From e3025cb3aa2f169471cf4e23eac04f5f21b2e9fa Mon Sep 17 00:00:00 2001 From: Shan-Weaviate <137914800+Shan-Weaviate@users.noreply.github.com> Date: Thu, 27 Nov 2025 12:58:07 +0000 Subject: [PATCH 1/6] Update --- _includes/wcs/wcs-landing-get-started.mdx | 5 +- _includes/wcs/wcs-landing-intro.mdx | 2 +- .../wcs/weaviate-cloud-edit-organization.mdx | 11 +- .../tutorial-recipe-recommender.mdx | 26 +-- docs/agents/personalization/usage.md | 19 +- docs/agents/query/tutorial-ecommerce.mdx | 2 +- docs/agents/query/usage.md | 2 +- .../tutorial-enrich-dataset.mdx | 6 +- docs/agents/transformation/usage.md | 7 +- docs/cloud/embeddings/administration.md | 6 +- docs/cloud/faq.mdx | 6 +- docs/cloud/manage-clusters/authentication.mdx | 22 ++- docs/cloud/manage-clusters/authorization.mdx | 28 +-- docs/cloud/manage-clusters/connect.mdx | 8 +- docs/cloud/manage-clusters/create.mdx | 6 +- docs/cloud/manage-clusters/status.mdx | 25 ++- docs/cloud/manage-clusters/upgrade.mdx | 6 +- docs/cloud/platform/create-account.mdx | 12 +- docs/cloud/platform/multi-factor-auth.mdx | 34 ++-- docs/cloud/platform/support-levels.mdx | 2 +- .../platform/users-and-organizations.mdx | 79 +++++--- docs/cloud/platform/version.mdx | 6 +- docs/cloud/quickstart.mdx | 34 ++-- docs/cloud/tools/query-tool.mdx | 22 ++- docs/contributor-guide/weaviate-core/setup.md | 14 +- docs/deploy/configuration/authentication.md | 31 +-- docs/deploy/index.mdx | 2 +- docs/deploy/installation-guides/index.md | 7 +- ...gent-workflow-with-weaviate-query-agent.md | 12 +- .../recipes/crewai-query-agent-as-tool.md | 9 +- .../recipes/haystack-query-agent-tool.md | 9 +- docs/weaviate/api/graphql/explore.md | 10 +- docs/weaviate/api/grpc.md | 8 +- .../_components/client.auth.wcs.mdx | 2 +- docs/weaviate/concepts/interface.md | 69 +++---- .../_enterprise-usage-collector.md | 38 ++-- .../compression/pq-compression.md | 2 +- docs/weaviate/connections/connect-cloud.mdx | 4 +- docs/weaviate/connections/connect-query.mdx | 18 +- .../model-providers/weaviate/index.md | 4 +- docs/weaviate/quickstart/index.md | 68 +++---- .../recipes/hybrid_search_mistral_embed.md | 13 +- .../recipes/multi-vector-colipali-rag.md | 43 ++++- .../rag_llama_3.1_nemotron_51b_instruct.md | 23 ++- .../recipes/weaviate_embeddings_service.md | 25 ++- .../weaviate/starter-guides/which-weaviate.md | 11 +- docs/weaviate/tutorials/_console.md | 18 +- .../weaviate/tutorials/collection-aliases.mdx | 2 +- docs/weaviate/tutorials/import.mdx | 2 +- .../tutorials/multi-vector-embeddings.md | 15 +- docs/weaviate/tutorials/query.md | 181 +++++++++--------- .../tutorials/vectorizer-migration.mdx | 4 +- docusaurus.config.js | 5 +- src/components/UTM/capture.js | 51 +++++ src/pages/go/console.jsx | 72 +++++++ 55 files changed, 705 insertions(+), 443 deletions(-) create mode 100644 src/components/UTM/capture.js create mode 100644 src/pages/go/console.jsx diff --git a/_includes/wcs/wcs-landing-get-started.mdx b/_includes/wcs/wcs-landing-get-started.mdx index baae663fc..438a20837 100644 --- a/_includes/wcs/wcs-landing-get-started.mdx +++ b/_includes/wcs/wcs-landing-get-started.mdx @@ -5,8 +5,9 @@ import CardsSection from "/src/components/CardsSection"; export const nextStepsData = [ { title: "Weaviate Cloud: Console", - description: " Go directly to the Weaviate Cloud console and create your first cluster.", - link: "https://weaviate.io/go/console?utm_source=docs&utm_content=others", + description: + " Go directly to the Weaviate Cloud console and create your first cluster.", + link: "/go/console?utm_source=docs&utm_content=others", icon: "fa fa-desktop", }, { diff --git a/_includes/wcs/wcs-landing-intro.mdx b/_includes/wcs/wcs-landing-intro.mdx index 6781c7c86..eaa4953d3 100644 --- a/_includes/wcs/wcs-landing-intro.mdx +++ b/_includes/wcs/wcs-landing-intro.mdx @@ -1,3 +1,3 @@ -**[Weaviate Cloud (WCD)](https://weaviate.io/go/console?utm_source=docs&utm_content=others)** is a fully managed vector database in the cloud. +**[Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=others)** is a fully managed vector database in the cloud. Weaviate Cloud manages the infrastructure so you can focus on innovation. Use Weaviate Cloud to simplify development and confidently deploy enterprise-ready AI applications. diff --git a/_includes/wcs/weaviate-cloud-edit-organization.mdx b/_includes/wcs/weaviate-cloud-edit-organization.mdx index 59a20b57f..4ba6e69d3 100644 --- a/_includes/wcs/weaviate-cloud-edit-organization.mdx +++ b/_includes/wcs/weaviate-cloud-edit-organization.mdx @@ -1,12 +1,15 @@ -import Link from '@docusaurus/Link'; -import OrganizationSettings from '/docs/cloud/img/weaviate-cloud-organization-settings.png'; +import Link from "@docusaurus/Link"; +import OrganizationSettings from "/docs/cloud/img/weaviate-cloud-organization-settings.png";
Now, you can open the{" "} - Explorer tool{" "} + + Explorer tool + {" "} to check the results of the transformation.
