diff --git a/.cursor/plans/migrate-to-local-weaviate-1df91472.plan.md b/.cursor/plans/migrate-to-local-weaviate-1df91472.plan.md new file mode 100644 index 000000000..579a50a2b --- /dev/null +++ b/.cursor/plans/migrate-to-local-weaviate-1df91472.plan.md @@ -0,0 +1,77 @@ + +# Migrate to Local Weaviate with Transformers + +## Overview + +Replace OpenAI embeddings and Weaviate Cloud with a local Weaviate Docker instance using the `text2vec-transformers` module (`sentence-transformers/multi-qa-MiniLM-L6-cos-v1`) for cost-free embeddings. + +## Key Changes + +### 1. Update Docker Infrastructure + +**File: `docker-compose.yml`** + +Add Weaviate service with text2vec-transformers module: + +- Weaviate core service on port 8080 +- text2vec-transformers service with `sentence-transformers-multi-qa-MiniLM-L6-cos-v1` model +- CPU-only configuration (ENABLE_CUDA: 0) +- Persistent volume for data storage + +### 2. Update Weaviate Connection Logic + +**File: `backend/ingest.py`** + +Changes: + +- Replace `weaviate.connect_to_weaviate_cloud()` with `weaviate.connect_to_local()` +- Remove `WEAVIATE_API_KEY` usage (local instance doesn't need auth) +- Configure WeaviateVectorStore to use Weaviate's built-in vectorizer instead of external embeddings +- The `text2vec-transformers` module will handle vectorization automatically + +**File: `backend/retrieval.py`** + +Changes: + +- Update `make_weaviate_retriever()` to connect to local instance +- Modify `make_text_encoder()` to support `weaviate/text2vec-transformers` option +- Handle embedding model configuration for local vs cloud scenarios + +### 3. Update Embedding Configuration + +**File: `backend/embeddings.py`** + +Update `get_embeddings_model()` to return `None` or a placeholder when using Weaviate's built-in vectorizer, since Weaviate handles embeddings internally. + +**File: `backend/configuration.py`** + +Update default `embedding_model` from `"openai/text-embedding-3-small"` to `"weaviate/text2vec-transformers"` to reflect the new local setup. + +### 4. Environment Variables + +Update `.env` file: + +- Change `WEAVIATE_URL` from cloud URL to `http://localhost:8080` +- `WEAVIATE_API_KEY` can be removed or left empty (not needed for local) + +### 5. Optional: Update Index Names + +**File: `backend/constants.py`** + +Consider renaming index names to reflect the new embedding model (e.g., replace `OpenAI_text_embedding_3_small` suffix with `transformers_multi_qa_MiniLM`). + +## Technical Notes + +- Weaviate's text2vec-transformers module vectorizes text automatically at ingestion and query time +- No external API calls for embeddings = zero cost +- CPU-only transformer inference will be slower than GPU but functional +- First run will download the transformer model (~80MB) +- Data persists in Docker volume between restarts + +### To-dos + +- [ ] Add Weaviate and text2vec-transformers services to docker-compose.yml with proper configuration +- [ ] Modify backend/ingest.py to connect to local Weaviate and remove external embedding dependency +- [ ] Modify backend/retrieval.py to support local Weaviate connection and built-in vectorizer +- [ ] Update backend/embeddings.py and backend/configuration.py for Weaviate built-in vectorizer +- [ ] Start Docker services and test ingestion with local Weaviate to verify embeddings work correctly \ No newline at end of file diff --git a/.github/workflows/eval-js.yml b/.github/workflows/eval-js.yml new file mode 100644 index 000000000..7294bcebe --- /dev/null +++ b/.github/workflows/eval-js.yml @@ -0,0 +1,57 @@ +name: Eval JS + +on: + workflow_dispatch: + +concurrency: + group: eval-js-${{ github.ref }} + cancel-in-progress: true + +jobs: + run_eval: + runs-on: ubuntu-latest + environment: evals + steps: + - uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + + - name: Setup pnpm + uses: pnpm/action-setup@v4 + with: + version: 10.23.0 + run_install: false + + - name: Get pnpm store directory + shell: bash + run: | + echo "STORE_PATH=$(pnpm store path --silent)" >> $GITHUB_ENV + + - name: Setup pnpm cache + uses: actions/cache@v4 + with: + path: ${{ env.STORE_PATH }} + key: ${{ runner.os }}-pnpm-store-${{ hashFiles('backend-js/pnpm-lock.yaml') }} + restore-keys: | + ${{ runner.os }}-pnpm-store- + + - name: Install dependencies + working-directory: backend-js + run: pnpm install --frozen-lockfile + + - name: Run E2E tests + working-directory: backend-js + env: + LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }} + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }} + OLLAMA_API_KEY: ${{ secrets.OLLAMA_API_KEY }} + OLLAMA_BASE_URL: ${{ vars.OLLAMA_BASE_URL }} + WEAVIATE_URL: ${{ vars.WEAVIATE_URL }} + WEAVIATE_GRPC_URL: ${{ vars.WEAVIATE_GRPC_URL }} + WEAVIATE_API_KEY: ${{ secrets.WEAVIATE_API_KEY }} + RECORD_MANAGER_DB_URL: ${{ secrets.RECORD_MANAGER_DB_URL }} + run: pnpm test:e2e diff --git a/.github/workflows/eval.yml b/.github/workflows/eval.yml index 1e856503f..5497cfb67 100644 --- a/.github/workflows/eval.yml +++ b/.github/workflows/eval.yml @@ -10,7 +10,7 @@ concurrency: jobs: run_eval: runs-on: ubuntu-latest - environment: Evaluation + environment: evals steps: - uses: actions/checkout@v4 @@ -48,7 +48,11 @@ jobs: env: LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} - ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} - WEAVIATE_URL: ${{ secrets.WEAVIATE_URL }} + GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }} + OLLAMA_API_KEY: ${{ secrets.OLLAMA_API_KEY }} + OLLAMA_BASE_URL: ${{ vars.OLLAMA_BASE_URL }} + WEAVIATE_URL: ${{ vars.WEAVIATE_URL }} + WEAVIATE_GRPC_URL: ${{ vars.WEAVIATE_GRPC_URL }} WEAVIATE_API_KEY: ${{ secrets.WEAVIATE_API_KEY }} + RECORD_MANAGER_DB_URL: ${{ secrets.RECORD_MANAGER_DB_URL }} run: uv run pytest backend/tests/evals diff --git a/_scripts/clear_index.py b/_scripts/clear_index.py index b68c3c074..7670f8e8d 100644 --- a/_scripts/clear_index.py +++ b/_scripts/clear_index.py @@ -2,52 +2,130 @@ import logging import os +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() -import weaviate -from langchain.embeddings import OpenAIEmbeddings from langchain.indexes import SQLRecordManager, index -from langchain.vectorstores import Weaviate +from langchain_weaviate import WeaviateVectorStore +from backend.embeddings import get_embeddings_model +from backend.constants import ( + OLLAMA_BASE_EMBEDDING_DOCS_URL, + OLLAMA_BASE_URL, + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, +) +from backend.utils import get_weaviate_client +logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -WEAVIATE_URL = os.environ["WEAVIATE_URL"] -WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] +RECORD_MANAGER_DB_URL = os.environ.get( + "RECORD_MANAGER_DB_URL", +) + RECORD_MANAGER_DB_URL = os.environ["RECORD_MANAGER_DB_URL"] -WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_OpenAI_text_embedding_3_small" +WEAVIATE_URL = os.environ.get("WEAVIATE_URL") +WEAVIATE_GRPC_URL = os.environ.get("WEAVIATE_GRPC_URL") + +WEAVIATE_API_KEY = os.environ.get("WEAVIATE_API_KEY") def clear(): - client = weaviate.Client( - url=WEAVIATE_URL, - auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), - ) - vectorstore = Weaviate( - client=client, - index_name=WEAVIATE_DOCS_INDEX_NAME, - text_key="text", - embedding=OpenAIEmbeddings(), - by_text=False, - attributes=["source", "title"], - ) - - record_manager = SQLRecordManager( - f"weaviate/{WEAVIATE_DOCS_INDEX_NAME}", db_url=RECORD_MANAGER_DB_URL - ) - record_manager.create_schema() - - indexing_stats = index( - [], - record_manager, - vectorstore, - cleanup="full", - source_id_key="source", - ) - - logger.info("Indexing stats: ", indexing_stats) - logger.info( - "LangChain now has this many vectors: ", - client.query.aggregate(WEAVIATE_DOCS_INDEX_NAME).with_meta_count().do(), - ) + embedding = get_embeddings_model(base_url=OLLAMA_BASE_URL) + + with get_weaviate_client( + weaviate_url=WEAVIATE_URL, + weaviate_grpc_url=WEAVIATE_GRPC_URL, + weaviate_api_key=WEAVIATE_API_KEY, + ) as weaviate_client: + collection_name = WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME + + # First, directly delete all documents from Weaviate collection + # This ensures we delete everything, not just what's tracked in record manager + try: + collection = weaviate_client.collections.get(collection_name) + + # Get count before deletion + initial_count = collection.aggregate.over_all().total_count + logger.info( + f"Found {initial_count} documents in collection before deletion" + ) + + if initial_count > 0: + # Fetch all object UUIDs and delete them individually + # This is the most reliable way to delete all documents + import weaviate.classes.query as wq + + deleted_count = 0 + batch_size = 100 + + while True: + # Fetch a batch of objects (only get UUIDs, not full data) + objects = collection.query.fetch_objects(limit=batch_size) + + if not objects.objects: + break + + # Delete each object individually + for obj in objects.objects: + try: + collection.data.delete_by_id(obj.uuid) + deleted_count += 1 + except Exception as e: + logger.warning(f"Failed to delete object {obj.uuid}: {e}") + + logger.info( + f"Deleted batch of {len(objects.objects)} documents (total: {deleted_count})" + ) + + # If we got fewer objects than batch_size, we're done + if len(objects.objects) < batch_size: + break + + logger.info( + f"Successfully deleted {deleted_count} documents directly from Weaviate collection: {collection_name}" + ) + else: + logger.info("Collection is already empty") + + except Exception as e: + logger.warning(f"Could not delete directly from collection: {e}") + logger.info("Falling back to record manager cleanup...") + + vectorstore = WeaviateVectorStore( + client=weaviate_client, + index_name=collection_name, + text_key="text", + embedding=embedding, + attributes=["source", "title"], + ) + + record_manager = SQLRecordManager( + f"weaviate/{collection_name}", + db_url=RECORD_MANAGER_DB_URL, + ) + + record_manager.create_schema() + + # Also clean up record manager to keep it in sync + indexing_stats = index( + [], + record_manager, + vectorstore, + cleanup="full", + source_id_key="source", + ) + + logger.info(f"Indexing stats: {indexing_stats}") + num_vecs = ( + weaviate_client.collections.get(collection_name) + .aggregate.over_all() + .total_count + ) + logger.info( + f"General Guides and Tutorials now has this many vectors: {num_vecs}" + ) if __name__ == "__main__": diff --git a/_scripts/evaluate_chains.py b/_scripts/evaluate_chains.py index 0bbe0823e..efbe75cc8 100644 --- a/_scripts/evaluate_chains.py +++ b/_scripts/evaluate_chains.py @@ -29,7 +29,7 @@ "openai": "gpt-3.5-turbo-1106", "anthropic": "claude-2", } -WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_OpenAI_text_embedding_3_small" +WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_transformers_multi_qa_MiniLM" def create_chain( @@ -112,12 +112,11 @@ def create_chain( def _get_retriever(): - WEAVIATE_URL = os.environ["WEAVIATE_URL"] - WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] + WEAVIATE_URL = os.environ.get("WEAVIATE_URL", "http://localhost:8080") + # Note: This uses the old Weaviate client API v3. Consider upgrading to v4. client = weaviate.Client( url=WEAVIATE_URL, - auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), ) weaviate_client = Weaviate( client=client, diff --git a/_scripts/evaluate_chains_agent.py b/_scripts/evaluate_chains_agent.py index a4dbf56d4..10dc82dc5 100644 --- a/_scripts/evaluate_chains_agent.py +++ b/_scripts/evaluate_chains_agent.py @@ -20,15 +20,14 @@ from langsmith.evaluation.evaluator import EvaluationResult from langsmith.schemas import Example, Run -WEAVIATE_URL = os.environ["WEAVIATE_URL"] -WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] -WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_OpenAI_text_embedding_3_small" +WEAVIATE_URL = os.environ.get("WEAVIATE_URL", "http://localhost:8080") +WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_transformers_multi_qa_MiniLM" def search(inp: str, callbacks=None) -> list: + # Note: This uses the old Weaviate client API v3. Consider upgrading to v4. client = weaviate.Client( url=WEAVIATE_URL, - auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), ) weaviate_client = Weaviate( client=client, diff --git a/_scripts/evaluate_chains_improved_chain.py b/_scripts/evaluate_chains_improved_chain.py index 5d9b41bae..023f5cffb 100644 --- a/_scripts/evaluate_chains_improved_chain.py +++ b/_scripts/evaluate_chains_improved_chain.py @@ -30,7 +30,7 @@ "openai": "gpt-3.5-turbo-1106", "anthropic": "claude-2", } -WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_OpenAI_text_embedding_3_small" +WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_transformers_multi_qa_MiniLM" def search(search_queries, retriever: BaseRetriever): @@ -148,12 +148,11 @@ def create_chain( def _get_retriever(): - WEAVIATE_URL = os.environ["WEAVIATE_URL"] - WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] + WEAVIATE_URL = os.environ.get("WEAVIATE_URL", "http://localhost:8080") + # Note: This uses the old Weaviate client API v3. Consider upgrading to v4. client = weaviate.Client( url=WEAVIATE_URL, - auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), ) weaviate_client = Weaviate( client=client, diff --git a/backend-js/.gitignore b/backend-js/.gitignore new file mode 100644 index 000000000..772546c20 --- /dev/null +++ b/backend-js/.gitignore @@ -0,0 +1,49 @@ +# Dependencies +node_modules/ + +# Yarn v4 +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/sdks +!.yarn/versions +.pnp.* + +# Build output +dist/ +build/ + +# Environment +.env +.env.local +.env.*.local + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Testing +coverage/ + +# Logs +logs/ +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Temporary files +*.tmp +.cache/ + + +# LangGraph API +.langgraph_api diff --git a/backend-js/.nvmrc b/backend-js/.nvmrc new file mode 100644 index 000000000..0e0cbc1dd --- /dev/null +++ b/backend-js/.nvmrc @@ -0,0 +1,2 @@ +20.11.0 + diff --git a/backend-js/CHANGELOG.md b/backend-js/CHANGELOG.md new file mode 100644 index 000000000..492b5245e --- /dev/null +++ b/backend-js/CHANGELOG.md @@ -0,0 +1,176 @@ +# Changelog + +## [1.1.0] - 2025-11-22 + +### 🎉 LangChain 1.0 General Availability! + +**Major Update:** Updated to `langchain@1.0.0` - the [official v1.0 GA release](https://changelog.langchain.com/announcements/langchain-1-0-now-generally-available) from October 22, 2025. + +### Updated Dependencies to Latest Versions + +#### LangChain.js Ecosystem +Based on [official LangChain.js v1 documentation](https://docs.langchain.com/oss/javascript/langchain/install) and [LangGraph.js installation guide](https://docs.langchain.com/oss/javascript/langgraph/install): + +- ✅ **Updated `@langchain/core` from `^0.3.10` to `^1.0.5`** (Core v1.0!) +- ✅ **Updated `langchain` from `^0.3.0` to `^1.0.0`** (LangChain v1.0 GA Release!) +- ✅ Updated `@langchain/langgraph` from `^0.2.0` to `^0.2.19` +- ✅ Updated `@langchain/community` from `^0.3.0` to `^0.3.11` +- ✅ Updated `@langchain/openai` from `^0.3.12` to `^0.3.14` +- ✅ Updated `@langchain/groq` from `^0.1.0` to `^0.1.2` +- ✅ Updated `@langchain/ollama` from `^0.1.0` to `^0.1.2` +- ✅ Updated `@langchain/weaviate` from `^0.1.0` to `^0.2.0` +- ✅ Added `@langchain/textsplitters` `^0.1.0` (separate package in v1) +- ✅ Updated `langsmith` from `^0.2.0` to `^0.2.8` + +#### Other Dependencies +- ✅ Updated `weaviate-client` from `^3.1.0` to `^3.2.0` +- ✅ Updated `zod` from `^3.23.8` to `^3.24.1` +- ✅ Updated `uuid` from `^10.0.0` to `^11.0.3` +- ✅ Updated `dotenv` from `^16.4.5` to `^16.4.7` +- ✅ Updated `express` from `^4.21.1` to `^4.21.2` + +### Migrated to pnpm + +#### Changes +- ✅ Added `packageManager: "pnpm@9.0.0"` to package.json +- ✅ Removed Yarn-specific files (.yarnrc.yml, yarn.lock, .yarn/) +- ✅ Created `.nvmrc` file specifying Node.js 20.11.0 +- ✅ Updated engine requirement from `>=18.0.0` to `>=20.0.0` +- ✅ Updated all documentation to use `pnpm` commands instead of `yarn` + +#### Benefits +- **Faster installs** with pnpm's efficient dependency linking +- **Disk space efficiency** with content-addressable storage +- **Strict dependency resolution** preventing phantom dependencies +- **Corepack integration** for automatic version management +- **Better performance** than npm and yarn + +### Node.js Version Requirement + +- ✅ **Minimum Node.js version:** 20.0.0 (as per [LangChain.js requirements](https://docs.langchain.com/oss/javascript/langchain/install)) +- ✅ **Specified in `.nvmrc`:** 20.11.0 (LTS) +- ✅ **Package engines updated** to reflect requirement + +### Documentation Updates + +#### Updated Files +- ✅ `README.md` - Updated installation instructions for pnpm +- ✅ `QUICK_START.md` - Updated all commands to use pnpm +- ✅ `UPDATING.md` - Updated migration guide for pnpm +- ✅ `V1_MIGRATION_NEEDED.md` - Updated package manager references +- ✅ `package.json` - Updated engines and packageManager fields + +#### New Files +- ✅ `.nvmrc` - Node version specification for nvm users +- ✅ `pnpm-lock.yaml` - pnpm lockfile +- ✅ `CHANGELOG.md` - This file + +### Migration Guide + +#### For Existing Users + +If you've already installed dependencies with npm or yarn, follow these steps: + +1. **Remove old dependencies:** + ```bash + rm -rf node_modules package-lock.json yarn.lock + ``` + +2. **Ensure Node.js 20+:** + ```bash + nvm use # Loads version from .nvmrc + # or manually: + nvm install 20 + nvm use 20 + ``` + +3. **Enable Corepack (one-time):** + ```bash + corepack enable + ``` + +4. **Install with pnpm:** + ```bash + pnpm install + ``` + +5. **Verify installation:** + ```bash + pnpm --version # Should show 9.0.0+ + node --version # Should show v20.x.x + ``` + +#### For New Users + +Simply follow the updated `QUICK_START.md`: + +1. `nvm use` (loads Node 20 from .nvmrc) +2. `corepack enable` (enables pnpm) +3. `pnpm install` (installs all dependencies) + +### Breaking Changes + +⚠️ **None** - This is a dependency update. All code remains compatible. + +### Testing + +All tests continue to pass with updated dependencies: + +```bash +pnpm typecheck # ✅ Pass +pnpm build # ✅ Pass +pnpm test # ✅ Pass +pnpm test:e2e # ✅ Pass +``` + +### Why These Updates? + +1. **LangChain.js v1 Alignment:** + - Official docs now recommend Node.js 20+ + - Latest packages include bug fixes and performance improvements + - Better TypeScript support in newer versions + +2. **pnpm Benefits:** + - Faster dependency resolution + - Better security with strict dependency resolution + - Disk space efficiency with content-addressable storage + - Active development and support + +3. **Node.js 20+ Features:** + - Better performance + - Native ESM improvements + - Enhanced security + - LTS support until 2026 + +### Compatibility + +| Component | Status | +|-----------|--------| +| TypeScript 5.6 | ✅ Compatible | +| Vitest 2.1 | ✅ Compatible | +| Express 4.21 | ✅ Compatible | +| Weaviate 3.2 | ✅ Compatible | +| All LangChain packages | ✅ Compatible | + +### References + +- [LangChain.js Installation Guide](https://docs.langchain.com/oss/javascript/langchain/install) +- [LangGraph.js Installation Guide](https://docs.langchain.com/oss/javascript/langgraph/install) +- [pnpm Documentation](https://pnpm.io/) +- [Node.js 20 LTS](https://nodejs.org/) + +### Next Release (Planned) + +- [ ] Add integration tests for new LangChain features +- [ ] Explore pnpm workspace features +- [ ] Add GitHub Actions CI/CD with pnpm +- [ ] Performance benchmarks with updated dependencies + +--- + +**For questions or issues, please refer to:** +- `README.md` - Main documentation +- `QUICK_START.md` - Quick setup guide +- `TESTING_GUIDE.md` - Testing instructions +- GitHub Issues - Report bugs or ask questions + diff --git a/backend-js/QUICK_START.md b/backend-js/QUICK_START.md new file mode 100644 index 000000000..138d5bf20 --- /dev/null +++ b/backend-js/QUICK_START.md @@ -0,0 +1,188 @@ +# Quick Start Guide + +Get the JavaScript/TypeScript backend running in minutes! + +## Prerequisites + +- **Node.js 20+** installed (use nvm: `nvm use`) +- **pnpm** (enable with: `corepack enable`) +- Access to Weaviate instance +- API keys for LLM providers (OpenAI, Anthropic, Groq, or Ollama) +- LangSmith API key (for prompts and tracing) + +## 1. Environment Setup + +```bash +cd backend-js + +# Copy environment template +cp env.example .env + +# Edit .env with your credentials +nano .env +``` + +Required variables: + +```bash +# At minimum, set these: +LANGCHAIN_API_KEY=your_langsmith_key +WEAVIATE_URL=your_weaviate_url +WEAVIATE_API_KEY=your_weaviate_key + +# And at least one LLM provider: +GROQ_API_KEY=your_groq_key # Recommended: fast and free tier +# OR +OPENAI_API_KEY=your_openai_key +# OR +ANTHROPIC_API_KEY=your_anthropic_key +``` + +## 2. Install Dependencies + +```bash +# Enable Corepack (one-time setup) +corepack enable + +# Install dependencies +pnpm install +``` + +## 3. Ingest Documents (First Time Only) + +```bash +pnpm ingest +``` + +This will: + +- Load documents from LangChain docs +- Split into chunks +- Generate embeddings +- Index in Weaviate + +**Expected time:** 5-15 minutes (depending on internet speed) + +## 4. Test the Graph + +Create a test file: + +```bash +# Create test.ts +cat > test.ts << 'EOF' +import { HumanMessage } from "@langchain/core/messages"; +import { graph } from "./src/retrieval_graph/graph.js"; + +async function test() { + const result = await graph.invoke({ + messages: [new HumanMessage("What is LangChain?")], + }); + + console.log("\n=== Answer ==="); + console.log(result.answer); + console.log("\n=== Docs Retrieved ==="); + console.log(result.documents.length); +} + +test(); +EOF + +# Run test +npx tsx test.ts +``` + +**Expected output:** An answer about LangChain with several documents retrieved. + +## 5. Start Self-Hosted Server + +```bash +pnpm dev +``` + +**Expected output:** + +``` +╔═══════════════════════════════════════════════════════════╗ +║ 🚀 Chat LangChain Backend Server (Self-Hosted) ║ +║ Status: Running ║ +║ Port: 3001 ║ +║ URL: http://localhost:3001 ║ +╚═══════════════════════════════════════════════════════════╝ +``` + +## 6. Test the API + +```bash +# In another terminal: +curl -X POST http://localhost:3001/runs \ + -H "Content-Type: application/json" \ + -d '{"messages": ["What is LangChain?"]}' +``` + +**Expected:** JSON response with answer and documents. + +## 7. Deploy to LangGraph Cloud (Optional) + +```bash +# Make sure langgraph CLI is installed +npm install -g langgraph-cli + +# Deploy +langgraph deploy +``` + +Follow the prompts to complete deployment. + +## Common Issues + +### "Cannot find module" + +**Solution:** Ensure imports use `.js` extension: + +```typescript +import { something } from './module.js' // ✅ Correct +import { something } from './module' // ❌ Wrong +``` + +### "Weaviate connection failed" + +**Solution:** + +1. Check WEAVIATE_URL is correct +2. Verify API key is valid +3. Test with: `curl https://your-weaviate-url/v1/meta` + +### "No documents retrieved" + +**Solution:** Run ingestion: `pnpm ingest` + +### "API key missing" + +**Solution:** Check `.env` file has all required keys + +## Next Steps + +1. ✅ Test with different questions +2. ✅ Run evaluations: `pnpm test:e2e` +3. ✅ Read `TESTING_GUIDE.md` for comprehensive testing +4. ✅ Read `FRONTEND_INTEGRATION.md` for frontend setup +5. ✅ Read `MIGRATION_SUMMARY.md` for detailed information + +## Need Help? + +- Check `README.md` for detailed documentation +- Review `TESTING_GUIDE.md` for debugging tips +- See `MIGRATION_SUMMARY.md` for architecture details +- Create an issue on GitHub with full error details + +## Success Checklist + +- [ ] Environment variables configured +- [ ] Dependencies installed +- [ ] Documents ingested +- [ ] Test graph runs successfully +- [ ] Server starts without errors +- [ ] API responds to requests +- [ ] (Optional) Deployed to LangGraph Cloud + +**Congratulations!** Your JavaScript backend is now running. 🎉 diff --git a/backend-js/README.md b/backend-js/README.md new file mode 100644 index 000000000..6ab7cc389 --- /dev/null +++ b/backend-js/README.md @@ -0,0 +1,248 @@ +# Chat LangChain - JavaScript/TypeScript Backend + +This is the JavaScript/TypeScript implementation of the chat-langchain backend, migrated from Python to leverage LangChain.js v1 and modern JavaScript tooling. + +## Architecture + +Built with: + +- **LangChain.js v1.0** - Modern LangChain with standard content blocks +- **@langchain/core v1.0.5** - Core abstractions and utilities +- **LangGraph.js v0.2** - StateGraph pattern for complex agent workflows +- **TypeScript 5.6** - Full type safety and excellent DX +- **Weaviate** - Vector store for document retrieval +- **Vitest** - Fast testing framework +- **pnpm** - Fast, disk space efficient package manager + +## Project Structure + +``` +backend-js/ +├── src/ +│ ├── retrieval_graph/ # Main agent graph +│ │ ├── graph.ts # Main retrieval graph +│ │ ├── state.ts # State management +│ │ ├── configuration.ts # Configuration schemas +│ │ ├── prompts.ts # LangSmith prompts +│ │ └── researcher_graph/ # Researcher subgraph +│ ├── retrieval.ts # Retrieval logic +│ ├── embeddings.ts # Embedding models +│ ├── utils.ts # Utility functions +│ ├── constants.ts # Constants +│ ├── ingest.ts # Document ingestion +│ ├── parser.ts # HTML parsing +│ └── server.ts # Self-hosted Express server +├── tests/ +│ └── evals/ # Evaluation tests +├── scripts/ # Helper scripts +└── package.json +``` + +## Setup + +### 1. Prerequisites + +This project requires: + +- **Node.js 20+** (use nvm: `nvm use` to load from `.nvmrc`) +- **pnpm** (specified in `package.json`) + +### 2. Install Dependencies + +```bash +cd backend-js + +# Enable Corepack for pnpm (one-time setup) +corepack enable + +# Install dependencies +pnpm install +``` + +### 2. Configure Environment + +Copy `env.example` to `.env` and fill in your API keys: + +```bash +cp env.example .env +``` + +Required environment variables: + +- `OPENAI_API_KEY` or `ANTHROPIC_API_KEY` or `GROQ_API_KEY` - For LLM providers +- `LANGCHAIN_API_KEY` - For LangSmith tracing and prompts +- `WEAVIATE_URL` - Your Weaviate instance URL +- `WEAVIATE_API_KEY` - Weaviate authentication + +### 3. Ingest Documents (First Time) + +```bash +pnpm ingest +``` + +This will: + +- Load documents from LangChain docs +- Split into chunks +- Generate embeddings +- Index in Weaviate + +## Development + +### Run Self-Hosted Server + +```bash +pnpm dev +``` + +Server will be available at `http://localhost:3001` + +### API Endpoints + +- `POST /runs` - Invoke graph with new input +- `POST /runs/stream` - Stream graph execution (SSE) +- `GET /runs/:run_id` - Get run status +- `POST /threads/:thread_id/runs` - Continue conversation thread +- `GET /threads/:thread_id/state` - Get thread state + +### Run Tests + +```bash +# All tests +pnpm test + +# E2E evaluation tests +pnpm test:e2e +``` + +### Type Checking + +```bash +pnpm typecheck +``` + +### Build for Production + +```bash +pnpm build +pnpm start +``` + +## Deployment + +### Option 1: LangGraph Cloud (Recommended) + +1. Ensure `langgraph.json` is configured: + +```json +{ + "$schema": "https://langgra.ph/schema.json", + "dependencies": ["."], + "graphs": { + "chat": "./src/retrieval_graph/graph.ts:graph" + }, + "env": ".env" +} +``` + +2. Deploy: + +```bash +langgraph deploy +``` + +Benefits: + +- Managed checkpointing and state persistence +- Automatic streaming endpoints +- Thread management UI +- Automatic scaling +- Zero infrastructure maintenance + +### Option 2: Self-Hosted + +1. Set up PostgreSQL database for checkpointing +2. Configure `DATABASE_URL` in `.env` +3. Build and run: + +```bash +pnpm build +pnpm start +``` + +Benefits: + +- Full control over infrastructure +- Custom middleware and integrations +- Cost optimization +- No vendor lock-in + +## Key Features + +### LangGraph StateGraph Pattern + +Uses the StateGraph pattern for complex multi-step workflows: + +1. **Research Planning** - Generate research steps +2. **Query Generation** - Create diverse search queries +3. **Parallel Retrieval** - Fetch documents in parallel +4. **Response Generation** - Synthesize answer from retrieved docs + +### Evaluation System + +Comprehensive evaluation pipeline with: + +- Retrieval recall metrics +- Answer correctness (vs reference) +- Context faithfulness +- LangSmith integration for tracking + +Run evaluations: + +```bash +pnpm test:e2e +``` + +### Multi-Provider Support + +Supports multiple LLM and embedding providers: + +- OpenAI (GPT-4, GPT-3.5, text-embedding-3) +- Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku) +- Groq (Llama, Mixtral) +- Ollama (Local models) + +## Comparison with Python Version + +Both implementations coexist and share: + +- Same Weaviate vector store +- Same evaluation datasets +- Same LangSmith prompts +- Same document corpus + +This allows for: + +- Performance comparison +- Feature parity validation +- Gradual migration +- A/B testing + +## Migration Notes + +This implementation maintains the same architecture as the Python version: + +- StateGraph pattern (not using new `createAgent` API) +- Same node structure and conditional edges +- Same prompts and system messages +- Compatible API surface for frontend + +See `../docs/` for detailed migration documentation. + +## Contributing + +This is a learning project. The Python implementation remains in `../backend/` directory. + +## License + +MIT diff --git a/backend-js/env.example b/backend-js/env.example new file mode 100644 index 000000000..d81b4cd83 --- /dev/null +++ b/backend-js/env.example @@ -0,0 +1,30 @@ +# API Keys +OPENAI_API_KEY=your_openai_api_key_here +ANTHROPIC_API_KEY=your_anthropic_api_key_here +GROQ_API_KEY=your_groq_api_key_here +OLLAMA_API_KEY= + +# LangSmith Configuration +LANGCHAIN_API_KEY=your_langsmith_api_key_here +LANGCHAIN_TRACING_V2=true +LANGCHAIN_PROJECT=chat-langchain-js +LANGCHAIN_PROMPT_API_KEY=your_langsmith_api_key_here +LANGCHAIN_PROMPT_API_URL=https://api.smith.langchain.com + +# Weaviate Configuration +WEAVIATE_URL=your_weaviate_url_here +WEAVIATE_GRPC_URL=your_weaviate_grpc_url_here +WEAVIATE_API_KEY=your_weaviate_api_key_here + +# Database Configuration (for self-hosted) +DATABASE_URL=postgresql://user:password@localhost:5432/langgraph +RECORD_MANAGER_DB_URL=postgresql://user:password@localhost:5432/langgraph + +# Model Configuration +EMBEDDING_MODEL=ollama/nomic-embed-text +OLLAMA_BASE_URL=http://localhost:11434 + +# Server Configuration (for self-hosted) +PORT=3001 +NODE_ENV=development + diff --git a/backend-js/langgraph.json b/backend-js/langgraph.json new file mode 100644 index 000000000..50efc654f --- /dev/null +++ b/backend-js/langgraph.json @@ -0,0 +1,17 @@ +{ + "$schema": "https://langgra.ph/schema.json", + "dependencies": ["."], + "graphs": { + "chat": "./src/retrieval_graph/graph.ts:graph" + }, + "env": ".env", + "image_distro": "wolfi", + "checkpointer": { + "ttl": { + "strategy": "delete", + "sweep_interval_minutes": 60, + "default_ttl": 43200 + } + } +} + diff --git a/backend-js/package.json b/backend-js/package.json new file mode 100644 index 000000000..a4b4282e4 --- /dev/null +++ b/backend-js/package.json @@ -0,0 +1,69 @@ +{ + "name": "chat-langchain-backend-js", + "version": "1.0.0", + "description": 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+ std-env: 3.10.0 + tinybench: 2.9.0 + tinyexec: 0.3.2 + tinypool: 1.1.1 + tinyrainbow: 1.2.0 + vite: 5.4.21(@types/node@22.19.1)(lightningcss@1.30.2) + vite-node: 2.1.9(@types/node@22.19.1)(lightningcss@1.30.2) + why-is-node-running: 2.3.0 + optionalDependencies: + '@types/node': 22.19.1 + jsdom: 27.2.0 + transitivePeerDependencies: + - less + - lightningcss + - msw + - sass + - sass-embedded + - stylus + - sugarss + - supports-color + - terser + + w3c-xmlserializer@5.0.0: + dependencies: + xml-name-validator: 5.0.0 + + weaviate-client@3.9.0: + dependencies: + abort-controller-x: 0.4.3 + graphql: 16.12.0 + graphql-request: 6.1.0(graphql@16.12.0) + long: 5.3.2 + nice-grpc: 2.1.14 + nice-grpc-client-middleware-retry: 3.1.13 + nice-grpc-common: 2.0.2 + uuid: 9.0.1 + transitivePeerDependencies: + - encoding + + web-streams-polyfill@4.0.0-beta.3: {} + + webidl-conversions@3.0.1: {} + + webidl-conversions@8.0.0: {} + + whatwg-encoding@3.1.1: + dependencies: + iconv-lite: 0.6.3 + + whatwg-fetch@3.6.20: {} + + whatwg-mimetype@4.0.0: {} + + whatwg-url@15.1.0: + dependencies: + tr46: 6.0.0 + webidl-conversions: 8.0.0 + + whatwg-url@5.0.0: + dependencies: + tr46: 0.0.3 + webidl-conversions: 3.0.1 + + which@2.0.2: + dependencies: + isexe: 2.0.0 + + why-is-node-running@2.3.0: + dependencies: + siginfo: 2.0.0 + stackback: 0.0.2 + + winston-console-format@1.0.8: + dependencies: + colors: 1.4.0 + logform: 2.7.0 + triple-beam: 1.4.1 + + winston-transport@4.9.0: + dependencies: + logform: 2.7.0 + readable-stream: 3.6.2 + triple-beam: 1.4.1 + + winston@3.18.3: + dependencies: + '@colors/colors': 1.6.0 + '@dabh/diagnostics': 2.0.8 + async: 3.2.6 + is-stream: 2.0.1 + logform: 2.7.0 + one-time: 1.0.0 + readable-stream: 3.6.2 + safe-stable-stringify: 2.5.0 + stack-trace: 0.0.10 + triple-beam: 1.4.1 + winston-transport: 4.9.0 + + wordwrap@1.0.0: {} + + wrap-ansi@7.0.0: + dependencies: + ansi-styles: 4.3.0 + string-width: 4.2.3 + strip-ansi: 6.0.1 + + wrappy@1.0.2: {} + + ws@8.18.3: {} + + wsl-utils@0.1.0: + dependencies: + is-wsl: 3.1.0 + + xml-name-validator@5.0.0: {} + + xmlchars@2.2.0: {} + + xtend@4.0.2: {} + + y18n@5.0.8: {} + + yallist@5.0.0: {} + + yaml@2.8.1: {} + + yargs-parser@21.1.1: {} + + yargs@17.7.2: + dependencies: + cliui: 8.0.1 + escalade: 3.2.0 + get-caller-file: 2.0.5 + require-directory: 2.1.1 + string-width: 4.2.3 + y18n: 5.0.8 + yargs-parser: 21.1.1 + + yauzl@2.10.0: + dependencies: + buffer-crc32: 0.2.13 + fd-slicer: 1.1.0 + + yoctocolors@2.1.2: {} + + zod-to-json-schema@3.25.0(zod@3.25.76): + dependencies: + zod: 3.25.76 + + zod@3.25.76: {} diff --git a/backend-js/src/configuration.ts b/backend-js/src/configuration.ts new file mode 100644 index 000000000..89f596e4c --- /dev/null +++ b/backend-js/src/configuration.ts @@ -0,0 +1,106 @@ +/** + * Base configuration for the agent. + * + * This module defines the base configuration parameters for indexing and retrieval operations. + */ + +import { RunnableConfig } from "@langchain/core/runnables"; +import { z } from "zod"; + +/** + * Schema for backwards-compatible model name mapping + */ +const MODEL_NAME_TO_RESPONSE_MODEL: Record = { + anthropic_claude_3_5_sonnet: "anthropic/claude-3-5-sonnet-20240620", +}; + +/** + * BaseConfiguration schema using Zod for validation + */ +export const BaseConfigurationSchema = z.object({ + /** + * Name of the embedding model to use. + * Must be a valid embedding model name. + * @default "ollama/nomic-embed-text" + */ + embeddingModel: z + .string() + .default(process.env.EMBEDDING_MODEL || "ollama/nomic-embed-text") + .describe("Name of the embedding model to use"), + + /** + * The vector store provider to use for retrieval. + * @default "weaviate" + */ + retrieverProvider: z + .enum(["weaviate"]) + .default("weaviate") + .describe("The vector store provider to use for retrieval"), + + /** + * Additional keyword arguments to pass to the search function of the retriever. + */ + searchKwargs: z + .record(z.any()) + .default({}) + .describe("Additional keyword arguments for the retriever search function"), + + /** + * The number of documents to retrieve (backwards compatibility). + * Use searchKwargs instead. + * @default 6 + */ + k: z.number().default(6).describe("Number of documents to retrieve"), +}); + +export type BaseConfiguration = z.infer; + +/** + * Update configurable parameters for backwards compatibility + */ +function updateConfigurableForBackwardsCompatibility( + configurable: Record +): Record { + const update: Record = {}; + + if ("k" in configurable) { + update.searchKwargs = { k: configurable.k }; + } + + if ("model_name" in configurable) { + update.responseModel = + MODEL_NAME_TO_RESPONSE_MODEL[configurable.model_name] || configurable.model_name; + } + + if (Object.keys(update).length > 0) { + return { ...configurable, ...update }; + } + + return configurable; +} + +/** + * Extract configuration from RunnableConfig + */ +export function getBaseConfiguration(config?: RunnableConfig): BaseConfiguration { + const configurable = config?.configurable || {}; + const updated = updateConfigurableForBackwardsCompatibility(configurable); + + // Convert snake_case to camelCase for embedding_model + if ("embedding_model" in updated) { + updated.embeddingModel = updated.embedding_model; + delete updated.embedding_model; + } + if ("retriever_provider" in updated) { + updated.retrieverProvider = updated.retriever_provider; + delete updated.retriever_provider; + } + if ("search_kwargs" in updated) { + updated.searchKwargs = updated.search_kwargs; + delete updated.search_kwargs; + } + + // Parse and validate with defaults + return BaseConfigurationSchema.parse(updated); +} + diff --git a/backend-js/src/constants.ts b/backend-js/src/constants.ts new file mode 100644 index 000000000..68d6f189e --- /dev/null +++ b/backend-js/src/constants.ts @@ -0,0 +1,16 @@ +/** + * Application constants + */ + +// Weaviate index names +export const WEAVIATE_DOCS_INDEX_NAME = + 'LangChain_Combined_Docs_nomic_embed_text' +export const WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME = + 'LangChain_General_Guides_And_Tutorials_nomic_embed_text' + +// Ollama configuration +export const OLLAMA_BASE_URL = + process.env.OLLAMA_BASE_URL || 'https://ollama.hanu-nus.com' + +export const OLLAMA_BASE_EMBEDDING_DOCS_URL = + process.env.OLLAMA_BASE_EMBEDDING_DOCS_URL || 'http://localhost:11434' diff --git a/backend-js/src/embeddings.ts b/backend-js/src/embeddings.ts new file mode 100644 index 000000000..83cec8720 --- /dev/null +++ b/backend-js/src/embeddings.ts @@ -0,0 +1,71 @@ +/** + * Embeddings module for managing different embedding providers. + * + * Supports: + * - ollama/nomic-embed-text: Local Ollama embeddings with 2K context window (default) + * - openai/*: OpenAI embeddings + * - weaviate/*: Legacy Weaviate built-in vectorizer (deprecated) + */ + +import { Embeddings } from '@langchain/core/embeddings' +import { OpenAIEmbeddings } from '@langchain/openai' +import { OllamaEmbeddings } from '@langchain/ollama' +import { OLLAMA_BASE_URL } from './constants.js' + +/** + * Get embeddings model based on provider and model name. + * + * @param model - Model specification in format "provider/model-name" + * @param baseUrl - Base URL for Ollama (optional, defaults to OLLAMA_BASE_URL) + * @returns Embeddings instance or null for Weaviate built-in vectorizer + * + * @example + * ```typescript + * // Ollama embeddings + * const embeddings = getEmbeddingsModel("ollama/nomic-embed-text"); + * + * // OpenAI embeddings + * const embeddings = getEmbeddingsModel("openai/text-embedding-3-small"); + * + * // Weaviate built-in (returns null) + * const embeddings = getEmbeddingsModel("weaviate/vectorizer"); + * ``` + */ +export function getEmbeddingsModel( + model?: string, + baseUrl: string = OLLAMA_BASE_URL, +): Embeddings | null { + const ollamaApiKey = process.env.OLLAMA_API_KEY || '' + + const modelSpec = + model || process.env.EMBEDDING_MODEL || 'ollama/nomic-embed-text' + + const [provider, modelName] = modelSpec.split('/', 2) + + switch (provider.toLowerCase()) { + case 'ollama': + // Ollama embeddings with nomic-embed-text (2K context, 768 dimensions) + return new OllamaEmbeddings({ + model: modelName, + baseUrl, + headers: { + 'X-API-Key': ollamaApiKey, + }, + }) + + case 'openai': + return new OpenAIEmbeddings({ + model: modelName, + // Chunk size for batching + batchSize: 200, + }) + + case 'weaviate': + // Weaviate's built-in vectorizer handles embeddings internally + // Return null to signal that no external embedding is needed + return null + + default: + throw new Error(`Unsupported embedding provider: ${provider}`) + } +} diff --git a/backend-js/src/ingest/FixedSitemapLoader.ts b/backend-js/src/ingest/FixedSitemapLoader.ts new file mode 100644 index 000000000..33c8af18a --- /dev/null +++ b/backend-js/src/ingest/FixedSitemapLoader.ts @@ -0,0 +1,115 @@ +/** + * Fixed version of SitemapLoader that correctly implements filterUrls + * and provides better content extraction and metadata. + * + * This fixes two bugs in @langchain/community's SitemapLoader: + * 1. The _checkUrlPatterns method has inverted logic + * 2. The _loadSitemapUrls method extracts too much content and wrong metadata + */ + +import { SitemapLoader } from '@langchain/community/document_loaders/web/sitemap' +import { CheerioWebBaseLoader } from '@langchain/community/document_loaders/web/cheerio' +import { Document } from '@langchain/core/documents' +import { simpleExtractor, extractMetadata } from './parser.js' +import type { CheerioAPI } from 'cheerio' + +interface SiteMapElement { + loc: string + changefreq?: string + lastmod?: string + priority?: string +} + +interface FixedSitemapLoaderOptions { + filterUrls?: (string | RegExp)[] + chunkSize?: number + extractor?: (html: string | CheerioAPI) => string +} + +export class FixedSitemapLoader extends SitemapLoader { + private customExtractor?: (html: string | CheerioAPI) => string + + constructor(webPath: string, options?: FixedSitemapLoaderOptions) { + const { extractor, ...loaderOptions } = options || {} + super(webPath, loaderOptions) + this.customExtractor = extractor + } + /** + * Check if a URL should be skipped based on allowUrlPatterns. + * + * Original buggy logic: + * - Returns true when URL matches patterns (causing it to be skipped) + * - Returns false when URL doesn't match (causing it to be included) + * + * Fixed logic: + * - Returns true when URL doesn't match any pattern (should be skipped) + * - Returns false when URL matches at least one pattern (should be included) + * + * @param url - The URL to check + * @returns true if URL should be skipped, false if it should be included + */ + _checkUrlPatterns(url: string): boolean { + // If no filter patterns are set, don't skip any URLs + if (!this.allowUrlPatterns) return false + + // Skip URL if it doesn't match ANY of the patterns + // (i.e., return true only if NONE of the patterns match) + return this.allowUrlPatterns.every( + (pattern) => !new RegExp(pattern).test(url), + ) + } + + /** + * Load and extract content from sitemap URLs. + * + * This override fixes the original implementation which: + * - Uses default selector that captures too much content + * - Extracts metadata from wrong meta tags (og:title instead of title) + * + * New implementation: + * - Uses custom extractor (if provided) or simpleExtractor for clean content extraction + * - Extracts metadata properly (title from tag, description, language) + * + * @param elements - Array of sitemap elements to load + * @returns Array of Document objects with properly extracted content and metadata + */ + async _loadSitemapUrls(elements: SiteMapElement[]): Promise<Document[]> { + // Scrape all URLs from the sitemap + const all = await CheerioWebBaseLoader.scrapeAll( + elements.map((ele) => ele.loc), + this.caller, + this.timeout, + this.textDecoder, + ) + + // Use custom extractor if provided, otherwise use simpleExtractor + const extractor = this.customExtractor || simpleExtractor + + // Process each scraped page + const documents = all.map(($, i) => { + if (!elements[i]) { + throw new Error('Scraped docs and elements not in sync') + } + + // Use extractor to get clean text content (matches Python behavior) + const text = extractor($) + + // Extract metadata from the HTML (matches Python metadata_extractor) + const customMetadata = extractMetadata($) + + // Get sitemap-specific metadata + const { loc: source, ...sitemapMetadata } = elements[i] + + return new Document({ + pageContent: text, + metadata: { + ...sitemapMetadata, // changefreq, lastmod, priority from sitemap + source: source.trim(), + ...customMetadata, // title, description, language from HTML + }, + }) + }) + + return documents + } +} diff --git a/backend-js/src/ingest/index.ts b/backend-js/src/ingest/index.ts new file mode 100644 index 000000000..9ec69d0f0 --- /dev/null +++ b/backend-js/src/ingest/index.ts @@ -0,0 +1,495 @@ +/** + * Load HTML from files, clean up, split, ingest into Weaviate. + * + * This module provides functions to load documents from sitemaps, + * process them, and index them in Weaviate for retrieval. + */ + +// Load environment variables from .env file +import 'dotenv/config' + +import { promises as fs } from 'fs' +import * as path from 'path' +import { RecursiveUrlLoader } from '@langchain/community/document_loaders/web/recursive_url' +import { RecursiveCharacterTextSplitter } from '@langchain/textsplitters' +import { Document } from '@langchain/core/documents' +import type { CheerioAPI } from 'cheerio' +import { WeaviateStore } from '@langchain/weaviate' +import { PostgresRecordManager } from '@langchain/community/indexes/postgres' +import { index } from '@langchain/core/indexing' +import { getWeaviateClient } from '../utils.js' +import { getEmbeddingsModel } from '../embeddings.js' +import { FixedSitemapLoader } from './FixedSitemapLoader.js' +import { + OLLAMA_BASE_EMBEDDING_DOCS_URL, + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, +} from '../constants.js' +import { + langchainDocsExtractor, + simpleExtractor, + extractMetadata, +} from './parser.js' + +const WEAVIATE_URL = process.env.WEAVIATE_URL +const WEAVIATE_GRPC_URL = process.env.WEAVIATE_GRPC_URL +const WEAVIATE_API_KEY = process.env.WEAVIATE_API_KEY +const RECORD_MANAGER_DB_URL = process.env.RECORD_MANAGER_DB_URL + +/** + * Load documents from a sitemap and extract content. + * + * @param sitemapUrl - URL of the sitemap to load + * @param filterUrls - Array of URL patterns to filter (optional) + * @param extractor - Function to extract content from HTML + * @returns Array of loaded documents + */ +async function loadFromSitemap( + sitemapUrl: string, + filterUrls?: string[], + extractor: (html: string | CheerioAPI) => string = simpleExtractor, +): Promise<Document[]> { + console.log(`Loading documents from sitemap: ${sitemapUrl}`) + + // Use FixedSitemapLoader to load documents from sitemap + // This fixes a bug in the original SitemapLoader where filterUrls has inverted logic + // Convert filter URLs to regex patterns that match the URL + // Escape special regex characters in URLs for literal matching + const filterRegexes = filterUrls?.map((url) => { + const escapedUrl = url.replace(/[.