RAG Pipeline Enhancement with Entity Extraction

Enhance RAG (Retrieval Augmented Generation) pipelines with entity extraction. Improve document chunking and retrieval for LLM applications.

The Problem

RAG pipelines often struggle with retrieval accuracy because vector similarity alone misses semantic connections. Without entity awareness, important context about people, organizations, and places gets lost during chunking.

The Solution

Enrich your document chunks with extracted entities during ingestion. Use entity metadata for hybrid search, filter retrieval results by entity type, and build knowledge graphs that improve LLM context quality.

Key Benefits

Enrich chunk metadata with entity information
Enable entity-based filtering in vector search
Build knowledge graphs from extracted relations
Improve retrieval accuracy for entity-specific queries
Reduce hallucinations with better context retrieval
Support hybrid search with entity tags

Code Example

python

import requests
from your_vector_db import VectorDB

def ingest_with_entities(document_chunks):
    """Ingest documents with entity enrichment"""
    enriched_chunks = []

    for chunk in document_chunks:
        # Extract entities from chunk
        response = requests.post(
            "https://api.entity-detector.com/v1/analyze",
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            json={"text": chunk["text"]}
        )

        entities = response.json()["entities"]

        # Enrich chunk with entity metadata
        enriched_chunk = {
            "id": chunk["id"],
            "text": chunk["text"],
            "embedding": chunk["embedding"],
            "metadata": {
                "persons": entities.get("persons", []),
                "organizations": entities.get("organizations", []),
                "locations": entities.get("locations", []),
                "source": chunk.get("source", "unknown")
            }
        }
        enriched_chunks.append(enriched_chunk)

    return enriched_chunks

# Later, filter retrieval by entity
results = vector_db.search(
    query_embedding,
    filter={"metadata.organizations": {"$contains": "OpenAI"}}
)

Example Output

json

{
  "chunk_id": "doc_123_chunk_5",
  "text": "OpenAI announced GPT-4 at their San Francisco headquarters...",
  "metadata": {
    "persons": [],
    "organizations": ["OpenAI"],
    "locations": ["San Francisco"],
    "source": "tech_news_2024.pdf"
  },
  "entities_extracted": 2,
  "relations": [
    {
      "source": "OpenAI",
      "target": "San Francisco",
      "type": "located_in"
    }
  ]
}

Ready to get started?

Try entity extraction for your rag pipeline enhancement workflow.

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