Weaviate Embeddings in the left sidebar (1).
@@ -47,6 +46,7 @@ import DisableWeaviateEmbeddings from '/docs/cloud/img/weaviate-cloud-disable-em
## Pricing and billing
+
If you would like to learn about the pricing model, you can visit the Weaviate Embeddings [product page](https://weaviate.io/product/embeddings).
The pricing works on a per-token basis. This means that you will only be billed for the tokens that are successfully consumed.
In other words, only requests that result in valid responses from the API are considered.
@@ -62,6 +62,6 @@ More info about billing in Weaviate Cloud can be found on [this page](/cloud/pla
## Support & feedback
-import SupportAndTrouble from '/_includes/wcs/support-and-troubleshoot.mdx';
+import SupportAndTrouble from '/\_includes/wcs/support-and-troubleshoot.mdx';
Available modules section under{" "}
Advanced options.
- Account dropdown menu in the lower left corner
@@ -32,7 +35,8 @@ import MFA from '/docs/cloud/img/weaviate-cloud-mfa.png';
Select Account Settings (2).
Enable MFA button (3).
+ Click on Enable MFA button (3
+ ).
Enable MFA button.
Add new organization (2).
+ Click on Add new organization (
+ 2).
Create button.
- Create button.
+
Organization settings (2).
+ Click on Organization settings (
+ 2).
Delete button (3).
+ Click on the Delete button (3
+ ).
GraphiQL - {' '} + {" "} is built into the query tool. GraphiQL provides many features that make GraphQL easier to use interactively:
@@ -48,17 +48,21 @@ import QueryToolPreview from '/docs/cloud/img/weaviate-cloud-query-tool-preview. To open the query tool for a cluster: -import QueryTool from '/docs/cloud/img/weaviate-cloud-query-tool.png'; +import QueryTool from "/docs/cloud/img/weaviate-cloud-query-tool.png";Query button and choose a cluster from the list.
+ Click on the Query button and choose a cluster from the
+ list.
@@ -591,11 +616,13 @@ for i, o in enumerate(response.objects):
```
Python output:
+
```text
The most relevant documents for the query "How does DeepSeek-V2 compare against the LLaMA family of LLMs?" by order of relevance:
1) MaxSim: 23.12, Title: "DeepSeek-V2: A Strong Economical and Efficient Mixture-of-Experts Language Model" (arXiv: 2405.04434), Page: 1
```
+
The retrieved page with the highest MaxSim score is indeed the page with the figure we mentioned earlier.
```python
@@ -752,6 +779,7 @@ qwenvl = QwenVL()
```
Python output:
+
```text
config.json: 0.00B [00:00, ?B/s]
@@ -781,6 +809,7 @@ You have video processor config saved in `preprocessor.json` file which is depre
chat_template.json: 0.00B [00:00, ?B/s]
```
+
The response from `Qwen2.5-VL-3B-Instruct` based on the retrieved PDF pages:
```python
@@ -788,9 +817,11 @@ qwenvl.query_images(query, result_images)
```
Python output:
+
```text
'DeepSeek-V2 achieves significantly stronger performance than the LLaMA family of LLMs, while also saving 42.5% of training costs and boosting the maximum generation throughput to 5.76 times.'
