NER for RAG & LLM Pipelines
Enhance your RAG (Retrieval Augmented Generation) pipelines with entity extraction. Improve document chunking, enrich metadata, and build better retrieval systems for LLM applications.
Key Points
- Extract entities during document ingestion
- Enrich chunk metadata with entity information
- Improve retrieval accuracy with entity-based filtering
- Build knowledge graphs from extracted entities
- Support hybrid search with entity tags
Example
python
import requests
def enrich_chunk_with_entities(chunk_text, chunk_id):
"""Enrich RAG chunk with entity metadata"""
response = requests.post(
"https://api.entity-detector.com/v1/analyze",
headers={"Authorization": "Bearer YOUR_API_KEY"},
json={"text": chunk_text}
)
result = response.json()
return {
"chunk_id": chunk_id,
"text": chunk_text,
"entities": result["entities"],
"relations": result["relations"],
# Use entities for metadata filtering in vector DB
"metadata": {
"persons": result["entities"].get("persons", []),
"organizations": result["entities"].get("organizations", []),
"locations": result["entities"].get("locations", [])
}
}Frequently Asked Questions
How does NER improve RAG retrieval?
By extracting entities during ingestion, you can filter retrieval results by entity type or specific entities, reducing noise and improving relevance for the LLM context window.
Should I extract entities from every chunk?
For best results, extract entities from each chunk and store them as metadata. This enables entity-based filtering and improves retrieval for entity-specific queries.
Try Entity Extraction Now
See entity extraction in action with our interactive demo or start building with the API.