docs-search

Semantic search on Markdown documentation works by generating text embeddings for document chunks and storing them in local indexes like FAISS or SQLite-VSS to match user queries based on meaning.

Yes, you can index RST and TXT files for information retrieval by creating embeddings for their text chunks and storing them in local vector indexes for similarity searches.

Semantic documentation search supports local indexes like FAISS or SQLite-VSS to store generated text embeddings and execute efficient similarity searches across your documentation files.

To retrieve relevant context from project documentation, query the indexed embeddings to find the most similar text chunks, returning the matching sections and their original source information.

The best way to search across multiple documentation formats like Markdown, TXT, and RST is generating unified text embeddings for all chunks and indexing them for semantic similarity searches.

Semantic search returns source information with results to identify exactly which documentation files and sections provided the retrieved context, ensuring developers can verify the information origin.