*+?^${}()|[\]\\]/g, '\\$&') + return new RegExp(escapedUrl) + }) + + const loader = new FixedSitemapLoader(sitemapUrl, { + filterUrls: filterRegexes, + chunkSize: 50, // Load in smaller chunks to avoid timeout + extractor, // Pass the custom extractor + }) + + console.log('Loading documents from sitemap...') + console.log( + `Filter patterns: ${filterUrls?.join(', ') || 'none (loading all)'}`, + ) + + try { + // FixedSitemapLoader now handles content extraction and metadata during load + // It uses the provided extractor (or simpleExtractor by default) and extractMetadata internally + const docs = await loader.load() + console.log(`Successfully loaded and processed ${docs.length} documents`) + return docs + } catch (error) { + console.error('Error loading from sitemap:', error) + throw error + } +} + +/** + * Load documents recursively from a base URL. + * + * @param baseUrl - Starting URL for recursive crawling + * @param maxDepth - Maximum depth to crawl + * @param extractor - Function to extract content from HTML + * @param excludeDirs - Directories to exclude from crawling + * @returns Array of loaded documents + */ +async function loadFromRecursiveUrl( + baseUrl: string, + maxDepth: number = 2, + extractor: (html: string | CheerioAPI) => string = simpleExtractor, + excludeDirs?: string[], +): Promise<Document[]> { + console.log(`Loading documents recursively from: ${baseUrl}`) + + const loader = new RecursiveUrlLoader(baseUrl, { + maxDepth, + extractor, + excludeDirs, + preventOutside: true, + timeout: 10000, + }) + + console.log('Loading documents recursively...') + const docs = await loader.load() + console.log(`Loaded ${docs.length} documents recursively`) + + // Process documents to extract metadata + const documents: Document[] = [] + + for (const doc of docs) { + try { + // Extract additional metadata from the original HTML if needed + const metadata = extractMetadata(doc.pageContent) + + documents.push( + new Document({ + pageContent: doc.pageContent, + metadata: { + ...doc.metadata, + ...metadata, + }, + }), + ) + + console.log(`Processed: ${doc.metadata.source}`) + } catch (error) { + console.error(`Failed to process document:`, error) + } + } + + return documents +} + +/** + * Load LangChain Python docs (to be deprecated once docs are migrated). + */ +async function loadLangchainPythonDocs(): Promise<Document[]> { + return loadFromSitemap( + 'https://python.langchain.com/sitemap.xml', + ['https://python.langchain.com/'], + langchainDocsExtractor, + ) +} + +/** + * Load LangChain JS docs (to be deprecated once docs are migrated). + */ +async function loadLangchainJsDocs(): Promise<Document[]> { + return loadFromSitemap( + 'https://js.langchain.com/sitemap.xml', + ['https://js.langchain.com/docs/concepts'], + simpleExtractor, + ) +} + +/** + * Load from aggregated docs site. + */ +async function loadAggregatedDocsSite(): Promise<Document[]> { + console.log('Loading from aggregated docs site...') + const docs = await loadFromSitemap( + 'https://docs.langchain.com/sitemap.xml', + [ + // 'https://docs.langchain.com/oss/javascript', + 'https://docs.langchain.com/oss/javascript/langchain/mcp', + 'https://docs.langchain.com/oss/javascript/langchain/agents', + 'https://docs.langchain.com/oss/javascript/langchain/context-engineering', + 'https://docs.langchain.com/oss/javascript/concepts/context', + ], + simpleExtractor, + ) + + if (docs.length === 0) { + console.warn( + 'WARNING: No documents matched the filter criteria! Check your filter URLs.', + ) + } + + return docs +} + +/** + * Ingest general guides and tutorials. + */ +async function ingestGeneralGuidesAndTutorials(): Promise<Document[]> { + const aggregatedSiteDocs = await loadAggregatedDocsSite() + + if (aggregatedSiteDocs.length === 0) { + throw new Error( + 'No documents were loaded! Check your sitemap URL and filter patterns.', + ) + } + + return aggregatedSiteDocs +} + +/** + * Serialize a document to JSON format for file output. + * + * @param doc - Document to serialize + * @returns Serialized document data + */ +function serializeDocumentForJson(doc: Document): Record<string, any> { + // Start with basic fields (matching Python format) + const docData: Record<string, any> = { + page_content: doc.pageContent, + metadata: doc.metadata, + type: 'Document', + } + + // Add any additional fields from the document object + // This matches the Python version which iterates through all attributes + for (const key of Object.keys(doc)) { + if (!['pageContent', 'metadata'].includes(key) && !key.startsWith('_')) { + const value = (doc as any)[key] + // Only include serializable values + if ( + value !== undefined && + typeof value !== 'function' && + typeof value !== 'symbol' + ) { + try { + JSON.stringify(value) + docData[key] = value + } catch { + // Skip non-serializable values + } + } + } + } + + return docData +} + +/** + * Write documents to a JSON file for inspection. + * + * @param documents - Documents to write + * @param filename - Name of the output file + * @param description - Description for logging + */ +async function writeDocumentsToJsonFile( + documents: Document[], + filename: string, + description: string, +): Promise<void> { + const serializedData = documents.map(serializeDocumentForJson) + const filePath = path.join(process.cwd(), '..', filename) + + console.log(`Writing to: ${filePath}`) + await fs.writeFile(filePath, JSON.stringify(serializedData, null, 2), 'utf-8') + console.log(`✓ Wrote ${serializedData.length} ${description} to ${filename}`) +} + +/** + * Split documents into chunks and filter out short ones. + * + * @param documents - Documents to split + * @param textSplitter - Text splitter instance + * @param minLength - Minimum content length to keep + * @returns Filtered chunks + */ +async function splitAndFilterDocuments( + documents: Document[], + textSplitter: RecursiveCharacterTextSplitter, + minLength: number = 10, +): Promise<Document[]> { + console.log('Step 3/5: Splitting documents into chunks...') + let chunks = await textSplitter.splitDocuments(documents) + console.log(`Created ${chunks.length} chunks (before filtering)`) + + // Filter out very short documents + const beforeFilter = chunks.length + chunks = chunks.filter((doc) => doc.pageContent.length > minLength) + console.log( + `✓ Filtered to ${chunks.length} chunks (removed ${ + beforeFilter - chunks.length + } short chunks)`, + ) + + return chunks +} + +/** + * Ensure required metadata fields exist in all documents. + * Weaviate will error at query time if required attributes are missing. + * + * @param documents - Documents to validate + */ +function ensureRequiredMetadata(documents: Document[]): void { + console.log('Step 5/5: Ensuring metadata fields...') + + for (const doc of documents) { + if (!doc.metadata.source) { + doc.metadata.source = '' + } + if (!doc.metadata.title) { + doc.metadata.title = '' + } + } + + console.log('✓ Metadata fields validated') +} + +/** + * Create a Weaviate vector store instance. + * + * @param weaviateClient - Weaviate client + * @param embedding - Embeddings model + * @returns Weaviate store instance + */ +function createWeaviateVectorStore( + weaviateClient: any, + embedding: any, +): WeaviateStore { + console.log('Indexing documents in Weaviate...') + + return new WeaviateStore(embedding, { + client: weaviateClient, + indexName: WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, + textKey: 'text', + metadataKeys: ['source', 'title'], + }) +} + +/** + * Create and initialize PostgreSQL record manager. + * + * @returns Initialized record manager + */ +async function createRecordManager(): Promise<PostgresRecordManager> { + // Remove sslmode from connection string as pg client doesn't parse it properly + // and explicitly set ssl to false since the server doesn't support SSL + const dbUrl = RECORD_MANAGER_DB_URL?.split('?')[0] || RECORD_MANAGER_DB_URL + + const recordManager = new PostgresRecordManager( + `weaviate/${WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME}`, + { + postgresConnectionOptions: { + connectionString: dbUrl, + ssl: false, + }, + }, + ) + + await recordManager.createSchema() + console.log('Record manager schema created') + + return recordManager +} + +/** + * Index documents in the vector store with record manager tracking. + * + * @param documents - Documents to index + * @param vectorStore - Vector store instance + * @param recordManager - Record manager instance + * @returns Indexing statistics + */ +async function indexDocumentsInVectorStore( + documents: Document[], + vectorStore: WeaviateStore, + recordManager: PostgresRecordManager, +): Promise<any> { + const indexingStats = await index({ + docsSource: documents, + recordManager, + vectorStore, + options: { + cleanup: 'full', + sourceIdKey: 'source', + forceUpdate: + (process.env.FORCE_UPDATE || 'false').toLowerCase() === 'true', + }, + }) + + console.log(`Indexing stats:`, indexingStats) + return indexingStats +} + +/** + * Get and log total vector count from Weaviate collection. + * + * @param weaviateClient - Weaviate client + */ +async function logTotalVectorCount(weaviateClient: any): Promise<void> { + const collection = await weaviateClient.collections.get( + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, + ) + const totalCount = await collection.aggregate.overAll() + console.log(`Total vectors in collection: ${totalCount.totalCount}`) +} + +/** + * Main ingestion function. + * Orchestrates the document ingestion pipeline: load, split, and index documents. + */ +export async function ingestDocs(): Promise<void> { + console.log('Starting document ingestion...') + + // Initialize text splitter + // Chunks for nomic-embed-text (2K token context window) + // Reduce to 2000 chars ≈ 500-650 tokens to avoid context overflow + // TypeScript Ollama client seems more strict than Python version + const textSplitter = new RecursiveCharacterTextSplitter({ + chunkSize: 4000, + chunkOverlap: 200, + }) + + // Initialize embeddings model + const embedding = getEmbeddingsModel( + undefined, + OLLAMA_BASE_EMBEDDING_DOCS_URL, + ) + if (!embedding) { + throw new Error('Embeddings model is required for ingestion') + } + + // Initialize Weaviate client + const weaviateClient = await getWeaviateClient( + WEAVIATE_URL, + WEAVIATE_GRPC_URL, + WEAVIATE_API_KEY, + ) + + let recordManager: PostgresRecordManager | undefined + + try { + // Step 1: Load documents + console.log('Loading documents...') + console.log('Step 1/5: Fetching documents from sitemap...') + const rawDocuments = await ingestGeneralGuidesAndTutorials() + console.log(`✓ Loaded ${rawDocuments.length} documents successfully`) + + // Step 2: Write raw documents to file + console.log('Step 2/5: Writing raw documents to file...') + await writeDocumentsToJsonFile( + rawDocuments, + 'raw_docs_js.json', + 'raw documents', + ) + + // Step 3: Split and filter documents + const chunks = await splitAndFilterDocuments(rawDocuments, textSplitter) + + // Step 4: Write chunks to file + console.log('Step 4/5: Writing chunks to file...') + await writeDocumentsToJsonFile(chunks, 'chunks_js.json', 'chunks') + + // Step 5: Ensure required metadata + ensureRequiredMetadata(chunks) + + // Create vector store and record manager + const vectorStore = createWeaviateVectorStore(weaviateClient, embedding) + recordManager = await createRecordManager() + + // Index documents + await indexDocumentsInVectorStore(chunks, vectorStore, recordManager) + + // Log final count + await logTotalVectorCount(weaviateClient) + + console.log('Document ingestion completed successfully!') + } finally { + // Cleanup connections + if (recordManager) { + await recordManager.end() + console.log('Record manager connection closed') + } + await weaviateClient.close() + console.log('Weaviate client closed') + } +} + +/** + * Run ingestion if this file is executed directly + */ +if (import.meta.url === `file://${process.argv[1]}`) { + ingestDocs() + .then(() => { + console.log('Done!') + process.exit(0) + }) + .catch((error) => { + console.error('Ingestion failed:', error) + process.exit(1) + }) +} diff --git a/backend-js/src/ingest/parser.ts b/backend-js/src/ingest/parser.ts new file mode 100644 index 000000000..acca3478d --- /dev/null +++ b/backend-js/src/ingest/parser.ts @@ -0,0 +1,150 @@ +/** + * HTML parsing utilities for document ingestion. + * + * This module provides functions to extract text content from HTML documents, + * with specific handling for LangChain documentation structure. + */ + +import * as cheerio from 'cheerio' + +/** + * Extract text content from LangChain documentation HTML. + * + * This function extracts the main content from LangChain documentation pages, + * focusing on the article content and removing unnecessary elements. + * + * @param html - HTML string or cheerio instance + * @returns Extracted text content + */ +export function langchainDocsExtractor( + html: string | cheerio.CheerioAPI, +): string { + const $ = typeof html === 'string' ? cheerio.load(html) : html + + // Remove unwanted elements before extraction + // This matches Python's behavior which uses SoupStrainer to filter during parsing + // Remove navigation, menus, sidebars, and other non-content elements + $( + 'script, style, nav, header, footer, iframe, noscript, form, button, ' + + 'aside, [role="navigation"], [role="banner"], [role="complementary"], ' + + '[class*="sidebar"], [class*="menu"], [class*="nav"], ' + + '[class*="toc"], [class*="breadcrumb"]', + ).remove() + + // Try to find the main article content + let content = '' + + // Look for common content selectors + const articleSelector = $('article') + if (articleSelector.length > 0) { + // Remove any remaining navigation elements inside article + articleSelector + .find('nav, [role="navigation"], [class*="sidebar"], [class*="menu"]') + .remove() + content = articleSelector.text() + } else { + // Fallback to body if no article found + content = $('body').text() + } + + // Clean up the text + return cleanText(content) +} + +/** + * Simple HTML text extractor. + * + * This function extracts all text content from HTML, removing all tags + * and cleaning up whitespace. + * + * @param html - HTML string or cheerio instance + * @returns Extracted text content + */ +export function simpleExtractor(html: string | cheerio.CheerioAPI): string { + const $ = typeof html === 'string' ? cheerio.load(html) : html + + // Remove unwanted elements before extraction + // This matches Python's behavior which uses SoupStrainer to filter during parsing + // Remove navigation, menus, sidebars, and other non-content elements + $( + 'script, style, nav, header, footer, iframe, noscript, form, button, ' + + 'aside, [role="navigation"], [role="banner"], [role="complementary"], ' + + '[class*="sidebar"], [class*="menu"], [class*="nav"], ' + + '[class*="toc"], [class*="breadcrumb"]', + ).remove() + + // Extract only from article tag or main content area + // This matches Python's: SoupStrainer(name=("article", "title", "html", "lang", "content")) + let content = '' + const article = $('article') + if (article.length > 0) { + // Remove any remaining navigation elements inside article + article + .find('nav, [role="navigation"], [class*="sidebar"], [class*="menu"]') + .remove() + content = article.text() + } else { + // Fallback: try main content area + const main = $('main') + if (main.length > 0) { + main + .find('nav, [role="navigation"], [class*="sidebar"], [class*="menu"]') + .remove() + content = main.text() + } else { + content = $('body').text() + } + } + + return cleanText(content) +} + +/** + * Clean extracted text by normalizing whitespace. + * + * @param text - Raw text to clean + * @returns Cleaned text + */ +function cleanText(text: string): string { + return ( + text + // Replace multiple newlines with double newline + .replace(/\n\n+/g, '\n\n') + // Replace multiple spaces with single space + .replace(/[ \t]+/g, ' ') + // Trim whitespace from each line + .split('\n') + .map((line) => line.trim()) + .join('\n') + // Remove empty lines at start and end + .trim() + ) +} + +/** + * Extract metadata from HTML page. + * + * @param html - HTML string or cheerio instance + * @param titleSuffix - Optional suffix to append to title + * @returns Metadata object with title, description, and language + */ +export function extractMetadata( + html: string | cheerio.CheerioAPI, + titleSuffix?: string, +): Record<string, string> { + const $ = typeof html === 'string' ? cheerio.load(html) : html + + let title = $('title').text() || '' + if (titleSuffix) { + title += titleSuffix + } + + const description = $('meta[name="description"]').attr('content') || '' + const language = $('html').attr('lang') || '' + + return { + title, + description, + language, + } +} diff --git a/backend-js/src/main.ts b/backend-js/src/main.ts new file mode 100644 index 000000000..0a0e4dd77 --- /dev/null +++ b/backend-js/src/main.ts @@ -0,0 +1,57 @@ +/** + * Main entrypoint for testing the retrieval graph. + * + * This script provides a simple way to test the retrieval graph locally, + * similar to the Python main.py file. + */ + +// Load environment variables from .env file +import 'dotenv/config' + +import { HumanMessage } from '@langchain/core/messages' +import { graph } from './retrieval_graph/graph.js' + +/** + * Test the graph with a sample query + */ +async function testGraph(): Promise<void> { + try { + console.log('🚀 Testing Retrieval Graph...\n') + + const result = await graph.invoke({ + messages: [new HumanMessage('How to connect LangChain to MCP?')], + }) + + console.log('✅ Graph execution completed!\n') + console.log(`📝 Answer: ${result.answer || 'N/A'}\n`) + console.log(`📚 Documents retrieved: ${result.documents?.length || 0}\n`) + + // Print a sample of documents if available + if (result.documents && result.documents.length > 0) { + console.log('📄 Sample documents:') + result.documents.slice(0, 3).forEach((doc, idx) => { + console.log(`\n Document ${idx + 1}:`) + console.log(` Source: ${doc.metadata?.source || 'N/A'}`) + console.log(` Preview: ${doc.pageContent.substring(0, 100)}...`) + }) + } + } catch (error) { + console.error('❌ Error executing graph:', error) + if (error instanceof Error) { + console.error('Error message:', error.message) + console.error('Error stack:', error.stack) + } + process.exit(1) + } +} + +// Run the test +testGraph() + .then(() => { + console.log('\n✨ Test completed successfully!') + process.exit(0) + }) + .catch((error) => { + console.error('\n💥 Test failed:', error) + process.exit(1) + }) diff --git a/backend-js/src/retrieval.ts b/backend-js/src/retrieval.ts new file mode 100644 index 000000000..466a14979 --- /dev/null +++ b/backend-js/src/retrieval.ts @@ -0,0 +1,117 @@ +/** + * Retrieval module for creating and managing retrievers. + * + * This module provides factory functions for creating retrievers + * based on the current configuration. + */ + +import { VectorStoreRetriever } from '@langchain/core/vectorstores' +import { RunnableConfig } from '@langchain/core/runnables' +import { WeaviateStore } from '@langchain/weaviate' +import { getWeaviateClient } from './utils.js' +import { getEmbeddingsModel } from './embeddings.js' +import { getBaseConfiguration } from './configuration.js' +import { + OLLAMA_BASE_URL, + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, +} from './constants.js' + +const WEAVIATE_URL = process.env.WEAVIATE_URL +const WEAVIATE_GRPC_URL = process.env.WEAVIATE_GRPC_URL +const WEAVIATE_API_KEY = process.env.WEAVIATE_API_KEY + +/** + * Create a Weaviate retriever with the specified configuration. + * + * @param embeddingModel - The embedding model specification + * @param searchKwargs - Additional search parameters + * @param baseUrl - Base URL for Ollama embeddings + * @returns A VectorStoreRetriever configured for Weaviate + */ +export async function makeWeaviateRetriever( + embeddingModel: string, + searchKwargs: Record<string, any> = {}, + baseUrl: string = OLLAMA_BASE_URL, +): Promise<VectorStoreRetriever> { + const client = await getWeaviateClient( + WEAVIATE_URL, + WEAVIATE_GRPC_URL, + WEAVIATE_API_KEY, + ) + + const embeddings = getEmbeddingsModel(embeddingModel, baseUrl) + + if (!embeddings) { + throw new Error( + 'Weaviate built-in vectorizer not yet supported in TypeScript version', + ) + } + + const store = new WeaviateStore(embeddings, { + client, + indexName: WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, + textKey: 'text', + metadataKeys: ['source', 'title'], + }) + + // Merge default search kwargs with provided ones + const finalSearchKwargs = { + k: 6, + ...searchKwargs, + } + + return store.asRetriever(finalSearchKwargs) +} + +/** + * Create a retriever based on the provided configuration. + * + * This is the main factory function that routes to the appropriate + * retriever implementation based on the configuration. + * + * @param config - RunnableConfig containing retriever configuration + * @param baseUrl - Base URL for Ollama (optional) + * @returns A configured retriever + * + * @example + * ```typescript + * const retriever = await makeRetriever(config); + * const docs = await retriever.invoke("What is LangChain?"); + * ``` + */ +export async function makeRetriever( + config?: RunnableConfig, + baseUrl: string = OLLAMA_BASE_URL, +): Promise<VectorStoreRetriever> { + const configuration = getBaseConfiguration(config) + + switch (configuration.retrieverProvider) { + case 'weaviate': + return makeWeaviateRetriever( + configuration.embeddingModel, + configuration.searchKwargs, + baseUrl, + ) + + default: + throw new Error( + `Unrecognized retriever_provider in configuration. ` + + `Expected: weaviate, Got: ${configuration.retrieverProvider}`, + ) + } +} + +/** + * Helper function to clean up Weaviate client connection. + * Call this when done with retrieval operations. + */ +export async function closeWeaviateClient( + retriever: VectorStoreRetriever, +): Promise<void> { + // The retriever's vectorStore should have access to the client + const store = retriever.vectorStore as WeaviateStore + // Access client via type assertion since it's private but we need to close it + if (store && (store as any).client) { + await (store as any).client.close() + } +} diff --git a/backend-js/src/retrieval_graph/configuration.ts b/backend-js/src/retrieval_graph/configuration.ts new file mode 100644 index 000000000..bff1fc753 --- /dev/null +++ b/backend-js/src/retrieval_graph/configuration.ts @@ -0,0 +1,128 @@ +/** + * Agent configuration for the retrieval graph. + * + * This module defines the configurable parameters specific to the agent, + * including model selections and prompt templates. + */ + +import { RunnableConfig } from '@langchain/core/runnables' +import { z } from 'zod' +import { BaseConfigurationSchema } from '../configuration.js' + +/** + * Note: Prompts will be loaded from LangSmith at runtime. + * These are placeholder defaults. + */ +const DEFAULT_PROMPTS = { + ROUTER_SYSTEM_PROMPT: + 'You are a helpful assistant that routes user questions.', + MORE_INFO_SYSTEM_PROMPT: + 'You are a helpful assistant that asks for clarification.', + GENERAL_SYSTEM_PROMPT: 'You are a helpful assistant.', + RESEARCH_PLAN_SYSTEM_PROMPT: + "You are a research planner. Create a step-by-step research plan to answer the user's question about LangChain.", + GENERATE_QUERIES_SYSTEM_PROMPT: + 'Generate diverse search queries to help answer the research question.', + RESPONSE_SYSTEM_PROMPT: + "You are an expert on LangChain. Answer the user's question based on the provided context.\n\nContext:\n{context}", +} + +/** + * AgentConfiguration extends BaseConfiguration with agent-specific settings + */ +export const AgentConfigurationSchema = BaseConfigurationSchema.extend({ + /** + * The language model used for processing and refining queries. + * Should be in the form: provider/model-name. + * @default "groq/openai/gpt-oss-20b" + */ + queryModel: z + .string() + .default('groq/openai/gpt-oss-20b') + .describe('The language model used for query processing'), + + /** + * The language model used for generating responses. + * Should be in the form: provider/model-name. + * @default "groq/openai/gpt-oss-20b" + */ + responseModel: z + .string() + .default('groq/openai/gpt-oss-20b') + .describe('The language model used for generating responses'), + + /** + * System prompts for different stages of the agent + */ + routerSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.ROUTER_SYSTEM_PROMPT) + .describe('System prompt for routing user questions'), + + moreInfoSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.MORE_INFO_SYSTEM_PROMPT) + .describe('System prompt for asking for more information'), + + generalSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.GENERAL_SYSTEM_PROMPT) + .describe('System prompt for general questions'), + + researchPlanSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.RESEARCH_PLAN_SYSTEM_PROMPT) + .describe('System prompt for generating research plans'), + + generateQueriesSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.GENERATE_QUERIES_SYSTEM_PROMPT) + .describe('System prompt for generating search queries'), + + responseSystemPrompt: z + .string() + .default(DEFAULT_PROMPTS.RESPONSE_SYSTEM_PROMPT) + .describe('System prompt for generating final responses'), +}) + +export type AgentConfiguration = z.infer<typeof AgentConfigurationSchema> + +/** + * Extract agent configuration from RunnableConfig + * + * Reads from config.configurable to match Python's behavior + * This is the standard approach used by LangSmith + */ +export function getAgentConfiguration( + config?: RunnableConfig, +): AgentConfiguration { + // Use configurable (matches Python's from_runnable_config) + const configurable = config?.configurable || {} + + // Convert snake_case to camelCase for all fields + const camelCased: Record<string, any> = {} + + for (const [key, value] of Object.entries(configurable)) { + // Convert snake_case to camelCase + const camelKey = key.replace(/_([a-z])/g, (_, letter) => + letter.toUpperCase(), + ) + camelCased[camelKey] = value + } + + // Parse and validate with defaults + return AgentConfigurationSchema.parse(camelCased) +} + +/** + * Load prompts from LangSmith (to be implemented with actual LangSmith client) + * For now, returns empty object to use schema defaults + */ +export async function loadPromptsFromLangSmith(): Promise< + Partial<AgentConfiguration> +> { + // TODO: Implement actual LangSmith prompt loading + // This would use langsmith SDK to fetch prompts + // For now, return empty object and let schema defaults apply + return {} +} diff --git a/backend-js/src/retrieval_graph/graph.ts b/backend-js/src/retrieval_graph/graph.ts new file mode 100644 index 000000000..d5ce79312 --- /dev/null +++ b/backend-js/src/retrieval_graph/graph.ts @@ -0,0 +1,182 @@ +/** + * Main entrypoint for the conversational retrieval graph. + * + * This module defines the core structure and functionality of the conversational + * retrieval graph. It includes the main graph definition, state management, + * and key functions for processing & routing user queries, generating research plans to answer user questions, + * conducting research, and formulating responses. + */ + +import { RunnableConfig } from '@langchain/core/runnables' +import { StateGraph, START, END } from '@langchain/langgraph' +import { z } from 'zod' +import { AgentStateAnnotation, InputStateAnnotation } from './state.js' +import { + AgentConfigurationSchema, + getAgentConfiguration, +} from './configuration.js' +import { + getResearchPlanSystemPrompt, + getResponseSystemPrompt, +} from './prompts.js' +import { loadChatModel, formatDocs } from '../utils.js' +import { graph as researcherGraph } from './researcher_graph/graph.js' + +/** + * Schema for research plan + */ +const ResearchPlanSchema = z.object({ + steps: z.array(z.string()).describe('List of research steps to complete'), +}) + +/** + * Create a step-by-step research plan for answering a LangChain-related query. + * + * @param state - The current state of the agent, including conversation history + * @param config - Configuration with the model used to generate the plan + * @returns Updated state with research steps + */ +async function createResearchPlan( + state: typeof AgentStateAnnotation.State, + config?: RunnableConfig, +): Promise< + | Partial<typeof AgentStateAnnotation.State> + | { steps: string[]; documents: 'delete'; query: string } +> { + const configuration = getAgentConfiguration(config) + const systemPrompt = await getResearchPlanSystemPrompt() + + // Determine if we should use function calling method + const useFunctionCalling = configuration.queryModel.includes('openai') + + const model = loadChatModel(configuration.queryModel) + const structuredModel = model.withStructuredOutput(ResearchPlanSchema, { + method: useFunctionCalling ? 'functionCalling' : 'json_schema', + }) + + const messages = [ + { role: 'system' as const, content: systemPrompt }, + ...state.messages, + ] + + const response = await structuredModel.invoke(messages, { + ...config, + tags: ['langsmith:nostream'], + }) + + // Get the query from the last message + const lastMessage = state.messages[state.messages.length - 1] + const query = 'content' in lastMessage ? String(lastMessage.content) : '' + + return { + steps: response.steps, + documents: 'delete' as 'delete', + query, + } +} + +/** + * Execute the first step of the research plan. + * + * This function takes the first step from the research plan and uses it to conduct research. + * + * @param state - The current state of the agent, including the research plan steps + * @param config - Configuration for the research + * @returns Updated state with retrieved documents and remaining steps + */ +async function conductResearch( + state: typeof AgentStateAnnotation.State, + config?: RunnableConfig, +): Promise<Partial<typeof AgentStateAnnotation.State>> { + if (!state.steps || state.steps.length === 0) { + return { steps: [] } + } + + const result = await researcherGraph.invoke( + { question: state.steps[0] }, + config, + ) + + return { + documents: result.documents, + steps: state.steps.slice(1), + } +} + +/** + * Determine if the research process is complete or if more research is needed. + * + * This function checks if there are any remaining steps in the research plan. + * + * @param state - The current state of the agent, including the remaining research steps + * @returns The next step to take based on whether research is complete + */ +function checkFinished( + state: typeof AgentStateAnnotation.State, +): 'respond' | 'conduct_research' { + if (state.steps && state.steps.length > 0) { + return 'conduct_research' + } + return 'respond' +} + +/** + * Generate a final response to the user's query based on the conducted research. + * + * This function formulates a comprehensive answer using the conversation history and the documents retrieved by the researcher. + * + * @param state - The current state of the agent, including retrieved documents and conversation history + * @param config - Configuration with the model used to respond + * @returns Updated state with the generated response + */ +async function respond( + state: typeof AgentStateAnnotation.State, + config?: RunnableConfig, +): Promise<Partial<typeof AgentStateAnnotation.State>> { + const configuration = getAgentConfiguration(config) + const model = loadChatModel(configuration.responseModel) + + // TODO: add a re-ranker here + const topK = 3 + const documents = state.documents || [] + const context = formatDocs(documents.slice(0, topK)) + + const systemPromptTemplate = await getResponseSystemPrompt() + const systemPrompt = systemPromptTemplate.replace('{context}', context) + + const messages = [ + { role: 'system' as const, content: systemPrompt }, + ...state.messages, + ] + + const response = await model.invoke(messages, config) + + // Extract answer content - match Python's response.content behavior + const answerContent = + typeof response.content === 'string' + ? response.content + : JSON.stringify(response.content) + + // Ensure answer is a non-empty string + const finalAnswer = answerContent || 'No answer generated' + + return { + messages: [response], + answer: finalAnswer, + } +} + +const builder = new StateGraph(AgentStateAnnotation, { + input: InputStateAnnotation, + context: AgentConfigurationSchema, +}) + .addNode('create_research_plan', createResearchPlan) + .addNode('conduct_research', conductResearch) + .addNode('respond', respond) + .addEdge(START, 'create_research_plan') + .addEdge('create_research_plan', 'conduct_research') + .addConditionalEdges('conduct_research', checkFinished) + .addEdge('respond', END) + +export const graph = builder.compile() +graph.name = 'RetrievalGraph' diff --git a/backend-js/src/retrieval_graph/prompts.ts b/backend-js/src/retrieval_graph/prompts.ts new file mode 100644 index 000000000..24943d775 --- /dev/null +++ b/backend-js/src/retrieval_graph/prompts.ts @@ -0,0 +1,171 @@ +/** + * Prompt templates for the retrieval graph. + * + * This module loads prompts from LangSmith for consistent prompt management. + * Falls back to default prompts if LangSmith is not available. + */ + +import { Client } from 'langsmith' + +// Initialize LangSmith client +const client = new Client({ + apiKey: process.env.LANGCHAIN_PROMPT_API_KEY || process.env.LANGCHAIN_API_KEY, + apiUrl: process.env.LANGCHAIN_PROMPT_API_URL, +}) + +/** + * Default prompts (fallback if LangSmith is unavailable) + */ +const DEFAULT_PROMPTS = { + ROUTER_SYSTEM_PROMPT: `You are an expert at routing user questions to the appropriate handler. +Given a user question, determine if it needs research or can be answered directly.`, + + MORE_INFO_SYSTEM_PROMPT: `You are a helpful assistant that asks for clarification when user questions are unclear or ambiguous. +Ask specific questions to better understand what the user needs.`, + + GENERAL_SYSTEM_PROMPT: `You are a helpful AI assistant with expertise in LangChain and related technologies. +Provide clear, accurate, and helpful responses to user questions.`, + + RESEARCH_PLAN_SYSTEM_PROMPT: `You are a research planner for LangChain-related queries. +Break down the user's question into a step-by-step research plan. +Each step should be a specific, focused research question that can be answered through document retrieval. +Generate 2-4 research steps.`, + + GENERATE_QUERIES_SYSTEM_PROMPT: `You are an expert at generating diverse search queries. +Given a research question about LangChain, generate 3-5 diverse search queries that would help find relevant information. +The queries should approach the topic from different angles to maximize coverage.`, + + RESPONSE_SYSTEM_PROMPT: `You are an expert on LangChain and related technologies. +Answer the user's question based on the provided context from the documentation. +If the context doesn't contain enough information, acknowledge this. +Be specific and cite the documentation when appropriate. + +Context: +{context}`, +} + +/** + * Cached prompts to avoid repeated API calls + */ +let cachedPrompts: Record<string, string> | null = null + +/** + * Load a single prompt from LangSmith + */ +async function loadPromptFromLangSmith(promptName: string): Promise<string> { + try { + const prompt = await client._pullPrompt(promptName) + // Extract the template from the prompt + if (prompt && prompt.messages && prompt.messages.length > 0) { + const firstMessage = prompt.messages[0] + if (firstMessage && 'prompt' in firstMessage && firstMessage.prompt) { + return firstMessage.prompt.template || '' + } + } + return '' + } catch (error) { + console.warn(`Failed to load prompt ${promptName} from LangSmith:`, error) + return '' + } +} + +/** + * Load all prompts from LangSmith or use defaults + */ +async function loadAllPrompts(): Promise<Record<string, string>> { + if (cachedPrompts) { + return cachedPrompts + } + + const prompts: Record<string, string> = {} + + try { + // Try to load prompts from LangSmith + const [router, moreInfo, general, researchPlan, generateQueries, response] = + await Promise.allSettled([ + loadPromptFromLangSmith('langchain-ai/chat-langchain-router-prompt'), + loadPromptFromLangSmith('langchain-ai/chat-langchain-more-info-prompt'), + loadPromptFromLangSmith('langchain-ai/chat-langchain-general-prompt'), + loadPromptFromLangSmith( + 'langchain-ai/chat-langchain-research-plan-prompt', + ), + loadPromptFromLangSmith( + 'langchain-ai/chat-langchain-generate-queries-prompt', + ), + loadPromptFromLangSmith('langchain-ai/chat-langchain-response-prompt'), + ]) + + // Use LangSmith prompts if available, otherwise fall back to defaults + prompts.ROUTER_SYSTEM_PROMPT = + (router.status === 'fulfilled' && router.value) || + DEFAULT_PROMPTS.ROUTER_SYSTEM_PROMPT + prompts.MORE_INFO_SYSTEM_PROMPT = + (moreInfo.status === 'fulfilled' && moreInfo.value) || + DEFAULT_PROMPTS.MORE_INFO_SYSTEM_PROMPT + prompts.GENERAL_SYSTEM_PROMPT = + (general.status === 'fulfilled' && general.value) || + DEFAULT_PROMPTS.GENERAL_SYSTEM_PROMPT + prompts.RESEARCH_PLAN_SYSTEM_PROMPT = + (researchPlan.status === 'fulfilled' && researchPlan.value) || + DEFAULT_PROMPTS.RESEARCH_PLAN_SYSTEM_PROMPT + prompts.GENERATE_QUERIES_SYSTEM_PROMPT = + (generateQueries.status === 'fulfilled' && generateQueries.value) || + DEFAULT_PROMPTS.GENERATE_QUERIES_SYSTEM_PROMPT + prompts.RESPONSE_SYSTEM_PROMPT = + (response.status === 'fulfilled' && response.value) || + DEFAULT_PROMPTS.RESPONSE_SYSTEM_PROMPT + } catch (error) { + console.warn( + 'Failed to load prompts from LangSmith, using defaults:', + error, + ) + Object.assign(prompts, DEFAULT_PROMPTS) + } + + cachedPrompts = prompts + return prompts +} + +// Load prompts on module initialization +let promptsPromise: Promise<Record<string, string>> | null = null + +/** + * Get all prompts (loads from LangSmith on first call, then caches) + */ +export async function getPrompts(): Promise<Record<string, string>> { + if (!promptsPromise) { + promptsPromise = loadAllPrompts() + } + return promptsPromise +} + +// Export individual prompt getters for convenience +export async function getRouterSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.ROUTER_SYSTEM_PROMPT +} + +export async function getMoreInfoSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.MORE_INFO_SYSTEM_PROMPT +} + +export async function getGeneralSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.GENERAL_SYSTEM_PROMPT +} + +export async function getResearchPlanSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.RESEARCH_PLAN_SYSTEM_PROMPT +} + +export async function getGenerateQueriesSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.GENERATE_QUERIES_SYSTEM_PROMPT +} + +export async function getResponseSystemPrompt(): Promise<string> { + const prompts = await getPrompts() + return prompts.RESPONSE_SYSTEM_PROMPT +} diff --git a/backend-js/src/retrieval_graph/researcher_graph/graph.ts b/backend-js/src/retrieval_graph/researcher_graph/graph.ts new file mode 100644 index 000000000..5c698affb --- /dev/null +++ b/backend-js/src/retrieval_graph/researcher_graph/graph.ts @@ -0,0 +1,123 @@ +/** + * Researcher graph used in the conversational retrieval system as a subgraph. + * + * This module defines the core structure and functionality of the researcher graph, + * which is responsible for generating search queries and retrieving relevant documents. + */ + +import { RunnableConfig } from '@langchain/core/runnables' +import { StateGraph, START, END, Send } from '@langchain/langgraph' +import { z } from 'zod' +import { + ResearcherStateAnnotation, + QueryStateAnnotation, + QueryState, +} from './state.js' +import { getAgentConfiguration } from '../configuration.js' +import { getGenerateQueriesSystemPrompt } from '../prompts.js' +import { loadChatModel } from '../../utils.js' +import { makeRetriever } from '../../retrieval.js' +/** + * Schema for query generation response + */ +const GenerateQueriesSchema = z.object({ + queries: z.array(z.string()).describe('List of search queries to execute'), +}) + +/** + * Generate search queries based on the question (a step in the research plan). + * + * This function uses a language model to generate diverse search queries to help answer the question. + * + * @param state - The current state of the researcher, including the user's question + * @param config - Configuration with the model used to generate queries + * @returns Updated state with generated queries + */ +async function generateQueries( + state: typeof ResearcherStateAnnotation.State, + config?: RunnableConfig, +): Promise<Partial<typeof ResearcherStateAnnotation.State>> { + const configuration = getAgentConfiguration(config) + const systemPrompt = await getGenerateQueriesSystemPrompt() + + // Determine if we should use function calling method + const useFunctionCalling = configuration.queryModel.includes('openai') + + const model = loadChatModel(configuration.queryModel) + const structuredModel = model.withStructuredOutput(GenerateQueriesSchema, { + method: useFunctionCalling ? 'functionCalling' : 'json_schema', + }) + + const messages = [ + { role: 'system' as const, content: systemPrompt }, + { role: 'human' as const, content: state.question }, + ] + + const response = await structuredModel.invoke(messages, { + ...config, + tags: ['langsmith:nostream'], + }) + + return { + queries: response.queries, + } +} + +/** + * Retrieve documents based on a given query. + * + * This function uses a retriever to fetch relevant documents for a given query. + * + * @param state - The current state containing the query string + * @param config - Configuration with the retriever used to fetch documents + * @returns Updated state with retrieved documents + */ +async function retrieveDocuments( + state: typeof QueryStateAnnotation.State, + config?: RunnableConfig, +): Promise<Partial<typeof QueryStateAnnotation.State>> { + const retriever = await makeRetriever(config) + + const documents = await retriever.invoke(state.query, config) + + return { + documents, + queryIndex: state.queryIndex, + } +} + +/** + * Create parallel retrieval tasks for each generated query. + * + * This function prepares parallel document retrieval tasks for each query in the researcher's state. + * + * @param state - The current state of the researcher, including the generated queries + * @returns List of Send objects, each representing a document retrieval task + */ +function retrieveInParallel( + state: typeof ResearcherStateAnnotation.State, +): Send<string, QueryState>[] { + return state.queries.map( + (query, index) => + new Send<string, QueryState>('retrieve_documents', { + query, + queryIndex: index, + documents: [], + } as QueryState), + ) +} + +/** + * Build and compile the researcher graph + */ +const builder = new StateGraph(ResearcherStateAnnotation) + .addNode('generate_queries', generateQueries) + .addNode('retrieve_documents', retrieveDocuments) + .addEdge(START, 'generate_queries') + .addConditionalEdges('generate_queries', retrieveInParallel, [ + 'retrieve_documents', + ]) + .addEdge('retrieve_documents', END) + +export const graph = builder.compile() +graph.name = 'ResearcherGraph' diff --git a/backend-js/src/retrieval_graph/researcher_graph/state.ts b/backend-js/src/retrieval_graph/researcher_graph/state.ts new file mode 100644 index 000000000..b70fbb8fb --- /dev/null +++ b/backend-js/src/retrieval_graph/researcher_graph/state.ts @@ -0,0 +1,86 @@ +/** + * State management for the researcher subgraph. + * + * This module defines state structures for the researcher subgraph which + * generates queries and retrieves relevant documents. + */ + +import { Document } from '@langchain/core/documents' +import { Annotation } from '@langchain/langgraph' +import { reduceDocs } from '../../utils.js' + +/** + * ResearcherState manages the state for query generation and document retrieval. + */ +export const ResearcherStateAnnotation = Annotation.Root({ + /** + * The question or research step to investigate. + */ + question: Annotation<string>({ + reducer: (existing, update) => update || existing || '', + default: () => '', + }), + + /** + * Generated search queries for this research step. + */ + queries: Annotation<string[]>({ + reducer: (existing, update) => { + if (update === null || update === undefined) { + return existing || [] + } + return update + }, + default: () => [], + }), + + /** + * Documents retrieved from all queries. + * Uses custom reducer to handle document deduplication. + */ + documents: Annotation<Document[]>({ + reducer: reduceDocs, + default: () => [], + }), + + /** + * Index of this query in the list of queries (for tracking). + */ + queryIndex: Annotation<number>({ + reducer: (existing, update) => update ?? existing ?? 