When using a Knowledge Graph (KG) for Retrieval Augmented Generation (RAG), getting the nodes is just the first step. The real power comes from being able to retrieve the original, relevant passages from your documents that back up the facts in your graph. This is crucial for providing the Large Language Model (LLM) with the rich context it needs to generate accurate and grounded answers.
When using a Knowledge Graph (KG) for Retrieval Augmented Generation (RAG), getting the nodes is just the first step. The real power comes from being able to retrieve the original, relevant passages from your documents that back up the facts in your graph. This is crucial for providing the Large Language Model (LLM) with the rich context it needs to generate accurate and grounded answers.
There are a few effective strategies to achieve this, ranging from simpler KG-only methods to more robust hybrid approaches:
When using a Knowledge Graph (KG) for Retrieval Augmented Generation (RAG), getting the nodes is just the first step. The real power comes from being able to retrieve the original, relevant passages from your documents that back up the facts in your graph. This is crucial for providing the Large Language Model (LLM) with the rich context it needs to generate accurate and grounded answers.
There are a few effective strategies to achieve this, ranging from simpler KG-only methods to more robust hybrid approaches:
Strategy 1: Store Content Directly on Paragraph/Chunk Nodes (Simpler KG-Only)
This is a straightforward approach where the actual text content of your document chunks is stored as a property on the Paragraph (or Chunk) nodes within your Neo4j graph.
How it works:
During Ingestion:
When you extract entities and relationships from a text chunk, you also create a Paragraph node for that chunk itself.
Store the page_content of the chunk as a property (e.g., content) on this Paragraph node.
Establish relationships from your extracted entities (like Concept, Person, Organization) back to the Paragraph node that mentions or discusses them (e.g., [:DISCUSSES], [:MENTIONED_IN]).
During Retrieval:
First, perform your multi-hop query on the Knowledge Graph to find the relevant entities and relationships.
As part of the same Cypher query, add a step to traverse from those relevant entities to the Paragraph nodes that are connected to them.
Retrieve the content property from these Paragraph nodes.
Example Cypher Modification for Retrieval:
MATCH (concept:Concept)<-[:DEVELOPED_BY]-(org:Organization)
WHERE org.name = 'Google'
RETURN concept.name AS ConceptDevelopedByGoogle
MATCH (concept:Concept)<-[:DEVELOPED_BY]-(org:Organization)
WHERE org.name = 'Google'
// Now, find paragraphs that discuss this concept
MATCH (p:Paragraph)-[:DISCUSSES]->(concept)
RETURN concept.name AS ConceptDevelopedByGoogle, COLLECT(DISTINCT p.content) AS RelevantPassages
Pros:
All data (structured facts and raw text) is in one place (Neo4j).
Simplifies initial setup if you're already ingesting into Neo4j.
Single query language (Cypher) for both relational facts and text retrieval.
Cons:
Performance and Scalability: Neo4j is optimized for graph traversals, not for storing and retrieving large amounts of raw text. Storing massive text content directly on nodes can lead to a larger database size and potentially slower performance for text-heavy operations if you have a very large corpus.
No Semantic Search on Text: You lose the benefit of vector similarity search on the raw text content itself. If a user asks a question that isn't directly answerable by the graph structure but requires semantic understanding of the passages, you can't easily retrieve relevant chunks based on similarity alone.
Strategy 2: Hybrid Approach (Knowledge Graph + Vector Database) - Recommended
This is generally considered the most robust and scalable approach for production-grade RAG systems. It leverages the strengths of both database types.
How it works:
Separate Storage:
Vector Database (e.g., ChromaDB, Pinecone, Weaviate): Store all your original document chunks here. Each chunk should have a unique identifier (e.g., a UUID or a simple chunk_id). Include useful metadata with each chunk (like document_title, section_title, order, etc.). The vector database is optimized for storing text and performing fast semantic similarity searches.
Knowledge Graph (Neo4j): Store your extracted entities and relationships.
Linking Mechanism:
When your LLM extracts a Concept (or Person, Technology, etc.) from a specific chunk, store the chunk_id (or a list of chunk_ids) as a property on the corresponding Concept node in your Neo4j graph. You can also create explicit relationships like (Concept)-[:MENTIONED_IN]->(ChunkNode) if you want ChunkNodes to be part of the graph (though often just storing the ID as a property is sufficient if the ChunkNode isn't heavily queried for graph traversals).
During Retrieval (RAG Workflow):
Step A: Structured Retrieval from KG:
Take the user's query and use it to formulate a multi-hop Cypher query on your Neo4j graph.