```
+
As you can see, the multimodal RAG pipeline was able to answer the original query: "How does DeepSeek-V2 compare against the LLaMA family of LLMs?". For this, the ColQwen2 retrieval model retrieved the correct PDF page from the
"DeepSeek-V2: A Strong Economical and Efficient Mixture-of-Experts Language Model" paper and used both the text and visual from the retrieved PDF page to answer the question.
diff --git a/docs/weaviate/recipes/rag_llama_3.1_nemotron_51b_instruct.md b/docs/weaviate/recipes/rag_llama_3.1_nemotron_51b_instruct.md
index cb65eabef..15bc6926f 100644
--- a/docs/weaviate/recipes/rag_llama_3.1_nemotron_51b_instruct.md
+++ b/docs/weaviate/recipes/rag_llama_3.1_nemotron_51b_instruct.md
@@ -5,8 +5,9 @@ title: "Generate new content with NVIDIA models and RAG"
featured: False
integration: False
agent: False
-tags: ['Generative Search', 'RAG', 'NVIDIA']
+tags: ["Generative Search", "RAG", "NVIDIA"]
---
+
[](https://colab.research.google.com/github/weaviate/recipes/blob/main/weaviate-features/model-providers/nvidia/rag_llama_3.1_nemotron_51b_instruct.ipynb)
# Generative Search with NVIDIA
@@ -16,10 +17,11 @@ In this demo, we will use an embedding and generative model on NVIDIA to generat
## Requirements
1. Weaviate cluster
- 1. You can create a 14-day free sandbox on [WCD](https://weaviate.io/go/console?utm_source=docs&utm_content=recipe/)
- 2. [Embedded Weaviate](https://docs.weaviate.io/deploy/installation-guides/embedded)
- 3. [Local deployment](https://docs.weaviate.io/deploy/installation-guides/docker-installation)
- 4. [Other options](https://docs.weaviate.io/deploy)
+
+ 1. You can create a 14-day free sandbox on [WCD](/go/console?utm_source=docs&utm_content=recipe/)
+ 2. [Embedded Weaviate](https://docs.weaviate.io/deploy/installation-guides/embedded)
+ 3. [Local deployment](https://docs.weaviate.io/deploy/installation-guides/docker-installation)
+ 4. [Other options](https://docs.weaviate.io/deploy)
2. NVIDIA NIM API key. Grab one [here](https://build.nvidia.com/models).
3. Weaviate client version `4.11.0` or newer
@@ -61,9 +63,11 @@ print(client.is_ready())
```
Python output:
+
```text
True
```
+
**Embedded Weaviate**
```python
@@ -97,6 +101,7 @@ True
```
## Create a collection
+
Collection stores your data and vector embeddings.
Full list of [generative models](https://weaviate.io/developers/weaviate/model-providers/octoai/generative#available-models)
@@ -129,9 +134,11 @@ print("Successfully created collection: BlogChunks.")
```
Python output:
+
```text
Successfully created collection: BlogChunks.
```
+
## Chunk and Import Data
We need to break our blog posts into smaller chunks
@@ -172,9 +179,11 @@ blog_chunks[0]
```
Python output:
+
```text
'---\ntitle: What is Ref2Vec and why you need it for your recommendation system\nslug: ref2vec-centroid\nauthors: [connor]\ndate: 2022-11-23\ntags: [\'integrations\', \'concepts\']\nimage: ./img/hero.png\ndescription: "Weaviate introduces Ref2Vec, a new module that utilises Cross-References for Recommendation!"\n---\n\n\n\n\nWeaviate 1.16 introduced the [Ref2Vec](/developers/weaviate/modules/retriever-vectorizer-modules/ref2vec-centroid) module. In this article, we give you an overview of what Ref2Vec is and some examples in which it can add value such as recommendations or representing long objects. ## What is Ref2Vec? The name Ref2Vec is short for reference-to-vector, and it offers the ability to vectorize a data object with its cross-references to other objects. The Ref2Vec module currently holds the name ref2vec-**centroid** because it uses the average, or centroid vector, of the cross-referenced vectors to represent the **referencing** object.'
```
+
```python
# Insert the objects (chunks) into the Weaviate cluster
@@ -219,6 +228,7 @@ for o in response.objects:
```
Python output:
+
```text
{
"content": "---\ntitle: What is Ref2Vec and why you need it for your recommendation system\nslug: ref2vec-centroid\nauthors: [connor]\ndate: 2022-11-23\ntags: ['integrations', 'concepts']\nimage: ./img/hero.png\ndescription: \"Weaviate introduces Ref2Vec, a new module that utilises Cross-References for Recommendation!\"\n---\n\n\n\n\nWeaviate 1.16 introduced the [Ref2Vec](/developers/weaviate/modules/retriever-vectorizer-modules/ref2vec-centroid) module. In this article, we give you an overview of what Ref2Vec is and some examples in which it can add value such as recommendations or representing long objects. ## What is Ref2Vec? The name Ref2Vec is short for reference-to-vector, and it offers the ability to vectorize a data object with its cross-references to other objects. The Ref2Vec module currently holds the name ref2vec-**centroid** because it uses the average, or centroid vector, of the cross-referenced vectors to represent the **referencing** object."