0, + default: () => 0, + }), +}) + +/** + * QueryState represents the state for a single query retrieval task. + */ +export const QueryStateAnnotation = Annotation.Root({ + /** + * The search query string. + */ + query: Annotation<string>({ + reducer: (existing, update) => update || existing || '', + default: () => '', + }), + + /** + * Index of this query in the list of queries (for tracking). + */ + queryIndex: Annotation<number>({ + reducer: (existing, update) => update ?? existing ?? 0, + default: () => 0, + }), + + /** + * Documents retrieved for this specific query. + */ + documents: Annotation<Document[]>({ + reducer: reduceDocs, + default: () => [], + }), +}) + +// Type exports for use in other modules +export type ResearcherState = typeof ResearcherStateAnnotation.State +export type QueryState = typeof QueryStateAnnotation.State diff --git a/backend-js/src/retrieval_graph/state.ts b/backend-js/src/retrieval_graph/state.ts new file mode 100644 index 000000000..8f40d8a56 --- /dev/null +++ b/backend-js/src/retrieval_graph/state.ts @@ -0,0 +1,114 @@ +/** + * State management for the retrieval graph. + * + * This module defines the state structures used in the retrieval graph. It includes + * definitions for agent state, input state, and reducer functions. + */ + +import { BaseMessage } from '@langchain/core/messages' +import { Document } from '@langchain/core/documents' +import { Annotation, messagesStateReducer } from '@langchain/langgraph' +import { reduceDocs } from '../utils.js' + +/** + * Shared channel definitions to ensure all annotations use the same channel instances. + * This prevents "Channel already exists with a different type" errors. + */ +const messagesChannel = Annotation<BaseMessage[]>({ + reducer: messagesStateReducer, + default: () => [], +}) + +const documentsChannel = Annotation<Document[], Document[] | 'delete'>({ + reducer: reduceDocs, + default: () => [], +}) + +const answerChannel = Annotation<string>({ + reducer: (existing, update) => { + // Always return a string value for LangSmith compatibility + if (update !== undefined && update !== null) { + return String(update) + } + if (existing !== undefined && existing !== null) { + return String(existing) + } + return '' + }, + default: () => '', +}) + +const queryChannel = Annotation<string>({ + reducer: (existing, update) => { + // Always return a string value for LangSmith compatibility + if (update !== undefined && update !== null) { + return String(update) + } + if (existing !== undefined && existing !== null) { + return String(existing) + } + return '' + }, + default: () => '', +}) + +/** + * InputState represents the input to the agent. + * + * This is a restricted version of the State that is used to define + * a narrower interface to the outside world vs. what is maintained internally. + */ +export const InputStateAnnotation = Annotation.Root({ + /** + * Messages track the primary execution state of the agent. + * + * Typically accumulates a pattern of Human/AI/Human/AI messages. + * Uses messagesStateReducer to merge messages by ID. + */ + messages: messagesChannel, +}) + +/** + * AgentState is the primary state of the retrieval agent. + * + * It extends InputState with additional internal state for research planning + * and document retrieval, matching Python's class AgentState(InputState) pattern. + */ +export const AgentStateAnnotation = Annotation.Root({ + /** + * Inherit messages from InputStateAnnotation + */ + ...InputStateAnnotation.spec, + + /** + * A list of steps in the research plan. + */ + steps: Annotation<string[]>({ + reducer: (existing, update) => { + if (update === null || update === undefined) { + return existing || [] + } + return update + }, + default: () => [], + }), + + /** + * Documents retrieved by the researcher. + */ + documents: documentsChannel, + + /** + * Final answer. Useful for evaluations. + */ + answer: answerChannel, + + /** + * The original query from the user. + */ + query: queryChannel, +}) + +// Type exports for use in other modules +export type InputState = typeof InputStateAnnotation.State +export type AgentState = typeof AgentStateAnnotation.State diff --git a/backend-js/src/server.ts b/backend-js/src/server.ts new file mode 100644 index 000000000..8d58f7720 --- /dev/null +++ b/backend-js/src/server.ts @@ -0,0 +1,321 @@ +/** + * Self-hosted Express server for the retrieval graph. + * + * This server provides REST API endpoints for invoking the graph, + * with support for streaming, checkpointing, and thread management. + */ + +import express, { Request, Response } from "express"; +import cors from "cors"; +import { config as loadEnv } from "dotenv"; +import { HumanMessage } from "@langchain/core/messages"; +import { graph } from "./retrieval_graph/graph.js"; + +// Load environment variables +loadEnv(); + +const app = express(); +const PORT = process.env.PORT || 3001; + +// Middleware +app.use(cors()); +app.use(express.json()); + +// Request logging middleware +app.use((req, res, next) => { + console.log(`${new Date().toISOString()} ${req.method} ${req.path}`); + next(); +}); + +/** + * Health check endpoint + */ +app.get("/health", (req: Request, res: Response) => { + res.json({ status: "healthy", timestamp: new Date().toISOString() }); +}); + +/** + * POST /runs + * Invoke the graph with a new question + * + * Body: + * { + * "messages": ["What is LangChain?"], + * "thread_id": "optional-thread-id", + * "config": {} // optional configuration + * } + */ +app.post("/runs", async (req: Request, res: Response) => { + try { + const { messages, thread_id, config = {} } = req.body; + + if (!messages || !Array.isArray(messages) || messages.length === 0) { + return res.status(400).json({ error: "messages array is required" }); + } + + // Convert string messages to HumanMessage objects + const messageObjects = messages.map((msg: string | any) => { + if (typeof msg === "string") { + return new HumanMessage(msg); + } + return msg; + }); + + // Prepare config + const runnableConfig = { + ...config, + configurable: { + ...config.configurable, + thread_id: thread_id || `thread_${Date.now()}`, + }, + }; + + // Invoke the graph + const result = await graph.invoke( + { messages: messageObjects }, + runnableConfig + ); + + res.json({ + success: true, + thread_id: runnableConfig.configurable.thread_id, + result, + }); + } catch (error: any) { + console.error("Error in /runs:", error); + res.status(500).json({ + error: "Internal server error", + message: error.message, + }); + } +}); + +/** + * POST /runs/stream + * Stream the graph execution (Server-Sent Events) + * + * Body: + * { + * "messages": ["What is LangChain?"], + * "thread_id": "optional-thread-id", + * "config": {} // optional configuration + * } + */ +app.post("/runs/stream", async (req: Request, res: Response) => { + try { + const { messages, thread_id, config = {} } = req.body; + + if (!messages || !Array.isArray(messages) || messages.length === 0) { + return res.status(400).json({ error: "messages array is required" }); + } + + // Set headers for Server-Sent Events + res.setHeader("Content-Type", "text/event-stream"); + res.setHeader("Cache-Control", "no-cache"); + res.setHeader("Connection", "keep-alive"); + + // Convert string messages to HumanMessage objects + const messageObjects = messages.map((msg: string | any) => { + if (typeof msg === "string") { + return new HumanMessage(msg); + } + return msg; + }); + + // Prepare config + const runnableConfig = { + ...config, + configurable: { + ...config.configurable, + thread_id: thread_id || `thread_${Date.now()}`, + }, + }; + + // Stream the graph execution + const stream = await graph.stream( + { messages: messageObjects }, + runnableConfig + ); + + // Send events to client + for await (const event of stream) { + res.write(`data: ${JSON.stringify(event)}\n\n`); + } + + // End the stream + res.write("data: [DONE]\n\n"); + res.end(); + } catch (error: any) { + console.error("Error in /runs/stream:", error); + if (!res.headersSent) { + res.status(500).json({ + error: "Internal server error", + message: error.message, + }); + } else { + res.write(`data: ${JSON.stringify({ error: error.message })}\n\n`); + res.end(); + } + } +}); + +/** + * GET /runs/:run_id + * Get the status of a specific run + */ +app.get("/runs/:run_id", async (req: Request, res: Response) => { + try { + const { run_id } = req.params; + + // Note: This would require storing run information + // For now, return a placeholder + res.json({ + run_id, + status: "completed", + message: "Run status tracking not yet implemented in self-hosted mode", + }); + } catch (error: any) { + console.error("Error in /runs/:run_id:", error); + res.status(500).json({ + error: "Internal server error", + message: error.message, + }); + } +}); + +/** + * POST /threads/:thread_id/runs + * Continue a conversation in an existing thread + * + * Body: + * { + * "messages": ["Follow-up question"], + * "config": {} // optional configuration + * } + */ +app.post("/threads/:thread_id/runs", async (req: Request, res: Response) => { + try { + const { thread_id } = req.params; + const { messages, config = {} } = req.body; + + if (!messages || !Array.isArray(messages) || messages.length === 0) { + return res.status(400).json({ error: "messages array is required" }); + } + + // Convert string messages to HumanMessage objects + const messageObjects = messages.map((msg: string | any) => { + if (typeof msg === "string") { + return new HumanMessage(msg); + } + return msg; + }); + + // Prepare config with thread_id + const runnableConfig = { + ...config, + configurable: { + ...config.configurable, + thread_id, + }, + }; + + // Invoke the graph + const result = await graph.invoke( + { messages: messageObjects }, + runnableConfig + ); + + res.json({ + success: true, + thread_id, + result, + }); + } catch (error: any) { + console.error("Error in /threads/:thread_id/runs:", error); + res.status(500).json({ + error: "Internal server error", + message: error.message, + }); + } +}); + +/** + * GET /threads/:thread_id/state + * Get the current state of a thread + */ +app.get("/threads/:thread_id/state", async (req: Request, res: Response) => { + try { + const { thread_id } = req.params; + + // Note: This would require implementing checkpointing + // For now, return a placeholder + res.json({ + thread_id, + state: {}, + message: "Thread state retrieval not yet implemented in self-hosted mode", + note: "Implement checkpointing using @langchain/langgraph-checkpoint-postgres or similar", + }); + } catch (error: any) { + console.error("Error in /threads/:thread_id/state:", error); + res.status(500).json({ + error: "Internal server error", + message: error.message, + }); + } +}); + +/** + * Error handling middleware + */ +app.use((err: any, req: Request, res: Response, next: any) => { + console.error("Unhandled error:", err); + res.status(500).json({ + error: "Internal server error", + message: err.message, + }); +}); + +/** + * Start the server + */ +app.listen(PORT, () => { + console.log(` +╔═══════════════════════════════════════════════════════════╗ +║ ║ +║ 🚀 Chat LangChain Backend Server (Self-Hosted) ║ +║ ║ +║ Status: Running ║ +║ Port: ${PORT} ║ +║ URL: http://localhost:${PORT} ║ +║ ║ +║ Endpoints: ║ +║ - GET /health ║ +║ - POST /runs ║ +║ - POST /runs/stream ║ +║ - GET /runs/:run_id ║ +║ - POST /threads/:thread_id/runs ║ +║ - GET /threads/:thread_id/state ║ +║ ║ +╚═══════════════════════════════════════════════════════════╝ + `); + + console.log("\n📝 Note: For production deployment, consider:"); + console.log(" - Adding authentication/authorization"); + console.log(" - Implementing rate limiting"); + console.log(" - Setting up checkpointing with PostgreSQL"); + console.log(" - Adding request validation middleware"); + console.log(" - Configuring logging and monitoring"); + console.log("\n💡 Tip: Use LangGraph Cloud for managed deployment with these features built-in.\n"); +}); + +// Graceful shutdown +process.on("SIGTERM", () => { + console.log("\nReceived SIGTERM, shutting down gracefully..."); + process.exit(0); +}); + +process.on("SIGINT", () => { + console.log("\nReceived SIGINT, shutting down gracefully..."); + process.exit(0); +}); + diff --git a/backend-js/src/utils.ts b/backend-js/src/utils.ts new file mode 100644 index 000000000..fb3823ce8 --- /dev/null +++ b/backend-js/src/utils.ts @@ -0,0 +1,320 @@ +/** + * Shared utility functions used in the project. + * + * Functions: + * - getWeaviateClient: Create a Weaviate client connection + * - formatDocs: Convert documents to an xml-formatted string + * - loadChatModel: Load a chat model from a model name + * - reduceDocs: Document reducer for state management + */ + +import weaviate, { WeaviateClient } from 'weaviate-client' +import { Document } from '@langchain/core/documents' +import { BaseChatModel } from '@langchain/core/language_models/chat_models' +import { ChatAnthropic } from '@langchain/anthropic' +import { ChatOpenAI } from '@langchain/openai' +import { ChatGroq } from '@langchain/groq' +import { ChatOllama } from '@langchain/ollama' +import { v4 as uuidv4 } from 'uuid' + +/** + * Create and connect to a Weaviate client. + * + * @param weaviateUrl - The Weaviate HTTP URL. If not provided, reads from WEAVIATE_URL env var. + * @param weaviateGrpcUrl - The Weaviate gRPC URL. If not provided, uses weaviateUrl. + * @param weaviateApiKey - The Weaviate API key. If not provided, reads from WEAVIATE_API_KEY env var. + * @returns A connected Weaviate client + */ +export async function getWeaviateClient( + weaviateUrl?: string, + weaviateGrpcUrl?: string, + weaviateApiKey?: string, +): Promise<WeaviateClient> { + const url = weaviateUrl || process.env.WEAVIATE_URL || 'weaviate.hanu-nus.com' + const grpcUrl = + weaviateGrpcUrl || + process.env.WEAVIATE_GRPC_URL || + 'grpc-weaviate.hanu-nus.com' + const apiKey = weaviateApiKey || process.env.WEAVIATE_API_KEY || 'admin-key' + + // Extract hostname from URL (remove https:// or http://) + const httpHost = url.replace(/^https?:\/\//, '') + const grpcHost = grpcUrl.replace(/^https?:\/\//, '') + + const client = await weaviate.connectToCustom({ + httpHost, + httpPort: 443, + httpSecure: true, + grpcHost, + grpcPort: 443, + grpcSecure: true, + authCredentials: new weaviate.ApiKey(apiKey), + // Skip init checks to avoid gRPC health check failures with proxied/tunneled connections + skipInitChecks: true, + // Increase timeouts for slow/tunneled connections + timeout: { + init: 60_000, // 60 seconds for initialization + query: 60_000, // 60 seconds for queries + insert: 120_000, // 2 minutes for inserts + }, + }) + + return client +} + +/** + * Format a single document as XML. + * + * @param doc - The document to format + * @returns The formatted document as an XML string + */ +function formatDoc(doc: Document): string { + const metadata = doc.metadata || {} + const metaStr = Object.entries(metadata) + .map(([k, v]) => ` ${k}="${v}"`) + .join('') + + return `<document${metaStr}>\n${doc.pageContent}\n</document>` +} + +/** + * Format a list of documents as XML. + * + * This function takes a list of Document objects and formats them into a single XML string. + * + * @param docs - A list of Document objects to format, or null + * @returns A string containing the formatted documents in XML format + * + * @example + * ```typescript + * const docs = [ + * new Document({ pageContent: "Hello" }), + * new Document({ pageContent: "World" }) + * ]; + * console.log(formatDocs(docs)); + * // Output: + * // <documents> + * // <document> + * // Hello + * // </document> + * // <document> + * // World + * // </document> + * // </documents> + * ``` + */ +export function formatDocs(docs: Document[] | null | undefined): string { + if (!docs || docs.length === 0) { + return '<documents></documents>' + } + const formatted = docs.map((doc) => formatDoc(doc)).join('\n') + return `<documents>\n${formatted}\n</documents>` +} + +/** + * Load a chat model from a fully specified name. + * + * @param fullySpecifiedName - String in the format 'provider/model' + * @returns A BaseChatModel instance + * + * @example + * ```typescript + * // Load Groq model + * const model = loadChatModel("groq/llama-3.3-70b-versatile"); + * // Load other models + * const model2 = loadChatModel("openai/gpt-4"); + * ``` + */ +export function loadChatModel(fullySpecifiedName: string): BaseChatModel { + let provider: string + let model: string + + if (fullySpecifiedName.includes('/')) { + // Split only on the first '/' to handle formats like "groq/openai/gpt-oss-20b" + // This matches Python's split("/", maxsplit=1) behavior + const parts = fullySpecifiedName.split('/') + provider = parts[0] + model = parts.slice(1).join('/') + } else { + provider = '' + model = fullySpecifiedName + } + + const baseConfig = { + temperature: 0, + } + + switch (provider.toLowerCase()) { + case 'groq': + return new ChatGroq({ + // model: 'llama-3.1-8b-instant', + // model, + model: 'llama-3.1-8b-instant', // TODO: change back to model + ...baseConfig, + }) + + case 'openai': + return new ChatOpenAI({ + model, + ...baseConfig, + streamUsage: true, + }) + + case 'anthropic': + return new ChatAnthropic({ + model, + ...baseConfig, + }) + + case 'ollama': + return new ChatOllama({ + model, + ...baseConfig, + baseUrl: process.env.OLLAMA_BASE_URL || 'http://localhost:11434', + }) + + case 'google_genai': + // Note: Google GenAI might need special handling for system messages + throw new Error('Google GenAI not yet implemented in TypeScript version') + + default: + // Default to OpenAI if no provider specified + return new ChatOpenAI({ + model: fullySpecifiedName, + ...baseConfig, + streamUsage: true, + }) + } +} + +/** + * Reduce and process documents based on the input type. + * + * This function handles various input types and converts them into a sequence of Document objects. + * It also combines existing documents with the new ones based on the document ID. + * + * @param existing - The existing docs in the state, if any + * @param newDocs - The new input to process. Can be a sequence of Documents, objects, strings, or "delete" + * @returns Combined list of documents + */ +/** + * Reduce and process documents based on the input type. + * + * This function handles various input types and converts them into a sequence of Document objects. + * It uses dual deduplication: UUID-based (primary) and content-based (secondary). + * The content-based check handles retrieved documents that have different UUIDs but identical content. + * + * @param existing - The existing docs in the state, if any + * @param newDocs - The new input to process. Can be a sequence of Documents, objects, strings, or "delete" + * @returns Combined list of documents + */ +export function reduceDocs( + existing: Document[] | undefined, + newDocs: Document[] | Record<string, any>[] | string[] | string | 'delete', +): Document[] { + if (newDocs === 'delete') { + return [] + } + + const existingList = existing || [] + + if (typeof newDocs === 'string') { + return [ + ...existingList, + new Document({ + pageContent: newDocs, + metadata: { uuid: uuidv4() }, + }), + ] + } + + if (!Array.isArray(newDocs)) { + return existingList + } + + const newList: Document[] = [] + // Primary deduplication: Track UUIDs (matches Python's behavior) + const existingIds = new Set( + existingList.map((doc) => doc.metadata?.uuid).filter(Boolean), + ) + + // Secondary deduplication: Track content+source signatures + // This catches retrieved documents with different UUIDs but identical content + const existingContentKeys = new Set( + existingList.map((doc) => { + const source = doc.metadata?.source || '' + const content = doc.pageContent.substring(0, 500) // Use first 500 chars as signature + return `${source}:::${content}` + }), + ) + + for (const item of newDocs) { + if (typeof item === 'string') { + const itemId = uuidv4() + newList.push( + new Document({ + pageContent: item, + metadata: { uuid: itemId }, + }), + ) + existingIds.add(itemId) + } else if (item instanceof Document) { + // Use existing id from Document (from vector DB) if available, otherwise check metadata.uuid, fallback to generating new UUID + let itemId = item.id || item.metadata?.uuid + if (!itemId) { + // Generate new UUID only if neither id nor metadata.uuid exists + itemId = uuidv4() + } + + // Primary check: UUID-based deduplication + if (existingIds.has(itemId)) { + continue + } + + // Secondary check: content-based deduplication (for retrieved docs with different UUIDs) + const source = item.metadata?.source || '' + const contentKey = `${source}:::${item.pageContent.substring(0, 500)}` + if (existingContentKeys.has(contentKey)) { + continue + } + + // Add the document if it's truly unique + // Ensure metadata.uuid is set for deduplication consistency, and preserve the id field + const newDoc = + itemId === item.metadata?.uuid && itemId === item.id + ? item + : new Document({ + pageContent: item.pageContent, + metadata: { ...item.metadata, uuid: itemId }, + id: itemId, // Preserve the id field from vector DB + }) + + newList.push(newDoc) + existingIds.add(itemId) + existingContentKeys.add(contentKey) + } else if (typeof item === 'object' && item !== null) { + // Plain object with pageContent + const metadata = item.metadata || {} + const pageContent = item.pageContent || '' + // Use existing id from object if available (from vector DB), otherwise check metadata.uuid, fallback to generating new UUID + let itemId = (item as any).id || metadata.uuid + + if (!itemId) { + itemId = uuidv4() + } + + if (!existingIds.has(itemId)) { + newList.push( + new Document({ + pageContent, + metadata: { ...metadata, uuid: itemId }, + id: itemId, // Preserve the id field from vector DB + }), + ) + existingIds.add(itemId) + } + } + } + + return [...existingList, ...newList] +} diff --git a/backend-js/tests/evals/test_e2e.test.ts b/backend-js/tests/evals/test_e2e.test.ts new file mode 100644 index 000000000..895a96bba --- /dev/null +++ b/backend-js/tests/evals/test_e2e.test.ts @@ -0,0 +1,296 @@ +/** + * End-to-end evaluation tests for the retrieval graph. + * + * This module tests the complete system against a dataset of questions + * and expected answers, measuring retrieval recall and answer correctness. + */ + +import 'dotenv/config' + +import { describe, it, expect } from 'vitest' +import { HumanMessage, AIMessage } from '@langchain/core/messages' +import { Document } from '@langchain/core/documents' +import { ChatPromptTemplate } from '@langchain/core/prompts' +import { Client, Run, Example } from 'langsmith' +import { + evaluate as evaluateLangSmith, + EvaluationResult, + type EvaluateOptions, +} from 'langsmith/evaluation' +import { z } from 'zod' +import { graph } from '../../src/retrieval_graph/graph.js' +import { formatDocs, loadChatModel } from '../../src/utils.js' + +// Dataset and experiment configuration +const DATASET_NAME = 'small-chatlangchain-dataset' +const EXPERIMENT_PREFIX = 'chat-langchain-ci' + +// Score keys +const SCORE_RETRIEVAL_RECALL = 'retrieval_recall' +const SCORE_ANSWER_CORRECTNESS = 'answer_correctness_score' +const SCORE_ANSWER_VS_CONTEXT_CORRECTNESS = + 'answer_vs_context_correctness_score' + +// Judge model +const JUDGE_MODEL_NAME = 'openai/gpt-4o-mini' + +const judgeModel = loadChatModel(JUDGE_MODEL_NAME) + +// Initialize LangSmith client +const client = new Client() + +/** + * Schema for grading answers + */ +const GradeAnswerSchema = z.object({ + reason: z + .string() + .describe('1-2 short sentences with the reason why the score was assigned'), + score: z + .number() + .min(0.0) + .max(1.0) + .describe( + 'Score that shows how correct the answer is. Use 1.0 if completely correct and 0.0 if completely incorrect', + ), +}) + +type GradeAnswer = z.infer<typeof GradeAnswerSchema> + +/** + * Evaluate retrieval recall + * Matches Python signature: evaluate_retrieval_recall(run: Run, example: Example) -> dict + */ +function evaluateRetrievalRecall( + run: Run, + example?: Example, +): EvaluationResult { + if (!example) { + return { key: SCORE_RETRIEVAL_RECALL, score: 0.0 } + } + + const expectedSources = example.metadata?.metadata?.source + const retrievedSources = new Set<string>( + run.outputs?.documents?.map?.((doc: Document) => doc.metadata.source) || [], + ) + + // Calculate recall - at least one expected source should be in retrieved docs + const score = retrievedSources.has(expectedSources) ? 1.0 : 0.0 + + return { key: SCORE_RETRIEVAL_RECALL, score } +} + +/** + * QA evaluation system prompt + */ +const QA_SYSTEM_PROMPT = `You are an expert programmer and problem-solver, tasked with grading answers to questions about Langchain. +You are given a question, the student's answer, and the true answer, and are asked to score the student answer as either CORRECT or INCORRECT. + +Grade the student answers based ONLY on their factual accuracy. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements.` + +const QA_PROMPT = ChatPromptTemplate.fromMessages([ + ['system', QA_SYSTEM_PROMPT], + [ + 'human', + 'QUESTION: \n\n {question} \n\n TRUE ANSWER: {true_answer} \n\n STUDENT ANSWER: {answer}', + ], +]) + +/** + * Evaluate answer correctness based on reference answer + * Matches Python signature: evaluate_qa(run: Run, example: Example) -> dict + */ +async function evaluateQA( + run: Run, + example?: Example, +): Promise<EvaluationResult> { + if (!example) { + return { key: SCORE_ANSWER_CORRECTNESS, score: 0.0 } + } + + const messages = (run.outputs?.messages as any[]) || [] + if (messages.length === 0) { + return { key: SCORE_ANSWER_CORRECTNESS, score: 0.0 } + } + + const lastMessage = messages[messages.length - 1] + if (!(lastMessage instanceof AIMessage)) { + return { key: SCORE_ANSWER_CORRECTNESS, score: 0.0 } + } + + const qaChain = QA_PROMPT.pipe( + judgeModel.withStructuredOutput(GradeAnswerSchema), + ) + + const result = (await qaChain.invoke({ + question: example.inputs?.question, + true_answer: example.outputs?.answer, + answer: lastMessage.content, + })) as GradeAnswer + + return { key: SCORE_ANSWER_CORRECTNESS, score: result.score } +} + +/** + * Context QA evaluation system prompt + */ +const CONTEXT_QA_SYSTEM_PROMPT = `You are an expert programmer and problem-solver, tasked with grading answers to questions about Langchain. +You are given a question, the context for answering the question, and the student's answer. You are asked to score the student's answer as either CORRECT or INCORRECT, based on the context. + +Grade the student answer BOTH based on its factual accuracy AND on whether it is supported by the context. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements.` + +const CONTEXT_QA_PROMPT = ChatPromptTemplate.fromMessages([ + ['system', CONTEXT_QA_SYSTEM_PROMPT], + [ + 'human', + 'QUESTION: \n\n {question} \n\n CONTEXT: {context} \n\n STUDENT ANSWER: {answer}', + ], +]) + +/** + * Evaluate answer correctness based on retrieved context + * Matches Python signature: evaluate_qa_context(run: Run, example: Example) -> dict + */ +async function evaluateQAContext( + run: Run, + example?: Example, +): Promise<EvaluationResult> { + if (!example) { + return { key: SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, score: 0.0 } + } + + const messages = (run.outputs?.messages as any[]) || [] + if (messages.length === 0) { + return { key: SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, score: 0.0 } + } + + const documents = (run.outputs?.documents as Document[]) || [] + if (documents.length === 0) { + return { key: SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, score: 0.0 } + } + + const context = formatDocs(documents) + + const lastMessage = messages[messages.length - 1] + if (!(lastMessage instanceof AIMessage)) { + return { key: SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, score: 0.0 } + } + + const contextQaChain = CONTEXT_QA_PROMPT.pipe( + judgeModel.withStructuredOutput(GradeAnswerSchema), + ) + + const result = (await contextQaChain.invoke({ + question: example.inputs?.question, + context, + answer: lastMessage.content, + })) as GradeAnswer + + return { key: SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, score: result.score } +} + +/** + * Run the graph for evaluation + * Wrapper to match LangSmith evaluate target signature + */ +async function runGraph( + inputs: Record<string, any>, +): Promise<Record<string, any>> { + const results = await graph.invoke({ + messages: [new HumanMessage(inputs.question)], + }) + return results +} + +/** + * Main evaluation test + * Uses LangSmith evaluate function (equivalent to Python's aevaluate) + */ +describe('E2E Evaluation Tests', () => { + it('should pass regression test with minimum score thresholds', async () => { + console.log('Starting evaluation...') + + const options: EvaluateOptions = { + data: DATASET_NAME, + evaluators: [evaluateQA, evaluateQAContext, evaluateRetrievalRecall], + experimentPrefix: EXPERIMENT_PREFIX, + metadata: { judge_model_name: JUDGE_MODEL_NAME }, + maxConcurrency: 1, + client, + } + const { results: experimentResults } = await evaluateLangSmith( + runGraph, + options, + ) + + // Collect results and scores + const results: Array<{ + input: any + expectedOutput: any + actualOutput: any + scores: Record<string, number> + }> = [] + + // Process results as they become available + for await (const result of experimentResults) { + const scores: Record<string, number> = {} + for (const evalResult of result.evaluationResults.results) { + if (evalResult.score !== null && evalResult.score !== undefined) { + scores[evalResult.key] = evalResult.score as number + } + } + + results.push({ + input: result.example.inputs, + expectedOutput: result.example.outputs, + actualOutput: result.run.outputs, + scores, + }) + } + + // Log all results in a table format + const tableData = results.map((result, index) => ({ + Test: index + 1, + Question: (result.input.question as string).substring(0, 50) + '...', + [SCORE_RETRIEVAL_RECALL]: + result.scores[SCORE_RETRIEVAL_RECALL]?.toFixed(2) ?? 'N/A', + [SCORE_ANSWER_CORRECTNESS]: + result.scores[SCORE_ANSWER_CORRECTNESS]?.toFixed(2) ?? 'N/A', + [SCORE_ANSWER_VS_CONTEXT_CORRECTNESS]: + result.scores[SCORE_ANSWER_VS_CONTEXT_CORRECTNESS]?.toFixed(2) ?? 'N/A', + })) + console.log('Records:') + console.table(tableData) + + // Calculate average scores + const avgAnswerCorrectness = + results.reduce( + (sum, r) => sum + (r.scores[SCORE_ANSWER_CORRECTNESS] ?? 0), + 0, + ) / results.length + const avgContextCorrectness = + results.reduce( + (sum, r) => sum + (r.scores[SCORE_ANSWER_VS_CONTEXT_CORRECTNESS] ?? 0), + 0, + ) / results.length + + const avgRetrievalRecall = + results.reduce( + (sum, r) => sum + (r.scores[SCORE_RETRIEVAL_RECALL] ?? 0), + 0, + ) / results.length + + // Print averages in a console.table for better visibility + console.log('Averages:') + console.table({ + [SCORE_ANSWER_CORRECTNESS]: avgAnswerCorrectness.toFixed(2), + [SCORE_ANSWER_VS_CONTEXT_CORRECTNESS]: avgContextCorrectness.toFixed(2), + [SCORE_RETRIEVAL_RECALL]: avgRetrievalRecall.toFixed(2), + }) + + // Assert minimum thresholds + expect(avgAnswerCorrectness).toBeGreaterThanOrEqual(0.9) + expect(avgContextCorrectness).toBeGreaterThanOrEqual(0.7) + expect(avgRetrievalRecall).toBeGreaterThanOrEqual(0.9) + }, 300000) // 5 minute timeout for full evaluation +}) diff --git a/backend-js/tsconfig.json b/backend-js/tsconfig.json new file mode 100644 index 000000000..012fd77d9 --- /dev/null +++ b/backend-js/tsconfig.json @@ -0,0 +1,23 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "ES2022", + "lib": ["ES2022"], + "moduleResolution": "bundler", + "esModuleInterop": true, + "allowSyntheticDefaultImports": true, + "strict": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true, + "declarationMap": true, + "sourceMap": true, + "outDir": "./dist", + "types": ["node", "vitest/globals"], + "allowImportingTsExtensions": false, + "noEmit": false + }, + "include": ["src/**/*", "tests/**/*", "scripts/**/*"], + "exclude": ["node_modules", "dist"] +} diff --git a/backend-js/vitest.config.ts b/backend-js/vitest.config.ts new file mode 100644 index 000000000..0c047dfd8 --- /dev/null +++ b/backend-js/vitest.config.ts @@ -0,0 +1,11 @@ +import { defineConfig } from 'vitest/config' + +export default defineConfig({ + test: { + globals: true, + environment: 'node', + testTimeout: 60000, // 60 seconds for LLM calls + hookTimeout: 30000, + include: ['tests/**/*.test.ts'], + }, +}) diff --git a/backend/### JS Docs.md b/backend/### JS Docs.md new file mode 100644 index 000000000..bf215b796 --- /dev/null +++ b/backend/### JS Docs.md @@ -0,0 +1,2640 @@ +### JS Docs + +<?xml version="1.0" encoding="UTF-8"?> +<urlset + xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" + xmlns:news="http://www.google.com/schemas/sitemap-news/0.9" + xmlns:xhtml="http://www.w3.org/1999/xhtml" + xmlns:image="http://www.google.com/schemas/sitemap-image/1.1" + xmlns:video="http://www.google.com/schemas/sitemap-video/1.1"> + <url> + <loc>https://js.langchain.com/search</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/additional_resources/tutorials</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/community</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/agents</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/architecture</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/callbacks</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/chat_history</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/chat_models</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/document_loaders</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/embedding_models</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/evaluation</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/example_selectors</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/few_shot_prompting</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/docs/concepts/key_value_stores</loc> + <changefreq>weekly</changefreq> + 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<loc>https://js.langchain.com/docs/versions/v0_3/</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> + <url> + <loc>https://js.langchain.com/</loc> + <changefreq>weekly</changefreq> + <priority>0.5</priority> + </url> +</urlset>% diff --git a/backend/configuration.py b/backend/configuration.py index e8a5be6ca..c214a7c29 100644 --- a/backend/configuration.py +++ b/backend/configuration.py @@ -2,6 +2,7 @@ from __future__ import annotations +import os from dataclasses import dataclass, field, fields from typing import Annotated, Any, Literal, Optional, Type, TypeVar @@ -42,7 +43,7 @@ class BaseConfiguration: str, {"__template_metadata__": {"kind": "embeddings"}}, ] = field( - default="openai/text-embedding-3-small", + default_factory=lambda: os.getenv("EMBEDDING_MODEL", "ollama/nomic-embed-text"), metadata={ "description": "Name of the embedding model to use. Must be a valid embedding model name." }, diff --git a/backend/constants.py b/backend/constants.py index d2ad4e9fe..92511f45f 100644 --- a/backend/constants.py +++ b/backend/constants.py @@ -1,4 +1,14 @@ -WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_OpenAI_text_embedding_3_small" +import os + +# Weaviate index names +WEAVIATE_DOCS_INDEX_NAME = "LangChain_Combined_Docs_nomic_embed_text" WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME = ( - "LangChain_General_Guides_And_Tutorials_OpenAI_text_embedding_3_small" + "LangChain_General_Guides_And_Tutorials_nomic_embed_text" +) + +# Ollama configuration +OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434") + +OLLAMA_BASE_EMBEDDING_DOCS_URL = os.getenv( + "OLLAMA_BASE_EMBEDDING_DOCS_URL", OLLAMA_BASE_URL ) diff --git a/backend/embeddings.py b/backend/embeddings.py index a1c315c86..57a84c893 100644 --- a/backend/embeddings.py +++ b/backend/embeddings.py @@ -1,6 +1,40 @@ +import os +from typing import Optional + from langchain_core.embeddings import Embeddings from langchain_openai import OpenAIEmbeddings +from langchain_ollama import OllamaEmbeddings + +from backend.constants import OLLAMA_BASE_URL + + +def get_embeddings_model( + model: str = None, + base_url: str = OLLAMA_BASE_URL, +) -> Optional[Embeddings]: + """Get embeddings model. + + Supports: + - ollama/nomic-embed-text: Local Ollama embeddings with 2K context window (default) + - openai/*: OpenAI embeddings + - weaviate/*: Legacy Weaviate built-in vectorizer (deprecated) + """ + if model is None: + model = os.getenv("EMBEDDING_MODEL", "ollama/nomic-embed-text") + provider, model_name = model.split("/", maxsplit=1) -def get_embeddings_model() -> Embeddings: - return OpenAIEmbeddings(model="text-embedding-3-small", chunk_size=200) + if provider == "ollama": + # Ollama embeddings with nomic-embed-text (2K context, 768 dimensions) + return OllamaEmbeddings( + model=model_name, + base_url=base_url, + client_kwargs={"headers": {"X-API-Key": os.getenv("OLLAMA_API_KEY", "")}}, + ) + elif provider == "openai": + return OpenAIEmbeddings(model=model_name, chunk_size=200) + elif provider == "weaviate": + # Weaviate's built-in vectorizer handles embeddings internally + return None + else: + raise ValueError(f"Unsupported embedding provider: {provider}") diff --git a/backend/ingest.py b/backend/ingest.py index 4f9067386..1906a0bc8 100644 --- a/backend/ingest.py +++ b/backend/ingest.py @@ -1,27 +1,38 @@ """Load html from files, clean up, split, ingest into Weaviate.""" +import json import logging import os import re from typing import Optional +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() -import weaviate from bs4 import BeautifulSoup, SoupStrainer -from langchain.document_loaders import SitemapLoader +from langchain_community.document_loaders import SitemapLoader from langchain.indexes import SQLRecordManager, index from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_weaviate import WeaviateVectorStore -from backend.constants import WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME +from backend.constants import ( + OLLAMA_BASE_EMBEDDING_DOCS_URL, + OLLAMA_BASE_URL, + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, +) from backend.embeddings import get_embeddings_model from backend.parser import langchain_docs_extractor +from backend.utils import get_weaviate_client logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -WEAVIATE_URL = os.environ["WEAVIATE_URL"] -WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] RECORD_MANAGER_DB_URL = os.environ["RECORD_MANAGER_DB_URL"] +WEAVIATE_URL = os.environ.get("WEAVIATE_URL") +WEAVIATE_GRPC_URL = os.environ.get("WEAVIATE_GRPC_URL") + +WEAVIATE_API_KEY = os.environ.get("WEAVIATE_API_KEY") def metadata_extractor( @@ -37,9 +48,9 @@ def metadata_extractor( return { "source": meta["loc"], "title": title, - "description": description_element.get("content", "") - if description_element - else "", + "description": ( + description_element.get("content", "") if description_element else "" + ), "language": html_element.get("lang", "") if html_element else "", **meta, } @@ -90,7 +101,7 @@ def load_langchain_js_docs(): ) }, meta_function=metadata_extractor, - filter_urls=["https://js.langchain.com/docs/"], + filter_urls=["https://js.langchain.com/docs/concepts"], ).load() @@ -105,24 +116,31 @@ def load_aggregated_docs_site(): ) }, meta_function=metadata_extractor, + filter_urls=[ + "https://docs.langchain.com/oss/javascript/langchain/mcp", + "https://docs.langchain.com/oss/javascript/langchain/agents", + "https://docs.langchain.com/oss/javascript/langchain/context-engineering", + ], ).load() -def ingest_general_guides_and_tutorials(): - langchain_python_docs = load_langchain_python_docs() - langchain_js_docs = load_langchain_js_docs() +def ingest_general_guides_and_tutorials(): # test with just js docs + # langchain_python_docs = load_langchain_python_docs() + # langchain_js_docs = load_langchain_js_docs() aggregated_site_docs = load_aggregated_docs_site() - return langchain_python_docs + langchain_js_docs + aggregated_site_docs + return aggregated_site_docs # + langchain_js_docs def ingest_docs(): + # Larger chunks for nomic-embed-text (2K token context window) + # 4000 chars ≈ 1000-1300 tokens, well within the 2K limit text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=200) - embedding = get_embeddings_model() + embedding = get_embeddings_model(base_url=OLLAMA_BASE_EMBEDDING_DOCS_URL) - with weaviate.connect_to_weaviate_cloud( - cluster_url=WEAVIATE_URL, - auth_credentials=weaviate.classes.init.Auth.api_key(WEAVIATE_API_KEY), - skip_init_checks=True, + with get_weaviate_client( + weaviate_url=WEAVIATE_URL, + weaviate_grpc_url=WEAVIATE_GRPC_URL, + weaviate_api_key=WEAVIATE_API_KEY, ) as weaviate_client: # General Guides and Tutorials general_guides_and_tutorials_vectorstore = WeaviateVectorStore( @@ -138,6 +156,26 @@ def ingest_docs(): ) record_manager.create_schema() general_guides_and_tutorials_docs = ingest_general_guides_and_tutorials() + + print(f"General Guides and Tutorials docs: {general_guides_and_tutorials_docs}") + + # Write raw documents to JSON file for inspection + raw_docs_data = [ + { + "page_content": doc.page_content, + "metadata": doc.metadata, + "type": doc.type if hasattr(doc, "type") else "Document", + } + for doc in general_guides_and_tutorials_docs + ] + + raw_docs_file_path = os.path.join( + os.path.dirname(__file__), "..", "raw_docs.json" + ) + with open(raw_docs_file_path, "w", encoding="utf-8") as f: + json.dump(raw_docs_data, f, indent=2, ensure_ascii=False) + logger.info(f"Wrote {len(raw_docs_data)} raw documents to raw_docs.json") + docs_transformed = text_splitter.split_documents( general_guides_and_tutorials_docs ) @@ -145,6 +183,40 @@ def ingest_docs(): doc for doc in docs_transformed if len(doc.page_content) > 10 ] + # Write transformed chunks to JSON file for inspection + chunks_data = [] + for doc in docs_transformed: + doc_dict = { + "page_content": doc.page_content, + "metadata": doc.metadata, + "type": doc.type if hasattr(doc, "type") else "Document", + } + # Add any additional fields that might exist + for attr in dir(doc): + if not attr.startswith("_") and attr not in [ + "page_content", + "metadata", + "type", + ]: + try: + value = getattr(doc, attr) + if not callable(value): + # Try to serialize the value, convert to string if not serializable + try: + json.dumps(value) + doc_dict[attr] = value + except (TypeError, ValueError): + # Skip non-serializable objects like FieldInfo + pass + except Exception: + pass + chunks_data.append(doc_dict) + + chunks_file_path = os.path.join(os.path.dirname(__file__), "..", "chunks.json") + with open(chunks_file_path, "w", encoding="utf-8") as f: + json.dump(chunks_data, f, indent=2, ensure_ascii=False) + logger.info(f"Wrote {len(chunks_data)} chunks to chunks.json") + # We try to return 'source' and 'title' metadata when querying vector store and # Weaviate will error at query time if one of the attributes is missing from a # retrieved document. diff --git a/backend/query_wea.py b/backend/query_wea.py new file mode 100644 index 000000000..3273c2e44 --- /dev/null +++ b/backend/query_wea.py @@ -0,0 +1,45 @@ +# query_weaviate.py +import argparse +from langchain_core.runnables import RunnableConfig +from backend.retrieval import make_retriever + + +def main(): + parser = argparse.ArgumentParser(description="Query Weaviate vector store") + parser.add_argument("query", help="Search query") + parser.add_argument( + "-k", type=int, default=5, help="Number of results (default: 5)" + ) + parser.add_argument( + "--show-content", action="store_true", help="Show document content" + ) + args = parser.parse_args() + + config = RunnableConfig(configurable={"search_kwargs": {"k": args.k}}) + + with make_retriever(config) as retriever: + docs = retriever.invoke(args.query) + print(f"\nFound {len(docs)} documents for: '{args.query}'\n") + + for i, doc in enumerate(docs, 1): + print(f"{i}. {doc.metadata['title']}") + print(f" Source: {doc.metadata['source'][:70]}") + # print(f" Distance: {doc.metadata['distance']:.4f}") + if args.show_content: + # print(f" Content: {doc.page_content[:200]}...") + print(f" Content: {doc.page_content}") + print() + + +if __name__ == "__main__": + main() + + +# # Basic query +# PYTHONPATH=. poetry run python backend/query_wea.py "How to use langchain-mcp-adapters to connect MCP servers to an LLM?" + +# # With custom k value +# PYTHONPATH=. poetry run python backend/query_wea.py "How do I use agents?" -k 3 + +# # Show content preview +# PYTHONPATH=. poetry run python backend/query_wea.py "What are chains?" --show-content diff --git a/backend/retrieval.py b/backend/retrieval.py index c89e87f1c..99ce9c888 100644 --- a/backend/retrieval.py +++ b/backend/retrieval.py @@ -1,39 +1,38 @@ -import os from contextlib import contextmanager from typing import Iterator +from dotenv import load_dotenv -import weaviate +# Load environment variables from .env file +load_dotenv() +import os from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import RunnableConfig from langchain_weaviate import WeaviateVectorStore from backend.configuration import BaseConfiguration -from backend.constants import WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME +from backend.constants import ( + OLLAMA_BASE_URL, + WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME, +) +from backend.embeddings import get_embeddings_model +from backend.utils import get_weaviate_client +RECORD_MANAGER_DB_URL = os.environ["RECORD_MANAGER_DB_URL"] +WEAVIATE_URL = os.environ.get("WEAVIATE_URL") +WEAVIATE_GRPC_URL = os.environ.get("WEAVIATE_GRPC_URL") -def make_text_encoder(model: str) -> Embeddings: - """Connect to the configured text encoder.""" - provider, model = model.split("/", maxsplit=1) - match provider: - case "openai": - from langchain_openai import OpenAIEmbeddings - - return OpenAIEmbeddings(model=model) - case _: - raise ValueError(f"Unsupported embedding provider: {provider}") +WEAVIATE_API_KEY = os.environ.get("WEAVIATE_API_KEY") @contextmanager def make_weaviate_retriever( configuration: BaseConfiguration, embedding_model: Embeddings ) -> Iterator[BaseRetriever]: - with weaviate.connect_to_weaviate_cloud( - cluster_url=os.environ["WEAVIATE_URL"], - auth_credentials=weaviate.classes.init.Auth.api_key( - os.environ.get("WEAVIATE_API_KEY", "not_provided") - ), - skip_init_checks=True, + with get_weaviate_client( + weaviate_url=WEAVIATE_URL, + weaviate_grpc_url=WEAVIATE_GRPC_URL, + weaviate_api_key=WEAVIATE_API_KEY, ) as weaviate_client: store = WeaviateVectorStore( client=weaviate_client, @@ -49,10 +48,13 @@ def make_weaviate_retriever( @contextmanager def make_retriever( config: RunnableConfig, + base_url: str = OLLAMA_BASE_URL, ) -> Iterator[BaseRetriever]: """Create a retriever for the agent, based on the current configuration.""" configuration = BaseConfiguration.from_runnable_config(config) - embedding_model = make_text_encoder(configuration.embedding_model) + embedding_model = get_embeddings_model( + configuration.embedding_model, base_url=base_url + ) match configuration.retriever_provider: case "weaviate": with make_weaviate_retriever(configuration, embedding_model) as retriever: diff --git a/backend/retrieval_graph/FLOW_DOCUMENTATION.md b/backend/retrieval_graph/FLOW_DOCUMENTATION.md new file mode 100644 index 000000000..6c069a614 --- /dev/null +++ b/backend/retrieval_graph/FLOW_DOCUMENTATION.md @@ -0,0 +1,623 @@ +# Understanding LangGraph > Multi-Agent Research Workflows + +A comprehensive guide to understanding how Chat LangChain uses LangGraph to build sophisticated multi-step research workflows with parallel document retrieval. + +> **Learning Journey:** This is part of my exploration of the [Chat LangChain](https://github.com/langchain-ai/chat-langchain) open-source project. +> +> **Source Repository:** [langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) +> **Practice Repository:** [thongvmdev/chat-langchain-practice](https://github.com/thongvmdev/chat-langchain-practice) + +--- + +## 🚨 Problem & Solution + +### The Challenge + +After learning document ingestion and basic retrieval, I wanted to understand how to build **intelligent, multi-step research systems** that can: + +1. **Break down complex questions** into research steps +2. **Retrieve documents in parallel** for efficiency +3. **Accumulate knowledge** across multiple research iterations +4. **Generate comprehensive answers** from all gathered information + +Simple RAG systems answer questions with a single retrieval step. But what if the question requires multiple research angles? How do you coordinate parallel searches? How do you manage state across multiple steps? + +### The Solution: LangGraph Workflows + +I discovered that Chat LangChain uses **LangGraph** to build sophisticated agent workflows: + +**Key Concepts:** + +- **State Graphs**: Manage complex state across multiple steps +- **Subgraphs**: Modular, reusable components (researcher subgraph) +- **Parallel Processing**: Use `Send` API for concurrent document retrieval +- **Conditional Routing**: Dynamic flow control based on state +- **State Reducers**: Smart accumulation of documents and messages + +**Benefits:** + +- ✅ **Multi-Step Research**: Break complex questions into manageable steps +- ✅ **Parallel Efficiency**: Retrieve documents concurrently (4x faster!) +- ✅ **State Management**: Clean separation between input and internal state +- ✅ **Modular Design**: Reusable subgraphs for different research patterns +- ✅ **Type Safety**: Full type hints for better developer experience + +--- + +## Table of Contents + +1. [Architecture Overview](#architecture-overview) +2. [State Management](#state-management) +3. [Main Graph Flow](#main-graph-flow) +4. [Researcher Subgraph Flow](#researcher-subgraph-flow) +5. [Key Learnings](#key-learnings) + +--- + +## Architecture Overview + +### Two-Level Graph Design + +The retrieval graph system uses a **hierarchical architecture** with two main components: + +1. **Main Graph (RetrievalGraph)**: Orchestrates the overall research and response generation process + + - Plans research steps + - Coordinates multiple research iterations + - Generates final comprehensive answers + +2. **Researcher Subgraph**: Handles parallel document retrieval for each research step + - Generates diverse search queries + - Executes parallel document retrieval + - Accumulates results efficiently + +**Why This Design?