Semantic Search Engine
Search that finds things by meaning, not just matching keywords.
Overview
A retrieval system that turns a set of documents into vectors and answers plain-language questions by meaning. It's the RAG foundation that lets the agents answer from real, cited context instead of guessing.
The problem
Keyword search misses what you meant. It can't tell that 'reset my access' and 'forgot password' are the same thing. Any agent that answers from documents needs retrieval that gets the meaning, not just the words.
So the building block was a fast, accurate index that ranks by relevance in a way you can trust.
The solution
Documents get chunked, embedded, and stored in a vector index. A query gets embedded the same way and matched by similarity, with a re-ranking pass to sharpen the top results. Each chunk carries its source, so answers can cite where they came from.
The whole pipeline is built for grounding. The same retrieval feeds the agents, so what they say stays tied to a real source.
Architecture
An ingestion pipeline chunks and embeds documents into a vector store. A query gets embedded, matched, and re-ranked, and the results come back with their sources.
- Chunking tuned for retrieval quality
- Vector similarity search with a re-ranking pass
- Sources attached, so answers can be cited
- The same retrieval the agents use for grounding
Screens & diagrams
Key features
Finds by meaning
Turns up the right content even when the wording is completely different.
Re-ranking
A second pass sharpens the top results so they're actually the best matches.
Cites its sources
Every result carries where it came from, so answers can point back at it.
Ready for RAG
Drops straight into an agent as its retrieval layer.
Implementation highlights
- Tuned the chunking and re-ranking, which noticeably improved how relevant the top results were.
- Put retrieval behind a clean API that I reused across projects.
- Batched the embedding calls with backoff to stay inside the provider's limits.
Challenges solved
Getting chunking right
Balancing chunk size against context and precision took a few rounds of testing.
Relevance
The re-ranking pass was what closed the gap between 'related' and 'actually the answer'.
Technologies
Future improvements
- Blend keyword and vector search
- Measure retrieval quality automatically