@@ -230,9 +240,11 @@ Python output:
"content": "Although all the query does is provide the ID of the User object, Ref2Vec has done the hard work by inferring a centroid vector from the User's references to other vectors. And as the set of references continues to evolve, the Ref2Vec vectors will continue to evolve also, ensuring that the User vector remains up-to-date with their latest interests. Whether your goal is to construct a Home Feed interface for users or to pair with search queries, Ref2Vec provides a strong foundation to enable Recommendation with fairly low overhead. For example, it can achieve personalized re-ranking, also known as a session-based recommendation, without persisting user data over a long sequence of interactions. A new user could have personalization available after a few interactions on the app which will help them quickly settle in and feel at home, helping to overcome what is known as the cold-start problem."
}
```
+
### Generative Search Query
Here is what happens in the below:
+
1. We will retrieve 3 relevant chunks from our vector database
2. We will pass the 3 chunks to NVIDIA to generate the short paragraph about Ref2Vec
@@ -253,6 +265,7 @@ for o in response.objects:
```
Python output:
+
```text
Here is a short paragraph about Ref2Vec:
diff --git a/docs/weaviate/recipes/weaviate_embeddings_service.md b/docs/weaviate/recipes/weaviate_embeddings_service.md
index c6fd95b2f..20442aac0 100644
--- a/docs/weaviate/recipes/weaviate_embeddings_service.md
+++ b/docs/weaviate/recipes/weaviate_embeddings_service.md
@@ -5,17 +5,19 @@ title: "How to Use Weaviate Embedding Service"
featured: True
integration: False
agent: False
-tags: ['Weaviate Embeddings', 'Weaviate Cloud']
+tags: ["Weaviate Embeddings", "Weaviate Cloud"]
---
+
[](https://colab.research.google.com/github/weaviate/recipes/weaviate-services/embedding-service/weaviate_embeddings_service.ipynb)
# Weaviate Embedding Service
-[Weaviate Embeddings](https://docs.weaviate.io/cloud/embeddings) enables you to generate embeddings directly from a [Weaviate Cloud](https://weaviate.io/go/console?utm_source=docs&utm_content=recipe/) database instance.
+[Weaviate Embeddings](https://docs.weaviate.io/cloud/embeddings) enables you to generate embeddings directly from a [Weaviate Cloud](/go/console?utm_source=docs&utm_content=recipe/) database instance.
-*Please note this service is part of Weaviate Cloud and cannot be accessed through open-source. Additionally, this service is currently under technical preview, and you can request access [here](https://events.weaviate.io/embeddings-preview).*
+_Please note this service is part of Weaviate Cloud and cannot be accessed through open-source. Additionally, this service is currently under technical preview, and you can request access [here](https://events.weaviate.io/embeddings-preview)._
This notebook will show you how to:
+
1. Define a Weaviate Collection
1. Run a vector search query
1. Run a hybrid search query
@@ -24,7 +26,7 @@ This notebook will show you how to:
## Requirements
-1. Weaviate Cloud (WCD) account: You can register [here](https://weaviate.io/go/console?utm_source=docs&utm_content=recipe/)
+1. Weaviate Cloud (WCD) account: You can register [here](/go/console?utm_source=docs&utm_content=recipe/)
1. Create a cluster on WCD: A sandbox or serverless cluster is fine. You will need to grab the cluster URL and admin API key
1. OpenAI key to access `GPT-4o mini`
@@ -72,9 +74,11 @@ print(client.is_ready())
```
Python output:
+
```text
True
```
+
## Define Collection
```python
@@ -108,9 +112,11 @@ print("Successfully created collection: JeopardyQuestion.")
```
Python output:
+
```text
Successfully created collection: JeopardyQuestion.
```
+
## Import Data
We will use the small jeopardy dataset as an example. It has 1,000 objects.
@@ -145,9 +151,11 @@ else:
```
Python output:
+
```text
Insert complete.
```
+
```python
# count the number of objects
@@ -158,9 +166,11 @@ print(response.total_count)
```
Python output:
+
```text
1000
```
+
## Query Time
### Vector Search
@@ -178,6 +188,7 @@ for item in response.objects:
```
Python output:
+
```text
Data: {
"value": "NaN",
@@ -193,6 +204,7 @@ Data: {
"category": "MAMMALS"
}
```
+
### Hybrid Search
The goal of this notebook is to show you how to use the embedding service. For more information on hybrid search, check out [this folder](https://github.com/weaviate/recipes/tree/main/weaviate-features/hybrid-search) and/or the [documentation](https://docs.weaviate.io/weaviate/search/hybrid).