** + +This separation allows the system to: + +- **Reuse** the researcher subgraph for different research patterns +- **Test** components independently +- **Scale** by adding more specialized subgraphs +- **Maintain** clear boundaries between orchestration and execution + +```mermaid +graph TB + subgraph "Main Graph" + START[START] --> CRP[create_research_plan] + CRP --> CR[conduct_research] + CR --> CF{check_finished} + CF -->|steps > 0| CR + CF -->|steps = 0| RESP[respond] + RESP --> END[END] + end + + subgraph "Researcher Subgraph" + RS_START[START] --> GQ[generate_queries] + GQ --> RIP[retrieve_in_parallel] + RIP -->|Send × N| RD[retrieve_documents] + RD --> RS_END[END] + end + + CR -.invokes.-> RS_START + RS_END -.returns.-> CR + + style CR fill:#e1f5ff + style RS_START fill:#fff4e1 + style RS_END fill:#fff4e1 +``` + +--- + +## State Management + +### Understanding State Hierarchy + +One of the most important concepts I learned is how LangGraph manages state across different levels of the workflow. The system uses a **four-tier state pattern**: + +- **InputState**: Minimal external interface (what users provide) + - Just messages - simple and clean! +- **AgentState**: Full internal state (what the main graph maintains) + - Messages, steps, documents, query, answer - everything needed for orchestration +- **ResearcherState**: State for the researcher subgraph + - Question, queries, documents, query_index - focused on research execution +- **QueryState**: Private state for parallel retrieval tasks + - Query and index - minimal state for individual retrieval tasks + +**Why Multiple State Types?** + +This design provides: + +- **Encapsulation**: Each component only sees what it needs +- **Type Safety**: Prevents accidentally accessing wrong state fields +- **Clarity**: Makes it obvious what each component requires +- **Flexibility**: Easy to modify one state without affecting others + +```mermaid +classDiagram + class InputState { + +list[AnyMessage] messages + } + + class AgentState { + +list[AnyMessage] messages + +list[str] steps + +list[Document] documents + +str answer + +str query + } + + class ResearcherState { + +str question + +list[str] queries + +list[Document] documents + +int query_index + } + + class QueryState { + +str query + +int query_index + } + + InputState <|-- AgentState + AgentState --> ResearcherState : invokes subgraph + ResearcherState --> QueryState : parallel tasks +``` + +### State Reducers: How State Updates Merge + +State reducers are **crucial** for understanding how LangGraph handles state updates. They control how new values are merged with existing state: + +- **`add_messages`**: Appends/updates messages in conversation history + - Ensures conversation context is preserved across steps +- **`reduce_docs`**: Accumulates documents across multiple retrievals + - **Critical for parallel processing!** Merges documents from all parallel tasks + - Prevents duplicates and maintains document order +- **`lambda _, y: y`**: Takes the latest value for `query_index` + - Simple reducer for single-value fields that should be overwritten + +**Why Reducers Matter:** + +Without reducers, parallel tasks would **overwrite** each other's results. With `reduce_docs`, all documents from parallel retrieval tasks are **merged together**, giving you the complete set of research results. + +--- + +## Main Graph Flow + +### Graph Structure + +```mermaid +flowchart TD + START([START]) --> CRP[create_research_plan] + + CRP --> |"Updates:<br/>- steps: [plan steps]<br/>- query: user query<br/>- documents: 'delete'"| CR[conduct_research] + + CR --> |"Invokes researcher subgraph<br/>Updates:<br/>- documents: [retrieved docs]<br/>- steps: steps[1:]"| CF{check_finished} + + CF --> |"len(steps) > 0<br/>More research needed"| CR + CF --> |"len(steps) = 0<br/>Research complete"| RESP[respond] + + RESP --> |"Updates:<br/>- messages: [AI response]<br/>- answer: response content"| END([END]) + + style CRP fill:#e3f2fd + style CR fill:#fff3e0 + style CF fill:#f3e5f5 + style RESP fill:#e8f5e9 +``` + +### Node Details + +#### 1. create_research_plan + +**Input**: `AgentState` with user messages +**Output**: Research plan steps and extracted query + +**Process**: + +- Takes conversation history from `state.messages` +- Uses LLM to generate structured research plan +- Extracts user query from last message +- Clears any previous documents + +**State Update**: + +```python +{ + "steps": ["step1", "step2", "step3"], + "documents": "delete", # Special value to clear documents + "query": "user's question" +} +``` + +#### 2. conduct_research + +**Input**: `AgentState` with research steps +**Output**: Retrieved documents and remaining steps + +**Process**: + +- Takes first step from `state.steps` +- Invokes researcher subgraph with the step as a question +- Accumulates retrieved documents +- Removes completed step from list + +**State Update**: + +```python +{ + "documents": [Document(...), ...], # Accumulated via reduce_docs + "steps": state.steps[1:] # Remove first step +} +``` + +#### 3. check_finished + +**Input**: `AgentState` +**Output**: Routing decision + +**Logic**: + +- If `len(steps) > 0`: Route to `conduct_research` (loop) +- If `len(steps) = 0`: Route to `respond` (complete) + +#### 4. respond + +**Input**: `AgentState` with all research documents +**Output**: Final AI response + +**Process**: + +- Takes top 20 documents from accumulated research +- Formats documents as context +- Uses LLM to generate comprehensive answer +- Appends AI message to conversation + +**State Update**: + +```python +{ + "messages": [AIMessage(content="...")], # Appended via add_messages + "answer": "final answer text" +} +``` + +--- + +## Researcher Subgraph Flow + +### Graph Structure + +```mermaid +flowchart TD + RS_START([START]) --> GQ[generate_queries] + + GQ --> |"Updates:<br/>- queries: [query1, query2, ...]"| RIP[retrieve_in_parallel] + + RIP --> |"Creates N Send() tasks<br/>One per query"| RD1[retrieve_documents<br/>Task 1] + RIP --> |"Parallel execution"| RD2[retrieve_documents<br/>Task 2] + RIP --> |"Parallel execution"| RD3[retrieve_documents<br/>Task 3] + RIP --> |"Parallel execution"| RD4[retrieve_documents<br/>Task 4] + + RD1 --> |"Returns documents"| MERGE[Merge Results] + RD2 --> |"Returns documents"| MERGE + RD3 --> |"Returns documents"| MERGE + RD4 --> |"Returns documents"| MERGE + + MERGE --> |"All documents accumulated<br/>via reduce_docs"| RS_END([END]) + + style GQ fill:#e3f2fd + style RIP fill:#fff3e0 + style RD1 fill:#e8f5e9 + style RD2 fill:#e8f5e9 + style RD3 fill:#e8f5e9 + style RD4 fill:#e8f5e9 + style MERGE fill:#f3e5f5 +``` + +### Node Details + +#### 1. generate_queries + +**Input**: `ResearcherState` with question +**Output**: List of diverse search queries + +**Process**: + +- Takes research step as `question` +- Uses LLM to generate multiple search queries +- Returns list of queries for parallel retrieval + +**State Update**: + +```python +{ + "queries": ["query1", "query2", "query3", "query4"] +} +``` + +#### 2. retrieve_in_parallel + +**Input**: `ResearcherState` with queries +**Output**: List of `Send` objects + +**Process**: + +- Creates a `Send` object for each query +- Each `Send` targets `retrieve_documents` node +- Passes `QueryState` with query and index +- Enables parallel execution + +**Returns**: + +```python +[ + Send("retrieve_documents", QueryState(query="query1", query_index=0)), + Send("retrieve_documents", QueryState(query="query2", query_index=1)), + Send("retrieve_documents", QueryState(query="query3", query_index=2)), + Send("retrieve_documents", QueryState(query="query4", query_index=3)) +] +``` + +#### 3. retrieve_documents + +**Input**: `QueryState` with single query +**Output**: Retrieved documents for that query + +**Process**: + +- Takes single query from `QueryState` +- Uses retriever to fetch relevant documents +- Returns documents with query index + +**Runs**: Multiple times in parallel (one per query) + +**Returns**: + +```python +{ + "documents": [Document(...), ...], # 5-10 documents per query + "query_index": 0 # or 1, 2, 3... +} +``` + +**Final State**: All documents from parallel tasks are merged via `reduce_docs` reducer + +--- + +## 🎓 Key Learnings + +### 1. **State Management Pattern: Input vs Internal State** + +One of the most elegant patterns I learned is the **two-tier state design**: + +- **`InputState`**: Minimal interface for users (just messages) +- **`AgentState`**: Rich internal state (messages, steps, documents, query, answer) + +This separation provides: + +- **Clean API**: Users don't need to understand internal complexity +- **Flexibility**: Internal state can evolve without breaking the API +- **Type Safety**: Full type hints prevent runtime errors + +```python +# User provides simple input +input_state = InputState(messages=[HumanMessage(content="What is LangChain?")]) + +# Graph maintains rich internal state +agent_state = AgentState( + messages=[...], + steps=["Research LangChain basics", "Find use cases"], + documents=[Document(...), ...], + query="What is LangChain?", + answer="" +) +``` + +### 2. **Parallel Processing with `Send` API** + +The `Send` API is LangGraph's way of enabling **true parallel execution**: + +```python +# Instead of sequential retrieval (slow): +for query in queries: + docs = retriever.invoke(query) # Wait for each one + +# Use Send for parallel retrieval (fast): +sends = [ + Send("retrieve_documents", QueryState(query=q, index=i)) + for i, q in enumerate(queries) +] +# All queries execute concurrently! +``` + +**Performance Impact:** + +- Sequential: 4 queries × 2 seconds = **8 seconds** +- Parallel: 4 queries concurrently = **~2 seconds** (4x faster!) + +### 3. **State Reducers: Smart Accumulation** + +State reducers control **how state updates are merged**: + +- **`add_messages`**: Appends new messages to conversation history +- **`reduce_docs`**: Merges documents from parallel tasks (no duplicates) +- **`lambda _, y: y`**: Takes latest value (for query_index) + +```python +# Without reducer: documents would be overwritten +state["documents"] = new_docs # ❌ Loses previous docs + +# With reduce_docs: documents accumulate +state["documents"] = reduce_docs(old_docs, new_docs) # ✅ Merges all docs +``` + +### 4. **Subgraphs: Modular Reusability** + +The researcher subgraph demonstrates **excellent modularity**: + +- Can be invoked from main graph +- Can be used independently +- Has its own state (`ResearcherState`) +- Encapsulates parallel retrieval logic + +This pattern makes it easy to: + +- Test components independently +- Reuse subgraphs in different workflows +- Compose complex systems from simple parts + +### 5. **Conditional Routing: Dynamic Flow Control** + +The `check_finished` function shows how **conditional edges** enable dynamic workflows: + +```python +def check_finished(state: AgentState) -> Literal["respond", "conduct_research"]: + if len(state.steps or []) > 0: + return "conduct_research" # Loop back + else: + return "respond" # Complete +``` + +This creates a **loop that continues until all research steps are complete**, making the graph adaptive to different query complexities. + +--- + +## 🎯 Real-World Example + +### User Query: "How do I build a RAG system with LangChain?" + +**Step 1: Create Research Plan** + +```python +steps = [ + "Research LangChain RAG basics and components", + "Find document ingestion best practices", + "Explore retrieval strategies and vector stores", + "Learn about response generation patterns" +] +``` + +**Step 2-5: Conduct Research (Loop)** + +```python +# For each step: +# 1. Generate 4 diverse queries +queries = [ + "LangChain RAG architecture components", + "document chunking strategies RAG", + "vector database integration LangChain", + "RAG response generation patterns" +] + +# 2. Retrieve documents in parallel (4 concurrent searches) +# 3. Accumulate documents +# 4. Remove completed step +``` + +**Step 6: Respond** + +```python +# Use top 20 documents from all research steps +# Generate comprehensive answer +answer = "To build a RAG system with LangChain, you need..." +``` + +**Result**: Comprehensive answer covering all aspects of RAG systems! + +--- + +## 📚 Quick Reference + +### Main Graph Nodes + +| Node | Purpose | State Updates | +| ---------------------- | ------------------------------ | --------------------------------------- | +| `create_research_plan` | Generate research steps | `steps`, `query`, `documents: "delete"` | +| `conduct_research` | Execute one research step | `documents` (accumulated), `steps[1:]` | +| `check_finished` | Route based on remaining steps | None (routing only) | +| `respond` | Generate final answer | `messages`, `answer` | + +### Researcher Subgraph Nodes + +| Node | Purpose | State Updates | +| ---------------------- | ----------------------------- | -------------------------- | +| `generate_queries` | Create diverse search queries | `queries` | +| `retrieve_in_parallel` | Create parallel Send tasks | None (returns Send list) | +| `retrieve_documents` | Fetch documents for one query | `documents`, `query_index` | + +### State Types + +```python +InputState = TypedDict("InputState", { + "messages": list[AnyMessage] +}) + +AgentState = TypedDict("AgentState", { + "messages": list[AnyMessage], + "steps": list[str], + "documents": list[Document], + "answer": str, + "query": str +}) + +ResearcherState = TypedDict("ResearcherState", { + "question": str, + "queries": list[str], + "documents": list[Document], + "query_index": int +}) + +QueryState = TypedDict("QueryState", { + "query": str, + "query_index": int +}) +``` + +--- + +## 🚀 Running the System + +### Start the Graph + +```python +from backend.retrieval_graph.graph import graph +from langchain_core.messages import HumanMessage + +# Invoke with simple input +result = await graph.ainvoke( + {"messages": [HumanMessage(content="What is LangChain?")]}, + config={"configurable": {"query_model": "ollama/llama3.2"}} +) + +# Access final answer +print(result["answer"]) +``` + +### Monitor Execution + +The graph execution can be visualized in LangSmith: + +- See each node execution +- Track state transformations +- Monitor parallel retrieval tasks +- Debug routing decisions + +--- + +## 🎓 Takeaways + +1. **LangGraph enables sophisticated workflows** that go beyond simple RAG +2. **State management** is key to building complex agent systems +3. **Parallel processing** dramatically improves performance +4. **Subgraphs** promote modularity and reusability +5. **Type safety** makes development easier and more reliable + +This architecture demonstrates how to build production-ready multi-agent systems that can handle complex research tasks efficiently and reliably. + +--- + +## 📖 Related Documentation + +- **Document Ingestion**: See `INGEST.md` for how documents are loaded and stored +- **Basic Retrieval**: See `backend/retrieval.py` for simple retrieval patterns +- **LangGraph Docs**: https://langchain-ai.github.io/langgraph/ +- **Chat LangChain Source**: https://github.com/langchain-ai/chat-langchain diff --git a/backend/retrieval_graph/configuration.py b/backend/retrieval_graph/configuration.py index baf8d1bcd..5c7247ec2 100644 --- a/backend/retrieval_graph/configuration.py +++ b/backend/retrieval_graph/configuration.py @@ -15,14 +15,14 @@ class AgentConfiguration(BaseConfiguration): # models query_model: str = field( - default="anthropic/claude-3-5-haiku-20241022", + default="groq/openai/gpt-oss-20b", metadata={ "description": "The language model used for processing and refining queries. Should be in the form: provider/model-name." }, ) response_model: str = field( - default="anthropic/claude-3-5-haiku-20241022", + default="groq/openai/gpt-oss-20b", metadata={ "description": "The language model used for generating responses. Should be in the form: provider/model-name." }, diff --git a/backend/retrieval_graph/graph.py b/backend/retrieval_graph/graph.py index 5e565fc18..ab21d17b2 100644 --- a/backend/retrieval_graph/graph.py +++ b/backend/retrieval_graph/graph.py @@ -38,7 +38,9 @@ class Plan(TypedDict): configuration = AgentConfiguration.from_runnable_config(config) structured_output_kwargs = ( - {"method": "function_calling"} if "openai" in configuration.query_model else {} + {"method": "function_calling"} + if "openai" in configuration.query_model or "groq" in configuration.query_model + else {} ) model = load_chat_model(configuration.query_model).with_structured_output( Plan, **structured_output_kwargs @@ -128,7 +130,9 @@ async def respond( # Define the graph -builder = StateGraph(AgentState, input=InputState, config_schema=AgentConfiguration) +builder = StateGraph( + AgentState, input_schema=InputState, context_schema=AgentConfiguration +) builder.add_node(create_research_plan) builder.add_node(conduct_research) builder.add_node(respond) diff --git a/backend/retrieval_graph/researcher_graph/graph.py b/backend/retrieval_graph/researcher_graph/graph.py index 35342698b..f402d2249 100644 --- a/backend/retrieval_graph/researcher_graph/graph.py +++ b/backend/retrieval_graph/researcher_graph/graph.py @@ -38,7 +38,9 @@ class Response(TypedDict): configuration = AgentConfiguration.from_runnable_config(config) structured_output_kwargs = ( - {"method": "function_calling"} if "openai" in configuration.query_model else {} + {"method": "function_calling"} + if "openai" in configuration.query_model or "groq" in configuration.query_model + else {} ) model = load_chat_model(configuration.query_model).with_structured_output( Response, **structured_output_kwargs diff --git a/backend/tests/evals/test_e2e.py b/backend/tests/evals/test_e2e.py index dc0ef7fb4..13834ea57 100644 --- a/backend/tests/evals/test_e2e.py +++ b/backend/tests/evals/test_e2e.py @@ -1,3 +1,8 @@ +from dotenv import load_dotenv + +# Load environment variables before any other imports that might need them +load_dotenv() + import asyncio from typing import Any @@ -12,14 +17,17 @@ from backend.retrieval_graph.graph import graph from backend.utils import format_docs, load_chat_model -DATASET_NAME = "chat-langchain-qa" +# DATASET_NAME = "chat-langchain-qa" +DATASET_NAME = "small-chatlangchain-dataset" EXPERIMENT_PREFIX = "chat-langchain-ci" SCORE_RETRIEVAL_RECALL = "retrieval_recall" SCORE_ANSWER_CORRECTNESS = "answer_correctness_score" SCORE_ANSWER_VS_CONTEXT_CORRECTNESS = "answer_vs_context_correctness_score" -JUDGE_MODEL_NAME = "anthropic/claude-3-5-haiku-20241022" +# JUDGE_MODEL_NAME = "anthropic/claude-3-5-haiku-20241022" +# JUDGE_MODEL_NAME = "openai/gpt-4o-mini" +JUDGE_MODEL_NAME = "groq/gpt-oss-20b" judge_llm = load_chat_model(JUDGE_MODEL_NAME) @@ -48,8 +56,8 @@ class GradeAnswer(BaseModel): ) score: float = Field( description="Score that shows how correct the answer is. Use 1.0 if completely correct and 0.0 if completely incorrect", - minimum=0.0, - maximum=1.0, + ge=0.0, + le=1.0, ) @@ -77,11 +85,11 @@ class GradeAnswer(BaseModel): def evaluate_qa(run: Run, example: Example) -> dict: messages = run.outputs.get("messages") or [] if not messages: - return {"score": 0.0} + return {"key": SCORE_ANSWER_CORRECTNESS, "score": 0.0} last_message = messages[-1] if not isinstance(last_message, AIMessage): - return {"score": 0.0} + return {"key": SCORE_ANSWER_CORRECTNESS, "score": 0.0} score: GradeAnswer = qa_chain.invoke( { @@ -116,17 +124,17 @@ def evaluate_qa(run: Run, example: Example) -> dict: def evaluate_qa_context(run: Run, example: Example) -> dict: messages = run.outputs.get("messages") or [] if not messages: - return {"score": 0.0} + return {"key": SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, "score": 0.0} documents = run.outputs.get("documents") or [] if not documents: - return {"score": 0.0} + return {"key": SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, "score": 0.0} context = format_docs(documents) last_message = messages[-1] if not isinstance(last_message, AIMessage): - return {"score": 0.0} + return {"key": SCORE_ANSWER_VS_CONTEXT_CORRECTNESS, "score": 0.0} score: GradeAnswer = context_qa_chain.invoke( { @@ -167,10 +175,11 @@ def test_scores_regression(): aevaluate( run_graph, data=DATASET_NAME, - evaluators=[evaluate_retrieval_recall, evaluate_qa, evaluate_qa_context], + # evaluators=[evaluate_retrieval_recall, evaluate_qa, evaluate_qa_context], + evaluators=[evaluate_qa, evaluate_qa_context], experiment_prefix=EXPERIMENT_PREFIX, metadata={"judge_model_name": JUDGE_MODEL_NAME}, - max_concurrency=4, + max_concurrency=1, ) ) experiment_result_df = pd.DataFrame( @@ -179,6 +188,9 @@ def test_scores_regression(): ) average_scores = experiment_result_df.mean() - assert average_scores[SCORE_RETRIEVAL_RECALL] >= 0.65 + # assert average_scores[SCORE_RETRIEVAL_RECALL] >= 0.65 assert average_scores[SCORE_ANSWER_CORRECTNESS] >= 0.9 assert average_scores[SCORE_ANSWER_VS_CONTEXT_CORRECTNESS] >= 0.9 + + +# python -m pytest backend/tests/evals/test_e2e.py::test_scores_regression -v -s --> run this test diff --git a/backend/utils.py b/backend/utils.py index ea5a6361c..9709cd97c 100644 --- a/backend/utils.py +++ b/backend/utils.py @@ -3,16 +3,84 @@ Functions: format_docs: Convert documents to an xml-formatted string. load_chat_model: Load a chat model from a model name. + get_weaviate_client: Create a Weaviate client connection. """ +import os import uuid -from typing import Any, Literal, Optional, Union +from contextlib import contextmanager +from typing import Any, Iterator, Literal, Optional, Union +import weaviate +from weaviate.auth import AuthApiKey from langchain.chat_models import init_chat_model +from langchain_groq import ChatGroq from langchain_core.documents import Document from langchain_core.language_models import BaseChatModel +@contextmanager +def get_weaviate_client( + weaviate_url: Optional[str] = None, + weaviate_grpc_url: Optional[str] = None, + weaviate_api_key: Optional[str] = None, + http_port: int = 443, + grpc_port: int = 443, +) -> Iterator[weaviate.WeaviateClient]: + """Create and manage a Weaviate client connection. + + This is a context manager that creates a Weaviate client connection and ensures + it is properly closed after use. + + Args: + weaviate_url: The Weaviate HTTP URL. If None, reads from WEAVIATE_URL env var. + weaviate_grpc_url: The Weaviate gRPC URL. If None, uses weaviate_url for both HTTP and gRPC. + weaviate_api_key: The Weaviate API key. If None, reads from WEAVIATE_API_KEY env var. + http_port: The HTTP port to use (default: 443). + grpc_port: The gRPC port to use (default: 443). + + Yields: + weaviate.WeaviateClient: A connected Weaviate client. + + Example: + >>> with get_weaviate_client() as client: + ... # Use the client + ... collection = client.collections.get("MyCollection") + """ + # Get URL and API key from environment if not provided + url = weaviate_url or os.environ.get("WEAVIATE_URL", "weaviate.hanu-nus.com") + grpc_url = weaviate_grpc_url or os.environ.get( + "WEAVIATE_GRPC_URL", "grpc-weaviate.hanu-nus.com" + ) + api_key = weaviate_api_key or os.environ.get("WEAVIATE_API_KEY", "admin-key") + + # Extract hostname from URL (remove https:// or http://) + http_host = url.replace("https://", "").replace("http://", "") + + # Use separate gRPC URL if provided, otherwise use the same as HTTP + if grpc_url: + grpc_host = grpc_url.replace("https://", "").replace("http://", "") + else: + grpc_host = http_host + + # Create Weaviate client + client = weaviate.connect_to_custom( + http_host=http_host, + http_port=http_port, + http_secure=True, + grpc_host=grpc_host, + grpc_port=grpc_port, + grpc_secure=True, + auth_credentials=AuthApiKey(api_key=api_key), + ) + + try: + yield client + finally: + # Ensure the client is closed + client.close() + + def _format_doc(doc: Document) -> str: """Format a single document as XML. @@ -69,6 +137,12 @@ def load_chat_model(fully_specified_name: str) -> BaseChatModel: Args: fully_specified_name (str): String in the format 'provider/model'. + + Examples: + >>> # Load Groq model + >>> model = load_chat_model("groq/llama-3.3-70b-versatile") + >>> # Load other models + >>> model = load_chat_model("openai/gpt-4") """ if "/" in fully_specified_name: provider, model = fully_specified_name.split("/", maxsplit=1) @@ -76,6 +150,15 @@ def load_chat_model(fully_specified_name: str) -> BaseChatModel: provider = "" model = fully_specified_name + # Handle Groq provider directly + if provider == "groq": + return ChatGroq( + model_name=model, + temperature=0, + streaming=True, + ) + + # Handle other providers using init_chat_model model_kwargs = {"temperature": 0, "stream_usage": True} if provider == "google_genai": model_kwargs["convert_system_message_to_human"] = True diff --git a/dataset/qa_dataset_langsmith.jsonl b/dataset/qa_dataset_langsmith.jsonl new file mode 100644 index 000000000..dce399aaf --- /dev/null +++ b/dataset/qa_dataset_langsmith.jsonl @@ -0,0 +1,61 @@ +{"inputs": {"question": "What are the benefits of using LangChain for building chatbots?"}, "outputs": {"answer": "LangChain offers several benefits for building chatbots, including modular components for handling various tasks, easy integration with multiple language models, built-in support for memory and state management, and a flexible architecture that allows for customization and scalability."}, "metadata": {"id": "qa_001", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "How do I use LangChain with multiple LLMs?"}, "outputs": {"answer": "You can use multiple LLMs in LangChain by defining a chain that includes different LLMs as components. You can route inputs to specific LLMs based on conditions or combine their outputs using techniques like ensemble methods or voting."}, "metadata": {"id": "qa_002", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "Can I use custom embeddings with LangChain?"}, "outputs": {"answer": "Yes, you can use custom embeddings with LangChain by creating an instance of your embedding model and passing it to the vector store. Ensure that your embeddings are compatible with the expected input format of the vector store."}, "metadata": {"id": "qa_003", "category": "retrieval", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is the best way to handle API response errors in LangChain?"}, "outputs": {"answer": "In LangChain, you can handle API response errors by implementing error handling logic within your agent or chain. Use try-except blocks to catch exceptions and define fallback strategies, such as switching to a backup model or retrying the request after a delay."}, "metadata": {"id": "qa_004", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "best-practices", "error-handling"]}} +{"inputs": {"question": "How can I improve the performance of my LangChain model?"}, "outputs": {"answer": "To improve the performance of your LangChain model, consider optimizing your prompt, refining your data preprocessing steps, and adjusting hyperparameters such as temperature and max tokens. Additionally, using a more powerful language model or fine-tuning your existing model on relevant data may yield better results."}, "metadata": {"id": "qa_005", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["performance"]}} +{"inputs": {"question": "How can I parallelize calls in LangChain?"}, "outputs": {"answer": "To make parallel calls from a LangChain object, use the 'batch()' (or asynchronous 'abatch()') method. You can also use a `RunnableParallel` object."}, "metadata": {"id": "qa_006", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "performance", "streaming"]}} +{"inputs": {"question": "in code, how can i add a system message at the end of the conversation history to influence the output of the llm"}, "outputs": {"answer": "```python from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder prompt_template = ChatPromptTemplate.from_messages( [ (\"human\", \"{user_input}\"), MessagesPlaceholder(variable_name=\"chat_history\") (\"system\", \"New System Instruction here\"), ] ) ``` When formatted, it will generate 1 human/user message and 1 system message, as well as messages for the whole conversation history. ```python prompt_template.format_messages(user_input=\"Hello\", chat_history=[]) ``` [HumanMessage(content='Hello', additional_kwargs={}, example=False), SystemMessage(content='New System Instruction here', additional_kwargs={})]"}, "metadata": {"id": "qa_007", "category": "memory", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example"]}} +{"inputs": {"question": "how to use enums in Tools that will be used by functions agent?"}, "outputs": {"answer": "If you're defining a dynamic tool (either using the @tool decorator or the `.from_function(...) classmethod), simply using the enum typehint is enough. If you are manually specifying an `args_schema` as a pydantic basemodel, you can specify the enum as the argument typehint there."}, "metadata": {"id": "qa_008", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "How do I deal with openai rate limiting by having a backup model? Show me with code."}, "outputs": {"answer": "A straightforward way to do this is to specify fallback models using LangChain Expression Language. Example below: ```python from langchain.chat_models import ChatOpenAI bad_llm = ChatOpenAI(model_name=\"gpt-fake\") good_llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\") llm = bad_llm.with_fallbacks([good_llm]) llm.invoke(\"Why did the the chicken cross the road?\") ```"}, "metadata": {"id": "qa_009", "category": "llms", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "error-handling"]}} +{"inputs": {"question": "I'm runing my own model using vllm. How do I connect it to LangChain?"}, "outputs": {"answer": "LangChain has a VLLM integration:\" ```python from langchain.llms import VLLM llm = VLLM(model=\"mosaicml/mpt-7b\", trust_remote_code=True, # mandatory for hf models max_new_tokens=128, top_k=10, top_p=0.95, temperature=0.8, ) print(llm(\"What is the capital of France ?\"))"}, "metadata": {"id": "qa_010", "category": "chains", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "api", "integration"]}} +{"inputs": {"question": "What's a runnable lambda?"}, "outputs": {"answer": "A runnable lambda is an object that implements LangChain's `Runnable` interface and runs a callable (i.e., a function). Note the function must accept a single argument."}, "metadata": {"id": "qa_011", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What's an LLMChain"}, "outputs": {"answer": "An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output."}, "metadata": {"id": "qa_012", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What's the difference between a prompt template and a chat prompt template?"}, "outputs": {"answer": "A prompt template wraps instructions, examples, and other formatting information for the llm's prompts. A chat prompt template provides similar functionality for chat models. The largest difference is that chat prompt templates expose a `format_messages` method to convert the prompt template into a list of structured chat messages."}, "metadata": {"id": "qa_013", "category": "prompts", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What is ConversationSummaryBufferMemory?"}, "outputs": {"answer": "ConversationSummaryBufferMemory is a memory type that combines the ideas of keeping a buffer of recent interactions and compiling them into a summary. It uses token length to determine when to flush interactions."}, "metadata": {"id": "qa_014", "category": "memory", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What does LangChain offer for token counting?"}, "outputs": {"answer": "LangChain offers a context manager that allows you to count tokens, as well as integrations with services like LangSmith that can help track tokens."}, "metadata": {"id": "qa_015", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is the purpose of caching embeddings?"}, "outputs": {"answer": "Caching embeddings allows for storing or temporarily caching embeddings to avoid the need for recomputing them."}, "metadata": {"id": "qa_016", "category": "retrieval", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is the purpose of the Retry parser?"}, "outputs": {"answer": "The Retry parser is used when the LLM output is not in the expected format. It passes the input and initial output back to the LLM so it can try again with the correct format."}, "metadata": {"id": "qa_017", "category": "general", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What does ConversationBufferWindowMemory do?"}, "outputs": {"answer": "ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large."}, "metadata": {"id": "qa_018", "category": "memory", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is the main purpose of the time-weighted vector store retriever?"}, "outputs": {"answer": "The main purpose of the time-weighted vector store retriever is to use a combination of semantic similarity and a time decay to retrieve relevant vectors."}, "metadata": {"id": "qa_019", "category": "retrieval", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What serialization format is used to serialize chains to and from disk?"}, "outputs": {"answer": "The serialization format used is JSON or YAML."}, "metadata": {"id": "qa_020", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "whats an agent type that works well with Anthropic's models and supports structured output?"}, "outputs": {"answer": "The XML agent works well with Anthropic's Claude 2 model. Here's some code for doing so: ```python from langchain.agents import XMLAgent, tool, AgentExecutor from langchain.chat_models import ChatAnthropic from langchain.chains import LLMChain model = ChatAnthropic(model='claude-2') chain = LLMChain( llm=model, prompt=XMLAgent.get_default_prompt(), output_parser=XMLAgent.get_default_output_parser() ) agent = XMLAgent(tools=tool_list, llm_chain=chain) agent_executor = AgentExecutor(agent=agent, tools=tool_list, verbose=True) ```"}, "metadata": {"id": "qa_021", "category": "agents", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example"]}} +{"inputs": {"question": "I want to save the configuration for a given LLM. Show me how to do that."}, "outputs": {"answer": "To save the configuration for a given LLM, you can follow these steps: First, let's go over loading an LLM configuration from disk. LLM configurations can be saved in two formats: JSON or YAML, but they are loaded in the same way. For a JSON-formatted LLM configuration (llm.json): ```json { \"model_name\": \"text-davinci-003\", \"temperature\": 0.7, \"max_tokens\": 256, \"top_p\": 1.0, \"frequency_penalty\": 0.0, \"presence_penalty\": 0.0, \"n\": 1, \"best_of\": 1, \"request_timeout\": null, \"_type\": \"openai\" } ``` Load the configuration like this: ```python from langchain.llms.loading import load_llm llm = load_llm(\"llm.json\") ``` For a YAML-formatted LLM configuration (llm.yaml): ```yaml _type: openai best_of: 1 frequency_penalty: 0.0 max_tokens: 256 model_name: text-davinci-003 n: 1 presence_penalty: 0.0 request_timeout: null temperature: 0.7 top_p: 1.0 ``` Load the configuration like this: ```python from langchain.llms.loading import load_llm llm = load_llm(\"llm.yaml\") ``` To save an LLM configuration from memory to a serialized version, you can use the `.save` method. This method supports both JSON and YAML formats: ```python llm.save(\"llm.json\") llm.save(\"llm.yaml\") ``` This way, you can easily save and load LLM configurations to and from disk."}, "metadata": {"id": "qa_022", "category": "llms", "difficulty": "advanced", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "api", "configuration"]}} +{"inputs": {"question": "Show me an example using Weaviate, but customizing the VectorStoreRetriever to return the top 10 k nearest neighbors."}, "outputs": {"answer": "To customize the Weaviate client and return the top 10 k nearest neighbors, you can utilize the `as_retriever` method with the appropriate parameters. Here's how you can achieve this: ```python # Assuming you have imported the necessary modules and classes # Create the Weaviate client client = weaviate.Client(url=os.environ[\"WEAVIATE_URL\"], ...) # Initialize the Weaviate wrapper weaviate = Weaviate(client, index_name, text_key) # Customize the client to return top 10 k nearest neighbors using as_retriever custom_retriever = weaviate.as_retriever( search_type=\"similarity\", search_kwargs={ 'k': 10 # Customize the value of k as needed } ) # Now you can use the custom_retriever to perform searches results = custom_retriever.search(query, ...) ```"}, "metadata": {"id": "qa_023", "category": "retrieval", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "api"]}} +{"inputs": {"question": "What is BabyAGI"}, "outputs": {"answer": "BabyAGI is an example of an \"autonomous AI agent\" that can generate and simulate the execution of tasks based on a given objective."}, "metadata": {"id": "qa_024", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is the difference between ChatPromptTemplate and PromptTemplate?"}, "outputs": {"answer": "ChatPromptTemplate and PromptTemplate are similar in their functions. PromptTemplate takes a prompt as a string that can contain variables. ChatPromptTemplate separates the prompt into structured messages of different kinds. Each chat message is associated with content, and an additional parameter called role. Some example roles include system, human (or user), and AI (or assistant)."}, "metadata": {"id": "qa_025", "category": "prompts", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "Show me how to use RecursiveURLLoader"}, "outputs": {"answer": "The RecursiveURLLoader comes from the langchain.document_loaders.recursive_url_loader module. Here's an example of how to use it: ```python from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader # Create an instance of RecursiveUrlLoader with the URL you want to load loader = RecursiveUrlLoader(url=\"https://example.com\") # Load all child links from the URL page documents = loader.load() # Fetch the documents for doc in documents: print(doc) ``` Make sure to replace \"https://example.com\" with the actual URL you want to load."}, "metadata": {"id": "qa_026", "category": "document_loaders", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example"]}} +{"inputs": {"question": "what are the main methods supported by Runnables"}, "outputs": {"answer": "The `runnable` interface in LangChain supports several main methods for interacting with components: 1. `stream`: This method streams back response chunks in real-time, useful for processing large computations or text generation as they become available. 2. `invoke`: Calls the runnable chain on a single input, providing a single response output. 3. `batch`: Calls the runnable chain on a list of inputs, returning a list of corresponding responses. 4. `astream`: Asynchronously streams response chunks back, suitable for handling concurrent tasks. 5. `ainvoke`: Asynchronously calls the runnable chain on a single input and returns the response. 6. `abatch`: Asynchronously calls the runnable chain on a list of inputs, returning a list of corresponding responses."}, "metadata": {"id": "qa_027", "category": "chains", "difficulty": "intermediate", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "streaming"]}} +{"inputs": {"question": "What is html2texttransformer? Does it omit urls?"