chore: cleanup unncessary comments/casts to ignore type warnings (#1392)

Author: georgeh0

examples/custom_output_files/main.py

@@ -96,7 +96,7 @@ def mutate(
 
 @cocoindex.op.function()
 def markdown_to_html(text: str) -> str:
-    return _markdown_it.render(text)  # type: ignore
+    return _markdown_it.render(text)
 
 
 @cocoindex.flow_def(name="CustomOutputFiles")

examples/face_recognition/main.py

@@ -85,7 +85,7 @@ def extract_face_embedding(
         np.array(img),
         known_face_locations=[(0, img.width - 1, img.height - 1, 0)],
     )[0]
-    return embedding  # type: ignore
+    return embedding
 
 
 @cocoindex.flow_def(name="FaceRecognition")

examples/fastapi_server_docker/main.py

@@ -109,7 +109,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 fastapi_app = FastAPI(lifespan=lifespan)
 
 
-@fastapi_app.get("/search")  # type: ignore
+@fastapi_app.get("/search")
 def search_endpoint(
     request: Request,
     q: str = Query(..., description="Search query"),

examples/image_search/colpali_main.py

@@ -100,7 +100,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 
 
 # --- Search API ---
-@app.get("/search")  # type: ignore
+@app.get("/search")
 def search(
     q: str = Query(..., description="Search query"),
     limit: int = Query(5, description="Number of results"),

examples/image_search/main.py

@@ -36,7 +36,7 @@ def embed_query(text: str) -> list[float]:
     inputs = processor(text=[text], return_tensors="pt", padding=True)
     with torch.no_grad():
         features = model.get_text_features(**inputs)
-    return features[0].tolist()  # type: ignore
+    return features[0].tolist()
 
 
 @cocoindex.op.function(cache=True, behavior_version=1, gpu=True)
@@ -51,7 +51,7 @@ def embed_image(
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         features = model.get_image_features(**inputs)
-    return features[0].tolist()  # type: ignore
+    return features[0].tolist()
 
 
 # CocoIndex flow: Ingest images, extract captions, embed, export to Qdrant
@@ -140,7 +140,7 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 
 
 # --- Search API ---
-@app.get("/search")  # type: ignore
+@app.get("/search")
 def search(
     q: str = Query(..., description="Search query"),
     limit: int = Query(5, description="Number of results"),

examples/manuals_llm_extraction/main.py

@@ -5,7 +5,6 @@
 from marker.models import create_model_dict
 from marker.output import text_from_rendered
 from marker.config.parser import ConfigParser
-from typing import cast
 
 import cocoindex
 
@@ -32,7 +31,7 @@ def __call__(self, content: bytes) -> str:
             temp_file.write(content)
             temp_file.flush()
             text_any, _, _ = text_from_rendered(self._converter(temp_file.name))
-            return cast(str, text_any)
+            return text_any
 
 
 @dataclasses.dataclass

examples/paper_metadata/main.py

@@ -9,7 +9,6 @@
 from marker.models import create_model_dict
 from marker.output import text_from_rendered
 from functools import cache
-from typing import cast
 from pypdf import PdfReader, PdfWriter
 
 
@@ -68,7 +67,7 @@ def pdf_to_markdown(content: bytes) -> str:
         temp_file.write(content)
         temp_file.flush()
     text_any, _, _ = text_from_rendered(get_marker_converter()(temp_file.name))
-    return cast(str, text_any)
+    return text_any
 
 
 @cocoindex.flow_def(name="PaperMetadata")

examples/patient_intake_extraction_dspy/main.py

@@ -77,7 +77,7 @@ class Patient(BaseModel):
 
 
 # DSPy Signature for patient information extraction from images
-class PatientExtractionSignature(dspy.Signature):  # type: ignore[misc]
+class PatientExtractionSignature(dspy.Signature):
     """Extract structured patient information from a medical intake form image."""
 
     form_images: list[dspy.Image] = dspy.InputField(
@@ -88,7 +88,7 @@ class PatientExtractionSignature(dspy.Signature):  # type: ignore[misc]
     )
 
 
-class PatientExtractor(dspy.Module):  # type: ignore[misc]
+class PatientExtractor(dspy.Module):
     """DSPy module for extracting patient information from intake form images."""
 
     def __init__(self) -> None:
@@ -98,7 +98,7 @@ def __init__(self) -> None:
     def forward(self, form_images: list[dspy.Image]) -> Patient:
         """Extract patient information from form images and return as a Pydantic model."""
         result = self.extract(form_images=form_images)
-        return result.patient  # type: ignore
+        return result.patient
 
 
 @cocoindex.op.function(cache=True, behavior_version=1)
@@ -123,7 +123,7 @@ def extract_patient(pdf_content: bytes) -> Patient:
     extractor = PatientExtractor()
     patient = extractor(form_images=form_images)
 
-    return patient  # type: ignore
+    return patient
 
 
 @cocoindex.flow_def(name="PatientIntakeExtractionDSPy")

examples/pdf_elements_embedding/main.py

@@ -7,7 +7,6 @@
 from dataclasses import dataclass
 from pypdf import PdfReader
 from transformers import CLIPModel, CLIPProcessor
-from typing import cast
 
 
 QDRANT_GRPC_URL = "http://localhost:6334"
@@ -36,7 +35,7 @@ def clip_embed_image(img_bytes: bytes) -> list[float]:
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         features = model.get_image_features(**inputs)
-    return cast(list[float], features[0].tolist())
+    return features[0].tolist()
 
 
 def clip_embed_query(text: str) -> list[float]:
@@ -47,7 +46,7 @@ def clip_embed_query(text: str) -> list[float]:
     inputs = processor(text=[text], return_tensors="pt", padding=True)
     with torch.no_grad():
         features = model.get_text_features(**inputs)
-    return cast(list[float], features[0].tolist())
+    return features[0].tolist()
 
 
 @cocoindex.transform_flow()

examples/pdf_embedding/main.py

@@ -34,7 +34,7 @@ def __call__(self, content: bytes) -> str:
             temp_file.write(content)
             temp_file.flush()
             text_any, _, _ = text_from_rendered(self._converter(temp_file.name))
-            return text_any  # type: ignore
+            return text_any
 
 
 @cocoindex.transform_flow()