@@ -215,6 +227,7 @@ for item in response.objects:
```
Python output:
+
```text
Data: {
"value": "NaN",
@@ -230,6 +243,7 @@ Data: {
"category": "MAMMALS"
}
```
+
### Fetch Objects with Metadata Filters
Learn more about the different filter operators [here](https://docs.weaviate.io/weaviate/search/filters).
@@ -247,6 +261,7 @@ for item in response.objects:
```
Python output:
+
```text
Data: {
"value": "$200",
@@ -262,6 +277,7 @@ Data: {
"category": "BUSINESS & INDUSTRY"
}
```
+
### Generative Search (RAG)
```python
@@ -278,6 +294,7 @@ print(f"Generated output: {response.generated}")
```
Python output:
+
```text
Generated output: People thought these animals were unicorn-like for a few reasons:
diff --git a/docs/weaviate/starter-guides/which-weaviate.md b/docs/weaviate/starter-guides/which-weaviate.md
index f2b1fc2f4..d072087e1 100644
--- a/docs/weaviate/starter-guides/which-weaviate.md
+++ b/docs/weaviate/starter-guides/which-weaviate.md
@@ -15,6 +15,7 @@ This page helps you to find the right combination for your project.
## Deploy Weaviate
Weaviate can be deployed in the following ways:
+
- [Embedded Weaviate](/deploy/installation-guides/embedded.md)
- [Docker-Compose](/deploy/installation-guides/docker-installation.md)
- [Weaviate Cloud (WCD)](/deploy/installation-guides/weaviate-cloud.md)
@@ -24,6 +25,7 @@ Weaviate can be deployed in the following ways:
## Vectorization options
When adding data objects to Weaviate, you have two choices:
+
- Specify the object vector directly.
- Use a Weaviate vectorizer module to generate the object vector.
@@ -44,7 +46,7 @@ If you are evaluating Weaviate, we recommend using one of these instance types t
- [Weaviate Cloud (WCD)](/cloud) sandbox
- [Embedded Weaviate](/deploy/installation-guides/embedded)
-Use an inference-API based text vectorizer with your instance, for example, `text2vec-cohere`, `text2vec-huggingface`, `text2vec-openai`, or `text2vec-google`.
+Use an inference-API based text vectorizer with your instance, for example, `text2vec-cohere`, `text2vec-huggingface`, `text2vec-openai`, or `text2vec-google`.
The [Quickstart guide](/weaviate/quickstart) uses a WCD sandbox and an API based vectorizer to run the examples.
@@ -52,7 +54,7 @@ The [Quickstart guide](/weaviate/quickstart) uses a WCD sandbox and an API based
For development, we recommend using
-- [Weaviate Cloud (WCD)](https://weaviate.io/go/console?utm_source=docs&utm_content=tutorial) or [Docker Compose](/deploy/installation-guides/docker-installation.md).
+- [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=tutorial) or [Docker Compose](/deploy/installation-guides/docker-installation.md).
- A vectorization strategy that matches your production vectorization strategy.
#### Docker-Compose vs. Weaviate Cloud (WCD)
@@ -68,12 +70,14 @@ Note that Embedded Weaviate is currently not recommended for serious development
For development, we recommend using a vectorizer module that at least approximates your needs.
As a first point, you must choose:
+
- Whether to vectorize data yourself and import it into Weaviate, or
- To use a Weaviate vectorizer module.
Then, we recommend choosing a vectorizer module that is as close as possible to your production needs. For example, if search quality is of paramount importance, we suggest using your preferred vectorizer module in development as well.
Keep in mind two other factors, which are cost, and their footprint.
+
- Vectorization, such as with an API-based vectorizer, can be expensive. This is especially true if you are dealing with very large datasets.
- Vector lengths can vary by a factor of ~5, which will impact both your storage and memory requirements. This can ultimately impact cost down the line.
@@ -99,9 +103,8 @@ Some model types such as Ollama or Transformers models are locally hosted, while
We recommend reviewing from the available [model integrations](../model-providers/index.md) and their availability in different Weaviate setups. Then, choose the one that best fits your needs.
-
## Questions and feedback
-import DocsFeedback from '/_includes/docs-feedback.mdx';
+import DocsFeedback from '/\_includes/docs-feedback.mdx';
Now, you can open the{" "} - - Explorer tool - {" "} - to check the results of the transformation. + Explorer tool to + check the results of the transformation.
Weaviate Embeddings in the left sidebar (1).
diff --git a/docs/cloud/faq.mdx b/docs/cloud/faq.mdx
index 9a3d50736..7f8b00d11 100644
--- a/docs/cloud/faq.mdx
+++ b/docs/cloud/faq.mdx
@@ -5,7 +5,7 @@ description: "Frequently asked questions and answers about Weaviate Cloud (WCD)
image: og/wcd/faq.jpg
---
-Frequently asked questions (FAQs) about [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=cloud).