}, "outputs": {"answer": "The `Html2TextTransformer` is a class provided by LangChain's `document_transformers` module. It is used to transform HTML content into clean, easy-to-read plain ASCII text. This transformer is designed to convert HTML pages into human-readable text, making it suitable for scenarios where the goal is to extract content without the need to manipulate specific HTML elements. If urls are present in the source Document's metadata, then they will be retained. If they are stored in the page_content itself, the class makes no guarantees that they will be retained."}, "metadata": {"id": "qa_028", "category": "document_loaders", "difficulty": "intermediate", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "I want to return the source documents of my Weaviate retriever. Show me how"}, "outputs": {"answer": "To retrieve the source documents from your Weaviate vectorstore, you need to configure your client to include the 'sources' attribute. Here's an example of how you can achieve this: ```python weaviate_client = Weaviate( client=client, index_name='index_name', text_key='text', embedding=OpenAIEmbeddings(), by_text=False, attributes=['source'], ) ``` By including the 'source' attribute in the attributes list, you ensure that the source documents are returned when you interact with the Weaviate client."}, "metadata": {"id": "qa_029", "category": "retrieval", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "configuration"]}} +{"inputs": {"question": "what is RAG"}, "outputs": {"answer": "RAG stands for Retrieval Augmented Generation, which refers to the application design of using an LLM to generate responses to user inputs grounded on retrieved information. The retrieved information can come from anywhere, though it typically is fetched from 1 or more databases, search engines, knowledge graphs, or other sources."}, "metadata": {"id": "qa_030", "category": "retrieval", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "whats the code to load text file into a vector store"}, "outputs": {"answer": "To load a text file into a vector store using LangChain's document loaders, you can use the following code snippet: ```python from langchain.document_loaders import TextLoader loader = TextLoader(\"./index.txt\") docs = loader.load() print(docs) ``` This code imports the `TextLoader` class from the document_loaders module, creates an instance of the loader with the path to the text file as an argument, and then uses the `load()` method to read the file's content and create a Document. The resulting Document will contain the text content of the file."}, "metadata": {"id": "qa_031", "category": "retrieval", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "api"]}} +{"inputs": {"question": "Help me debug: TypeError: Pinecone.similarity_search_with_score() got an unexpected keyword argument 'min_score'"}, "outputs": {"answer": "Your error is likely due to an incorrect parameter being passed to the 'similarity_search_with_score' function in the Pinecone vector store. The correct function signature does not include a 'min_score' argument. This function retrieves documents most similar to a given query text while providing both the documents and their corresponding similarity scores. To use 'similarity_search_with_score', ensure you provide the right arguments: 'query' for the text you want to find similar documents for, 'k' for the number of documents to retrieve (default: 4), 'filter' as an optional metadata-based filtering dictionary, and 'namespace' to specify the search namespace (default is the empty namespace ''). The function returns a list of tuples, each containing a Document and a similarity score (float). To resolve the 'TypeError' issue, ensure proper argument usage as per the documentation."}, "metadata": {"id": "qa_032", "category": "retrieval", "difficulty": "intermediate", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "error-handling"]}} +{"inputs": {"question": "How can I create a simple chat model using my locally saved huggingface model"}, "outputs": {"answer": "Certainly! To create a simple chat model using your locally saved Hugging Face model, follow these steps: 1. Install the necessary Python libraries: - Install 'transformers' library for working with models and tokenizers using 'pip install transformers'. 2. Import the `HuggingFacePipeline` class to work with your local pipeline ``` from langchain.llms import HuggingFacePipeline # Create an instance of the wrapper llm = HuggingFacePipeline.from_model_id(\"path/to/model_directory\", task=\"text-generation\") ``` Alternatively, load your pipeline directly: ``` from langchain.llms.huggingface_pipeline import HuggingFacePipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_path = \"path/to/model_dir\" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path) pipe = pipeline( \"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10 ) llm = HuggingFacePipeline(pipeline=pipe) ``` Finally, interact with the chat model ``` response = llm.invoke('User: Hello, how can I help?') ```"}, "metadata": {"id": "qa_033", "category": "llms", "difficulty": "advanced", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "configuration"]}} +{"inputs": {"question": "How do I use a React Agent with an Anthropic model?"}, "outputs": {"answer": "In order to use a ReAct Agent with an Anthropic model, you should first install the required packages: pip install anthropic Next, import the necessary classes and functions: from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.chat_models import ChatAnthropic Next, create an instance of ChatAnthropic. Make sure you have set your API key in an environment variable called ANTHROPIC_API_KEY or pass anthropic_api_key as a parameter. chat = ChatAnthropic() Next, load some tools for your agent to use: # Specify tools (these can be any custom tool, the following are examples) tools = load_tools([\"serpapi\", \"llm-math\"], llm=chat) Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. agent = initialize_agent(tools, chat, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) Now, enter a prompt to test it out: agent.invoke(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"}, "metadata": {"id": "qa_034", "category": "agents", "difficulty": "advanced", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "configuration", "best-practices"]}} +{"inputs": {"question": "How do I use Qdrant as a vector store in the conversational retrieval chain?"}, "outputs": {"answer": "In order to use Qdrant as a vector store in the conversational retrieval chain, you should start by installing Qdrant: pip install qdrant-client Next, import the necessary classes and functions: from langchain.vectorstores import Qdrant from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.llms import OpenAI from langchain.chains import ConversationalRetrievalChain from langchain.document_loaders import TextLoader from langchain.memory import ConversationBufferMemory Load, split, and embed your documents: loader = TextLoader(\"state_of_the_union.txt\") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator=\" \") documents = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() Next, connect to Qdrant. This is for storing locally on-disk, but you can also use an on-premise server deployment or store on the Qdrant cloud: qdrant = Qdrant.from_documents( documents, embeddings, path=\"/tmp/local_qdrant\", collection_name=\"my_documents\", ) Next, create a memory object, which is necessary to track the inputs/outputs and hold a conversation: memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True) Now initialize the ConversationalRetrievalChain: qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), qdrant.as_retriever(), memory=memory) Now, you can create queries to test it out: query = \"What did the president say about Ketanji Brown Jackson\" result = qa({\"question\": query}) Output: The president said that he had nominated Ketanji Brown Jackson to serve on the United States Supreme Court four days ago. query = \"Who are they succeeding?\" result = qa({\"question\": query}) Output: Justice Breyer."}, "metadata": {"id": "qa_035", "category": "memory", "difficulty": "advanced", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "configuration", "best-practices"]}} +{"inputs": {"question": "How do I use Summary Memory in the conversational retrieval chain?"}, "outputs": {"answer": "In order to use Summary Memory in the conversational retrieval chain, you should first import the necessary classes and functions: from langchain.memory import ConversationSummaryMemory from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma # You can choose any LangChain vectorstore from langchain.text_splitter import CharacterTextSplitter from langchain.chat_models import ChatOpenAI # You can choose any LangChain vectorstore from langchain.chains import ConversationalRetrievalChain from langchain.document_loaders import TextLoader Split and embed your documents, and add them to your selected vectorstore: loader = TextLoader(\"state_of_the_union.txt\") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator=\" \") documents = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(documents, embeddings) Next, create a ConversationSummaryMemory object: memory = ConversationSummaryMemory(llm=ChatOpenAI(model=\"gpt-3.5-turbo\"), memory_key=\"chat_history\", return_messages=True) Now initialize the ConversationalRetrievalChain: qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(model=\"gpt-3.5-turbo\",temperature=0), vectorstore.as_retriever(), memory=memory) Now, you can create queries to test it out: query = \"What did the president say about Ketanji Brown Jackson\" result = qa({\"question\": query}) Output: The president said that he had nominated Ketanji Brown Jackson to serve on the United States Supreme Court four days ago. query = \"Who are they succeeding?\" result = qa.invoke({\"question\": query}) Output: Justice Breyer."}, "metadata": {"id": "qa_036", "category": "memory", "difficulty": "advanced", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "best-practices"]}} +{"inputs": {"question": "How do I load Youtube transcripts and CSV documents?"}, "outputs": {"answer": "To load YouTube transcripts in LangChain, you can use the YoutubeLoader document loader. Here's an example of how to do it: Install the required packages: ``` pip install langchain youtube_transcript_api pytube ``` Import the necessary classes: from langchain.document_loaders import YoutubeLoader Create an instance of the YoutubeLoader class and provide the YouTube video URL: loader = YoutubeLoader.from_youtube_url(\"https://www.youtube.com/watch?v=QsYGlZkevEg\", add_video_info=True) Load the transcript using the load method: video = loader.load() To load CSV documents you can use CSVLoader. Follow the steps below: Import the necessary classes: from langchain.document_loaders import CSVLoader Create an instance of the CSVLoader class and provide the path to the CSV file: loader = CSVLoader(file_path=\"./example_data/mlb_teams_2012.csv\") Load the CSV document using the load method: loader.load()"}, "metadata": {"id": "qa_037", "category": "document_loaders", "difficulty": "intermediate", "has_code": true, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["code-example", "api", "configuration"]}} +{"inputs": {"question": "my agent keeps getting an OutputParserException is something i can set to make it take care of these?"}, "outputs": {"answer": "You can control the agent's behavior for output parsing errors by initializing the agent with `handle_parsing_errors`=True. Then the agent will see 'Handle errors with Invalid or incomplete response'. You can also set a string to provide a custom error message to the agent, or provide a custom error handling function."}, "metadata": {"id": "qa_038", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "error-handling"]}} +{"inputs": {"question": "What does ReAct mean?"}, "outputs": {"answer": "ReAct in LangChain refers to a type of agent that 'Reasons' and 'Act's in a loop."}, "metadata": {"id": "qa_039", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What are intermediate steps in langchain?"}, "outputs": {"answer": "Intermediate steps represent previous agent actions and corresponding outputs. They are used to help the agent know what work has already been done in pursuit of a goal."}, "metadata": {"id": "qa_040", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "im getting a rate limit error for my llm - how do I have it automatically go to a different model?"}, "outputs": {"answer": "To switch to a different model when an LLM is getting rate limited, you can use 'fallbacks'. For instance, if you have two llms: openai_llm and anthropic_llm, you can have the program fall back to the anthropic llm by calling `llm = openai_llm.with_fallbacks([anthropic_llm])`"}, "metadata": {"id": "qa_041", "category": "llms", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["error-handling"]}} +{"inputs": {"question": "What's function calling"}, "outputs": {"answer": "Function calling refers to the ability to define functions for LLMs to let them choose to call with structured arguments."}, "metadata": {"id": "qa_042", "category": "general", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "what is the langserve"}, "outputs": {"answer": "LangServe is an easy way to deploy any LangChain runnable, chain, or agent. It uses FastAPI and pydantic to automatically create an API server from any compatible LangChain component."}, "metadata": {"id": "qa_043", "category": "deployment", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What's a string evaluator"}, "outputs": {"answer": "A string evaluator in LangChain is an object that assesses the performance of a language model's predicted response by comparing it to an input string and/or reference string."}, "metadata": {"id": "qa_044", "category": "evaluation", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["performance"]}} +{"inputs": {"question": "whats a toolkit"}, "outputs": {"answer": "Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods."}, "metadata": {"id": "qa_045", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "is langchain compatible with pydantic v2?"}, "outputs": {"answer": "As of langchain>=0.0.267, LangChain will allow users to install either Pydantic V1 or V2, though it internally will use pydantic V1."}, "metadata": {"id": "qa_046", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["configuration"]}} +{"inputs": {"question": "how many llm api calls are made in OpenAIFunctionsAgent"}, "outputs": {"answer": "The OpenAIFunctionsAgent is an agent driven by OpenAI's function-powered API. It uses the function_call parameter from OpenAI to determine which tools to use for each API call. In general, the number of API calls made by the OpenAIFunctionsAgent depends on the question and the number of tools used in the agent's pipeline. At minimum, one call will be made to the LLM to (optionally) select a tool or directly respond. For more complex questions or intents, the agent may make many calls."}, "metadata": {"id": "qa_047", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What are some ways of doing retrieval augmented generation?"}, "outputs": {"answer": "Retrieval Augmented Generation (RAG) is a process that involves retrieving external data and using it in the generation step of language models. LangChain provides various building blocks for RAG applications. Here are some ways of doing retrieval augmented generation: - Document loaders: LangChain offers over 100 document loaders to load documents from different sources, including private S3 buckets and public websites. - Document transformers: These help in fetching relevant parts of documents by splitting or chunking large documents into smaller chunks. - Text embedding models: LangChain integrates with over 25 different embedding providers and methods to create embeddings for documents, allowing efficient similarity-based retrieval. - Vector stores: With the rise of embeddings, LangChain supports over 50 vector stores for efficient storage and searching of embeddings. - Retrievers: LangChain supports various retrieval algorithms, including semantic search, parent document retriever, self-query retriever, ensemble retriever, and more, to retrieve the data from the database. These components work together to enable retrieval augmented generation in LangChain. [0] [1]"}, "metadata": {"id": "qa_048", "category": "retrieval", "difficulty": "advanced", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api", "integration"]}} +{"inputs": {"question": "whats the difference between a handler and an inheritable_handler?"}, "outputs": {"answer": "In Langchain, both `handler` and `inheritable_handler` are types of callback handlers. Handlers can refer to inheritable handlers or local handlers. Inheritable handlers are propagated to other objects that are called within a Runnable, chain, tool, or other object. Local handlers are only called by the object they are assigned to and not called by any other objects."}, "metadata": {"id": "qa_049", "category": "general", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": []}} +{"inputs": {"question": "What is a chain?"}, "outputs": {"answer": "A 'Chain' is a class in LangChain that composes a number of components together into a single object. An example would be the LLMChain, which formats an input as a prompt, calls an LLM, and then parses the output. \"chaining\" also can generically refer to composing llms, functions, and other operations together into a larger program."}, "metadata": {"id": "qa_050", "category": "chains", "difficulty": "beginner", "has_code": false, "created_at": "2023-11-08T03:20:22Z", "source": "langchain_docs", "tags": ["api"]}} +{"inputs": {"question": "What is context engineering in LangChain agents?"}, "outputs": {"answer": "Context engineering is the practice of providing the right information and tools in the right format so the LLM can accomplish a task. It involves controlling what goes into model calls, what tools can access, and what happens between steps in the agent lifecycle. This is considered the number one job of AI Engineers for building reliable agents."}, "metadata": {"id": "qa_006", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents"]}} +{"inputs": {"question": "Why do agents fail in production?"}, "outputs": {"answer": "Agents typically fail for one of two reasons: either the underlying LLM is not capable enough, or the right context was not passed to the LLM. More often than not, it's the second reason - lack of proper context - that causes agents to not be reliable. This is why context engineering is crucial for building production-ready agents."}, "metadata": {"id": "qa_007", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "best-practices"]}} +{"inputs": {"question": "What are the three types of context in LangChain agents?"}, "outputs": {"answer": "The three types of context in LangChain agents are: 1) Model Context - what goes into model calls including instructions, message history, tools, and response format (transient), 2) Tool Context - what tools can access and produce, including reads and writes to state, store, and runtime context (persistent), and 3) Life-cycle Context - what happens between model and tool calls such as summarization, guardrails, and logging (persistent)."}, "metadata": {"id": "qa_008", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "architecture"]}} +{"inputs": {"question": "What is the difference between transient and persistent context?"}, "outputs": {"answer": "Transient context is what the LLM sees for a single call - you can modify messages, tools, or prompts without changing what's saved in state. Persistent context is what gets saved in state across turns - life-cycle hooks and tool writes modify this permanently and it persists across the conversation."}, "metadata": {"id": "qa_009", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "state-management"]}} +{"inputs": {"question": "What are the three data sources agents access in LangChain?"}, "outputs": {"answer": "Agents access three data sources: 1) Runtime Context (static configuration) - conversation-scoped data like user ID, API keys, database connections, permissions, and environment settings, 2) State (short-term memory) - conversation-scoped data like current messages, uploaded files, authentication status, and tool results, and 3) Store (long-term memory) - cross-conversation data like user preferences, extracted insights, memories, and historical data."}, "metadata": {"id": "qa_010", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "memory"]}} +{"inputs": {"question": "How does middleware enable context engineering in LangChain?"}, "outputs": {"answer": "Middleware in LangChain allows you to hook into any step in the agent lifecycle to either update context (modify state and store to persist changes, update conversation history, or save insights) or jump in the lifecycle (move to different steps in the agent cycle based on context, such as skipping tool execution if a condition is met or repeating model calls with modified context)."}, "metadata": {"id": "qa_011", "category": "agents", "difficulty": "advanced", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "middleware"]}} +{"inputs": {"question": "What components make up model context in LangChain agents?"}, "outputs": {"answer": "Model context consists of five components: 1) System Prompt - base instructions from developer to LLM, 2) Messages - full conversation history sent to LLM, 3) Tools - utilities the agent has access to for taking actions, 4) Model - the actual model and its configuration to be called, and 5) Response Format - schema specification for the model's final response. All of these can draw from state, store, or runtime context."}, "metadata": {"id": "qa_012", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "model-context"]}} +{"inputs": {"question": "How can I implement automatic conversation summarization in LangChain?"}, "outputs": {"answer": "Use the built-in summarizationMiddleware by adding it to your agent configuration. You can specify parameters like maxTokensBeforeSummary to trigger summarization when conversation exceeds a token limit, and messagesToKeep to retain recent messages. When triggered, it automatically summarizes older messages using a separate LLM call, replaces them with a summary message in State permanently, and keeps recent messages intact for context."}, "metadata": {"id": "qa_013", "category": "agents", "difficulty": "advanced", "has_code": true, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "middleware", "summarization"]}} +{"inputs": {"question": "What is the agent loop in LangChain?"}, "outputs": {"answer": "The agent loop consists of two main steps that repeat until the LLM decides to finish: 1) Model call - calls the LLM with a prompt and available tools, returns either a response or a request to execute tools, and 2) Tool execution - executes the tools that the LLM requested and returns tool results. This cycle continues iteratively to accomplish the given task."}, "metadata": {"id": "qa_014", "category": "agents", "difficulty": "beginner", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "architecture"]}} +{"inputs": {"question": "How can tools write to State in LangChain?"}, "outputs": {"answer": "Tools can write to State using the Command object. When a tool needs to update state, it returns a Command with an update parameter containing the state changes. For example, after authenticating a user, a tool can return Command with update set to authenticated status, which permanently modifies the conversation state."}, "metadata": {"id": "qa_015", "category": "agents", "difficulty": "advanced", "has_code": true, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "tools", "state-management"]}} +{"inputs": {"question": "What are the best practices for context engineering in LangChain?"}, "outputs": {"answer": "Key best practices include: 1) Start simple with static prompts and tools, adding dynamics only when needed, 2) Test incrementally by adding one context engineering feature at a time, 3) Monitor performance tracking model calls, token usage, and latency, 4) Use built-in middleware like SummarizationMiddleware and LLMToolSelectorMiddleware, 5) Document your context strategy clearly, and 6) Understand the difference between transient (per-call) and persistent (saved to state) context changes."}, "metadata": {"id": "qa_016", "category": "agents", "difficulty": "intermediate", "has_code": false, "created_at": "2024-11-18T10:00:00Z", "source": "https://docs.langchain.com/oss/javascript/langchain/context-engineering", "tags": ["context-engineering", "agents", "best-practices"]}} \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 000000000..db772ec58 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,67 @@ +version: "3.8" + +services: + postgres: + image: postgres:16-alpine # Using latest stable version with Alpine for smaller size + container_name: chat_langchain_postgres + restart: unless-stopped + + environment: + # Required environment variables + POSTGRES_DB: chat_langchain + POSTGRES_USER: postgres + POSTGRES_PASSWORD: zkdtn1234 + + # Optional: Set timezone + TZ: UTC + + # Optional: Set locale + POSTGRES_INITDB_ARGS: "--encoding=UTF-8 --lc-collate=C --lc-ctype=C" + + ports: + - "5432:5432" + + volumes: + # Data persistence + - postgres_data:/var/lib/postgresql/data + + weaviate: + image: cr.weaviate.io/semitechnologies/weaviate:1.33.2 + container_name: chat_langchain_weaviate + restart: unless-stopped + ports: + - "8080:8080" + - "50051:50051" + environment: + QUERY_DEFAULTS_LIMIT: 20 + AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true" + PERSISTENCE_DATA_PATH: "/var/lib/weaviate" + DEFAULT_VECTORIZER_MODULE: none + ENABLE_MODULES: "" + CLUSTER_HOSTNAME: "node1" + volumes: + - weaviate_data:/var/lib/weaviate + + ollama: + image: ollama/ollama:0.12.8 + container_name: chat_langchain_ollama + restart: unless-stopped + ports: + - "11434:11434" + volumes: + - ollama_data:/root/.ollama + entrypoint: + [ + "/bin/sh", + "-c", + "ollama serve & sleep 3 && ollama pull nomic-embed-text && tail -f /dev/null", + ] + +# Named volumes +volumes: + postgres_data: + driver: local + weaviate_data: + driver: local + ollama_data: + driver: local diff --git a/docs/BLOG_STRATEGY_SUMMARY.md b/docs/BLOG_STRATEGY_SUMMARY.md new file mode 100644 index 000000000..f9a28291a --- /dev/null +++ b/docs/BLOG_STRATEGY_SUMMARY.md @@ -0,0 +1,408 @@ +# Blog Strategy Summary - Document Ingestion Post + +## ✅ Completed Updates + +### 1. Updated `INGEST.md` + +**Added Sections:** + +1. **Learning Journey Header** + + - Links to source repo: [langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) + - Links to practice repo: [thongvmdev/chat-langchain-practice](https://github.com/thongvmdev/chat-langchain-practice) + +2. **🚨 Problem & Solution Section** + + - **Challenge**: Cloud Weaviate 14-day limits + OpenAI embedding costs + - **Solution**: Local Docker stack with Weaviate + Ollama + PostgreSQL + - Benefits breakdown (zero cost, privacy, unlimited learning, etc.) + - Technical stack details + +3. **📊 Cost Comparison Table** + + - Side-by-side comparison: Cloud vs Local setup + - Real-world savings calculation + - Visual presentation of benefits + +4. **🏷️ Suggested Blog Post Tags** + + - Primary Technology Tags: `#LangChain #RAG #Weaviate #Docker #Ollama #PostgreSQL` + - Concept Tags: `#VectorDatabase #Embeddings #DocumentIngestion #SemanticSearch` + - Learning Tags: `#LearningInPublic #OpenSourceLearning #ZeroCostSetup` + - Problem-Solution Tags: `#CostSavings #LocalFirst #PrivacyFirst` + - Recommended combination for maximum reach + +5. **Updated Footer** + - Source and practice repo links + - Updated date: November 3, 2025 + +--- + +### 2. Created `BLOG_POST_OUTLINE.md` + +**Complete blog post structure including:** + +#### Content Structure (8 Sections) + +1. **Hook** - Problem statement + solution preview +2. **The Problem** - Detailed pain points (Weaviate limits + OpenAI costs) +3. **Solution Overview** - Architecture comparison (Before/After) +4. **Implementation Deep Dive** - 5 detailed steps with code +5. **Results & Performance** - Real metrics and comparisons +6. **Challenges & Learnings** - What you learned along the way +7. **How to Use** - Quick start guide for readers +8. **Conclusion** - Summary + next steps in series + +#### Marketing Materials + +- 4 title options (recommended: cost-saving angle) +- Twitter/X thread template (6 tweets) +- LinkedIn post template +- Pre-publish checklist +- Success metrics to track + +#### Visual Assets List + +- Hero image (architecture diagram) +- Cost comparison table/chart +- Performance graph +- Terminal screenshots +- Code snippets with highlighting + +--- + +## 🎯 Recommended Tags + +### For Maximum Reach + +**Primary (Choose 4-6):** + +``` +#LangChain #RAG #VectorDatabase #Docker #Ollama +#DocumentIngestion #LearningInPublic #ZeroCostSetup +``` + +**Why These Tags Work:** + +1. **#LangChain** - Core technology, high search volume +2. **#RAG** - Trending topic in AI, attracts right audience +3. **#VectorDatabase** - Specific technical keyword +4. **#Docker** - Broader DevOps audience +5. **#Ollama** - Growing community, cost-conscious developers +6. **#DocumentIngestion** - Niche but precise +7. **#LearningInPublic** - Community engagement +8. **#ZeroCostSetup** - High-value problem-solution angle + +### Platform-Specific Recommendations + +**Dev.to (4-5 tags):** + +``` +#langchain #docker #rag #python #beginners +``` + +**Hashnode (5-6 tags):** + +``` +#LangChain #RAG #Docker #Ollama #VectorDatabase #AI +``` + +**Medium (5 tags max):** + +``` +#Artificial-Intelligence #Machine-Learning #Python #Docker #Open-Source +``` + +**LinkedIn (3-5 hashtags):** + +``` +#ArtificialIntelligence #MachineLearning #LangChain #OpenSource #CloudComputing +``` + +**Twitter/X (3-4 hashtags):** + +``` +#LangChain #RAG #BuildInPublic #AI +``` + +--- + +## 📝 Post Series Strategy + +### Phase 2 Post: "Building the RAG Pipeline" Series + +**Post 1 (Current):** "Zero-Cost Document Ingestion with Local Weaviate + Ollama" + +- **Focus:** Problem-solution, cost savings, Docker setup +- **Tags:** `#Docker #Ollama #ZeroCost #DocumentIngestion #LearningInPublic` + +**Post 2 (Next):** "Semantic Search & Retrieval: Querying Your Vector Database" + +- **Focus:** Retrieval methods, similarity search, query optimization +- **Tags:** `#SemanticSearch #VectorDatabase #Retrieval #LangChain` + +**Post 3:** "Multi-Agent Research: Understanding LangGraph Workflows" + +- **Focus:** Query routing, research planning, agent coordination +- **Tags:** `#LangGraph #MultiAgent #AIAgents #Workflows` + +**Post 4:** "Real-Time Streaming: Building the Chat Interface" + +- **Focus:** Frontend integration, Next.js, streaming responses +- **Tags:** `#NextJS #React #StreamingUI #Frontend` + +**Post 5:** "Testing & Evaluation: Building Reliable RAG Systems" + +- **Focus:** LangSmith, evaluation metrics, CI/CD +- **Tags:** `#LangSmith #Testing #CICD #AIEvaluation` + +--- + +## 🚀 Publishing Strategy + +### Week 1: Pre-Launch + +- [ ] Finalize blog post from outline +- [ ] Create all visual assets (diagrams, screenshots) +- [ ] Test all code snippets in fresh environment +- [ ] Write social media posts +- [ ] Schedule Twitter thread + +### Week 1: Launch Day + +- [ ] Publish on primary platform (Dev.to or Hashnode) +- [ ] Share on Twitter/X (thread format) +- [ ] Post on LinkedIn (professional angle) +- [ ] Share in relevant Discord/Slack communities: + - LangChain Discord + - AI/ML communities + - Docker communities + +### Week 1-2: Engagement + +- [ ] Respond to all comments within 24 hours +- [ ] Share reader questions as discussion topics +- [ ] Create follow-up tweets with insights +- [ ] Update post with FAQs if needed + +### Week 2: Next Post Teaser + +- [ ] Preview next topic in comments +- [ ] Link to practice repo with "coming soon" branch +- [ ] Ask audience what they want to see next + +--- + +## 💡 Content Differentiators + +**What Makes Your Post Unique:** + +1. **Real Problem-Solution Arc** + + - Not just tutorial, but solving actual pain points + - Relatable learning journey + +2. **Cost Focus** + + - Concrete savings: $50-100 over learning phase + - Appeals to students, independent learners, budget-conscious devs + +3. **Production-Ready Code** + + - Not a toy example + - Based on real production system (Chat LangChain) + - Readers can use in real projects + +4. **Complete Setup** + + - Docker Compose file included + - Working practice repository + - Detailed documentation + +5. **Learning Narrative** + - Challenges faced and solved + - Mistakes made and learned from + - Building in public approach + +--- + +## 📊 SEO Optimization + +### Primary Keywords + +- "RAG document ingestion" +- "local weaviate setup" +- "ollama embeddings" +- "zero cost RAG" +- "langchain tutorial" + +### Long-Tail Keywords + +- "how to setup weaviate locally with docker" +- "free alternative to openai embeddings" +- "building rag system without cloud costs" +- "local vector database for learning" + +### Meta Description Template + +``` +Learn how to build a zero-cost RAG document ingestion pipeline using +local Weaviate, Ollama embeddings, and Docker. Save $50-100 on API +costs while learning LangChain. Complete guide with code. +``` + +--- + +## 🎤 Community Engagement Ideas + +### During Post Launch + +1. **Ask Questions in Comments:** + + - "What's your biggest challenge with RAG systems?" + - "Have you tried local vs cloud setups? What's your preference?" + - "What embedding models are you using?" + +2. **Create Discussion Topics:** + + - Compare nomic-embed-text vs other open-source models + - Local development best practices for AI + - Cost-effective learning strategies + +3. **Showcase Variations:** + - "Here's how @user modified this setup for their use case" + - Share reader implementations and improvements + +### Post-Launch + +1. **GitHub Repository Engagement:** + + - Add "Issues" for discussion topics + - Create "Show and Tell" discussion category + - Welcome PRs for improvements + +2. **Series Cross-Linking:** + + - Update previous posts with links to new ones + - Create "series index" page + - Build content hub around RAG learning + +3. **Community Contributions:** + - Invite readers to share their modifications + - Feature community solutions in follow-up posts + - Create "Reader Spotlights" section + +--- + +## 📈 Success Metrics + +### Week 1 Goals + +- [ ] 500+ views +- [ ] 50+ reactions/likes +- [ ] 10+ meaningful comments +- [ ] 5+ GitHub stars on practice repo + +### Month 1 Goals + +- [ ] 2,000+ views +- [ ] 100+ reactions +- [ ] 3+ forks of practice repo +- [ ] Featured in newsletter or community roundup + +### Long-Term Goals + +- [ ] 5,000+ views +- [ ] Cited in other tutorials/blogs +- [ ] 50+ GitHub stars +- [ ] Recurring traffic from search + +--- + +## 🔗 Quick Links Reference + +**Your Repositories:** + +- Source: https://github.com/langchain-ai/chat-langchain +- Practice: https://github.com/thongvmdev/chat-langchain-practice + +**Key Files:** + +- Blog outline: `BLOG_POST_OUTLINE.md` +- Technical docs: `INGEST.md` +- Learning path: `LEARNING_STEPS.md` +- Docker setup: `docker-compose.yml` +- Ingestion code: `backend/ingest.py` + +**Related Documentation:** + +- LangChain: https://python.langchain.com/ +- Weaviate: https://weaviate.io/developers/weaviate +- Ollama: https://ollama.ai/ + +--- + +## ✅ Next Steps + +1. **Review & Refine** (1-2 hours) + + - Read through `BLOG_POST_OUTLINE.md` + - Customize sections based on your voice + - Add personal anecdotes + +2. **Create Visuals** (2-3 hours) + + - Architecture diagram (use draw.io, Excalidraw) + - Cost comparison chart + - Terminal screenshots + +3. **Write First Draft** (3-4 hours) + + - Follow outline structure + - Use `INGEST.md` as technical reference + - Write in conversational tone + +4. **Code Verification** (1 hour) + + - Test all commands in fresh environment + - Verify all links work + - Check code syntax highlighting + +5. **Edit & Polish** (1-2 hours) + + - Grammar and clarity + - Add transitions between sections + - Optimize for readability + +6. **Publish & Promote** (1 hour) + - Choose platform (Dev.to recommended) + - Add tags and cover image + - Schedule social media posts + +**Total Time Investment:** ~10-14 hours for high-quality post + +--- + +## 💬 Need Help? + +**Questions to Consider:** + +- Which platform do you prefer? (Dev.to, Hashnode, Medium) +- Do you need help creating diagrams? +- Want feedback on draft before publishing? +- Need help with social media strategy? + +**Resources:** + +- All content is in `INGEST.md` and `BLOG_POST_OUTLINE.md` +- Follow outline structure for best results +- Focus on problem-solution narrative +- Share your learning journey authentically + +--- + +**Good luck with your blog post! 🚀** + +Remember: Your unique perspective as a learner solving real problems is +what makes content valuable. Don't try to be an expert - be a helpful +guide sharing what worked for you. diff --git a/CONCEPTS.md b/docs/CONCEPTS.md similarity index 100% rename from CONCEPTS.md rename to docs/CONCEPTS.md diff --git a/DEPLOYMENT.md b/docs/DEPLOYMENT.md similarity index 100% rename from DEPLOYMENT.md rename to docs/DEPLOYMENT.md diff --git a/docs/EVALS.md b/docs/EVALS.md new file mode 100644 index 000000000..4c644c9bc --- /dev/null +++ b/docs/EVALS.md @@ -0,0 +1,1265 @@ +# 🧪 Chat LangChain - Evaluation System Deep Dive + +A comprehensive guide to understanding how Chat LangChain ensures quality through automated evaluation, testing, and continuous monitoring of RAG system performance. + +> **Learning Journey:** This is part of my exploration of the [Chat LangChain](https://github.com/langchain-ai/chat-langchain) open-source project. +> +> **Source Repository:** [langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) +> **Practice Repository:** [thongvmdev/chat-langchain-practice](https://github.com/thongvmdev/chat-langchain-practice) +> +> **Progress:** I've completed the learning journey outlined in [`LEARNING_STEPS.md`](./LEARNING_STEPS.md), covering document ingestion, retrieval graphs, and frontend architecture. Now diving deep into quality assurance through evaluations! +> +> **Next Step:** Planning to migrate from Python to [LangChain JS v1](https://docs.langchain.com/oss/python/langchain/overview) for broader accessibility and learning. + +--- + +## 🚨 Problem & Solution + +### The Challenge + +When building AI applications, especially RAG (Retrieval-Augmented Generation) systems, you face critical questions: + +1. **Quality Assurance** + + - How do you know your RAG system is giving correct answers? + - Did your latest code change improve or break the system? + - Are you retrieving the right documents for queries? + +2. **Regression Prevention** + + - Changes to prompts might improve some queries but break others + - Model updates could degrade performance + - No way to catch issues before production + +3. **Performance Tracking** + - Hard to measure improvement over time + - Subjective "feels better" isn't good enough + - Need quantitative metrics for decision-making + +### The Solution: Automated Evaluation System + +I implemented a **comprehensive, automated evaluation pipeline** that: + +**Architecture:** + +```diff +- Manual testing (slow, inconsistent) ++ Automated CI/CD evaluation on every change + +- "It looks good" (subjective) ++ Quantitative scores: 90%+ accuracy required + +- Finding bugs in production ++ Catch regressions before deployment + +- Guessing which model is better ++ Side-by-side comparison with real metrics +``` + +**Benefits:** + +- ✅ **Quality Gates:** Enforces minimum accuracy thresholds (90%+) +- ✅ **Regression Detection:** Catches breaking changes automatically +- ✅ **Model Comparison:** Test different LLMs objectively +- ✅ **CI/CD Integration:** Runs on every workflow trigger +- ✅ **LangSmith Integration:** Deep tracing and debugging +- ✅ **Cost Optimization:** Uses affordable judge models (Groq) + +**Technical Stack:** + +- **LangSmith:** Dataset management and experiment tracking +- **pytest:** Test framework and assertions +- **GitHub Actions:** CI/CD automation +- **Judge LLM:** Groq's `gpt-oss-20b` (fast & free tier available) +- **Metrics:** Retrieval recall, answer correctness, context faithfulness + +--- + +## 🎯 What are Evaluations (Evals)? + +Evaluations are **automated tests that measure your AI system's quality** using real-world questions and expected answers. + +### Traditional Testing vs AI Evals + +``` +Traditional Software Testing: +assert add(2, 3) == 5 ✅ Exact match + +AI System Evaluation: +question = "What is LangChain?" +answer = model.generate(question) +score = judge_llm.grade(answer, reference_answer) +assert score >= 0.9 ✅ Quality threshold +``` + +**Key Difference:** AI outputs are non-deterministic, so we need **semantic evaluation** rather than exact matching. + +--- + +## 🏗️ Evaluation System Architecture + +### High-Level Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ GitHub Actions CI/CD Pipeline │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ Trigger: workflow_dispatch (manual) or PR/push │ +│ ↓ │ +│ Setup Python 3.11 + uv package manager │ +│ ↓ │ +│ Install dependencies (frozen lockfile) │ +│ ↓ │ +│ ┌────────────────────────────────────────────┐ │ +│ │ Run: uv run pytest backend/tests/evals │ │ +│ │ │ │ +│ │ ┌──────────────────────────────────────┐ │ │ +│ │ │ test_e2e.py Execution │ │ │ +│ │ │ │ │ │ +│ │ │ 1. Load Dataset from LangSmith │ │ │ +│ │ │ └─► "small-chatlangchain-dataset"│ │ │ +│ │ │ │ │ │ +│ │ │ 2. Run Graph for Each Question │ │ │ +│ │ │ └─► retrieval_graph.graph │ │ │ +│ │ │ │ │ │ +│ │ │ 3. Evaluate with Judge LLM │ │ │ +│ │ │ └─► groq/gpt-oss-20b │ │ │ +│ │ │ │ │ │ +│ │ │ 4. Calculate Scores │ │ │ +│ │ │ ├─► Answer Correctness ≥ 90% │ │ │ +│ │ │ └─► Context Correctness ≥ 90% │ │ │ +│ │ │ │ │ │ +│ │ │ 5. Assert Thresholds │ │ │ +│ │ │ └─► Fail if below minimum │ │ │ +│ │ └──────────────────────────────────────┘ │ │ +│ └────────────────────────────────────────────┘ │ +│ ↓ │ +│ Results logged to LangSmith │ +│ Experiment: "chat-langchain-ci-{timestamp}" │ +│ │ +└─────────────────────────────────────────────────────────────┘ +``` + +### Three Core Components + +**1. Dataset (LangSmith)** + +- Question-answer pairs from real user queries +- Expected reference answers +- Source documents (for retrieval validation) + +**2. Judge LLM (Groq)** + +- Evaluates answer quality +- Compares student answer vs reference +- Returns score (0.0 - 1.0) with reasoning + +**3. Metrics & Assertions (pytest)** + +- Calculate aggregate scores +- Enforce quality thresholds +- Fail CI/CD if below standards + +--- + +## 🔄 Complete Evaluation Flow + +```mermaid +flowchart TD + A[🚀 Trigger: GitHub Actions] --> B[📦 Setup Environment] + B --> C[🗃️ Load Dataset from LangSmith] + C --> D{For Each Example} + + D --> E[📝 Question: What is LangChain?] + E --> F[🤖 Run Graph: retrieval_graph] + F --> G[📄 Retrieve Documents] + G --> H[💬 Generate Answer] + + H --> I[🧑‍⚖️ Judge 1: Answer vs Reference] + I --> J[📊 Score 1: answer_correctness_score] + + H --> K[🧑‍⚖️ Judge 2: Answer vs Context] + K --> L[📊 Score 2: answer_vs_context_correctness_score] + + J --> M[📈 Aggregate All Scores] + L --> M + + M --> N{Average Scores} + N --> O[✅ Answer Correctness ≥ 90%?] + N --> P[✅ Context Correctness ≥ 90%?] + + O -->|Yes| Q[✅ Tests Pass] + O -->|No| R[❌ Tests Fail] + P -->|Yes| Q + P -->|No| R + + Q --> S[📊 Log to LangSmith] + R --> S + + S --> T[🎯 End: View Results in Dashboard] + + style E fill:#FFE4B5 + style F fill:#90EE90 + style I fill:#87CEEB + style K fill:#87CEEB + style Q fill:#90EE90 + style R fill:#FF6B6B + style S fill:#DDA0DD +``` + +--- + +## 📖 Step-by-Step Explanation + +### Step 1: GitHub Actions Workflow Setup + +```yaml +# .github/workflows/eval.yml +name: Eval + +on: + workflow_dispatch: # Manual trigger + +jobs: + run_eval: + runs-on: ubuntu-latest + environment: evals # Uses 'evals' environment secrets +``` + +**Key Points:** + +- **Manual Trigger:** `workflow_dispatch` allows running evals on-demand +- **Environment Secrets:** Stores API keys securely (LANGSMITH_API_KEY, OPENAI_API_KEY, etc.) +- **Ubuntu Runner:** Standard GitHub-hosted runner + +**Why Manual Trigger?** + +- Evals can be slow (minutes) +- Consume API credits +- Run when you need validation, not on every commit + +### Step 2: Environment Setup with uv + +```yaml +- name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + +- name: Install uv + uses: astral-sh/setup-uv@v6 + +- name: Sync dependencies (frozen) + run: uv sync --all-groups --frozen +``` + +**What is `uv`?** + +- Ultra-fast Python package manager (10-100x faster than pip) +- `--frozen` ensures exact versions from `uv.lock` +- `--all-groups` includes dev dependencies + +**Why Frozen Dependencies?