Semantic search alone won't solve relational queries in your LLM retrieval pipeline.

We’ve examined and built multiple RAG systems, and here's the hard truth: semantic search alone fails when queries demand relational depth. Sure, vector embeddings excel at similarity—but they stumble when your query relies on precise relationships.

Take this scenario: A customer asks, "Why is my internet slow?" Semantic search returns pages of generic troubleshooting articles. But it won't pinpoint local outages, connection types (fiber vs DSL), or network path dependencies. Without structured relational context, your LLM is flying blind.

Here's an approach that solves this issue:

Hybrid Retrieval: Combine semantic vector searches with structured graph queries. Semantic vectors handle broad contextual matches; graph queries surface exact relationships and dependencies.

Use a low-latency graph database:
Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions.

Orchestrate logic with LangChain & LangGraph: LangChain simplifies LLM interactions and data integration; LangGraph manages complex workflows as directed graphs, maintaining state contextually across nodes.

Accuracy isn't optional—it's mandatory. Pure semantic retrieval won't cut it anymore. Hybrid retrieval is the only viable path forward for high-stakes, relationship-driven queries.

Have you tested hybrid retrieval yet?

more here: https://www.falkordb.com/blog/graphrag-workflow-falkordb-langchain/

Comments

[Rafał Groń]

The relational gap is real, but there's another layer worth separating: the retrieval problem vs the query understanding problem. For e-commerce specifically, "Why is my internet slow?" equivalent is "wireless headphones under $80, not Sony" — semantic vectors find the headphones, but the price filter and brand exclusion need a different layer. We ended up adding an LLM parser on top of vector retrieval specifically for these constraint-based queries. Not full graph traversal, but a lightweight intent extraction step that handles negations, price ranges, and attribute filters before the vector search runs.