+Frequently asked questions (FAQs) about [Weaviate Cloud (WCD)](/go/console?utm_content=cloud).
## Features
@@ -25,7 +25,7 @@ Using Weaviate Cloud gives you access to a 14-day free sandbox for testing. You
Account dropdown menu in the lower left corner
diff --git a/docs/cloud/platform/support-levels.mdx b/docs/cloud/platform/support-levels.mdx
index 07c43ae49..7718d577d 100644
--- a/docs/cloud/platform/support-levels.mdx
+++ b/docs/cloud/platform/support-levels.mdx
@@ -5,7 +5,7 @@ description: "Available support tiers and plans for Weaviate Cloud users with di
image: og/wcd/user_guides.jpg
---
-[Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=cloud) offers multiple levels of support integrated into each pricing plan. Choose the plan that best meets your needs.
+[Weaviate Cloud (WCD)](/go/console?utm_content=cloud) offers multiple levels of support integrated into each pricing plan. Choose the plan that best meets your needs.
## Available plans
diff --git a/docs/cloud/platform/users-and-organizations.mdx b/docs/cloud/platform/users-and-organizations.mdx
index 5e8dc9ed0..9f9e8372c 100644
--- a/docs/cloud/platform/users-and-organizations.mdx
+++ b/docs/cloud/platform/users-and-organizations.mdx
@@ -49,10 +49,7 @@ import AddOrganization from "/docs/cloud/img/weaviate-cloud-add-organization.png
Query button and choose a cluster from the
diff --git a/docs/contributor-guide/weaviate-core/setup.md b/docs/contributor-guide/weaviate-core/setup.md
index 6aebf4944..66ceb26f8 100644
--- a/docs/contributor-guide/weaviate-core/setup.md
+++ b/docs/contributor-guide/weaviate-core/setup.md
@@ -75,7 +75,7 @@ tools/dev/restart_dev_environment.sh --qna && ./tools/dev/run_dev_server.sh loca
The above commands are subject to change as we add more modules and require specific combinations for local testing. You can always inspect [restart_dev_environment.sh](https://github.com/weaviate/weaviate/blob/master/tools/dev/restart_dev_environment.sh) and [run_dev_server.sh](https://github.com/weaviate/weaviate/blob/master/tools/dev/run_dev_server.sh) to see which options are available. The first option without any arguments is always guaranteed to work.
-To make queries from a web interface, use the [WCD console](/go/console?utm_source=docs&utm_content=others) to connect to `localhost:8080`.
+To make queries from a web interface, use the [WCD console](/go/console?utm_content=others) to connect to `localhost:8080`.
## Further resources
diff --git a/docs/deploy/configuration/authentication.md b/docs/deploy/configuration/authentication.md
index f6a652811..d6ed7a8ae 100644
--- a/docs/deploy/configuration/authentication.md
+++ b/docs/deploy/configuration/authentication.md
@@ -306,7 +306,7 @@ Configuring the OIDC token issuer is outside the scope of this document, but her
- For simple use-cases such as for a single user, you can use Weaviate Cloud (WCD) as the OIDC token issuer. To do so:
- - Make sure you have a WCD account (you can [sign up here](/go/console?utm_source=docs&utm_content=deploy)).
+ - Make sure you have a WCD account (you can [sign up here](/go/console?utm_content=deploy)).
- In the Docker Compose file (e.g. `docker-compose.yml`), specify:
- `https://auth.wcs.api.weaviate.io/auth/realms/SeMI` as the issuer (in `AUTHENTICATION_OIDC_ISSUER`),
- `wcs` as the client id (in `AUTHENTICATION_OIDC_CLIENT_ID`), and
diff --git a/docs/deploy/index.mdx b/docs/deploy/index.mdx
index 08ceeb01a..cd8390719 100644
--- a/docs/deploy/index.mdx
+++ b/docs/deploy/index.mdx
@@ -8,7 +8,7 @@ import CardsSection from "/src/components/CardsSection";
import DeploymentCards from "/src/components/DeploymentCards";
import styles from "/src/components/CardsSection/styles.module.scss";
-Weaviate is available as a hosted service, [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=deploy), or as a self managed instance. If you manage your own instance, you can host it locally or with a cloud provider. Self-managed instances use the same Weaviate Database as WCD.
+Weaviate is available as a hosted service, [Weaviate Cloud (WCD)](/go/console?utm_content=deploy), or as a self managed instance. If you manage your own instance, you can host it locally or with a cloud provider. Self-managed instances use the same Weaviate Database as WCD.