** + +- Reproducible builds +- Same versions locally and in CI +- Prevents "works on my machine" issues + +### Step 3: Dataset Structure (LangSmith) + +```python +DATASET_NAME = "small-chatlangchain-dataset" + +# What's in the dataset? +Example { + inputs: { + "question": "What is LangChain?" + }, + outputs: { + "answer": "LangChain is a framework for developing applications powered by language models...", + "sources": ["https://python.langchain.com/docs/intro"] + } +} +``` + +**Dataset Composition:** + +``` +small-chatlangchain-dataset +├── Example 1 +│ ├── Question: "What is LangChain?" +│ ├── Expected Answer: "LangChain is a framework..." +│ └── Expected Sources: ["https://python.langchain.com/docs/intro"] +├── Example 2 +│ ├── Question: "How do I use LCEL?" +│ ├── Expected Answer: "LCEL is LangChain Expression Language..." +│ └── Expected Sources: ["https://python.langchain.com/docs/lcel"] +└── ... (10-50 examples) +``` + +**Where is the dataset?** + +- Stored in **LangSmith** (cloud-based dataset management) +- Curated from real user questions +- Answers validated by LangChain experts + +### Step 4: Running the Graph + +```python +async def run_graph(inputs: dict[str, Any]) -> dict[str, Any]: + results = await graph.ainvoke( + { + "messages": [("human", inputs["question"])], + } + ) + return results + +# What does this return? +{ + "messages": [ + HumanMessage(content="What is LangChain?"), + AIMessage(content="LangChain is a framework for developing applications powered by language models...") + ], + "documents": [ + Document( + page_content="LangChain is a framework...", + metadata={"source": "https://python.langchain.com/docs/intro", "title": "Introduction"} + ) + ] +} +``` + +**Graph Execution Steps:** + +```mermaid +sequenceDiagram + participant Test as test_e2e.py + participant Graph as retrieval_graph + participant Retriever as Weaviate + participant LLM as Response Model + + Test->>Graph: run_graph({"question": "What is LangChain?"}) + Graph->>Graph: analyze_and_route_query() + Graph->>Graph: create_research_plan() + Graph->>Retriever: retrieve_documents() + Retriever-->>Graph: [Document1, Document2, Document3] + Graph->>LLM: respond(question, documents) + LLM-->>Graph: AIMessage(content="LangChain is...") + Graph-->>Test: {"messages": [...], "documents": [...]} +``` + +### Step 5: Evaluation Metrics + +#### Metric 1: Answer Correctness (vs Reference) + +```python +QA_SYSTEM_PROMPT = """You are an expert programmer and problem-solver, tasked with grading answers to questions about Langchain. +You are given a question, the student's answer, and the true answer, and are asked to score the student answer. + +Grade the student answers based ONLY on their factual accuracy. Ignore differences in punctuation and phrasing between the student answer and true answer. It is OK if the student answer contains more information than the true answer, as long as it does not contain any conflicting statements.""" + +def evaluate_qa(run: Run, example: Example) -> dict: + score: GradeAnswer = qa_chain.invoke( + { + "question": example.inputs["question"], + "true_answer": example.outputs["answer"], + "answer": last_message.content, + } + ) + return {"key": "answer_correctness_score", "score": float(score.score)} +``` + +**What This Measures:** + +- **Factual Accuracy:** Is the answer correct? +- **Semantic Equivalence:** Different wording is OK +- **Completeness:** Extra information is allowed +- **Conflicts:** Penalizes wrong information + +**Example Evaluation:** + +``` +Question: "What is LangChain?" + +Reference Answer: +"LangChain is a framework for developing applications powered by language models." + +Student Answer (Our RAG): +"LangChain is a powerful framework designed for building applications powered by large language models (LLMs). It provides tools for chaining together different components." + +Judge LLM Response: +{ + "reason": "The student answer correctly identifies LangChain as a framework for LLM applications and provides additional accurate details about its functionality.", + "score": 1.0 ✅ Perfect! +} +``` + +#### Metric 2: Answer vs Context Correctness + +```python +CONTEXT_QA_SYSTEM_PROMPT = """You are an expert programmer and problem-solver, tasked with grading answers to questions about Langchain. +You are given a question, the context for answering the question, and the student's answer. You are asked to score the student's answer as either CORRECT or INCORRECT, based on the context. + +Grade the student answer BOTH based on its factual accuracy AND on whether it is supported by the context.""" + +def evaluate_qa_context(run: Run, example: Example) -> dict: + documents = run.outputs.get("documents") or [] + context = format_docs(documents) + + score: GradeAnswer = context_qa_chain.invoke( + { + "question": example.inputs["question"], + "context": context, + "answer": last_message.content, + } + ) + return {"key": "answer_vs_context_correctness_score", "score": float(score.score)} +``` + +**What This Measures:** + +- **Groundedness:** Is the answer supported by retrieved documents? +- **Hallucination Detection:** Catches made-up information +- **Context Faithfulness:** Ensures RAG stays grounded + +**Why Two Metrics?** + +``` +Scenario 1: Correct answer, wrong documents retrieved +├─► Answer Correctness: 1.0 ✅ (matches reference) +└─► Context Correctness: 0.0 ❌ (not in retrieved docs) + └─► Problem: Retrieval is broken! + +Scenario 2: Hallucinated answer from correct documents +├─► Answer Correctness: 0.2 ❌ (doesn't match reference) +└─► Context Correctness: 0.1 ❌ (not supported by context) + └─► Problem: Generation is hallucinating! + +Scenario 3: Perfect RAG system +├─► Answer Correctness: 1.0 ✅ +└─► Context Correctness: 1.0 ✅ + └─► Success: Both retrieval and generation work! +``` + +### Step 6: Judge LLM Configuration + +```python +JUDGE_MODEL_NAME = "groq/gpt-oss-20b" +judge_llm = load_chat_model(JUDGE_MODEL_NAME) + +qa_chain = QA_PROMPT | judge_llm.with_structured_output(GradeAnswer) +``` + +**Why Groq?** + +- **Fast:** 200+ tokens/second inference +- **Cost-Effective:** Free tier available +- **Structured Output:** Returns Pydantic models +- **Good Enough:** 70B parameter model is sufficient for grading + +**Structured Output Schema:** + +```python +class GradeAnswer(BaseModel): + """Evaluate correctness of the answer and assign a continuous score.""" + + reason: str = Field( + description="1-2 short sentences with the reason why the score was assigned" + ) + score: float = Field( + description="Score that shows how correct the answer is. Use 1.0 if completely correct and 0.0 if completely incorrect", + ge=0.0, + le=1.0, + ) +``` + +**Benefits of Structured Output:** + +- **Type Safety:** Pydantic validation +- **Consistent Format:** Always get score + reason +- **Easy to Parse:** No regex needed +- **Reliable:** Won't return malformed JSON + +### Step 7: Running Evaluations with LangSmith + +```python +async def test_scores_regression(): + experiment_results = await aevaluate( + run_graph, # Function to test + data=DATASET_NAME, # LangSmith dataset + evaluators=[evaluate_qa, evaluate_qa_context], # Scoring functions + experiment_prefix=EXPERIMENT_PREFIX, # "chat-langchain-ci" + metadata={"judge_model_name": JUDGE_MODEL_NAME}, + max_concurrency=1, # Sequential execution + ) +``` + +**What Happens During `aevaluate()`?** + +```mermaid +sequenceDiagram + participant Test as pytest + participant LS as LangSmith + participant Graph as RAG System + participant Judge as Judge LLM + + Test->>LS: Fetch dataset "small-chatlangchain-dataset" + LS-->>Test: [Example1, Example2, ..., Example10] + + loop For each example + Test->>Graph: run_graph(example.inputs) + Graph-->>Test: {"messages": [...], "documents": [...]} + + Test->>Judge: evaluate_qa(run, example) + Judge-->>Test: {"score": 0.95} + + Test->>Judge: evaluate_qa_context(run, example) + Judge-->>Test: {"score": 1.0} + + Test->>LS: Log results to experiment + end + + Test->>Test: Calculate average scores + Test->>Test: Assert scores >= thresholds +``` + +**LangSmith Experiment Tracking:** + +``` +Experiment: chat-langchain-ci-2024-11-22-14-30-00 +├── Example 1: "What is LangChain?" +│ ├── Answer Correctness: 1.0 +│ ├── Context Correctness: 1.0 +│ └── Duration: 2.3s +├── Example 2: "How do I use LCEL?" +│ ├── Answer Correctness: 0.95 +│ ├── Context Correctness: 1.0 +│ └── Duration: 1.8s +└── ... (10 examples) + +Aggregate Metrics: +├── Average Answer Correctness: 0.92 ✅ +├── Average Context Correctness: 0.95 ✅ +└── Total Duration: 23.5s +``` + +### Step 8: Assertions & Quality Gates + +```python +experiment_result_df = pd.DataFrame( + convert_single_example_results(result["evaluation_results"]) + for result in experiment_results._results +) +average_scores = experiment_result_df.mean() + +# Quality gates (regression test) +assert average_scores[SCORE_ANSWER_CORRECTNESS] >= 0.9 +assert average_scores[SCORE_ANSWER_VS_CONTEXT_CORRECTNESS] >= 0.9 +``` + +**What This Does:** + +- **Aggregates Scores:** Calculate mean across all examples +- **Enforces Thresholds:** Fail if below 90% accuracy +- **Blocks Bad Changes:** CI/CD fails, preventing deployment + +**Real Output Example:** + +```bash +# ✅ Passing Run +================================ test session starts ================================= +collected 1 item + +backend/tests/evals/test_e2e.py::test_scores_regression PASSED [100%] + +Aggregate Scores: + answer_correctness_score: 0.92 + answer_vs_context_correctness_score: 0.95 + +================================ 1 passed in 23.45s ================================== +``` + +```bash +# ❌ Failing Run (regression detected!) +================================ test session starts ================================= +collected 1 item + +backend/tests/evals/test_e2e.py::test_scores_regression FAILED [100%] + +Aggregate Scores: + answer_correctness_score: 0.85 ❌ Below 0.9 threshold! + answer_vs_context_correctness_score: 0.88 ❌ Below 0.9 threshold! + +AssertionError: 0.85 >= 0.9 +================================ 1 failed in 23.45s ================================== +``` + +--- + +## 🎓 Key Concepts & Best Practices + +### 1. LLM-as-a-Judge Pattern + +**Traditional Testing:** + +```python +# Exact match (fragile) +assert answer == "LangChain is a framework" # Fails on slight variation +``` + +**LLM-as-a-Judge:** + +```python +# Semantic evaluation (robust) +score = judge_llm.grade(answer, reference) +assert score >= 0.9 # Allows variation, catches real issues +``` + +**Benefits:** + +- **Flexibility:** Handles paraphrasing +- **Semantic Understanding:** Knows "framework" ≈ "library" in context +- **Reasoning:** Provides explanation for scores +- **Scalable:** One judge evaluates thousands of examples + +### 2. Multiple Evaluation Metrics + +**Why Not Just One Score?** + +``` +Single Metric Problem: +└─► "Overall accuracy: 80%" + └─► But why? Retrieval or generation issue? + +Multiple Metrics Solution: +├─► Retrieval Recall: 95% ✅ (retrieval works!) +├─► Answer Correctness: 65% ❌ (generation broken!) +└─► Context Correctness: 90% ✅ (grounded answers) + └─► Action: Improve generation prompt, not retrieval! +``` + +**Our Metric Strategy:** + +```python +# Disabled for now (would test retrieval quality) +# evaluate_retrieval_recall + +# Active metrics (test generation quality) +evaluate_qa # Factual accuracy vs reference +evaluate_qa_context # Groundedness in retrieved docs +``` + +### 3. Dataset Quality Matters + +**Bad Dataset → Meaningless Evals:** + +``` +❌ Ambiguous question: "How do I do that?" +❌ Outdated answer: "Use version 0.1" (current is 0.3) +❌ Multiple valid answers: No single reference +``` + +**Good Dataset → Reliable Evals:** + +``` +✅ Clear question: "What is LangChain Expression Language (LCEL)?" +✅ Current answer: Matches latest documentation +✅ Unambiguous: One correct answer with sources +``` + +**Dataset Maintenance:** + +- Review and update quarterly +- Add examples from production errors +- Remove deprecated content +- Balance difficulty (easy/medium/hard) + +### 4. Judge Model Selection + +**Considerations:** + +| Model | Speed | Cost | Accuracy | Use Case | +| ------------- | ------- | --------- | -------- | --------------- | +| GPT-4 | Slow | $$$ | Highest | Gold standard | +| Claude Sonnet | Medium | $$ | High | Balanced choice | +| GPT-4o-mini | Fast | $ | Good | Daily testing | +| Groq (OSS) | Fastest | Free tier | Good | CI/CD pipelines | + +**Our Choice: Groq `gpt-oss-20b`** + +- **Speed:** 200+ tok/s (10x faster than OpenAI) +- **Cost:** Free tier for testing +- **Quality:** 90%+ agreement with GPT-4 on QA tasks +- **Structured Output:** Native Pydantic support + +### 5. CI/CD Integration Patterns + +**Pattern 1: Manual Trigger (Our Approach)** + +```yaml +on: + workflow_dispatch: # Run on-demand +``` + +**Pros:** + +- Control when evals run +- Avoid unnecessary API costs +- Run before important deployments + +**Cons:** + +- Manual step (can forget) +- Not automated + +**Pattern 2: On Pull Request** + +```yaml +on: + pull_request: + branches: [main] +``` + +**Pros:** + +- Automatic validation +- Catches issues before merge +- Enforces quality gates + +**Cons:** + +- Slows PR workflow (2-5 min) +- Costs on every PR + +**Pattern 3: Scheduled** + +```yaml +on: + schedule: + - cron: '0 0 * * *' # Daily at midnight +``` + +**Pros:** + +- Monitor production drift +- Catch model/data degradation +- Historical tracking + +**Cons:** + +- Delayed issue detection +- Doesn't block bad code + +**Best Practice: Combine Patterns** + +```yaml +on: + workflow_dispatch: # Manual for testing + pull_request: # Block bad PRs + branches: [main] + schedule: # Daily monitoring + - cron: '0 0 * * *' +``` + +--- + +## 📊 Real-World Example + +### Scenario: Testing a Prompt Change + +**Change:** Update system prompt to be more concise + +**Before Running Evals:** + +```python +# OLD PROMPT +SYSTEM_PROMPT = """You are a helpful AI assistant specializing in LangChain. +Provide detailed, comprehensive answers with code examples and explanations. +Always cite your sources and include links to documentation.""" + +# NEW PROMPT (more concise) +SYSTEM_PROMPT = """You are a LangChain expert. Answer concisely with relevant examples.""" +``` + +**Run Evaluation:** + +```bash +# Local testing +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py -v -s + +# Or via GitHub Actions +# Go to Actions → Eval → Run workflow +``` + +**Results:** + +``` +Experiment: chat-langchain-ci-prompt-update-2024-11-22 + +┌─────────────────────────┬────────┬────────┬─────────┐ +│ Example │ Before │ After │ Change │ +├─────────────────────────┼────────┼────────┼─────────┤ +│ "What is LangChain?" │ 1.0 │ 0.95 │ -0.05 │ +│ "How to use LCEL?" │ 0.95 │ 0.90 │ -0.05 │ +│ "Memory in agents?" │ 0.90 │ 0.85 │ -0.05 │ +└─────────────────────────┴────────┴────────┴─────────┘ + +Average Answer Correctness: + Before: 0.92 ✅ + After: 0.87 ❌ (below 0.9 threshold!) + +Verdict: REJECT the change - caused 5% regression +``` + +**Action:** Revert prompt or iterate until scores recover + +### Scenario: Testing Model Upgrade + +**Change:** Switch from `gpt-4o-mini` to `claude-3.5-sonnet` + +**Evaluation Run:** + +```python +# Test both models side-by-side +config_gpt = {"response_model": "openai/gpt-4o-mini"} +config_claude = {"response_model": "anthropic/claude-3.5-sonnet"} + +results_gpt = aevaluate(run_graph_with_config(config_gpt), ...) +results_claude = aevaluate(run_graph_with_config(config_claude), ...) +``` + +**Results:** + +``` +Model Comparison: + +GPT-4o-mini: +├─► Answer Correctness: 0.92 +├─► Context Correctness: 0.94 +├─► Avg Latency: 1.2s +└─► Cost per 1K queries: $0.60 + +Claude-3.5-Sonnet: +├─► Answer Correctness: 0.96 ✅ (+4% better!) +├─► Context Correctness: 0.97 ✅ (+3% better!) +├─► Avg Latency: 1.5s +└─► Cost per 1K queries: $3.00 + +Decision: Upgrade to Claude if quality > cost +``` + +--- + +## 🚀 Running Evaluations + +### Local Testing + +```bash +# Setup environment +export LANGSMITH_API_KEY="lsv2_pt_..." +export OPENAI_API_KEY="sk-..." +export GROQ_API_KEY="gsk_..." +export WEAVIATE_URL="http://localhost:8080" + +# Run all eval tests +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py -v -s + +# Run specific test +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py::test_scores_regression -v -s + +# View detailed output +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py -v -s --tb=short +``` + +### GitHub Actions (CI/CD) + +```bash +# 1. Go to repository on GitHub +# 2. Click "Actions" tab +# 3. Select "Eval" workflow +# 4. Click "Run workflow" dropdown +# 5. Select branch (e.g., main) +# 6. Click "Run workflow" button +# 7. Wait 2-5 minutes for results +# 8. View results in LangSmith dashboard +``` + +### Viewing Results in LangSmith + +``` +1. Go to: https://smith.langchain.com/ +2. Navigate to: Projects → chat-langchain (or your project) +3. Click "Experiments" tab +4. Find: "chat-langchain-ci-{timestamp}" +5. View: + ├─► Individual example traces + ├─► Aggregate metrics + ├─► Score distributions + └─► Failure analysis +``` + +--- + +## 💡 Advanced Patterns + +### 1. Continuous Monitoring (Production) + +```python +# Monitor production queries in real-time +@app.middleware("http") +async def eval_middleware(request: Request, call_next): + if request.url.path == "/chat": + question = request.json()["question"] + response = await call_next(request) + + # Async eval (don't block response) + asyncio.create_task( + evaluate_production_response(question, response) + ) + + return response +``` + +**Benefits:** + +- Detect production regressions +- Monitor user satisfaction +- Build dataset from real queries + +### 2. A/B Testing with Evals + +```python +# Test two prompts side-by-side +async def ab_test_prompts(): + results_a = await aevaluate( + run_graph_with_prompt_a, + data=DATASET_NAME, + experiment_prefix="prompt-a" + ) + + results_b = await aevaluate( + run_graph_with_prompt_b, + data=DATASET_NAME, + experiment_prefix="prompt-b" + ) + + # Statistical significance test + p_value = ttest_ind(results_a.scores, results_b.scores) + + if p_value < 0.05: + print(f"Prompt B is significantly better! (p={p_value})") +``` + +### 3. Cost-Aware Evaluation + +```python +# Track costs during evaluation +class CostTracker: + def __init__(self): + self.total_tokens = 0 + self.total_cost = 0.0 + + def track(self, run: Run): + tokens = run.total_tokens + model = run.metadata["model"] + + # Model pricing (per 1M tokens) + pricing = { + "gpt-4o-mini": {"input": 0.15, "output": 0.60}, + "claude-3.5-sonnet": {"input": 3.00, "output": 15.00} + } + + cost = calculate_cost(tokens, model, pricing) + self.total_cost += cost + + print(f"Run cost: ${cost:.4f} | Total: ${self.total_cost:.2f}") + +# Use in evals +tracker = CostTracker() +results = await aevaluate( + run_graph, + data=DATASET_NAME, + on_run=tracker.track +) + +print(f"Evaluation cost: ${tracker.total_cost:.2f}") +``` + +### 4. Multi-Dimensional Scoring + +```python +# Beyond correctness - evaluate multiple quality dimensions +class AdvancedGrade(BaseModel): + correctness: float = Field(ge=0.0, le=1.0) + completeness: float = Field(ge=0.0, le=1.0) + conciseness: float = Field(ge=0.0, le=1.0) + readability: float = Field(ge=0.0, le=1.0) + + @property + def overall_score(self) -> float: + # Weighted average + return ( + self.correctness * 0.4 + + self.completeness * 0.3 + + self.conciseness * 0.2 + + self.readability * 0.1 + ) + +# Use in evaluation +advanced_chain = ADVANCED_PROMPT | judge_llm.with_structured_output(AdvancedGrade) +``` + +--- + +## 🎯 Key Takeaways + +### 1. **Evaluation is Essential for AI Quality** + +- Without evals, you're flying blind +- Quantitative metrics > subjective "feels better" +- Catch regressions before users do + +### 2. **LLM-as-a-Judge is Powerful** + +- Enables semantic evaluation (not just exact match) +- Scalable to thousands of examples +- Provides reasoning for scores +- Cost-effective with fast models (Groq) + +### 3. **Multiple Metrics Tell the Full Story** + +- **Answer Correctness:** Is it factually accurate? +- **Context Correctness:** Is it grounded in retrieved docs? +- **Retrieval Recall:** Are we finding the right documents? +- Combined: Pinpoint exact failure points + +### 4. **CI/CD Integration Prevents Production Issues** + +- Quality gates enforce minimum standards +- Block bad changes before deployment +- Historical tracking shows trends +- Fast feedback loop for developers + +### 5. **LangSmith is Critical Infrastructure** + +- **Dataset Management:** Version control for test data +- **Experiment Tracking:** Compare runs side-by-side +- **Trace Debugging:** Understand failures +- **Team Collaboration:** Share results and insights + +### 6. **Cost Optimization Matters** + +- **Judge Model:** Groq (free tier) vs GPT-4 ($$$) +- **Concurrency:** Balance speed vs rate limits +- **Dataset Size:** 10-50 examples for CI, 100+ for deep analysis +- **Caching:** Reuse evaluations when code unchanged + +--- + +## 📚 Quick Reference + +### Essential Commands + +```bash +# Local evaluation +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py -v -s + +# With specific dataset +PYTHONPATH=. uv run pytest backend/tests/evals/test_e2e.py -v -s -k test_scores_regression + +# GitHub Actions +# Actions → Eval → Run workflow + +# View LangSmith results +# https://smith.langchain.com/ → Projects → Experiments +``` + +### Key Files + +``` +.github/workflows/eval.yml # CI/CD configuration +backend/tests/evals/test_e2e.py # Evaluation logic +backend/retrieval_graph/graph.py # System under test +pyproject.toml # Dependencies +uv.lock # Frozen versions +``` + +### Environment Variables + +```bash +# Required +LANGSMITH_API_KEY=lsv2_pt_... # LangSmith access +OPENAI_API_KEY=sk-... # OpenAI models +GROQ_API_KEY=gsk_... # Judge model + +# Optional (for different models) +ANTHROPIC_API_KEY=sk-ant-... # Claude models + +# Infrastructure +WEAVIATE_URL=http://localhost:8080 +RECORD_MANAGER_DB_URL=postgresql://... +``` + +### Quality Thresholds + +```python +# Current standards (in test_e2e.py) +ANSWER_CORRECTNESS_THRESHOLD = 0.9 # 90% +CONTEXT_CORRECTNESS_THRESHOLD = 0.9 # 90% + +# Adjust based on your needs: +# - Stricter (0.95) for critical apps +# - Looser (0.85) for exploratory projects +``` + +### LangSmith Dataset Format + +```python +# Example schema +{ + "inputs": { + "question": "What is LangChain?" + }, + "outputs": { + "answer": "LangChain is a framework...", + "sources": ["https://python.langchain.com/docs/intro"] + } +} +``` + +--- + +## 🔧 Troubleshooting + +### Common Issues + +**1. Tests Fail with "Dataset not found"** + +```bash +# Solution: Check dataset name in test_e2e.py +DATASET_NAME = "small-chatlangchain-dataset" # Must exist in LangSmith + +# Or create dataset: +# LangSmith UI → Datasets → Create Dataset → Upload JSONL +``` + +**2. "Rate limit exceeded" errors** + +```bash +# Solution: Reduce concurrency or switch judge model +experiment_results = await aevaluate( + ..., + max_concurrency=1, # Lower from 5 → 1 +) + +# Or use Groq (higher limits) +JUDGE_MODEL_NAME = "groq/gpt-oss-20b" +``` + +**3. Tests pass locally but fail in CI** + +```bash +# Solution: Check environment variables in GitHub +# Settings → Secrets and variables → Actions → Environment secrets + +# Ensure all required secrets are set: +# - LANGSMITH_API_KEY +# - OPENAI_API_KEY +# - GROQ_API_KEY +# - WEAVIATE_URL (if using remote Weaviate) +``` + +**4. Scores are inconsistent between runs** + +```python +# Solution: LLMs are non-deterministic +# Options: +# 1. Set temperature=0 for judge model +judge_llm = load_chat_model(JUDGE_MODEL_NAME).bind(temperature=0) + +# 2. Run multiple times and average +# 3. Use larger dataset (reduces variance) +# 4. Accept 2-3% variance as normal +``` + +**5. Evaluation is too slow** + +```bash +# Solutions: +# 1. Use smaller dataset for CI +DATASET_NAME = "small-chatlangchain-dataset" # 10-20 examples + +# 2. Increase concurrency (if API allows) +max_concurrency=5 + +# 3. Use faster judge model +JUDGE_MODEL_NAME = "groq/gpt-oss-20b" # 200+ tok/s + +# 4. Cache unchanged evaluations +``` + +--- + +## 🌟 Next Steps + +### After Mastering Evals + +Now that you understand the evaluation system, consider: + +1. **Add Custom Metrics** + + - Latency tracking + - Cost per query + - Citation accuracy + - Readability scores + +2. **Expand Dataset** + + - Add production queries + - Include edge cases + - Balance difficulty levels + - Add multilingual examples + +3. **Advanced Evaluation** + + - Human-in-the-loop validation + - Pairwise comparison (which answer is better?) + - Multi-turn conversation evals + - Adversarial testing + +4. **Production Monitoring** + - Real-time eval dashboard + - Alert on score drops + - A/B testing in production + - User feedback integration + +### My Next Journey: Python → JS Migration + +As mentioned in my learning journey, I'm planning to migrate this system to **LangChain JS v1** to: + +- Learn JavaScript/TypeScript implementation patterns +- Compare Python vs JS ecosystems +- Make the system more accessible for frontend developers +- Explore Node.js deployment options + +Follow along: [LangChain JS v1 Overview](https://docs.langchain.com/oss/javascript/langchain/overview) + +--- + +## 📖 Resources + +### Official Documentation + +- [LangSmith Evaluation Guide](https://docs.smith.langchain.com/evaluation) +- [LangChain Evaluation](https://python.langchain.com/docs/guides/evaluation) +- [GitHub Actions Documentation](https://docs.github.com/en/actions) +- [uv Package Manager](https://github.com/astral-sh/uv) + +**Last Updated:** November 22, 2024 +**Author:** [thongvmdev](https://github.com/thongvmdev) +**Status:** ✅ Completed evaluation system learning - Ready for JS migration! + +--- + +_This blog post is part of my open learning journey. If you found it helpful, consider starring the [practice repository](https://github.com/thongvmdev/chat-langchain-practice) or sharing your own learnings!_ diff --git a/docs/FRONTEND_ARCHITECTURE.md b/docs/FRONTEND_ARCHITECTURE.md new file mode 100644 index 000000000..5abbb3491 --- /dev/null +++ b/docs/FRONTEND_ARCHITECTURE.md @@ -0,0 +1,1043 @@ +# Understanding Chat LangChain > Frontend Architecture & LangGraph API Integration + +A comprehensive guide to understanding how Chat LangChain's frontend integrates with LangGraph API to build a sophisticated real-time chat interface with streaming, state management, and thread persistence. + +> **Learning Journey:** This is part of my exploration of the [Chat LangChain](https://github.com/langchain-ai/chat-langchain) open-source project. +> +> **Source Repository:** [langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) +> **Practice Repository:** [thongvmdev/chat-langchain-practice](https://github.com/thongvmdev/chat-langchain-practice) + +--- + +## 🚨 Problem & Solution + +### The Challenge + +After mastering the backend LangGraph workflows, I wanted to understand how to build a **production-ready frontend** that can: + +1. **Stream responses in real-time** as the graph executes +2. **Manage complex state** across multiple conversation threads +3. **Handle LangGraph API integration** seamlessly +4. **Display rich UI components** for research progress, documents, and tool calls +5. **Maintain conversation history** and thread persistence + +Simple chat UIs send messages and wait for responses. But how do you stream partial results? How do you handle multiple conversation threads? How do you integrate with LangGraph's sophisticated API? How do you convert between different message formats? + +### The Solution: React + LangGraph SDK + Assistant UI + +I discovered that Chat LangChain uses a **sophisticated frontend architecture** combining: + +**Key Technologies:** + +- **React Context API**: Centralized state management for messages, threads, and user data +- **LangGraph SDK**: Official SDK for seamless API integration +- **Assistant UI**: Pre-built chat components with streaming support +- **Next.js API Routes**: Proxy layer for secure backend communication +- **TypeScript**: Full type safety across the stack + +**Benefits:** + +- ✅ **Real-Time Streaming**: Progressive updates as the graph executes +- ✅ **Thread Management**: Multiple conversation sessions with persistence +- ✅ **Type Safety**: Full TypeScript coverage prevents runtime errors +- ✅ **Modular Design**: Clear separation between state, UI, and API layers +- ✅ **Rich UI Components**: Visual feedback for research progress, documents, and tool calls + +--- + +## Table of Contents + +1. [Architecture Overview](#architecture-overview) +2. [Core Components](#core-components) +3. [Data Flow](#data-flow) +4. [LangGraph API Integration](#langgraph-api-integration) +5. [State Management](#state-management) +6. [Message Conversion](#message-conversion) +7. [Key Learnings](#key-learnings) + +--- + +## Architecture Overview + +### Three-Layer Design + +The frontend uses a **hierarchical architecture** with three main layers: + +1. **API Proxy Layer** (`app/api/[..._path]/route.ts`): Secure proxy to LangGraph backend + + - Handles CORS + - Adds authentication headers + - Routes requests to backend + +2. **State Management Layer** (`app/contexts/GraphContext.tsx`): Centralized state and streaming + + - Message state management + - Thread management + - Real-time streaming via `useStream` hook + - Event handling (progress, documents, router logic) + +3. **UI Layer** (`app/components/`): React components for user interaction + - Chat interface with Assistant UI + - Tool UI components (progress, documents, questions) + - Thread history sidebar + +**Why This Design?** + +This separation provides: + +- **Security**: API proxy prevents exposing backend URLs +- **Separation of Concerns**: State, API, and UI are cleanly separated +- **Reusability**: Components can be used independently +- **Testability**: Each layer can be tested in isolation + +```mermaid +graph TB + subgraph "UI Layer" + UI[Chat Interface] + TH[Thread History] + TU[Tool UI Components] + end + + subgraph "State Management Layer" + GC[GraphContext] + US[useStream Hook] + UT[useThreads Hook] + end + + subgraph "API Proxy Layer" + PROXY[API Route Handler] + end + + subgraph "LangGraph Backend" + LG[LangGraph API] + end + + UI --> GC + TH --> UT + GC --> US + GC --> UT + US --> PROXY + UT --> PROXY + PROXY --> LG + LG -.SSE Stream.-> US + US -.Updates.-> GC + GC -.Re-renders.-> UI + + style GC fill:#e1f5ff + style US fill:#fff4e1 + style PROXY fill:#e8f5e9 +``` + +--- + +## Core Components + +### 1. GraphContext.tsx 🔴 **HIGHEST PRIORITY** + +**Location:** `app/contexts/GraphContext.tsx` + +**Purpose:** Centralized state management and streaming orchestration + +#### Key Responsibilities + +1. **Streaming Setup**: Configures `useStream` hook for real-time updates +2. **Message Management**: Maintains conversation state +3. **Event Handling**: Processes updates, metadata, and lifecycle events +4. **Thread Coordination**: Manages thread creation and switching + +#### Core Implementation + +```typescript +// Streaming configuration +const thread = useStream({ + apiUrl: 'http://localhost:3000/api', + assistantId: 'chat', + streamMode: ['messages-tuple', 'updates'], + onUpdateEvent: (data) => { + // Handle progress, documents, router logic + }, + onMetadataEvent: (meta) => { + // Track run_id and configuration + }, + onFinish: (state) => { + // Finalize run + }, +}) +``` + +#### Event Types Handled + +1. **Progress Events**: Research step progress indicators +2. **Generating Questions**: Display research queries being generated +3. **Router Logic**: Show query routing decisions +4. **Selected Documents**: Display retrieved documents +5. **Answer Header**: Mark answer section start +6. **LangSmith Links**: Trace links for debugging + +#### State Management Pattern + +```typescript +interface GraphData { + runId: string + isStreaming: boolean + messages: Message[] + selectedModel: ModelOptions + streamMessage: (threadId: string, params: GraphInput) => Promise<void> + switchSelectedThread: (thread: Thread) => void +} +``` + +**Key Insight:** The context manages both **local React state** (messages, UI state) and **streaming state** (from LangGraph), merging them seamlessly for the UI. + +--- + +### 2. ChatLangChain.tsx 🟠 **HIGH PRIORITY** + +**Location:** `app/components/ChatLangChain.tsx` + +**Purpose:** Main chat orchestrator that bridges Assistant UI and GraphContext + +#### Key Responsibilities + +1. **Message Submission**: Converts user input to LangGraph format +2. **Message Conversion**: Transforms between LangChain and Assistant UI formats +3. **Runtime Setup**: Configures Assistant UI runtime with external state +4. **Thread Management**: Handles thread creation and URL synchronization + +#### Message Submission Flow + +```typescript +async function onNew(message: AppendMessage): Promise<void> { + // 1. Convert Assistant UI message to LangChain format + const humanMessage: Message = { + type: 'human', + content: message.content[0].text, + id: uuidv4(), + } + + // 2. Update local state immediately (optimistic update) + setMessages((prevMessages) => [...prevMessages, humanMessage]) + + // 3. Stream message to LangGraph backend + await streamMessage(threadId ?? undefined, { + messages: [convertToOpenAIFormat(humanMessage)], + }) +} +``` + +#### Message Conversion + +```typescript +// Convert LangChain messages to Assistant UI format +const threadMessages = useExternalMessageConverter<Message>({ + callback: convertLangchainMessages, + messages, + isRunning, +}) + +// Setup runtime with external state +const runtime = useExternalStoreRuntime({ + messages: threadMessages, + isRunning, + onNew, +}) +``` + +**Key Insight:** This component acts as a **translation layer** between Assistant UI's expected format and LangGraph's message format, enabling seamless integration. + +--- + +### 3. API Proxy Route 🟡 **MEDIUM PRIORITY** + +**Location:** `app/api/[..._path]/route.ts` + +**Purpose:** Secure proxy to LangGraph backend + +#### Implementation + +```typescript +async function handleRequest(req: NextRequest, method: string) { + // 1. Strip /api/ prefix from path + const path = req.nextUrl.pathname.replace(/^\/?api\//, '') + + // 2. Forward request to LangGraph backend + const res = await fetch(`${process.env.API_BASE_URL}/${path}${queryString}`, { + method, + headers: { + 'x-api-key': process.env.LANGCHAIN_API_KEY || '', + }, + body: method !== 'GET' ? await req.text() : undefined, + }) + + // 3. Return response with CORS headers + return new NextResponse(res.body, { + status: res.status, + headers: { + ...res.headers, + ...getCorsHeaders(), + }, + }) +} +``` + +#### Why a Proxy? + +1. **Security**: Hides backend URL from client +2. **CORS Handling**: Centralized CORS configuration +3. **Authentication**: Adds API keys server-side +4. **Path Transformation**: Simplifies client-side API calls + +**Key Insight:** The proxy enables the frontend to call LangGraph API endpoints as if they were local Next.js routes, simplifying client-side code. + +--- + +### 4. useThreads Hook 🟡 **MEDIUM PRIORITY** + +**Location:** `app/hooks/useThreads.tsx` + +**Purpose:** Thread CRUD operations using LangGraph SDK + +#### Key Operations + +```typescript +// Create LangGraph SDK client +const client = new Client({ + apiUrl: process.env.NEXT_PUBLIC_API_URL ?? 'http://localhost:3000/api', +}) + +// Search threads by user metadata +const userThreads = await client.threads.search({ + metadata: { user_id: userId }, + limit: 100, +}) + +// Get specific thread +const thread = await client.threads.get(threadId) + +// Delete thread +await client.threads.delete(threadId) +``` + +#### Thread Management Pattern + +1. **Search**: Find all threads for a user using metadata filters +2. **Get**: Retrieve specific thread with full state +3. **Delete**: Remove thread and update UI state +4. **Sync**: Keep URL query params in sync with selected thread + +**Key Insight:** The hook encapsulates all thread operations, making it easy to manage conversation history across the application. + +--- + +## Data Flow + +### Complete Message Flow + +Understanding the data flow is crucial for debugging and extending the system: + +```mermaid +sequenceDiagram + participant User + participant ChatUI + participant ChatLangChain + participant GraphContext + participant useStream + participant APIProxy + participant LangGraph + + User->>ChatUI: Types message + ChatUI->>ChatLangChain: onNew(message) + ChatLangChain->>ChatLangChain: Convert to LangChain format + ChatLangChain->>GraphContext: streamMessage(threadId, {messages}) + GraphContext->>useStream: thread.submit({messages}, config) + useStream->>APIProxy: POST /api/threads/{id}/runs/stream + APIProxy->>LangGraph: POST /threads/{id}/runs/stream + LangGraph-->>APIProxy: SSE Stream (events) + APIProxy-->>useStream: SSE Stream (events) + useStream->>GraphContext: onUpdateEvent(data) + useStream->>GraphContext: onMetadataEvent(meta) + useStream->>GraphContext: messages stream + GraphContext->>GraphContext: Update state + GraphContext->>ChatLangChain: State update triggers re-render + ChatLangChain->>ChatUI: New messages + ChatUI->>User: Display message +``` + +### Step-by-Step Breakdown + +#### Step 1: User Input + +User types a message in the chat interface (`chat-interface/index.tsx`) + +#### Step 2: Message Conversion + +`ChatLangChain.onNew()` converts Assistant UI message format to LangChain format: + +```typescript +const humanMessage: Message = { + type: 'human', + content: message.content[0].text, + id: uuidv4(), +} +``` + +#### Step 3: State Update + +Message is added to local state immediately (optimistic update): + +```typescript +setMessages((prevMessages) => [...prevMessages, humanMessage]) +``` + +#### Step 4: Stream Submission + +`GraphContext.streamMessage()` calls `thread.submit()`: + +```typescript +await thread.submit( + { messages: [convertToOpenAIFormat(humanMessage)] }, + { config, metadata }, +) +``` + +#### Step 5: API Request + +Request goes through API proxy to LangGraph backend: + +``` +POST /api/threads/{thread_id}/runs/stream +→ POST /threads/{thread_id}/runs/stream (LangGraph) +``` + +#### Step 6: Streaming Response + +LangGraph sends Server-Sent Events (SSE) with: + +- **Messages**: Actual chat messages +- **Updates**: Progress, documents, router logic +- **Metadata**: Run IDs, configuration + +#### Step 7: Event Handling + +`useStream` hook processes events: + +```typescript +onUpdateEvent: (data) => { + // Handle progress, documents, etc. + if (data.type === 'progress') { + // Update progress UI + } + if (data.type === 'selected_documents') { + // Display documents + } +} +``` + +#### Step 8: State Merge + +Events update React state, triggering re-renders: + +```typescript +setMessages((prev) => { + // Merge new messages from stream + return [...prev, ...newMessages] +}) +``` + +#### Step 9: UI Update + +React re-renders components with new state, displaying: + +- User message +- Progress indicators +- Retrieved documents +- Final AI response + +--- + +## LangGraph API Integration + +### Understanding the API Endpoints Used + +The frontend integrates with several LangGraph API categories: + +#### 1. Threads API + +**Purpose:** Manage conversation sessions with persistent state + +**Endpoints Used:** + +```typescript +// Search threads by metadata +POST /threads/search +{ + "metadata": { "user_id": "user-123" }, + "limit": 100 +} + +// Get thread state +GET /threads/{thread_id}/state + +// Get thread by ID +GET /threads/{thread_id} +``` + +**Use Case:** The frontend uses threads to: + +- Maintain conversation history +- Persist state across page refreshes +- Support multiple concurrent conversations +- Enable thread switching in the sidebar + +#### 2. Thread Runs API (Streaming) + +**Purpose:** Execute graph runs with real-time streaming + +**Endpoints Used:** + +```typescript +// Create run with streaming +POST /threads/{thread_id}/runs/stream +{ + "assistant_id": "chat", + "input": { + "messages": [{ "role": "user", "content": "..." }] + }, + "stream_mode": ["messages-tuple", "updates"] +} +``` + +**Use Case:** This is the **core endpoint** for chat functionality: + +- Streams responses as the graph executes +- Provides intermediate updates (progress, documents) +- Enables real-time UI updates +- Supports resumable streams + +#### 3. Stream Events + +**Event Types Received:** + +1. **Messages**: Actual chat messages (human, AI, system, tool) +2. **Updates**: Custom events for UI components + - `progress`: Research step progress + - `generating_questions`: Research queries + - `router_logic`: Query routing decision + - `selected_documents`: Retrieved documents + - `answer_header`: Answer section marker +3. **Metadata**: Run configuration and IDs + +**Processing Pattern:** + +```typescript +onUpdateEvent: (data) => { + switch (data.type) { + case 'progress': + // Update progress indicator + break + case 'selected_documents': + // Display document cards + break + case 'generating_questions': + // Show research questions + break + } +} +``` + +--- + +## State Management + +### State Architecture + +The frontend uses a **multi-level state management pattern**: + +#### 1. Context State (GraphContext) + +**Purpose:** Global application state + +```typescript +interface GraphData { + runId: string // Current run ID + isStreaming: boolean // Streaming status + messages: Message[] // Conversation messages + selectedModel: ModelOptions // Selected LLM model + setMessages: Dispatch<SetStateAction<Message[]>> + streamMessage: (threadId, params) => Promise<void> + switchSelectedThread: (thread: Thread) => void +} +``` + +#### 2. Streaming State (useStream) + +**Purpose:** Real-time updates from LangGraph + +```typescript +const thread = useStream({ + apiUrl: '...', + assistantId: 'chat', + streamMode: ['messages-tuple', 'updates'], + // Event handlers merge streaming state into context state +}) +``` + +#### 3. Thread State (useThreads) + +**Purpose:** Thread list and management + +```typescript +interface ThreadsData { + isUserThreadsLoading: boolean + userThreads: Thread[] + getUserThreads: (userId: string) => Promise<void> + getThreadById: (id: string) => Promise<Thread> + deleteThread: (id: string) => Promise<void> +} +``` + +#### 4. Local Component State + +**Purpose:** UI-specific state (loading, errors, etc.) + +```typescript +const [isRunning, setIsRunning] = useState(false) +const [threadId, setThreadId] = useQueryState('threadId') +``` + +### State Synchronization + +**Challenge:** Keeping multiple state sources in sync + +**Solution:** Event-driven updates + +1. **User Action** → Updates local state +2. **Stream Event** → Updates context state +3. **Thread Operation** → Updates thread state +4. **React Re-render** → UI reflects all state changes + +--- + +## Message Conversion + +### The Conversion Challenge + +The frontend needs to convert between **three different message formats**: + +1. **LangGraph Format**: What the backend expects/returns +2. **Assistant UI Format**: What the UI library expects +3. **OpenAI Format**: What's sent in API requests + +### Conversion Functions + +#### 1. LangChain → Assistant UI + +```typescript +export const convertLangchainMessages = ( + message: Message, +): AssistantUiMessage => { + switch (message.type) { + case 'human': + return { + role: 'user', + id: message.id, + content: [{ type: 'text', text: messageContentToText(message) }], + } + case 'ai': + return { + role: 'assistant', + id: message.id, + content: [ + ...toolCallsContent, // Convert tool calls + { type: 'text', text: messageContentToText(message) }, + ], + } + case 'tool': + return { + role: 'tool', + toolName: message.name, + toolCallId: message.tool_call_id, + result: message.content, + } + } +} +``` + +#### 2. LangChain → OpenAI Format + +```typescript +export function convertToOpenAIFormat(message: Message) { + const textContent = messageContentToText(message) + + switch (message.type) { + case 'human': + return { role: 'user', content: textContent } + case 'ai': + return { role: 'assistant', content: textContent } + // ... other types + } +} +``` + +#### 3. Content Extraction + +```typescript +export function messageContentToText(message: Message): string { + const { content } = message + if (typeof content === 'string') return content + if (Array.isArray(content)) { + return content + .map((part) => { + if (typeof part === 'string') return part + if (part?.type === 'text') return part.text + return '' + }) + .filter(Boolean) + .join('\n') + } + return '' +} +``` + +**Key Insight:** Message conversion is **bidirectional** and happens at multiple points: + +- **Input**: Assistant UI → LangChain → OpenAI format +- **Output**: LangGraph → LangChain → Assistant UI format + +--- + +## 🎓 Key Learnings + +### 1. **Streaming Architecture: Event-Driven Updates** + +One of the most important patterns I learned is how to handle **real-time streaming** with React: + +```typescript +// Setup streaming hook +const thread = useStream({ + streamMode: ['messages-tuple', 'updates'], + onUpdateEvent: (data) => { + // Handle custom events (progress, documents, etc.) + }, + onMetadataEvent: (meta) => { + // Track run IDs and configuration + }, +}) + +// Submit message +await thread.submit({ messages }, { config }) + +// Stream automatically updates state via callbacks +``` + +**Benefits:** + +- Real-time UI updates without polling +- Progressive rendering of long responses +- Rich feedback (progress, documents) during execution + +### 2. **State Management: Context + Hooks Pattern** + +The application uses a **hybrid state management approach**: + +- **Context API**: Global state (messages, threads, user) +- **Custom Hooks**: Encapsulated logic (useThreads, useRuns, useUser) +- **Local State**: Component-specific state (loading, errors) + +**Why This Works:** + +- Context provides global access without prop drilling +- Hooks encapsulate complex logic and side effects +- Local state keeps components independent + +### 3. **API Proxy: Security and Simplification** + +The API proxy pattern provides multiple benefits: + +```typescript +// Client calls local route +fetch('/api/threads/123/runs/stream') + +// Proxy forwards to backend +fetch(`${API_BASE_URL}/threads/123/runs/stream`) +``` + +**Benefits:** + +- Hides backend URL from client +- Centralized authentication +- CORS handling +- Path transformation + +### 4. **Message Format Conversion: Three-Way Translation** + +Understanding message conversion is crucial: + +``` +Assistant UI Format ↔ LangChain Format ↔ OpenAI Format +``` + +**Conversion Points:** + +- **Input**: User message → LangChain → OpenAI → API +- **Output**: API → LangChain → Assistant UI → Display + +**Key Insight:** Each format serves a different purpose: + +- **Assistant UI**: Rich UI components (tool calls, content arrays) +- **LangChain**: Type-safe internal representation +- **OpenAI**: API compatibility + +### 5. **Thread Management: Metadata-Based Organization** + +Threads are organized using **metadata filters**: + +```typescript +// Search threads by user +await client.threads.search({ + metadata: { user_id: userId }, + limit: 100, +}) +``` + +**Benefits:** + +- Easy filtering and organization +- No need for separate user-thread mapping table +- Flexible querying by any metadata field + +### 6. **Type Safety: Full TypeScript Coverage** + +The entire frontend is **fully typed**: + +```typescript +// LangGraph SDK types +import type { Message, Thread } from '@langchain/langgraph-sdk' + +// Custom types +interface GraphData { + messages: Message[] + streamMessage: (threadId: string, params: GraphInput) => Promise<void> +} +``` + +**Benefits:** + +- Catch errors at compile time +- Better IDE autocomplete +- Self-documenting code +- Refactoring safety + +--- + +## 🎯 Real-World Example + +### User Query: "How do I build a RAG system?" + +**Step 1: User Types Message** + +```typescript +// User types in chat interface +const userMessage = 'How do I build a RAG system?' +``` + +**Step 2: Message Conversion** + +```typescript +// ChatLangChain.onNew() converts to LangChain format +const humanMessage: Message = { + type: 'human', + content: userMessage, + id: uuidv4(), +} + +// Convert to OpenAI format for API +const apiMessage = convertToOpenAIFormat(humanMessage) +// { role: "user", content: "How do I build a RAG system?" } +``` + +**Step 3: Stream Submission** + +```typescript +// GraphContext.streamMessage() submits to LangGraph +await thread.submit( + { messages: [apiMessage] }, + { + config: { configurable: { query_model: 'ollama/llama3.2' } }, + metadata: { user_id: userId }, + }, +) +``` + +**Step 4: Streaming Events** + +```typescript +// LangGraph sends events via SSE: + +// Event 1: Progress +onUpdateEvent({ type: "progress", step: 1, total: 4 }) + +// Event 2: Generating Questions +onUpdateEvent({ + type: "generating_questions", + questions: ["RAG architecture", "document ingestion", ...] +}) + +// Event 3: Selected Documents +onUpdateEvent({ + type: "selected_documents", + documents: [Document(...), ...] +}) + +// Event 4: Messages (AI response) +messages: [{ type: "ai", content: "To build a RAG system..." }] +``` + +**Step 5: UI Updates** + +```typescript +// Each event updates state and triggers re-render: + +// Progress bar updates +<ProgressToolUI step={1} total={4} /> + +// Questions display +<GeneratingQuestionsToolUI questions={[...]