Index a document into the knowledge base so it can be searched.

Reads the file at file_path, splits it into overlapping chunks, embeds each chunk using nomic-embed-text via Ollama, and stores the vectors in ChromaDB. Re-indexing the same file is safe — old chunks are replaced automatically.

Args: file_path: Absolute path to the file to index. Supported types: .pdf .md .txt .py .js .ts .json .yaml .yml

Returns: A summary string describing what was indexed.

Semantically search the knowledge base for content related to query.

The query is embedded using the same model used during indexing. ChromaDB finds the N most similar chunks using cosine similarity. Lower distance = more relevant.

Args: query: Natural-language question or keyword phrase. n_results: How many results to return (default 5, max 20). source_filter: If non-empty, restrict search to chunks from this specific document (exact filename, e.g. "notes.md").

Returns: Formatted string with matching chunks, their sources, and distances. Returns a helpful message if the knowledge base is empty.

List all documents currently indexed in the knowledge base.

Returns one entry per document with its filename, file type, number of chunks, and when it was last indexed.

Returns: Formatted string listing all documents, or a message if empty.

Remove a document and all its chunks from the knowledge base.

This is useful when a document has been updated (you'll delete then re-index it) or when you no longer need it to be searchable.

Args: source: The filename of the document to remove, e.g. "design_doc.pdf". Use list_indexed_docs to see valid filenames.

Returns: Confirmation message with the number of chunks deleted.