If you are upgrading from a previous version of Weaviate, see the [Migration guide](docs/deploy/migration/index.md) for any changes that may affect your installation.
diff --git a/docs/deploy/installation-guides/index.md b/docs/deploy/installation-guides/index.md
index b77761b8a..e834c8efd 100644
--- a/docs/deploy/installation-guides/index.md
+++ b/docs/deploy/installation-guides/index.md
@@ -5,7 +5,7 @@ image: og/docs/installation.jpg
# tags: ['installation']
---
-Weaviate is available as a hosted service, [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=deploy), or as a self managed instance. If you manage your own instance, you can host it locally or with a cloud provider. Self-managed instances use the same Weaviate Database as WCD.
+Weaviate is available as a hosted service, [Weaviate Cloud (WCD)](/go/console?utm_content=deploy), or as a self managed instance. If you manage your own instance, you can host it locally or with a cloud provider. Self-managed instances use the same Weaviate Database as WCD.
If you are upgrading from a previous version of Weaviate, see the [Migration Guide](/deploy/migration/index.md) for any changes that may affect your installation.
diff --git a/docs/integrations/recipes/agent-workflow-with-weaviate-query-agent.md b/docs/integrations/recipes/agent-workflow-with-weaviate-query-agent.md
index 3eb282151..b81c1ca2b 100644
--- a/docs/integrations/recipes/agent-workflow-with-weaviate-query-agent.md
+++ b/docs/integrations/recipes/agent-workflow-with-weaviate-query-agent.md
@@ -19,7 +19,7 @@ This notebook will show you how to define the Weaviate Query Agent as a tool thr
### Requirements
-1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_source=docs&utm_content=integrations).
+1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_content=integrations).
2. Install LlamaIndex with `pip install llama-index` (we used version `0.12.22` for this notebook)
3. Install the Weaviate Agents package with `pip install weaviate-agents`
4. You'll need a Weaviate cluster with data. If you don't have one, check out [this notebook](https://github.com/weaviate/recipes/blob/main/integrations/Weaviate-Import-Example.ipynb) to import the Weaviate Blogs.
diff --git a/docs/integrations/recipes/crewai-query-agent-as-tool.md b/docs/integrations/recipes/crewai-query-agent-as-tool.md
index 8004dd8be..e10783031 100644
--- a/docs/integrations/recipes/crewai-query-agent-as-tool.md
+++ b/docs/integrations/recipes/crewai-query-agent-as-tool.md
@@ -17,7 +17,7 @@ This notebook will show you how to define the Weaviate Query Agent as a tool thr
## Requirements
-1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_source=docs&utm_content=integrations).
+1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_content=integrations).
2. Install Crew AI with `pip install crewai`
3. Install the Weaviate Agents package with `pip install weaviate-agents`
4. You'll need a Weaviate cluster with data. If you don't have one, check out [this notebook](https://github.com/weaviate/recipes/blob/main/integrations/Weaviate-Import-Example.ipynb) to import the Weaviate Blogs.
diff --git a/docs/integrations/recipes/haystack-query-agent-tool.md b/docs/integrations/recipes/haystack-query-agent-tool.md
index 045e6ecd0..0e3b6a96a 100644
--- a/docs/integrations/recipes/haystack-query-agent-tool.md
+++ b/docs/integrations/recipes/haystack-query-agent-tool.md
@@ -19,7 +19,7 @@ This notebook will show you how to define the Weaviate Query Agent as a tool thr
### Requirements
-1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_source=docs&utm_content=integrations).
+1. Weaviate Cloud instance (WCD): The Weaviate Query Agent is only accessible through WCD at the moment. You can create a serverless cluster or a free 14-day sandbox [here](/go/console?utm_content=integrations).
1. Install Haystack with `pip install haystack-ai`
1. Install the Weaviate Agents package with `pip install weaviate-agents`
1. You'll need a Weaviate cluster with data. If you don't have one, check out [this notebook](https://github.com/weaviate/recipes/blob/main/integrations/Weaviate-Import-Example.ipynb) to import the Weaviate Blogs.
diff --git a/docs/weaviate/api/graphql/explore.md b/docs/weaviate/api/graphql/explore.md
index e54f2c365..d7acc7272 100644
--- a/docs/weaviate/api/graphql/explore.md
+++ b/docs/weaviate/api/graphql/explore.md
@@ -10,7 +10,7 @@ image: og/docs/api.jpg
The `Explore` function is disabled where multiple inference (e.g. `text2vec-xxx`) modules are enabled.
-As a result, `Explore` is not available on [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=api) as its clusters are pre-configured with multiple inference modules for AWS, Cohere, Google, OpenAI and so on.
+As a result, `Explore` is not available on [Weaviate Cloud (WCD)](/go/console?utm_content=api) as its clusters are pre-configured with multiple inference modules for AWS, Cohere, Google, OpenAI and so on.