Retrieve the relevant structured facts (e.g., "Transformer architecture developed by Google") AND the chunk_ids associated with those facts/concepts.
Step B: Contextual Retrieval from Vector DB:
Use the retrieved chunk_ids (from Step A) to directly fetch the full, original textual content from your Vector Database.
Optionally, you can also perform a semantic similarity search on the Vector DB using the original user query (or a reformulated query) to get additional, semantically similar chunks that might not have been directly linked by the KG but are still relevant.
Step C: Augmentation for LLM:
Combine the structured facts from the KG (e.g., "Fact: Transformer architecture was developed by Google. Fact: GPT-3 is based on Transformer architecture.")
Concatenate the full, relevant passages retrieved from the Vector Database.
Pass this combined, rich context to your LLM prompt.
Pros:
Optimal Performance: Each database specializes in its strength (graph traversals for KG, semantic search for Vector DB).
Scalability: Can handle very large document corpora and complex knowledge graphs independently.
Flexibility: Allows for sophisticated RAG strategies, combining structured knowledge with unstructured text effectively.
Reduced Duplication: Core text content is stored once in the Vector DB.
Cons:
Increased Complexity: Requires managing and orchestrating two different database systems.
More Involved Workflow: The RAG retrieval pipeline has more steps.
Example Python Sketch for Hybrid Approach:
# (Assuming Neo4j setup as before, and a ChromaDB client initialized)
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document as LC_Document
import uuid # For unique chunk IDs
# --- Simulated Chunking and Ingestion into Vector DB ---
# In a real scenario, this would come from your document loader/splitter
simulated_chunks = [
{
"content": "The Transformer architecture was introduced in the paper 'Attention Is All You Need' by Google researchers.",
"metadata": {"section": "Architectures", "doc_id": "doc_llm_overview"}
},
{
"content": "Many modern LLMs, like GPT-3, are based on the Transformer architecture.",
"metadata": {"section": "Architectures", "doc_id": "doc_llm_overview"}
},
# ... more chunks
]
# Simulate adding unique IDs and converting to LangChain Document format for Chroma
chroma_docs = []
for i, chunk_data in enumerate(simulated_chunks):
chunk_id = f"chunk_{uuid.uuid4()}" # Generate a unique ID for each chunk
chunk_data['metadata']['chunk_id'] = chunk_id # Add chunk_id to metadata
chroma_docs.append(LC_Document(page_content=chunk_data['content'], metadata=chunk_data['metadata']))
# Initialize a dummy ChromaDB (replace with your actual Chroma client)
# For this example, we won't actually embed, just simulate storing and retrieving by ID
class DummyChromaDB:
def __init__(self, documents):
self.documents_by_id = {doc.metadata['chunk_id']: doc.page_content for doc in documents}
self.metadata_by_id = {doc.metadata['chunk_id']: doc.metadata for doc in documents}
def get_by_ids(self, ids):
return {id: self.documents_by_id.get(id) for id in ids}
def query(self, query_texts, n_results=2):
# Simulate very basic semantic similarity search
# In real Chroma, this would be a vector search
results = []
for doc_id, content in self.documents_by_id.items():
if any(qt.lower() in content.lower() for qt in query_texts):
results.append(LC_Document(page_content=content, metadata=self.metadata_by_id.get(doc_id)))
if len(results) >= n_results:
break
return {"documents": [[doc.page_content for doc in results]], "metadatas": [[doc.metadata for doc in results]]}
vector_db = DummyChromaDB(chroma_docs)
print("Simulated Vector Database initialized with chunks.")
# --- Neo4j Ingestion (Modified to link Concepts to chunk_ids) ---
# ... (Neo4j driver and clear db part - same as previous example) ...
# Ingest data into Neo4j (UPDATED for chunk_ids)
# (Assume concepts_developed_by_google and model_based_on_concept are identified by LLM
# from specific chunks)
# In a real scenario, your LLM extraction would produce `chunk_id` for each extracted entity
# For this example, we'll manually link:
chunk_id_for_transformer_google = simulated_chunks_with_ids[0]['id']
chunk_id_for_gpt3_transformer = simulated_chunks_with_ids[1]['id']
with driver.session() as session:
# ... (ingest Document, Organization, Concepts as before) ...
# Link Concepts to their originating chunk_ids (NEW STEP for Hybrid)
# This stores the chunk ID on the concept node.
session.run("""
MATCH (c:Concept {name: 'Transformer architecture'})
SET c.originatingChunkIds = CASE
WHEN c.originatingChunkIds IS NULL THEN [$chunk_id]
ELSE c.originatingChunkIds + $chunk_id
END
""", {"chunk_id": chunk_id_for_transformer_google})
session.run("""
MATCH (c:Concept {name: 'GPT-3'})
SET c.originatingChunkIds = CASE
WHEN c.originatingChunkIds IS NULL THEN [$chunk_id]
ELSE c.originatingChunkIds + $chunk_id
END
""", {"chunk_id": chunk_id_for_gpt3_transformer})
# ... (Ingest other relationships as before) ...
print("Neo4j data ingestion (with chunk_ids) complete.")