} /> + +// Documents display +<SelectedDocumentsToolUI documents={[...]} /> + +// Final answer displays +<Message content="To build a RAG system..." /> +``` + +**Result:** User sees progressive updates as the graph executes, with rich visual feedback at each step! + +--- + +## 📚 Quick Reference + +### Core Components + +| Component | Purpose | Key Features | +| ------------------- | ---------------------------- | ----------------------------------------------- | +| `GraphContext.tsx` | State management & streaming | `useStream` hook, event handlers, message state | +| `ChatLangChain.tsx` | Chat orchestrator | Message conversion, runtime setup, thread sync | +| `API Proxy` | Backend communication | Request forwarding, CORS, authentication | +| `useThreads` | Thread management | Search, get, delete threads | + +### LangGraph API Endpoints Used + +| Endpoint | Purpose | When Used | +| -------------------------------- | -------------------- | ------------------------ | +| `POST /threads/search` | Find user threads | Loading thread list | +| `GET /threads/{id}` | Get thread state | Thread switching | +| `POST /threads/{id}/runs/stream` | Stream run execution | Every message submission | +| `DELETE /threads/{id}` | Remove thread | Thread deletion | + +### Message Types + +```typescript +// LangChain Message Types +type Message = + | { type: 'human'; content: string } + | { type: 'ai'; content: string; tool_calls?: ToolCall[] } + | { type: 'system'; content: string } + | { type: 'tool'; name: string; content: any } + +// Assistant UI Message Types +type AssistantUiMessage = + | { role: 'user'; content: ContentPart[] } + | { role: 'assistant'; content: ContentPart[] } + | { role: 'tool'; toolName: string; result: any } +``` + +### Event Types + +```typescript +// Custom Update Events +type UpdateEvent = + | { type: 'progress'; step: number; total: number } + | { type: 'generating_questions'; questions: string[] } + | { type: 'router_logic'; decision: string } + | { type: 'selected_documents'; documents: Document[] } + | { type: 'answer_header' } +``` + +### State Management Pattern + +```typescript +// Context provides global state +const { graphData, userData, threadsData } = useGraphContext() + +// graphData: Messages, streaming, model selection +// userData: User ID, user preferences +// threadsData: Thread list, thread operations +``` + +--- + +## 🚀 Running the Frontend + +### Development Setup + +```bash +# Install dependencies +cd frontend +yarn install + +# Set environment variables +# .env.local +NEXT_PUBLIC_API_URL=http://localhost:3000/api +NEXT_PUBLIC_ASSISTANT_ID=chat + +# Root .env +API_BASE_URL=http://localhost:8123 +LANGCHAIN_API_KEY=your-api-key + +# Run development server +yarn dev +``` + +### Key Files to Study + +1. **`app/contexts/GraphContext.tsx`** (lines 868-1170) + + - `useStream` hook setup + - Event handlers + - `streamMessage` function + +2. **`app/components/ChatLangChain.tsx`** (lines 62-105) + + - `onNew` function + - Message conversion + - Runtime setup + +3. **`app/api/[..._path]/route.ts`** + - Request forwarding logic + - Header handling + +--- + +## 🎓 Takeaways + +1. **Streaming enables real-time UX** - Progressive updates make the app feel responsive +2. **State management is critical** - Context + hooks pattern scales well +3. **API proxy simplifies integration** - Hides complexity from client code +4. **Message conversion is bidirectional** - Handle both input and output conversions +5. **Type safety prevents bugs** - Full TypeScript coverage catches errors early +6. **Thread management enables persistence** - Metadata-based organization is flexible +7. **Event-driven architecture scales** - Handle multiple event types cleanly + +This architecture demonstrates how to build production-ready chat interfaces that integrate seamlessly with LangGraph's sophisticated backend workflows. + +--- + +## 📖 Related Documentation + +- **Backend Architecture**: See `FLOW_DOCUMENTATION.md` for LangGraph workflows +- **LangGraph API**: See `LANGGRAPH_API_USE_CASES.md` for API reference +- **Document Ingestion**: See `INGEST.md` for document processing +- **LangGraph SDK**: https://github.com/langchain-ai/langgraph-sdk +- **Assistant UI**: https://www.assistant-ui.com/ +- **Chat LangChain Source**: https://github.com/langchain-ai/chat-langchain diff --git a/docs/GOAL_LEARNING.md b/docs/GOAL_LEARNING.md new file mode 100644 index 000000000..6e2cbed48 --- /dev/null +++ b/docs/GOAL_LEARNING.md @@ -0,0 +1,163 @@ +Based on my analysis of the Chat LangChain codebase, here's the comprehensive knowledge you'll gain after learning this sophisticated AI application: + +## 🧠 **Core AI & ML Knowledge** + +### **Retrieval-Augmented Generation (RAG)** + +- **Vector databases** - How to store, index, and query document embeddings using Weaviate +- **Semantic search** - Understanding similarity search and document retrieval strategies +- **Document processing** - Text splitting, chunking, and metadata extraction techniques +- **Embedding models** - Working with OpenAI, Voyage AI, and other embedding providers +- **Context management** - Handling large documents and conversation history + +### **Large Language Models (LLMs)** + +- **Multi-provider integration** - OpenAI GPT, Anthropic Claude, and model switching +- **Prompt engineering** - System prompts, query analysis, and response generation +- **Structured outputs** - Using function calling and schema validation +- **Stream processing** - Real-time response generation and UI updates + +## 🤖 **Advanced AI Architecture** + +### **Multi-Agent Systems** + +- **LangGraph workflows** - Building stateful, graph-based AI applications +- **Agent coordination** - Research planning, query routing, and decision trees +- **State management** - Managing conversation context across multiple steps +- **Conditional logic** - Dynamic routing based on query classification + +### **Intelligent Query Processing** + +- **Query analysis** - Automatic classification (LangChain vs general vs needs-more-info) +- **Research planning** - Breaking complex questions into manageable steps +- **Query expansion** - Generating multiple search queries for better retrieval +- **Multi-step reasoning** - Conducting research across multiple iterations + +## 💻 **Full-Stack Development Skills** + +### **Backend Development (Python)** + +- **FastAPI patterns** - Async API development and dependency injection +- **Poetry management** - Modern Python dependency and virtual environment management +- **LangChain integration** - Document loaders, text splitters, and retrievers +- **Database integration** - Vector stores, SQL record managers, and indexing strategies + +### **Frontend Development (TypeScript/React)** + +- **Next.js 14** - Modern React framework with app router +- **Real-time streaming** - WebSocket connections and progressive response rendering +- **State management** - Context API, custom hooks, and complex state handling +- **UI libraries** - Radix UI, Tailwind CSS, and component composition +- **Assistant UI** - Chat interfaces and conversation management + +## 🛠️ **DevOps & Production Skills** + +### **CI/CD Automation** + +- **GitHub Actions** - Automated testing, evaluation, and deployment pipelines +- **Quality gates** - Linting, formatting, and performance regression testing +- **Environment management** - Secrets handling and multi-environment deployment +- **Automated maintenance** - Scheduled document indexing and database updates + +### **Testing & Evaluation** + +- **LLM evaluation** - Automated testing of AI system performance +- **Regression testing** - Maintaining quality standards over time +- **Performance monitoring** - LangSmith integration for observability +- **A/B testing** - Comparing different models and approaches + +### **Infrastructure & Deployment** + +- **Terraform** - Infrastructure as Code for cloud deployment +- **Vercel deployment** - Frontend hosting and optimization +- **LangGraph Cloud** - Serverless AI application deployment +- **Database management** - Vector store optimization and maintenance + +## 📊 **Data Engineering** + +### **Document Ingestion Pipelines** + +- **Web scraping** - Sitemap parsing and content extraction with BeautifulSoup +- **ETL processes** - Extract, Transform, Load for document processing +- **Batch processing** - Handling large-scale document ingestion +- **Data quality** - Deduplication, validation, and metadata management + +### **Vector Database Operations** + +- **Weaviate administration** - Schema design, indexing, and querying +- **Embedding management** - Efficient storage and retrieval of high-dimensional vectors +- **Record management** - Tracking document updates and preventing duplicates +- **Performance optimization** - Query optimization and caching strategies + +## 🎯 **AI Product Development** + +### **User Experience Design** + +- **Conversational UI** - Chat interfaces and message threading +- **Progressive disclosure** - Showing research progress and intermediate results +- **Error handling** - Graceful degradation and user feedback +- **Accessibility** - Screen reader support and keyboard navigation + +### **System Integration** + +- **API design** - RESTful endpoints and streaming protocols +- **Third-party services** - LangSmith, OpenAI, Anthropic integrations +- **Configuration management** - Environment-based settings and feature flags +- **Monitoring & alerting** - System health and performance tracking + +## 🚀 **Advanced Technical Concepts** + +### **Distributed Systems** + +- **Async programming** - Concurrent processing and non-blocking operations +- **Stream processing** - Real-time data flow and progressive responses +- **Caching strategies** - Performance optimization and resource management +- **Load balancing** - Handling multiple concurrent users + +### **Security & Compliance** + +- **API key management** - Secure credential handling +- **Input validation** - Preventing injection and abuse +- **Rate limiting** - Resource protection and cost control +- **Data privacy** - Handling sensitive information securely + +## 💼 **Business & Strategic Skills** + +### **AI Product Strategy** + +- **Cost optimization** - Managing LLM API costs and resource usage +- **Performance metrics** - Measuring accuracy, relevance, and user satisfaction +- **Feature prioritization** - Balancing complexity vs. user value +- **Scalability planning** - Designing for growth and increased load + +### **Technical Leadership** + +- **Code organization** - Modular architecture and maintainable codebases +- **Documentation** - Technical writing and knowledge transfer +- **Code review** - Quality assurance and team collaboration +- **Mentoring** - Teaching complex AI concepts to others + +## 🎓 **Practical Applications** + +After mastering this codebase, you'll be able to: + +- **Build production RAG applications** for any domain or use case +- **Implement multi-agent AI systems** for complex reasoning tasks +- **Design conversational AI interfaces** with real-time streaming +- **Set up automated AI evaluation** and continuous integration +- **Deploy scalable AI applications** to production environments +- **Integrate multiple LLM providers** with fallback strategies +- **Create document processing pipelines** for knowledge bases +- **Implement advanced search** and retrieval systems + +## 🌟 **Career Impact** + +This knowledge positions you for roles in: + +- **AI/ML Engineering** - Building production AI systems +- **Full-Stack AI Development** - End-to-end AI application development +- **AI Product Management** - Understanding technical capabilities and limitations +- **DevOps for AI** - Specialized skills in AI system deployment and monitoring +- **AI Consulting** - Helping organizations implement RAG and multi-agent systems + +The Chat LangChain codebase represents a **comprehensive blueprint** for modern AI applications, giving you hands-on experience with the entire stack from data ingestion to production deployment! 🚀 diff --git a/docs/INGEST.md b/docs/INGEST.md new file mode 100644 index 000000000..10b7639e4 --- /dev/null +++ b/docs/INGEST.md @@ -0,0 +1,964 @@ +A comprehensive guide to understanding how Chat LangChain ingests, processes, and stores documents for semantic search using local Weaviate with Docker. + +> **Learning Journey:** This is part of my exploration of the [Chat LangChain](https://github.com/langchain-ai/chat-langchain) open-source project. +> +> **Source Repository:** [langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) +> **Practice Repository:** [thongvmdev/chat-langchain-practice](https://github.com/thongvmdev/chat-langchain-practice) + +--- + +## 🚨 Problem & Solution + +### The Challenge + +While learning RAG systems from the Chat LangChain project, I encountered two major obstacles: + +1. **Cloud Weaviate Limitations** + + - Free tier cluster expires after **14 days** + - Not suitable for continuous learning and experimentation + - Need to recreate clusters repeatedly + - Limited storage and query capacity + +2. **OpenAI Embedding Costs** + - OpenAI embeddings charge per token + - For learning purposes, re-running ingestion multiple times adds up quickly + - Example: Ingesting 500 documents × 3 practice runs = significant costs + - Not cost-effective for experimentation and iteration + +### The Solution: Local Docker Stack + +I solved both problems by switching to a **completely local, zero-cost setup**: + +**Architecture Changes:** + +```diff +- Cloud Weaviate Cluster (14-day limit) ++ Local Weaviate Docker Container (unlimited) + +- OpenAI Embeddings ($$$) ++ Ollama + nomic-embed-text Model (free!) + +- External API dependencies ++ 100% local Docker Compose setup +``` + +**Benefits:** + +- ✅ **Zero Cost:** No API fees, no cloud limits +- ✅ **Privacy:** All data stays on your machine +- ✅ **Unlimited Learning:** Run ingestion as many times as needed +- ✅ **Offline Capable:** Work without internet connection +- ✅ **Fast Iteration:** No rate limits or network latency + +**Technical Stack:** + +- **PostgreSQL** (Record Manager): Tracks indexed documents for deduplication +- **Weaviate** (Vector Store): Stores embeddings and document metadata +- **Ollama** (Embedding Service): Generates vectors using `nomic-embed-text` + - 768-dimensional vectors + - 2K token context window + - Runs locally on CPU/GPU + +**Setup:** See `docker-compose.yml` for the complete configuration + +## 🎯 What is Document Ingestion? + +Document ingestion converts raw documents (web pages) into searchable vectors stored in a database. This enables AI assistants to search by **meaning** rather than keywords. + +## 🐳 Docker Infrastructure + +### Three-Container Architecture + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Docker Compose Network │ +├─────────────────────────────────────────────────────────────┤ +│ │ +│ ┌──────────────────┐ ┌──────────────────┐ │ +│ │ PostgreSQL │ │ Weaviate │ │ +│ │ :5432 │ │ :8080 │ │ +│ │ │ │ :50051 │ │ +│ │ Record Manager │ │ Vector Store │ │ +│ │ (Deduplication) │ │ │ │ +│ └──────────────────┘ └──────────────────┘ │ +│ │ +│ ┌──────────────────────┐ │ +│ │ Ollama │ │ +│ │ :11434 │ │ +│ │ │ │ +│ │ nomic-embed-text │ │ +│ │ (2K context, 768d) │ │ +│ └──────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ +``` + +### Container Roles + +**1. PostgreSQL** (`:5432`) + +- Tracks indexed documents for deduplication +- Stores content hashes and timestamps +- Prevents duplicate processing + +**2. Weaviate** (`:8080`) + +- Vector database server +- Stores vectors + text + metadata +- No built-in vectorizer (uses external embeddings) + +**3. Ollama** (`:11434`) + +- Embedding model: `nomic-embed-text` +- Converts text → 768-dimensional vectors +- 2K token context window +- Called directly by Python via LangChain + +--- + +## 🔄 High-Level System Flow + +### Ingestion Process + +``` +[Web Scraping] + │ Load 32 pages from js.langchain.com + ↓ +[Text Splitting] + │ Split into ~120 chunks (4000 chars each) + ↓ +[Python: ingest.py] + │ Send text to Ollama + ↓ +[Ollama Server] + │ Generate 768-dim vectors with nomic-embed-text + ↓ +[Python: ingest.py] + │ Send vectors + text to Weaviate + ↓ +[Weaviate Server] + │ Store: {text, vector, source, title} + ↓ +[Record Manager (PostgreSQL)] + │ Track: source URL + content hash + └─► Deduplication on next run +``` + +### Retrieval Process + +``` +[User Query] + │ "What is LangChain?" + ↓ +[Python: retrieval.py] + │ retriever.invoke(query) + ↓ +[Ollama Server] + │ Generate query vector with nomic-embed-text + ↓ +[Python: retrieval.py] + │ Send query vector to Weaviate + ↓ +[Weaviate Server] + │ Find similar vectors (cosine similarity) + │ Return top-k documents with distance scores + ↓ +[Python: retrieval.py] + └─► List[Document] with metadata +``` + +--- + +## 🔄 Complete Ingestion Flow + +```mermaid +flowchart TD + A[📄 Load Documents from Web] --> B[🔪 Split into Chunks] + B --> C[📋 Extract Metadata] + C --> D{For Each Chunk} + + D --> E[📝 Generate Document Key] + E --> F[🔐 Generate Content Hash] + F --> G{Check Record Manager<br/>Does Key Exist?} + + G -->|No - New Document| H[🧠 Call Embedding API] + H --> I[💾 Insert to Vector DB] + I --> J[✅ Record in Record Manager] + J --> K[Status: ADDED] + + G -->|Yes - Exists| L{Compare Content Hash<br/>Has Content Changed?} + + L -->|No - Same| M[⏭️ Skip Processing] + M --> N[Status: SKIPPED] + + L -->|Yes - Changed| O[🧠 Call Embedding API] + O --> P[🔄 Update Vector DB] + P --> Q[🔄 Update Record Manager] + Q --> R[Status: UPDATED] + + K --> S[📊 Return Stats] + N --> S + R --> S + + style H fill:#90EE90 + style O fill:#90EE90 + style M fill:#FFD700 + style I fill:#87CEEB + style P fill:#87CEEB +``` + +## 📖 Step-by-Step Explanation + +### Step 1: Load Documents from Web + +```python +# Load from sitemaps +langchain_python_docs = load_langchain_python_docs() +langchain_js_docs = load_langchain_js_docs() +aggregated_site_docs = load_aggregated_docs_site() +``` + +**What does `load_langchain_js_docs()` return?** + +A list of LangChain `Document` objects loaded from the sitemap: + +```python +[ + Document( + page_content="LangChain.js\n\nIntroduction\n\nLangChain is a framework for developing applications powered by language models...", + metadata={ + "source": "https://js.langchain.com/docs/get_started/introduction", + "title": "Introduction | 🦜️🔗 LangChain.js", + "description": "LangChain is a framework for developing applications powered by language models.", + "language": "en", + "loc": "https://js.langchain.com/docs/get_started/introduction", + "lastmod": "2024-10-15T10:30:00+00:00", + "changefreq": "weekly", + "priority": 0.8 + } + ), + Document( + page_content="Installation\n\nTo get started with LangChain.js, install the package...", + metadata={ + "source": "https://js.langchain.com/docs/get_started/installation", + "title": "Installation | 🦜️🔗 LangChain.js", + ... + } + ), + # ... hundreds more documents from the sitemap +] +``` + +**Key Details:** + +- **Return Type**: `List[Document]` (typically 100-500 documents) +- **Filtering**: Only URLs matching `https://js.langchain.com/docs/` are included +- **Content**: Clean text extracted via `simple_extractor()` (HTML tags removed, excessive newlines collapsed) +- **Metadata**: Source URL, title, description, language from HTML elements + +### Step 2: Split Documents into Chunks + +```python +text_splitter = RecursiveCharacterTextSplitter( + chunk_size=4000, # Max 4000 characters per chunk + chunk_overlap=400 # 400 characters overlap between chunks +) + +docs_transformed = text_splitter.split_documents(general_guides_and_tutorials_docs) + +# Filter out tiny chunks +docs_transformed = [doc for doc in docs_transformed if len(doc.page_content) > 10] +``` + +**Transformation Process:** + +```mermaid +flowchart TD + A[📄 Input: 1 Large Document<br/>8000 characters] --> B[🔪 RecursiveCharacterTextSplitter<br/>chunk_size=4000, overlap=200] + B --> C[📋 Chunk 1<br/>chars 0-4000<br/>Same metadata] + B --> D[📋 Chunk 2<br/>chars 3800-7800<br/>200 char overlap<br/>Same metadata] + B --> E[📋 Chunk 3<br/>chars 7600-8000<br/>200 char overlap<br/>Same metadata] + + C --> F[✅ Filter: len > 10] + D --> F + E --> F + + F --> G[📦 Output: 3 Chunks<br/>Ready for embedding] + + style A fill:#FFE4B5 + style C fill:#90EE90 + style D fill:#90EE90 + style E fill:#90EE90 + style G fill:#87CEEB +``` + +**Before Splitting:** + +```python +# 1 document (8000 characters) +Document( + page_content="LangChain.js\n\nIntroduction\n\nLangChain is a framework..." (8000 chars), + metadata={ + "source": "https://js.langchain.com/docs/intro", + "title": "Introduction | 🦜️🔗 LangChain.js" + } +) +``` + +**After Splitting:** + +```python +# Becomes 3 smaller chunks +[ + Document( + page_content="LangChain.js\n\nIntroduction\n\nLangChain is a framework..." (4000 chars), + metadata={ + "source": "https://js.langchain.com/docs/intro", # Same! + "title": "Introduction | 🦜️🔗 LangChain.js" # Same! + } + ), + Document( + page_content="...powered by language models\n\nThe main value props..." (4000 chars, 200 overlap), + metadata={ + "source": "https://js.langchain.com/docs/intro", # Same! + "title": "Introduction | 🦜️🔗 LangChain.js" # Same! + } + ), + Document( + page_content="...for accomplishing specific tasks..." (400 chars, 200 overlap), + metadata={ + "source": "https://js.langchain.com/docs/intro", # Same! + "title": "Introduction | 🦜️🔗 LangChain.js" # Same! + } + ) +] +``` + +**Key Points:** + +- **Input**: ~100-500 large documents (potentially 5000+ chars each) +- **Output**: ~500-2000+ smaller chunks (max 4000 chars each) +- **Metadata preserved**: All chunks keep the same `source` URL (critical for record management!) +- **200-char overlap**: Ensures context isn't lost at boundaries + +**Why split documents?** + +- Embedding models have size limits +- Smaller chunks = more precise search results +- Overlap ensures context isn't lost between chunks + +### Step 3: Generate Embeddings (Server-Side) + +```python +# Python code generates embeddings via Ollama, then stores in Weaviate +vectorstore.add_documents([ + Document(page_content="LangChain is a framework...", metadata={...}) +]) + +# What happens internally: +# 1. Python calls Ollama at localhost:11434 +# 2. Ollama/nomic-embed-text returns 768-dim vector [0.023, -0.015, ..., 0.008] +# 3. Python sends {text, vector, metadata} to Weaviate +# 4. Weaviate stores the complete document +``` + +**What is a vector?** + +- Mathematical representation of text meaning +- 768 floating-point numbers (nomic-embed-text) +- Similar texts have similar vectors +- Enables "meaning-based" search +- 2K token context window for better understanding +- **Generated locally via Ollama** (no API costs!) + +### Step 4: Check Record Manager & Smart Deduplication + +The Record Manager uses `source_id_key="source"` to **group chunks by their source URL** and track changes. + +```python +# For each chunk after splitting +for doc in docs_transformed: + # Extract document key (unique identifier) + doc_key = doc.metadata["source"] # e.g., "https://js.langchain.com/docs/intro" + + # Generate content hash + content_hash = hash(doc.page_content + doc_key) # e.g., "a3f5c9b2e8d1..." + + # Check if already indexed + record = record_manager.get(key=doc_key) +``` + +**How Record Management Works:** + +```mermaid +flowchart TD + A[📄 Run 1: Load URL<br/>example.com/page1] --> B[🔪 Split into 3 Chunks] + B --> C[📝 Chunk 1: hash_h1<br/>source: page1] + B --> D[📝 Chunk 2: hash_h2<br/>source: page1] + B --> E[📝 Chunk 3: hash_h3<br/>source: page1] + + C --> F[💾 Record Manager<br/>Groups by source_id_key] + D --> F + E --> F + + F --> G[📊 Stored Record<br/>source: page1<br/>hashes: h1, h2, h3<br/>vector_ids: v1, v2, v3] + + G --> H[📄 Run 2: Same URL<br/>Content Updated!] + + H --> I[🔪 Split into 3 New Chunks] + I --> J[📝 Chunk 1: hash_h1_v2<br/>source: page1] + I --> K[📝 Chunk 2: hash_h2_v2<br/>source: page1] + I --> L[📝 Chunk 3: hash_h3_v2<br/>source: page1] + + J --> M{🔍 Compare by source: page1<br/>Old: h1, h2, h3<br/>New: h1_v2, h2_v2, h3_v2} + K --> M + L --> M + + M --> N[❌ Delete old vectors<br/>v1, v2, v3] + M --> O[✅ Insert new vectors<br/>v1_v2, v2_v2, v3_v2] + + N --> P[📊 Updated Record<br/>source: page1<br/>hashes: h1_v2, h2_v2, h3_v2<br/>vector_ids: v1_v2, v2_v2, v3_v2] + O --> P + + style G fill:#90EE90 + style M fill:#FFD700 + style N fill:#FF6B6B + style O fill:#4ECDC4 + style P fill:#90EE90 +``` + +**Real Example - First Run:** + +```python +# URL: https://js.langchain.com/docs/intro (8000 chars) +# After splitting → 3 chunks + +# Record Manager stores: +{ + "group_id": "https://js.langchain.com/docs/intro", # source_id_key + "doc_hashes": [ + "hash_abc123", # Chunk 1 hash + "hash_def456", # Chunk 2 hash + "hash_ghi789" # Chunk 3 hash + ], + "vector_ids": ["vec_1", "vec_2", "vec_3"], + "updated_at": "2024-10-25T10:00:00" +} +``` + +**Real Example - Second Run (Content Changed):** + +```python +# Same URL, but website content updated +# After splitting → 3 NEW chunks (different content) + +# Record Manager compares: +# - Groups by: "https://js.langchain.com/docs/intro" +# - Old hashes: [hash_abc123, hash_def456, hash_ghi789] +# - New hashes: [hash_xyz111, hash_xyz222, hash_xyz333] +# - Result: NO MATCHES! + +# Actions: +# 1. DELETE old vectors: vec_1, vec_2, vec_3 from Weaviate +# 2. INSERT new vectors: vec_4, vec_5, vec_6 to Weaviate +# 3. UPDATE record: +{ + "group_id": "https://js.langchain.com/docs/intro", + "doc_hashes": [ + "hash_xyz111", # NEW Chunk 1 hash + "hash_xyz222", # NEW Chunk 2 hash + "hash_xyz333" # NEW Chunk 3 hash + ], + "vector_ids": ["vec_4", "vec_5", "vec_6"], # NEW vector IDs + "updated_at": "2024-10-26T14:30:00" +} +``` + +**Key Insight: Grouping by `source_id_key`** + +The critical parameter is `source_id_key="source"` in the index function: + +```python +indexing_stats = index( + docs_transformed, + record_manager, + vectorstore, + cleanup="full", + source_id_key="source" # ← Groups all chunks by their 'source' URL! +) +``` + +This means: + +- All chunks from the **same URL are treated as a group** +- When content changes, **all old chunks are deleted** and **all new chunks are inserted** +- Record Manager tracks at the **source URL level**, not individual chunk level + +**Three possible outcomes:** + +#### A. New Document (Not Found) + +```python +# Record doesn't exist +if record is None: + # → Generate embedding + # → Insert to vector DB + # → Record in Record Manager + status = "ADDED" +``` + +#### B. Unchanged Document (Hash Matches) + +```python +# Record exists, hash matches +if record.content_hash == content_hash: + # → Skip! No processing needed + # → Save API costs and time + status = "SKIPPED" +``` + +#### C. Updated Document (Hash Different) + +```python +# Record exists, but content changed +if record.content_hash != content_hash: + # → Generate new embedding + # → Update vector DB + # → Update Record Manager + status = "UPDATED" +``` + +### Step 5: Store in Vector Database + +```python +# What gets stored in Weaviate +{ + "_id": "uuid-abc-123", + "vector": [0.023, -0.015, 0.034, ..., 0.008], # 1,536 numbers + "text": "LangChain is a framework for developing...", + "source": "https://python.langchain.com/docs/intro", + "title": "Introduction to LangChain" +} +``` + +### Step 6: Record in Record Manager + +```python +# What gets stored in PostgreSQL +{ + "key": "https://python.langchain.com/docs/intro", + "content_hash": "a3f5c9b2e8d1a4f7c3e9", + "updated_at": "2024-10-25 10:30:00", + "namespace": "weaviate/LangChain_general_guides" +} +``` + +## 🔍 How Search Works After Ingestion + +``` +User Query: "How does memory work?" + │ + ↓ +Python: retriever.invoke(query) + │ Sends raw text to Weaviate + ↓ +Weaviate Server + │ Calls text2vec-transformers:8080 + ↓ +text2vec-transformers + │ Returns query vector [0.019, -0.013, ...] + ↓ +Weaviate Server + │ Calculates cosine similarity with all stored vectors + │ Memory doc: distance = 0.12 (close!) + │ Chains doc: distance = 0.89 (far) + ↓ +Python Code + │ Receives top-k documents with distance scores + └─► Returns: List[Document] +``` + +**Example:** + +```python +from backend.retrieval import make_retriever + +config = RunnableConfig(configurable={"search_kwargs": {"k": 3}}) + +with make_retriever(config) as retriever: + # Uses Weaviate's near_text query (server-side vectorization) + docs = retriever.invoke("How does memory work in LangChain?") + + for doc in docs: + print(f"Title: {doc.metadata['title']}") + print(f"Distance: {doc.metadata['distance']:.4f}") + print(f"Content: {doc.page_content[:200]}...") +``` + +## 🎓 Record Management Summary + +### How Chunks Are Tracked Across Runs + +```mermaid +graph TB + subgraph "Run 1: Initial Load" + A1[URL: docs/intro<br/>Content: Version 1<br/>8000 chars] --> B1[Split] + B1 --> C1[Chunk 1: h1] + B1 --> C2[Chunk 2: h2] + B1 --> C3[Chunk 3: h3] + C1 --> D1[Record Manager] + C2 --> D1 + C3 --> D1 + D1 --> E1[Store Group:<br/>source: docs/intro<br/>hashes: h1,h2,h3] + end + + subgraph "Run 2: Content Changed" + A2[URL: docs/intro<br/>Content: Version 2<br/>8000 chars] --> B2[Split] + B2 --> C4[Chunk 1: h1v2] + B2 --> C5[Chunk 2: h2v2] + B2 --> C6[Chunk 3: h3v2] + C4 --> D2[Record Manager] + C5 --> D2 + C6 --> D2 + D2 --> E2{Compare by source:<br/>docs/intro} + E2 --> F1[Old hashes:<br/>h1, h2, h3] + E2 --> F2[New hashes:<br/>h1v2, h2v2, h3v2] + F1 --> G[❌ DELETE old vectors] + F2 --> H[✅ INSERT new vectors] + G --> I[Update Record:<br/>source: docs/intro<br/>hashes: h1v2,h2v2,h3v2] + H --> I + end + + E1 -.->|Next run| A2 + + style E1 fill:#90EE90 + style E2 fill:#FFD700 + style G fill:#FF6B6B + style H fill:#4ECDC4 + style I fill:#90EE90 +``` + +### Key Concepts + +**1. One Source URL → Multiple Chunks → One Record Group** + +``` +URL: https://js.langchain.com/docs/intro +├── Chunk 1 (hash: abc123) +├── Chunk 2 (hash: def456) +└── Chunk 3 (hash: ghi789) + +Record Manager Entry: +{ + group_id: "https://js.langchain.com/docs/intro", + hashes: ["abc123", "def456", "ghi789"] +} +``` + +**2. Comparison Happens at Group Level** + +- Record Manager groups all chunks by `source_id_key="source"` +- When the same URL is re-ingested: + - Compare: Old hash list vs New hash list + - If ANY hash is different → Update ALL chunks in that group + - If all hashes match → Skip ALL chunks in that group + +**3. `cleanup="full"` Removes Deleted Sources** + +```python +# Run 1: 3 URLs +- docs/intro → 3 chunks +- docs/install → 2 chunks +- docs/guide → 4 chunks + +# Run 2: docs/install removed from sitemap +- docs/intro → 3 chunks (unchanged, SKIP) +- docs/guide → 4 chunks (unchanged, SKIP) +# ❌ docs/install automatically DELETED from vector DB +``` + +**4. Time Savings Through Smart Tracking** + +``` +First Run: +- 32 URLs → 465 chunks +- Generate 465 embeddings (server-side) +- Time: ~8 seconds + +Second Run (no changes): +- 32 URLs → 465 chunks +- Generate 0 embeddings (all skipped!) +- Time: ~1 second +- Savings: 87.5% time! ⚡ + +Third Run (5 URLs changed): +- 27 URLs unchanged → 390 chunks SKIPPED +- 5 URLs changed → 75 chunks UPDATED +- Generate 75 embeddings (server-side) +- Time: ~2 seconds +- Savings: 75% time! ⚡ +``` + +## 📊 Real-World Example with Numbers + +### Scenario: Ingesting LangChain JS Documentation + +**First Run (Initial Load):** + +```python +# Documents loaded: 32 pages (from sitemap) +# After splitting: ~120 chunks (4000 chars each) +# Embeddings generated: ~120 (via Ollama, no cost!) +# Time: ~10-15 seconds (local generation) + +indexing_stats = { + 'num_added': 120, + 'num_updated': 0, + 'num_skipped': 0, + 'num_deleted': 0 +} +``` + +**Second Run (No Changes):** + +```python +# Documents loaded: 32 pages (same) +# After splitting: ~120 chunks (same) +# Embeddings generated: 0 ✅ (all skipped!) +# Time: ~1 second + +indexing_stats = { + 'num_added': 0, + 'num_updated': 0, + 'num_skipped': 120, # All documents unchanged + 'num_deleted': 0 +} +``` + +**Third Run (5 Pages Updated):** + +```python +# New documents: 0 +# Updated documents: 5 pages → ~19 chunks +# Unchanged: 27 pages → ~101 chunks +# Embeddings generated: 19 (only updated chunks) +# Time: ~3 seconds + +indexing_stats = { + 'num_added': 0, + 'num_updated': 19, # Changed chunks + 'num_skipped': 101, # Unchanged chunks + 'num_deleted': 0 +} +``` + +## 💡 Why This System is Smart + +### 1. **Local & Private** + +- **All processing happens locally** in Docker containers +- Ollama runs embeddings on your machine +- Your data never leaves your machine +- No API costs for embeddings! 🎉 +- 768-dimensional vectors for better semantic understanding + +### 2. **No Duplicates** + +- **Without Record Manager:** Each run adds duplicates + - Run 1: 120 vectors + - Run 2: 240 vectors (50% duplicates!) + - Run 3: 360 vectors (67% duplicates!) +- **With Record Manager:** Always exactly the right number + - Run 1: 120 vectors + - Run 2: 120 vectors (same) + - Run 3: 120 vectors (same) + +### 3. **Speed & Efficiency** + +- Skip unchanged documents (no re-processing) +- Only generate embeddings when content changes +- 85% faster on subsequent runs +- Larger chunks (4000 chars) = fewer vectors to process +- First run: ~10-15 seconds for 32 pages → ~120 vectors +- Second run (unchanged): ~1 second (all skipped) + +### 4. **Automatic Cleanup** + +Using `cleanup="full"` removes deleted documents: + +```python +# If a page is removed from the source website +# Record Manager detects: "This was indexed before but not in current batch" +# → Automatically deletes from Vector DB +# → Removes from Record Manager +# → Keeps database clean and up-to-date +``` + +## 🔧 Implementation in Code + +```python +def ingest_docs(): + # 1. Load and prepare documents + docs = ingest_general_guides_and_tutorials() # 32 HTML pages + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=4000, # 4000 chars per chunk (larger for better context) + chunk_overlap=400 # 400 chars overlap + ) + docs_transformed = text_splitter.split_documents(docs) # → ~120 chunks + + # 2. Get embedding model (OllamaEmbeddings for nomic-embed-text) + embedding = get_embeddings_model() # → OllamaEmbeddings + + # 3. Connect to local Weaviate + with weaviate.connect_to_local() as weaviate_client: + # 4. Create Vector Store (embedding=OllamaEmbeddings) + vectorstore = WeaviateVectorStore( + client=weaviate_client, + index_name="LangChain_General_Guides_And_Tutorials_nomic_embed_text", + text_key="text", + embedding=embedding, # ← Python handles vectorization via Ollama! + attributes=["source", "title"] + ) + + # 5. Initialize Record Manager + record_manager = SQLRecordManager( + "weaviate/LangChain_General_Guides_...", + db_url="postgresql://postgres:zkdtn1234@localhost:5432/chat_langchain" + ) + record_manager.create_schema() + + # 6. Index with smart deduplication + indexing_stats = index( + docs_transformed, + record_manager, + vectorstore, + cleanup="full", + source_id_key="source", # Group by URL + force_update=False + ) + + print(f"Indexing complete: {indexing_stats}") + # Output: {'num_added': 120, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0} +``` + +## 🎯 Key Takeaways + +1. **Docker-Based Local Setup:** + + - **PostgreSQL** = Tracks indexed documents (deduplication) + - **Weaviate** = Vector database server (storage only) + - **Ollama** = Embedding service with nomic-embed-text (768-dim vectors, 2K context) + - Python calls Ollama for embeddings, then stores in Weaviate + +2. **Client-Side Vectorization via Ollama:** + + - Python calls Ollama at `localhost:11434` + - Ollama generates vectors using nomic-embed-text + - Python sends vectors to Weaviate for storage + - No external API calls or costs + - Your data stays private and local + +3. **Document Pipeline:** + + - **Load**: 32 HTML pages from sitemap + - **Split**: 32 pages → ~120 chunks (4000 chars, 400 overlap) + - **Vectorize**: Via Ollama/nomic-embed-text (768 dimensions) + - **Store**: Weaviate stores {text, vector, metadata} + +4. **Smart Deduplication with Record Manager:** + + - Groups chunks by source URL (`source_id_key="source"`) + - Tracks content hashes in PostgreSQL + - **NEW**: First time seeing URL → insert all chunks + - **SKIP**: URL exists, hashes match → skip (huge savings!) + - **UPDATE**: URL exists, hash differs → delete old + insert new + - **DELETE**: URL removed from source → auto-cleanup + +5. **Production Benefits:** + - **Private**: All local, no data leaves your machine + - **Fast**: 85% faster on re-runs (skip unchanged) + - **Reliable**: No duplicates, automatic cleanup + - **Cost**: $0 for embeddings! 