Semantic Search Engine
Retrieval that understands meaning, not keywords.
Latency
−35%
Accuracy
89%+
Vectors
FAISS / Chroma
Metric profile
01 / Signal
−35%
Latency reduction
vs. baseline retrieval
02 / Signal
89%+
Retrieval accuracy
top-k relevance
03 / Signal
FAISS + Chroma
Index backends
swappable vector store
Overview
A production-shaped retrieval stack: documents in, embeddings out, semantic answers back — with PDF text extraction and multilingual handling built into the ingestion path.
Built during work that cut average query latency by 35% and reached 89%+ semantic retrieval accuracy on an end-to-end RAG pipeline.
What makes it work
Ingestion that handles reality
Automatic PDF text extraction, chunking with overlap, and language detection so messy corpora become searchable without manual prep.
Dual vector backends
ChromaDB for developer ergonomics, FAISS for scale — the retrieval interface stays identical either way.
Layered architecture
A CSR-style layered design with strict OOP boundaries cut coupling and reduced contributor onboarding time by roughly 40%.
Architecture
- FastAPI service exposing ingest and search endpoints
- Chunk + embed pipeline with multilingual support
- FAISS / ChromaDB vector index
- MongoDB for document metadata and provenance
Stack
- FastAPI
- ChromaDB
- FAISS
- MongoDB
Scalable semantic search engine with vector embeddings (FAISS/ChromaDB), PDF auto text extraction, and multilingual support.
Interested?