Anyone replaced pure vector search with GraphRAG in production? What was the accuracy difference?

AI & ML
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1

I’ve been running a fairly standard RAG pipeline for internal docs search: chunk documents, embed with an embedding model, store in Pinecone, retrieve top-k, stuff into prompt. It works… okay. But I keep hitting cases where the retrieved chunks miss important context because the relationships between concepts live across different documents.

For example, our engineering docs reference architecture decisions that link to security policies that reference compliance requirements. Pure vector similarity grabs the most semantically similar chunks, but it has no concept of “this document references that other document” or “these two things are related through a shared project.”

I’ve been reading about GraphRAG and hybrid retrieval approaches that combine vector search with knowledge graphs. The idea is you build a graph of entities and relationships from your corpus, then use both graph traversal AND vector similarity during retrieval. Some teams are reporting retrieval precision jumping from ~75% to 95%+ on complex queries.

But everything I’ve seen is either a research paper or a vendor pitch. I’m looking for real-world experiences:

  • How painful was it to build and maintain the knowledge graph? Did you use an LLM to extract entities automatically or did you hand-curate?
  • What graph database are you using? Neo4j? Something else?
  • How does the query latency compare to pure vector search?
  • Is the accuracy improvement worth the added complexity for a team of 3-4 engineers?

My stack is Python, LangChain, and Pinecone. I’m open to adding a graph layer but I don’t want to over-engineer this if the marginal accuracy gains are small for typical enterprise doc search.

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2

We migrated from pure vector search to a hybrid GraphRAG setup about four months ago for a similar internal docs platform, so I can share some concrete numbers and lessons.

The graph layer setup

We went with Neo4j (their Aura managed service) sitting alongside our existing Qdrant vector store. For entity extraction, we used an LLM-based pipeline that runs on document ingest. Basically:

# Simplified version of our extraction pipeline
from langchain_community.graphs import Neo4jGraph
from langchain_experimental.graph_transformers import LLMGraphTransformer

graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)

# Extract entities and relationships from each document
transformer = LLMGraphTransformer(
    llm=extraction_llm,
    allowed_nodes=["Document", "Policy", "Team", "Service", "Requirement"],
    allowed_relationships=["REFERENCES", "OWNED_BY", "DEPENDS_ON", "IMPLEMENTS"]
)

graph_docs = transformer.convert_to_graph_documents(documents)
graph.add_graph_documents(graph_docs, include_source=True)

We constrained the allowed node and relationship types to our domain. Without that constraint, the LLM goes wild and you end up with a messy graph that’s hard to query. Hand-curating wasn’t practical for us (10k+ docs), but we do have a manual review step for high-value policy documents.

Retrieval approach

At query time, we do both searches in parallel and merge results:

# Parallel retrieval
vector_results = qdrant_retriever.get_relevant_documents(query)  
graph_results = graph_chain.invoke({"query": query})

# Merge and deduplicate, graph results get a relevance boost
combined = merge_with_graph_boost(vector_results, graph_results, boost_factor=1.3)

The graph traversal catches exactly the scenario you described. When someone asks “what are the compliance requirements for service X?”, the graph follows the REFERENCES and IMPLEMENTS edges across documents that pure cosine similarity would never connect.

Real numbers

  • Retrieval precision on our eval set went from 72% to 89%. Not quite the 95% some vendors claim, but a significant jump for multi-hop queries specifically.
  • Single-hop factual queries saw almost no improvement, maybe 2-3%.
  • Query latency went from ~120ms (vector only) to ~280ms (hybrid). The graph traversal adds overhead, but it’s still fine for our use case.
  • The initial graph build for 10k documents took about 6 hours and cost around $40 in LLM API calls.

The honest downsides

Graph maintenance is the real cost. When documents update, you need to re-extract entities and relationships, handle stale edges, and deal with entity resolution (is “Auth Service” the same as “Authentication Service”?). We spend maybe 4-5 hours a week on graph hygiene.

For a team of 3-4 engineers, I’d say it’s worth it only if you’re seeing consistent failures on queries that require cross-document reasoning. If your users mostly ask straightforward “find me the doc about X” queries, stick with vector search and invest in better chunking strategies instead.

One middle ground: before going full GraphRAG, try adding document-level metadata and parent-child chunk relationships to your vector store. Pinecone supports metadata filtering, so you can do lightweight relational queries without a full graph database. That got us halfway there with a fraction of the complexity.