:::
diff --git a/docs/weaviate/api/grpc.md b/docs/weaviate/api/grpc.md
index ada02fb01..4decc0bda 100644
--- a/docs/weaviate/api/grpc.md
+++ b/docs/weaviate/api/grpc.md
@@ -30,7 +30,7 @@ As an example, the snippet below maps `50051` as the host port so that it can be
:::info
We suggest using the default port `50051` for gRPC calls. It can be modified through the `GRPC_PORT` [environment variable](/deploy/configuration/env-vars/index.md).
-Note that [Weaviate Cloud](/go/console?utm_source=docs&utm_content=api) uses port `443` for gRPC.
+Note that [Weaviate Cloud](/go/console?utm_content=api) uses port `443` for gRPC.
:::
````yaml:
diff --git a/docs/weaviate/client-libraries/_components/client.auth.wcs.mdx b/docs/weaviate/client-libraries/_components/client.auth.wcs.mdx
index a0887af8d..945088b44 100644
--- a/docs/weaviate/client-libraries/_components/client.auth.wcs.mdx
+++ b/docs/weaviate/client-libraries/_components/client.auth.wcs.mdx
@@ -1,5 +1,5 @@
:::tip WCD + Weaviate client
-Each Weaviate instance in [Weaviate Cloud (WCD)](/go/console?utm_source=docs&utm_content=others) is pre-configured to act as a token issuer for OIDC authentication.
+Each Weaviate instance in [Weaviate Cloud (WCD)](/go/console?utm_content=others) is pre-configured to act as a token issuer for OIDC authentication.
:::
[See our WCD authentication documentation](/cloud/manage-clusters/connect) for instructions on how to authenticate against WCD with your preferred Weaviate client.
diff --git a/docs/weaviate/concepts/interface.md b/docs/weaviate/concepts/interface.md
index 05042f962..da45c9c4e 100644
--- a/docs/weaviate/concepts/interface.md
+++ b/docs/weaviate/concepts/interface.md
@@ -131,7 +131,7 @@ This will not result in any user-facing API changes. As of May 2023, gRPC has be
## Weaviate Console
-The [Weaviate Console](/go/console?utm_source=docs&utm_content=others) is a dashboard to manage Weaviate clusters from WCD, and access Weaviate instances running elsewhere. You can use the Query Module to make GraphQL queries.
+The [Weaviate Console](/go/console?utm_content=others) is a dashboard to manage Weaviate clusters from WCD, and access Weaviate instances running elsewhere. You can use the Query Module to make GraphQL queries.

diff --git a/docs/weaviate/configuration/_enterprise-usage-collector.md b/docs/weaviate/configuration/_enterprise-usage-collector.md
index 35ede059c..1c11ecefa 100644
--- a/docs/weaviate/configuration/_enterprise-usage-collector.md
+++ b/docs/weaviate/configuration/_enterprise-usage-collector.md
@@ -11,7 +11,7 @@ When using Weaviate Enterprise, a proxy service is placed in between the user (o
## 1. Collect a Weaviate Enterprise Token
-- Login into the [Weaviate Console](/go/console?utm_source=docs&utm_content=howto).
+- Login into the [Weaviate Console](/go/console?utm_content=howto).
- Click the profile symbol in the top menu and collect the key, which is shown to you. Note, this key is a secret, and you should not make this available in public repositories.
## 2. Add the Weaviate Enterprise Usage Collector to your Docker Compose file
diff --git a/docs/weaviate/configuration/compression/pq-compression.md b/docs/weaviate/configuration/compression/pq-compression.md
index e0526e96a..d84d99a82 100644
--- a/docs/weaviate/configuration/compression/pq-compression.md
+++ b/docs/weaviate/configuration/compression/pq-compression.md
@@ -48,7 +48,7 @@ For new collections, use AutoPQ. AutoPQ automates triggering of the PQ training
AutoPQ requires asynchronous indexing.
- **Open-source Weaviate users**: To enable AutoPQ, set the environment variable `ASYNC_INDEXING=true` and restart your Weaviate instance.
-- [**Weaviate Cloud (WCD)**](/go/console?utm_source=docs&utm_content=howto/) users: Enable async indexing through the WCD Console and restart your Weaviate instance.
+- [**Weaviate Cloud (WCD)**](/go/console?utm_content=howto/) users: Enable async indexing through the WCD Console and restart your Weaviate instance.
### 2. Configure PQ
diff --git a/docs/weaviate/connections/connect-cloud.mdx b/docs/weaviate/connections/connect-cloud.mdx
index d5b71bf00..770e38def 100644
--- a/docs/weaviate/connections/connect-cloud.mdx
+++ b/docs/weaviate/connections/connect-cloud.mdx
@@ -19,11 +19,11 @@ import CSharpCode from "!!raw-loader!/_includes/code/csharp/ConnectionTest.cs";