🎉 + +## 📚 Quick Reference + +### Vector Database Schema (Weaviate) + +```json +{ + "vector": [768 floats], // nomic-embed-text embeddings + "text": "document content", + "source": "https://js.langchain.com/docs/...", + "title": "Page Title | 🦜️🔗 LangChain.js" +} +``` + +### Record Manager Schema (PostgreSQL) + +```json +{ + "key": "https://js.langchain.com/docs/...", + "content_hash": "a3f5c9b2e8d1...", + "updated_at": "2024-10-30 10:30:00", + "namespace": "weaviate/LangChain_General_Guides_..." +} +``` + +### Index Function Parameters + +```python +index( + docs, # Documents to process + record_manager, # Deduplication tracker + vectorstore, # Storage destination + cleanup="full", # Auto-delete removed documents + source_id_key="source", # Group by URL + force_update=False # Skip if unchanged +) +``` + +## 🚀 Running the System + +### Start Docker Containers + +```bash +# Start all services (PostgreSQL, Weaviate, Ollama) +docker-compose up -d + +# Check status +docker-compose ps + +# View logs +docker-compose logs -f weaviate +``` + +### Run Ingestion + +```bash +# Set environment (already configured in .env) +# RECORD_MANAGER_DB_URL=postgresql://postgres:zkdtn1234@localhost:5432/chat_langchain + +# Run ingestion (make sure Ollama is running and has pulled nomic-embed-text) +PYTHONPATH=. poetry run python backend/ingest.py + +# Output: +# Fetching pages: 100%|████████| 32/32 [00:08<00:00, 3.60it/s] +# INFO: Indexing stats: {'num_added': 120, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0} +# INFO: General Guides and Tutorials now has this many vectors: 120 +``` + +### Query Documents + +```bash +# Test retrieval +PYTHONPATH=. poetry run python -c " +from langchain_core.runnables import RunnableConfig +from backend.retrieval import make_retriever + +config = RunnableConfig(configurable={'search_kwargs': {'k': 3}}) +with make_retriever(config) as retriever: + docs = retriever.invoke('What is LangChain?') + print(f'Found {len(docs)} documents') + print(f'Top result: {docs[0].metadata[\"title\"][:60]}') +" +``` diff --git a/docs/LANGGRAPH_API_USE_CASES.md b/docs/LANGGRAPH_API_USE_CASES.md new file mode 100644 index 000000000..27b7c6866 --- /dev/null +++ b/docs/LANGGRAPH_API_USE_CASES.md @@ -0,0 +1,989 @@ +# LangGraph API: Purpose and Use Cases + +This document explains the purpose, use cases, and when to use each endpoint category in the LangGraph API. + +--- + +## 1. Assistants (Create, Search, Update, Delete) + +### Purpose +**Assistants** are reusable configurations of LangGraph workflows. They define how a graph should behave, what models to use, what parameters to apply, and any static context. Think of an assistant as a "template" or "blueprint" for running a specific type of AI workflow. + +### Key Concepts +- **Graph ID**: References a graph defined in your `langgraph.json` +- **Config**: Runtime configuration (model selection, temperature, etc.) +- **Context**: Static data that's always available to the assistant +- **Versions**: Assistants can have multiple versions for A/B testing or gradual rollouts + +### Use Cases + +#### 1.1 Create Assistant +**When to use:** +- Setting up a new AI workflow for your application +- Creating different assistant variants (e.g., "customer-support", "technical-assistant") +- Initializing assistants with specific configurations (different models, parameters) +- Creating assistants with static context (knowledge base, instructions, system prompts) + +**Example scenarios:** +```python +# Create a customer support assistant +POST /assistants +{ + "graph_id": "chat", + "name": "Customer Support Bot", + "config": { + "configurable": { + "model": "gpt-4", + "temperature": 0.7 + } + }, + "context": { + "company_name": "Acme Corp", + "support_policies": "Always be polite and helpful" + }, + "metadata": { + "department": "support", + "language": "en" + } +} + +# Create a technical documentation assistant +POST /assistants +{ + "graph_id": "chat", + "name": "Tech Docs Assistant", + "config": { + "configurable": { + "model": "gpt-4-turbo", + "temperature": 0.3 # Lower temperature for more factual responses + } + }, + "context": { + "documentation_urls": ["https://docs.example.com"], + "code_examples": true + } +} +``` + +#### 1.2 Search Assistants +**When to use:** +- Listing all assistants in your system +- Finding assistants by metadata (e.g., "all support assistants") +- Filtering assistants by graph type +- Building admin dashboards or management UIs + +**Example scenarios:** +```python +# Find all customer support assistants +POST /assistants/search +{ + "metadata": {"department": "support"}, + "limit": 50 +} + +# List all assistants using a specific graph +POST /assistants/search +{ + "graph_id": "chat", + "sort_by": "created_at", + "sort_order": "desc" +} +``` + +#### 1.3 Update Assistant +**When to use:** +- Updating assistant configuration without creating a new version +- Changing static context (e.g., updating knowledge base) +- Modifying metadata tags +- Adjusting model parameters or settings + +**Example scenarios:** +```python +# Update assistant's context with new information +PATCH /assistants/{assistant_id} +{ + "context": { + "updated_knowledge_base": "2024-01-15", + "new_features": ["feature1", "feature2"] + } +} + +# Change assistant name and description +PATCH /assistants/{assistant_id} +{ + "name": "Updated Assistant Name", + "description": "New description" +} +``` + +#### 1.4 Delete Assistant +**When to use:** +- Removing deprecated assistants +- Cleaning up test assistants +- Removing assistants that are no longer needed +- **Note**: Deletes all versions of the assistant + +**Example scenarios:** +```python +# Delete a test assistant +DELETE /assistants/{test_assistant_id} + +# Clean up old assistants (after archiving data) +DELETE /assistants/{old_assistant_id} +``` + +### Best Practices +- Create assistants once and reuse them across multiple threads +- Use metadata to organize and filter assistants +- Use versions for gradual rollouts (create new version, test, then set as latest) +- Store static, reusable data in `context` rather than passing it with each run + +--- + +## 2. Threads (State Management, History, Streaming) + +### Purpose +**Threads** are conversation sessions that maintain state and context across multiple interactions. They're the "memory" of your application, preserving conversation history, user preferences, and any accumulated state from previous runs. + +### Key Concepts +- **State Persistence**: Threads store state that persists between runs +- **Checkpoints**: Snapshots of state at specific points in time +- **History**: Complete record of all state changes +- **Streaming**: Real-time updates as the thread processes runs + +### Use Cases + +#### 2.1 Create Thread +**When to use:** +- Starting a new conversation session +- Initializing a user's interaction context +- Setting up a thread with initial state or metadata +- Creating threads with TTL for automatic cleanup + +**Example scenarios:** +```python +# Start a new customer conversation +POST /threads +{ + "metadata": { + "user_id": "user-123", + "session_id": "session-456", + "channel": "web" + }, + "ttl": { + "strategy": "delete", + "ttl": 10080 # Delete after 7 days of inactivity + } +} + +# Create thread with initial state +POST /threads +{ + "metadata": {"conversation_type": "support"}, + "supersteps": [{ + "as_node": "initialize", + "values": { + "user_preferences": {"language": "en"}, + "conversation_history": [] + } + }] +} +``` + +#### 2.2 Get Thread State +**When to use:** +- Retrieving current conversation state +- Checking what information the thread remembers +- Inspecting thread status (idle, busy, interrupted) +- Getting the latest checkpoint for resumption + +**Example scenarios:** +```python +# Check thread status before sending new message +GET /threads/{thread_id}/state + +# Get state including subgraphs +GET /threads/{thread_id}/state?subgraphs=true +``` + +#### 2.3 Update Thread State +**When to use:** +- Manually updating conversation state +- Injecting external data into the thread +- Correcting or modifying thread state +- Resetting or clearing certain state values + +**Example scenarios:** +```python +# Update user preferences mid-conversation +POST /threads/{thread_id}/state +{ + "values": { + "user_preferences": { + "language": "es", # User switched to Spanish + "theme": "dark" + } + } +} + +# Inject external data (e.g., from database) +POST /threads/{thread_id}/state +{ + "values": { + "user_profile": { + "name": "John Doe", + "subscription": "premium" + } + }, + "as_node": "load_user_data" +} +``` + +#### 2.4 Get Thread History +**When to use:** +- Reviewing conversation history +- Debugging what happened in previous runs +- Building conversation replay features +- Analyzing conversation patterns +- Implementing "undo" functionality + +**Example scenarios:** +```python +# Get last 20 checkpoints +GET /threads/{thread_id}/history?limit=20 + +# Get history before a specific checkpoint +POST /threads/{thread_id}/history +{ + "limit": 10, + "before": { + "checkpoint_id": "checkpoint-123" + } +} + +# Filter history by metadata +POST /threads/{thread_id}/history +{ + "limit": 50, + "metadata": {"run_type": "user_message"} +} +``` + +#### 2.5 Join Thread Stream +**When to use:** +- Real-time chat applications +- Streaming responses to users +- Monitoring thread activity +- Building live dashboards +- Implementing typing indicators + +**Example scenarios:** +```python +# Stream all events from a thread +GET /threads/{thread_id}/stream + +# Stream only lifecycle events (start/end) +GET /threads/{thread_id}/stream?stream_modes=lifecycle + +# Resume streaming from last event +GET /threads/{thread_id}/stream +Headers: Last-Event-ID: event-123 +``` + +#### 2.6 Copy Thread +**When to use:** +- Creating conversation branches +- Testing different approaches on same context +- Forking conversations for different users +- Creating backups before major changes + +**Example scenarios:** +```python +# Branch conversation for testing +POST /threads/{thread_id}/copy +# Returns new thread with same state + +# Create backup before risky operation +POST /threads/{thread_id}/copy +# Use backup thread if something goes wrong +``` + +### Best Practices +- Create one thread per user session/conversation +- Use TTLs to automatically clean up old threads +- Store user identifiers in metadata for easy lookup +- Use checkpoints to enable conversation resumption +- Stream for real-time user experiences + +--- + +## 3. Thread Runs (Execution on Threads) + +### Purpose +**Thread Runs** execute a graph/assistant on an existing thread, updating the thread's state. They're the mechanism for processing user inputs and generating responses while maintaining conversation context. + +### Key Concepts +- **Stateful Execution**: Runs update the thread's persistent state +- **Streaming**: Can stream intermediate results +- **Interrupts**: Can pause execution at specific nodes +- **Multitask Strategy**: How to handle concurrent runs + +### Use Cases + +#### 3.1 Create Background Run +**When to use:** +- Fire-and-forget operations +- Long-running tasks where you don't need immediate results +- Batch processing +- When you'll poll for results later + +**Example scenarios:** +```python +# Send user message, don't wait for response +POST /threads/{thread_id}/runs +{ + "assistant_id": "assistant-123", + "input": { + "messages": [{"role": "user", "content": "Hello!"}] + } +} +# Returns immediately with run_id +# Poll GET /threads/{thread_id}/runs/{run_id} for status +``` + +#### 3.2 Create Run, Stream Output +**When to use:** +- Real-time chat applications +- Showing progressive results to users +- Implementing typing indicators +- Streaming long-form responses + +**Example scenarios:** +```python +# Stream response as it's generated +POST /threads/{thread_id}/runs/stream +{ + "assistant_id": "assistant-123", + "input": {"messages": [{"role": "user", "content": "Tell me a story"}]}, + "stream_mode": ["values", "messages"] +} +# Returns SSE stream with incremental updates +``` + +#### 3.3 Create Run, Wait for Output +**When to use:** +- Synchronous operations +- When you need the final result before proceeding +- Simple request-response patterns +- Testing and debugging + +**Example scenarios:** +```python +# Wait for complete response +POST /threads/{thread_id}/runs/wait +{ + "assistant_id": "assistant-123", + "input": {"messages": [{"role": "user", "content": "What's 2+2?"}]} +} +# Returns only after run completes +``` + +#### 3.4 Join Run Stream +**When to use:** +- Reconnecting to an existing run's stream +- Resuming after network interruption +- Monitoring a run that's already in progress +- Building resumable streaming clients + +**Example scenarios:** +```python +# Resume streaming from last event +GET /threads/{thread_id}/runs/{run_id}/stream +Headers: Last-Event-ID: event-456 + +# Monitor run progress +GET /threads/{thread_id}/runs/{run_id}/stream?stream_mode=lifecycle +``` + +#### 3.5 Cancel Run +**When to use:** +- User cancels request +- Timeout handling +- Stopping erroneous runs +- Cleanup operations + +**Example scenarios:** +```python +# Cancel and interrupt +POST /threads/{thread_id}/runs/{run_id}/cancel?action=interrupt + +# Cancel and rollback (delete run and checkpoints) +POST /threads/{thread_id}/runs/{run_id}/cancel?action=rollback&wait=true +``` + +### Best Practices +- Use streaming for better UX in chat applications +- Use background runs for long operations +- Set appropriate multitask strategies (enqueue vs reject) +- Handle interrupts for user interaction points +- Monitor run status for error handling + +--- + +## 4. Stateless Runs (Temporary Execution) + +### Purpose +**Stateless Runs** execute a graph/assistant without maintaining persistent state. They create a temporary thread, execute, and optionally delete it afterward. Perfect for one-off operations that don't need conversation context. + +### Key Concepts +- **No Persistent State**: Each run is independent +- **Temporary Threads**: Created automatically, can be auto-deleted +- **One-off Operations**: No conversation history needed + +### Use Cases + +#### 4.1 Create Stateless Run +**When to use:** +- One-time queries that don't need context +- Batch processing of independent tasks +- Testing graph behavior without state +- Simple request-response without conversation history + +**Example scenarios:** +```python +# One-time translation request +POST /runs +{ + "assistant_id": "translator-assistant", + "input": { + "text": "Hello world", + "target_language": "es" + }, + "on_completion": "delete" # Clean up thread after +} + +# Batch process multiple independent tasks +POST /runs/batch +[ + {"assistant_id": "analyzer", "input": {"data": "task1"}}, + {"assistant_id": "analyzer", "input": {"data": "task2"}}, + {"assistant_id": "analyzer", "input": {"data": "task3"}} +] +``` + +#### 4.2 Stateless Run with Streaming +**When to use:** +- Streaming one-off operations +- Real-time processing without state +- Progressive result display + +**Example scenarios:** +```python +# Stream analysis results +POST /runs/stream +{ + "assistant_id": "analyzer-assistant", + "input": {"document": "..."}, + "stream_mode": ["values"], + "on_completion": "delete" +} +``` + +### When to Use Stateless vs Thread Runs + +**Use Stateless Runs when:** +- ✅ Each request is independent +- ✅ No conversation history needed +- ✅ One-off operations +- ✅ Batch processing +- ✅ Testing/debugging + +**Use Thread Runs when:** +- ✅ Conversation context matters +- ✅ User sessions need persistence +- ✅ Multi-turn interactions +- ✅ State accumulation needed +- ✅ Resumable conversations + +### Best Practices +- Use `on_completion: "delete"` to clean up temporary threads +- Use for batch operations where each item is independent +- Don't use for conversations that need context + +--- + +## 5. Crons (Scheduled Runs) + +### Purpose +**Crons** schedule periodic runs that execute on a schedule. They can run on new threads (isolated) or share state in an existing thread. Perfect for automated tasks, periodic reports, and scheduled workflows. + +### Key Concepts +- **Schedule**: Cron format (e.g., "0 0 * * *" for daily at midnight) +- **End Time**: When to stop scheduling +- **Isolated vs Shared**: Can run on new threads or same thread + +### Use Cases + +#### 5.1 Create Cron (Isolated Runs) +**When to use:** +- Daily/weekly reports +- Periodic data processing +- Scheduled notifications +- Automated tasks that don't need shared state + +**Example scenarios:** +```python +# Daily report generation +POST /runs/crons +{ + "assistant_id": "report-generator", + "schedule": "0 9 * * *", # 9 AM daily + "end_time": "2024-12-31T23:59:59Z", + "input": { + "report_type": "daily_summary" + }, + "metadata": {"type": "scheduled_report"} +} +``` + +#### 5.2 Create Thread Cron (Shared State) +**When to use:** +- Periodic updates to same conversation +- Maintaining state across scheduled runs +- Continuous monitoring with history +- Workflows that build on previous runs + +**Example scenarios:** +```python +# Periodic status updates on same thread +POST /threads/{thread_id}/runs/crons +{ + "assistant_id": "monitor-assistant", + "schedule": "*/5 * * * *", # Every 5 minutes + "end_time": "2024-12-31T23:59:59Z", + "input": { + "check_type": "system_health" + } +} +# All runs share the same thread, building history +``` + +### Use Cases + +1. **Daily Reports**: Generate and send daily summaries +2. **Health Checks**: Periodic system monitoring +3. **Data Sync**: Regular data synchronization tasks +4. **Reminders**: Scheduled notifications +5. **Cleanup Tasks**: Periodic cleanup operations +6. **Analytics**: Scheduled data analysis + +### Best Practices +- Set appropriate `end_time` to prevent infinite scheduling +- Use isolated crons for independent tasks +- Use thread crons when state needs to accumulate +- Monitor cron execution with search endpoints +- Clean up completed crons when no longer needed + +--- + +## 6. Store (Key-Value Storage) + +### Purpose +The **Store** provides persistent, cross-thread key-value storage. It's like a global database that any thread can access, perfect for storing user preferences, shared knowledge, or any data that needs to persist across conversations. + +### Key Concepts +- **Namespaces**: Hierarchical organization (like directories) +- **Cross-Thread**: Accessible from any thread +- **Persistent**: Survives thread deletion +- **Searchable**: Can search by namespace, filters, or semantic search + +### Use Cases + +#### 6.1 Store User Preferences +**When to use:** +- Storing user settings that persist across conversations +- User profiles and preferences +- Cross-session data + +**Example scenarios:** +```python +# Store user preferences +PUT /store/items +{ + "namespace": ["users", "preferences"], + "key": "user-123", + "value": { + "language": "en", + "theme": "dark", + "notifications": true + } +} + +# Retrieve user preferences +GET /store/items?key=user-123&namespace[]=users&namespace[]=preferences +``` + +#### 6.2 Store Shared Knowledge +**When to use:** +- Company-wide knowledge base +- Shared documentation +- Cross-conversation facts + +**Example scenarios:** +```python +# Store company policies +PUT /store/items +{ + "namespace": ["knowledge", "policies"], + "key": "refund_policy", + "value": { + "title": "Refund Policy", + "content": "30-day money-back guarantee...", + "last_updated": "2024-01-15" + } +} + +# Search for policies +POST /store/items/search +{ + "namespace_prefix": ["knowledge", "policies"], + "query": "refund", + "limit": 10 +} +``` + +#### 6.3 Store Conversation Summaries +**When to use:** +- Storing conversation summaries for quick access +- Building user history +- Cross-thread context + +**Example scenarios:** +```python +# Store conversation summary +PUT /store/items +{ + "namespace": ["users", "conversations"], + "key": "user-123-summary-2024-01", + "value": { + "summary": "User asked about product features...", + "topics": ["pricing", "features"], + "sentiment": "positive" + } +} +``` + +#### 6.4 Store Feature Flags / Configuration +**When to use:** +- Application configuration +- Feature flags +- A/B test configurations + +**Example scenarios:** +```python +# Store feature flags +PUT /store/items +{ + "namespace": ["config", "features"], + "key": "new_ui_enabled", + "value": { + "enabled": true, + "rollout_percentage": 50, + "user_ids": ["user-1", "user-2"] + } +} +``` + +### Use Cases Summary + +1. **User Data**: Preferences, profiles, settings +2. **Knowledge Base**: Shared information, documentation +3. **Conversation History**: Summaries, important facts +4. **Configuration**: Feature flags, app settings +5. **Cross-Thread Context**: Data shared across conversations +6. **Caching**: Store computed results for reuse + +### Best Practices +- Use hierarchical namespaces for organization +- Use semantic search for finding related items +- Set appropriate TTLs if needed (via thread TTL) +- Use filters for efficient queries +- Store structured data (JSON objects) as values + +--- + +## 7. A2A (Agent-to-Agent Protocol) + +### Purpose +**A2A (Agent-to-Agent Protocol)** exposes assistants as standardized agents that can communicate with other A2A-compliant systems. It uses JSON-RPC 2.0 for standardized agent communication. + +### Key Concepts +- **JSON-RPC 2.0**: Standardized protocol +- **Interoperability**: Works with other A2A systems +- **Threaded Conversations**: Supports conversation context +- **Task-Based**: Asynchronous task execution + +### Use Cases + +#### 7.1 Agent-to-Agent Communication +**When to use:** +- Integrating with other AI agent systems +- Multi-agent workflows +- Agent orchestration +- Standardized agent interfaces + +**Example scenarios:** +```python +# Send message to another agent +POST /a2a/{assistant_id} +{ + "jsonrpc": "2.0", + "id": "req-1", + "method": "message/send", + "params": { + "message": { + "role": "user", + "parts": [{ + "kind": "text", + "text": "Analyze this data" + }], + "messageId": "msg-1" + }, + "thread": { + "threadId": "thread-123" + } + } +} + +# Check task status +POST /a2a/{assistant_id} +{ + "jsonrpc": "2.0", + "id": "req-2", + "method": "tasks/get", + "params": { + "taskId": "task-456" + } +} +``` + +### Use Cases + +1. **Multi-Agent Systems**: Agents communicating with each other +2. **Agent Marketplaces**: Exposing agents via standard protocol +3. **Agent Orchestration**: Coordinating multiple agents +4. **Integration**: Connecting with A2A-compliant tools +5. **Workflow Automation**: Agent-based workflows + +### Best Practices +- Use for standardized agent interfaces +- Implement proper error handling for JSON-RPC +- Use thread context for multi-turn agent conversations +- Handle task status polling appropriately + +--- + +## 8. MCP (Model Context Protocol) + +### Purpose +**MCP (Model Context Protocol)** exposes assistants as MCP servers, enabling integration with MCP-compatible clients and tools. It's a protocol for standardized AI model interactions. + +### Key Concepts +- **Stateless**: Sessions are not persisted +- **JSON-RPC 2.0**: Uses JSON-RPC for communication +- **Streaming Support**: Can stream responses +- **Tool Integration**: Works with MCP tools + +### Use Cases + +#### 8.1 MCP Client Integration +**When to use:** +- Integrating with MCP-compatible clients +- Exposing assistants via MCP protocol +- Tool integration through MCP +- Standardized model access + +**Example scenarios:** +```python +# Initialize MCP session +POST /mcp/ +{ + "jsonrpc": "2.0", + "id": "1", + "method": "initialize", + "params": { + "clientInfo": { + "name": "my-client", + "version": "1.0.0" + }, + "protocolVersion": "2024-11-05", + "capabilities": {} + } +} + +# Send request to MCP server +POST /mcp/ +{ + "jsonrpc": "2.0", + "id": "2", + "method": "tools/call", + "params": { + "name": "search", + "arguments": {"query": "python"} + } +} +``` + +### Use Cases + +1. **MCP Client Integration**: Connect MCP clients to your assistants +2. **Tool Integration**: Use MCP-compatible tools +3. **Standardized Access**: Expose assistants via MCP protocol +4. **Development Tools**: Integrate with MCP development tools + +### Best Practices +- Use for MCP-specific integrations +- Handle stateless nature (no session persistence) +- Implement proper MCP protocol compliance +- Use streaming for better performance + +--- + +## 9. System (Health Checks, Metrics) + +### Purpose +**System endpoints** provide operational monitoring, health checks, and server information. Essential for production deployments and monitoring. + +### Key Concepts +- **Health Checks**: Verify system is running +- **Metrics**: Performance and operational metrics +- **Server Info**: Version and feature information + +### Use Cases + +#### 9.1 Health Check +**When to use:** +- Load balancer health checks +- Monitoring system availability +- Automated alerting +- Deployment verification + +**Example scenarios:** +```python +# Basic health check +GET /ok +# Returns: {"ok": true} + +# Health check with database connectivity +GET /ok?check_db=1 +# Verifies database connection too +``` + +#### 9.2 Server Information +**When to use:** +- Version checking +- Feature detection +- Debugging +- API discovery + +**Example scenarios:** +```python +# Get server info +GET /info +# Returns: { +# "version": "0.1.0", +# "langgraph_py_version": "x.x.x", +# "flags": {...}, +# "metadata": {...} +# } +``` + +#### 9.3 System Metrics +**When to use:** +- Performance monitoring +- Prometheus integration +- Operational dashboards +- Capacity planning + +**Example scenarios:** +```python +# Get Prometheus metrics +GET /metrics?format=prometheus +# Returns Prometheus-formatted metrics + +# Get JSON metrics +GET /metrics?format=json +# Returns structured JSON metrics +``` + +### Use Cases + +1. **Health Monitoring**: Regular health checks for uptime monitoring +2. **Load Balancing**: Health endpoints for load balancers +3. **Metrics Collection**: Prometheus/Grafana integration +4. **Debugging**: Version and feature information +5. **Alerting**: Automated alerts based on health status + +### Best Practices +- Set up regular health check monitoring +- Integrate metrics with monitoring systems (Prometheus, Datadog, etc.) +- Use database health checks for critical deployments +- Monitor metrics for capacity planning + +--- + +## Summary: When to Use Each Endpoint Category + +| Endpoint Category | Use When | Don't Use When | +|------------------|----------|----------------| +| **Assistants** | Setting up reusable workflows, configuring graphs | One-off configurations | +| **Threads** | Conversations, stateful interactions, multi-turn | One-off requests, stateless operations | +| **Thread Runs** | Executing on existing conversations | Independent operations | +| **Stateless Runs** | One-off operations, batch processing | Conversations needing context | +| **Crons** | Scheduled tasks, periodic operations | Real-time user interactions | +| **Store** | Cross-thread data, user preferences, shared knowledge | Thread-specific state (use thread state) | +| **A2A** | Agent-to-agent communication, multi-agent systems | Direct user interactions | +| **MCP** | MCP client integration, tool integration | General API usage | +| **System** | Monitoring, health checks, operations | Application logic | + +--- + +## Common Patterns + +### Pattern 1: Chat Application +``` +1. Create Assistant (once) +2. Create Thread per user session +3. Use Thread Runs with streaming for messages +4. Store user preferences in Store +5. Monitor with System endpoints +``` + +### Pattern 2: Batch Processing +``` +1. Create Assistant (once) +2. Use Stateless Runs for each item +3. Use Batch endpoint for parallel processing +4. Monitor with System metrics +``` + +### Pattern 3: Scheduled Reports +``` +1. Create Assistant (once) +2. Create Cron for scheduled execution +3. Use Store for report templates/config +4. Monitor Cron execution +``` + +### Pattern 4: Multi-Agent System +``` +1. Create multiple Assistants (one per agent) +2. Use A2A protocol for agent communication +3. Use Threads for agent conversations +4. Use Store for shared agent knowledge +``` + +--- + +*This document provides guidance on when and why to use each endpoint category. For detailed API specifications, refer to the main API documentation.* + diff --git a/LANGSMITH.md b/docs/LANGSMITH.md similarity index 100% rename from LANGSMITH.md rename to docs/LANGSMITH.md diff --git a/docs/LEARNING_STEPS.md b/docs/LEARNING_STEPS.md new file mode 100644 index 000000000..3fac41670 --- /dev/null +++ b/docs/LEARNING_STEPS.md @@ -0,0 +1,310 @@ +# 🦜️🔗 Chat LangChain - Learning Guide + +This comprehensive learning guide will help you understand the Chat LangChain codebase, a sophisticated RAG (Retrieval-Augmented Generation) application built with LangChain, LangGraph, and Next.js. + +## 📋 Overview + +**What is this?** A chatbot specifically focused on question answering over LangChain documentation, featuring intelligent multi-agent research, document ingestion, and real-time streaming responses. + +**Key Technologies:** + +- Backend: Python, LangChain, LangGraph, FastAPI +- Frontend: Next.js, React, TypeScript, Tailwind CSS +- Vector Database: Weaviate +- Deployment: LangGraph Cloud + +--- + +## 🎯 Phase 1: Foundation & Setup (Day 1-2) + +### Step 1: Understand the Project Structure + +- [x] **Read core documentation files** + + - `README.md` - Project overview and setup + - `CONCEPTS.md` - Core concepts (vector stores, indexing, retrieval) + - `MODIFY.md` - How to customize the application + - `PRODUCTION.md` - Production deployment considerations + +- [x] **Explore project structure** + ``` + /backend/ # Python backend with LangChain/LangGraph + /frontend/ # Next.js frontend + /terraform/ # Infrastructure as code + /_scripts/ # Evaluation and testing scripts + ``` + +### Step 2: Environment Setup + +- [x] **Install dependencies** + + - Backend: `poetry install` (Python dependencies) + - Frontend: `cd frontend && yarn install` + +- [x] **Understand configuration** + - `pyproject.toml` - Python dependencies and project metadata + - `frontend/package.json` - Node.js dependencies + - `langgraph.json` - LangGraph configuration + +--- + +## 🏗️ Phase 2: Backend Architecture (Day 3-5) + +### Step 3: Core Backend Components + +#### Data Ingestion Pipeline + +- [x] **Read CONCEPTS.md frist** + +- [x] **Study document ingestion** (`backend/ingest.py`) + + - How documents are loaded from LangChain documentation sites + - Text splitting with `RecursiveCharacterTextSplitter` + - Embedding generation and storage in Weaviate + - Metadata extraction and processing + +- [x] **Understand embeddings** (`backend/embeddings.py`) + + - Embedding model configuration + - Integration with various providers (OpenAI, Voyage, etc.) + +- [x] **Review parsing logic** (`backend/parser.py`) + - Document content extraction + - HTML parsing and cleaning + +### Step 4: The Retrieval Graph System + +#### Core Graph Architecture + +- [x] **Main retrieval graph** (`backend/retrieval_graph/graph.py`) + - `analyze_and_route_query()` - Query classification + - `create_research_plan()` - Multi-step research planning + - `conduct_research()` - Executing research steps + - `respond()` - Final response generation + - Graph flow: START → research plan → research loop → respond → END + +#### State Management + +- [x] **Agent state** (`backend/retrieval_graph/state.py`) + - `InputState` - Input message structure + - `AgentState` - Complete conversation state + - `Router` - Query classification types + +#### Configuration System + +- [x] **Agent configuration** (`backend/retrieval_graph/configuration.py`) + - Model selection (query_model, response_model) + - System prompts customization + - Retrieval provider configuration + +### Step 5: Researcher Sub-Graph + +- [x] **Researcher graph** (`backend/retrieval_graph/researcher_graph/`) + - `generate_queries()` - Creating search queries + - `retrieve_documents()` - Document retrieval + - Parallel query execution and result aggregation + +### Step 6: Retrieval & Utilities + +- [x] **Retrieval methods** (`backend/retrieval.py`) + + - Vector similarity search + - Multiple retrieval providers support + - Query rewriting and optimization + +- [x] **Utility functions** (`backend/utils.py`) + - Model loading and configuration + - Document formatting + - Helper functions + +--- + +## 🎨 Phase 3: Frontend Architecture (Day 6-8) + +### Step 7: React Application Structure + +#### Core Components + +- [x] **Main app structure** + - `frontend/app/page.tsx` - Root page component + - `frontend/app/layout.tsx` - Application layout + - `frontend/app/components/ChatLangChain.tsx` - Main chat interface + +#### Chat Interface + +- [x] **Chat system** (`frontend/app/components/chat-interface/`) + - `index.tsx` - Main chat component using assistant-ui + - `chat-composer.tsx` - Message input component + - `messages.tsx` - Message display and rendering + +#### UI Components + +- [x] **Custom UI components** (`frontend/app/components/ui/`) + - Built with Radix UI primitives + - Tailwind CSS styling + - `button.tsx`, `dialog.tsx`, `toast.tsx`, etc. + +#### Tool UI Components + +- [x] **Research visualization** (`frontend/app/components/`) + - `GeneratingQuestionsToolUI.tsx` - Query generation display + - `ProgressToolUI.tsx` - Research progress tracking + - `SelectedDocumentsToolUI.tsx` - Document selection display + +### Step 8: State Management & Context + +#### Context System + +- [x] **Graph context** (`frontend/app/contexts/GraphContext.tsx`) + - Message state management + - Thread management + - User data handling + - Real-time streaming integration + +#### Custom Hooks + +- [x] **React hooks** (`frontend/app/hooks/`) + - `useRuns.tsx` - LangSmith run tracking + - `useThreads.tsx` - Thread management + - `useUser.tsx` - User session management + - `use-toast.ts` - Toast notifications + +### Step 9: API Integration + +#### API Routes + +- [x] **Next.js API routes** (`frontend/app/api/`) + - `[...path]/route.ts` - LangGraph Cloud proxy + - `runs/feedback/route.ts` - LangSmith feedback + - `runs/share/route.ts` - Run sharing + +#### Message Conversion + +- [x] **Message handling** (`frontend/app/utils/convert_messages.ts`) + - LangChain to OpenAI format conversion + - Message type transformations + +--- + +## 🔬 Phase 4: Advanced Features & Quality Assurance (Day 9-12) + +### Step 10: LangSmith Integration + +- [ ] **Observability** (`LANGSMITH.md`) + - Trace collection and analysis + - Feedback systems + - Performance monitoring + +### Step 11: Evaluation & Testing System > Ignore + +- [ ] **Evaluation scripts** (`_scripts/`) + - `evaluate_chat_langchain.py` - End-to-end system evaluation + - `evaluate_chains.py` - Basic retrieval + response chain testing + - `evaluate_chains_agent.py` - Multi-agent research workflow testing + - `evaluate_chains_improved_chain.py` - Advanced retrieval with query expansion + - `clear_index.py` - Vector database maintenance and cleanup + +#### Understanding Each Evaluation Script + +- [ ] **`evaluate_chat_langchain.py`** - Simple end-to-end testing + + - Tests complete conversation flow + - Uses LangSmith datasets like "Chat LangChain Complex Questions" + - Supports different model providers (OpenAI, Anthropic) + +- [ ] **`evaluate_chains.py`** - Basic chain evaluation + + - Tests retrieval quality and response generation + - Includes chat history handling + - Measures QA accuracy and relevance + +- [ ] **`evaluate_chains_agent.py`** - Agent-based evaluation + + - Tests complex multi-step research planning + - Evaluates agent routing and decision-making + - Measures research effectiveness + +- [ ] **`evaluate_chains_improved_chain.py`** - Advanced evaluation + - Tests query expansion and search query generation + - Includes multiple retrieval strategies + - Performance regression testing with specific thresholds: + - Retrieval recall ≥ 65% + - Answer correctness ≥ 90% + - Answer vs context correctness ≥ 90% + +#### How to Use Evaluation Scripts + +```bash +# Run end-to-end evaluation +PYTHONPATH=$(PWD) python _scripts/evaluate_chat_langchain.py --dataset-name "Chat LangChain Complex Questions" --model-provider openai + +# Run component testing +PYTHONPATH=$(PWD) python _scripts/evaluate_chains.py --model-provider anthropic + +# Clear and reset vector database +python _scripts/clear_index.py +``` + +### Step 12: CI/CD Pipeline & GitHub Actions + +- [x] **Automated workflows** (`.github/workflows/`) + - `eval.yml` - Automated evaluation on PRs and pushes + - `lint.yml` - Code quality and style checking + - `update-index.yml` - Scheduled document indexing + - `clear-and-update-index.yml` - Manual index reset and refresh + - `deploy-cloud-run.yaml` - Production deployment to Vercel + +--- + +## 🚀 Phase 5: Deployment & Production (Day 13-15) > after miragtion + +### Step 14: Infrastructure + +- [ ] **Terraform configuration** (`terraform/`) + - `main.tf` - Main infrastructure + - `modules/chat_langchain_backend/` - Backend module + - Cloud deployment setup + +### Step 15: Production Considerations + +- [ ] **Production setup** (`PRODUCTION.md`, `DEPLOYMENT.md`) + - Security considerations + - Database setup + - Environment configuration + - Monitoring and logging + +### Step 16: Configuration & Customization + +- [ ] **Customization points** + - Model selection and configuration + - System prompt customization + - UI theme and branding + - Retrieval provider switching + +--- + +## 📚 Key Learning Resources + +### Core Documentation + +- [LangChain Documentation](https://python.langchain.com/) +- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) +- [LangSmith Documentation](https://docs.smith.langchain.com/) +- [Assistant UI Documentation](https://www.assistant-ui.com/) + +### Important Files to Study in Detail + +1. `backend/retrieval_graph/graph.py` - Core retrieval logic +2. `frontend/app/components/ChatLangChain.tsx` - Main chat component +3. `backend/ingest.py` - Document processing pipeline +4. `frontend/app/contexts/GraphContext.tsx` - State management + +### Architecture Patterns to Understand + +- **RAG (Retrieval-Augmented Generation)** - Core pattern +- **Multi-agent systems** - Research planning and execution +- **Graph-based workflows** - LangGraph state machines +- **Streaming responses** - Real-time UI updates +- **Vector similarity search** - Document retrieval +- **CI/CD automation** - Quality gates and deployment pipelines +- **Performance monitoring** - LangSmith integration and evaluation systems diff --git a/MODIFY.md b/docs/MODIFY.md similarity index 100% rename from MODIFY.md rename to docs/MODIFY.md diff --git a/PRODUCTION.md b/docs/PRODUCTION.md similarity index 100% rename from PRODUCTION.md rename to docs/PRODUCTION.md diff --git a/docs/RECORDS_MGMT.md b/docs/RECORDS_MGMT.md new file mode 100644 index 000000000..321274c38 --- /dev/null +++ b/docs/RECORDS_MGMT.md @@ -0,0 +1,163 @@ +I'll provide you with concrete examples showing how `SQLRecordManager` provides benefits in your ingest.py workflow. Let me first examine the related scripts to give you a complete picture. + +[1 tool called] + +Perfect! Now I can give you concrete examples showing how `SQLRecordManager` provides significant benefits. Let me demonstrate with practical scenarios: + +## 🚫 **WITHOUT SQLRecordManager (The Problems)** + +### Scenario: Re-running the ingest process + +```python +# WITHOUT SQLRecordManager - naive approach +def ingest_docs_naive(): + docs = load_all_docs() # 10,000 documents + text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=200) + docs_split = text_splitter.split_documents(docs) # 50,000 chunks + + # Every time you run this: + vectorstore.add_documents(docs_split) # ❌ Duplicates everything! +``` + +**Problems:** + +- **Run 1:** 50,000 vectors created ✅ +- **Run 2:** 100,000 vectors (50k duplicates) ❌ +- **Run 3:** 150,000 vectors (100k duplicates) ❌ +- **Cost:** ~$50 per run for embeddings (OpenAI API) +- **Performance:** Vector store becomes bloated, search quality degrades + +## ✅ **WITH SQLRecordManager (The Benefits)** + +### Real Example from your code: + +```python +def ingest_docs(): + # Load fresh documents from web + docs = ingest_general_guides_and_tutorials() # 1,500 docs + docs_transformed = text_splitter.split_documents(docs) # 7,500 chunks + + record_manager = SQLRecordManager( + f"weaviate/{WEAVIATE_GENERAL_GUIDES_AND_TUTORIALS_INDEX_NAME}", + db_url=RECORD_MANAGER_DB_URL, + ) + record_manager.create_schema() + + indexing_stats = index( + docs_transformed, + record_manager, + vectorstore, + cleanup="full", + source_id_key="source", # Uses 'source' URL as unique ID + ) +``` + +## 📊 **Concrete Benefits with Examples** + +### **1. Duplicate Prevention** + +```python +# First run +indexing_stats = { + 'num_added': 7500, # All documents indexed + 'num_updated': 0, # Nothing to update + 'num_skipped': 0, # Nothing skipped + 'num_deleted': 0 # Nothing deleted +} + +# Second run (no changes to docs) +indexing_stats = { + 'num_added': 0, # ✅ No duplicates created! + 'num_updated': 0, # No changes detected + 'num_skipped': 7500, # All docs skipped (already exist) + 'num_deleted': 0 # Nothing deleted +} +``` + +### **2. Incremental Updates** + +```python +# Third run (LangChain adds 100 new docs, updates 50 existing) +indexing_stats = { + 'num_added': 500, # Only new chunks added + 'num_updated': 200, # Only changed chunks updated + 'num_skipped': 7300, # Unchanged docs skipped + 'num_deleted': 0 # Nothing deleted +} +``` + +**Time & Cost Savings:** + +- **Without RecordManager:** Process 7,500 docs = 15 minutes + $15 API cost +- **With RecordManager:** Process 700 docs = 2 minutes + $1.50 API cost +- **Savings:** 87% time reduction, 90% cost reduction! 💰 + +### **3. Cleanup Management** + +```python +# When a document gets removed from the source site +indexing_stats = { + 'num_added': 100, # New docs + 'num_updated': 50, # Updated docs + 'num_skipped': 7200, # Unchanged docs + 'num_deleted': 250 # ✅ Removed outdated docs automatically! +} +``` + +### **4. State Tracking Example** + +The SQLRecordManager database tracks: + +```sql +-- Internal tracking table (simplified view) +SELECT source_id, content_hash, updated_at, group_id FROM record_manager; + +source_id | content_hash | updated_at | group_id +-----------------------------------|-------------|--------------------|--------- +https://docs.langchain.com/page1 | abc123... | 2024-01-15 10:30:00| weaviate/docs +https://docs.langchain.com/page2 | def456... | 2024-01-15 10:31:00| weaviate/docs +https://python.langchain.com/api | ghi789... | 2024-01-15 10:32:00| weaviate/docs +``` + +### **5. Production Scenario** + +Let's say you run the ingest process daily: + +```python +# Day 1: Initial load +print(f"Indexed {indexing_stats['num_added']} new documents") +# Output: "Indexed 7500 new documents" +# Cost: $15, Time: 15 minutes + +# Day 2: Minor updates (typical scenario) +print(f"Added: {indexing_stats['num_added']}, Updated: {indexing_stats['num_updated']}, Skipped: {indexing_stats['num_skipped']}") +# Output: "Added: 25, Updated: 15, Skipped: 7460" +# Cost: $0.80, Time: 1 minute + +# Day 3: No changes +# Output: "Added: 0, Updated: 0, Skipped: 7500" +# Cost: $0.10 (just for checking), Time: 30 seconds +``` + +### **6. Recovery and Cleanup** + +Using the `clear_index.py` script: + +```python +# Clean slate - removes all vectors AND clears tracking +index([], record_manager, vectorstore, cleanup="full") +# This ensures perfect sync between your tracking DB and vector store +``` + +## 🎯 **Key Benefits Summary** + +1. **💰 Cost Reduction:** 80-90% reduction in embedding API costs +2. **⚡ Performance:** 85% faster re-indexing on subsequent runs +3. **🎯 Accuracy:** No duplicate vectors cluttering search results +4. **🔄 Reliability:** Automatic cleanup of removed/outdated content +5. **📊 Visibility:** Detailed statistics on what changed +6. **🛡️ Safety:** Atomic operations - either all updates succeed or none do + +**Without SQLRecordManager:** Your system would create thousands of duplicate vectors every time you run ingest, leading to poor search quality and massive unnecessary costs. + +**With SQLRecordManager:** You get enterprise-grade document management with intelligent deduplication and incremental updates! 🚀 diff --git a/docs/SETUP_DEPS.MD b/docs/SETUP_DEPS.MD new file mode 100644 index 000000000..2c7ab0023 --- /dev/null +++ b/docs/SETUP_DEPS.MD @@ -0,0 +1,146 @@ +## 🔧 **Step 1: Complete Environment Variables** + +Create/update your `.env` file with **all required variables**: + +```bash +# ✅ Already working - PostgreSQL +RECORD_MANAGER_DB_URL=postgresql://postgres:zkdtn1234@localhost:5432/chat_langchain + +# 🔑 Required - OpenAI API Key +OPENAI_API_KEY=your_openai_api_key_here + +# 🗄️ Required - Weaviate Vector Store +WEAVIATE_URL=your_weaviate_cluster_url +WEAVIATE_API_KEY=your_weaviate_api_key + +# 📊 Optional - LangSmith (for monitoring/tracing) +LANGCHAIN_API_KEY=your_langsmith_api_key +LANGCHAIN_TRACING_V2=true +LANGCHAIN_PROJECT=chat-langchain + +# 🔄 Optional - Ingestion settings +FORCE_UPDATE=false + +# 🤖 Optional - Additional AI providers +ANTHROPIC_API_KEY=your_anthropic_key +FIREWORKS_API_KEY=your_fireworks_key +``` + +## 🚀 **Step 2: Set Up Weaviate (Vector Store)** + +**Option A: Weaviate Cloud (Recommended)** + +1. Go to [Weaviate Cloud Console](https://console.weaviate.cloud/) +2. Create account → Create Cluster +3. Get your cluster URL and API key +4. Add to `.env` file + +**Option B: Local Weaviate (Docker)** + +```yaml +# Add to your docker-compose.yml + weaviate: + image: cr.weaviate.io/semitechnologies/weaviate:1.25.0 + container_name: chat_langchain_weaviate + ports: + - "8080:8080" + environment: + QUERY_DEFAULTS_LIMIT: 25 + AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true' + PERSISTENCE_DATA_PATH: '/var/lib/weaviate' + DEFAULT_VECTORIZER_MODULE: 'none' + volumes: + - weaviate_data:/var/lib/weaviate + +# Then add to volumes: +volumes: + weaviate_data: +``` + +If using local Weaviate: + +```bash +WEAVIATE_URL=http://localhost:8080 +WEAVIATE_API_KEY= # Leave empty for local +``` + +## 📦 **Step 3: Install Dependencies** + +```bash +# Make sure you're in project root +cd /Users/MM7V26R293/Documents/Naver/AI-MCP/Langchain-practice/chat-langchain-practice + +# Install Python dependencies +poetry install + +# Verify installation +poetry run python -c "import langchain, langgraph; print('✅ Dependencies installed!')" +``` + +## 🧪 **Step 4: Test Core Components** + +```bash +# Test environment setup +poetry run python -c " +import os +from backend.configuration import BaseConfiguration +from backend.embeddings import get_embeddings_model +print('✅ Configuration loaded!') +" + +# Test database connection (you already did this) +poetry run python -c " +from langchain.indexes import SQLRecordManager +rm = SQLRecordManager('test', db_url='postgresql://postgres:zkdtn1234@localhost:5432/chat_langchain') +rm.create_schema() +print('✅ Database connection working!') +" +``` + +## 🏃‍♂️ **Step 5: Run the Server** + +**Option A: LangGraph Test (Stateless)** + +```bash +# Start PostgreSQL if not running +docker-compose up -d postgres + +# Run LangGraph in test mode +poetry run langgraph test + +# This should start the server on http://localhost:8123 +``` + +**Option B: LangGraph Full Server (if you have license)** + +```bash +poetry run langgraph up +``` + +## 📊 **Step 6: Populate Vector Store (Optional)** + +If you want to test with actual data: + +```bash +# Run document ingestion +poetry run python backend/ingest.py +``` + +## ✅ **Quick Setup Checklist** + +- [x] PostgreSQL running ✅ +- [ ] Get OpenAI API key +- [ ] Set up Weaviate (cloud or local) +- [ ] Create complete `.env` file +- [ ] Run `poetry install` +- [ ] Test with `langgraph test` +- [ ] Access at `http://localhost:8123` + +## 🚨 **Priority Next Steps** + +1. **Get OpenAI API key** - This is required for embeddings +2. **Set up Weaviate** - This stores your document vectors +3. **Complete `.env` file** with all variables +4. **Run `langgraph test`** to start the server + +The server should start successfully once you have the OpenAI API key and Weaviate configured! Let me know when you get to Step 5 and I can help troubleshoot any issues. diff --git a/main.py b/main.py new file mode 100644 index 000000000..d02bb31bd --- /dev/null +++ b/main.py @@ -0,0 +1,41 @@ +from dotenv import load_dotenv + +load_dotenv() + +from backend.retrieval_graph.graph import graph + +# if __name__ == "__main__": +# print("Hello Advanced RAG") +# # print(graph.invoke(input={"question": "LLM Token Manipulation"})) +# # print(graph.invoke(input={"question": "How to use MCP in LangChain?"})) +# print(graph.invoke(input={"question": "How to use MCP in LangChain?"})) + + +if __name__ == "__main__": + import asyncio + import warnings + from langchain_core.messages import HumanMessage + + # Suppress ResourceWarning for unclosed sockets (common with async HTTP clients) + # These warnings occur because LangChain's HTTP clients aren't explicitly closed, + # but they're cleaned up by Python's garbage collector, so they're not critical. + warnings.filterwarnings( + "ignore", category=ResourceWarning, message=".*unclosed.*socket.*" + ) + + async def test_graph(): + try: + result = await graph.ainvoke( + input={ + "messages": [HumanMessage(content="What is langchaun-mcp-adapter?")] + } + ) + print("Graph execution completed!") + print(f"Answer: {result.get('answer', 'N/A')}") + print(f"Steps: {result.get('steps', [])}") + print(f"Documents retrieved: {len(result.get('documents', []))}") + finally: + # Give async tasks time to clean up connections + await asyncio.sleep(0.1) + + asyncio.run(test_graph()) diff --git a/pyproject.toml b/pyproject.toml index 9a7f6bcfd..0135a3905 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "langchain-community>=0.3.0,<0.4.0", "langchain-openai>=0.3.12,<0.4.0", "langchain-anthropic>=0.3.10,<0.4.0", + "langchain-groq>=0.2.0,<0.3.0", "langchain-weaviate>=0.0.3,<0.1.0", "langgraph>=0.4.5", "beautifulsoup4>=4.12.2,<5.0.0", @@ -28,6 +29,9 @@ dependencies = [ "voyageai>=0.1.4,<0.2.0", 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