# --- RAG Workflow: Hybrid Retrieval ---
# User Query
user_query = "What concepts developed by Google are used as a basis for GPT-3 models? Provide details from the document."
# Step 1: Structured Retrieval from KG
target_developer = "Google"
target_user_model = "GPT-3"
kg_query = f"""
MATCH (concept:Concept)<-[:DEVELOPED_BY]-(org:Organization)
WHERE org.name = '{target_developer}'
MATCH (model:Concept)-[:BASED_ON]->(concept)
WHERE model.name = '{target_user_model}'
RETURN concept.name AS BridgingConcept, org.name AS Developer, model.name AS User, concept.originatingChunkIds AS ConceptChunkIds
"""
print(f"\n--- RAG Step 1: Querying Knowledge Graph ---\n{kg_query}")
kg_results = []
all_relevant_chunk_ids = set()
with driver.session() as session:
result = session.run(kg_query)
for record in result:
kg_results.append({
"BridgingConcept": record["BridgingConcept"],
"Developer": record["Developer"],
"User": record["User"]
})
if record["ConceptChunkIds"]:
all_relevant_chunk_ids.update(record["ConceptChunkIds"])
print("\nKG Results (Structured Facts):")
for res in kg_results:
print(f"- Concept: {res['BridgingConcept']}, Developer: {res['Developer']}, User: {res['User']}")
print(f"Relevant Chunk IDs from KG: {list(all_relevant_chunk_ids)}")
# Step 2: Contextual Retrieval from Vector DB
retrieved_passages = {}
if all_relevant_chunk_ids:
# Retrieve specific passages identified by KG
passages_by_id = vector_db.get_by_ids(list(all_relevant_chunk_ids))
for chunk_id, content in passages_by_id.items():
if content:
retrieved_passages[chunk_id] = content
print(f"\n--- RAG Step 2: Retrieving Specific Passages from Vector DB ---")
print(f"Passages retrieved by KG-provided IDs: {len(retrieved_passages)} chunks")
# Optional: Add top N semantically similar passages (if not already retrieved by ID)
# This handles cases where the KG doesn't have a direct link but text is relevant
print("\n--- RAG Step 2 (Optional): Retrieving Semantically Similar Passages from Vector DB ---")
semantic_results = vector_db.query(query_texts=[user_query], n_results=2)
for doc_content in semantic_results["documents"][0]:
# Only add if not already present from direct ID retrieval
found = False
for existing_passage in retrieved_passages.values():
if doc_content == existing_passage: # Simple content match
found = True
break
if not found:
# In a real setup, you'd get the ID from metadata and add it
# For this dummy, we just add content.
retrieved_passages[f"semantic_chunk_{len(retrieved_passages)}"] = doc_content
print(f" Added semantically similar passage: \"{doc_content[:50]}...\"")
print(f"\nTotal passages for LLM: {len(retrieved_passages)} chunks")
# Step 3: Augment LLM Prompt
llm_context = ""
llm_context += "Here are structured facts from a knowledge graph:\n"
for res in kg_results:
llm_context += f"- Concept: {res['BridgingConcept']}, Developed by: {res['Developer']}, Used by: {res['User']}\n"
llm_context += "\nHere are relevant document passages for context:\n"
for chunk_id, passage in retrieved_passages.items():
llm_context += f"--- Passage {chunk_id} ---\n{passage}\n\n"
llm_context += f"Based on the above information, answer the following question: {user_query}"
print("\n--- RAG Step 3: Combined Context for LLM ---")
print(llm_context)
driver.close()
Summary and Recommendation:
For small, self-contained documents where text content is not excessively large and semantic search on raw text is not a primary concern, Strategy 1 (KG-only with content on Paragraph nodes) can work. It keeps everything in Neo4j and simplifies your toolchain.
For larger document corpuses, or when you need sophisticated semantic search capabilities, the Hybrid Approach (Strategy 2) is highly recommended. It separates concerns, leveraging vector databases for what they do best (text embeddings and similarity search) and knowledge graphs for their strength in structured data and relational querying. This combined power allows your RAG system to answer complex, multi-faceted questions with high accuracy and grounded responses.
