Results for "rag"
6 packages found
The `embedding` executor is a native capability compiled into the `kdeps` binary. It provides a SQLite-backed keyword store for indexing, searching, upserting, and deleting text documents. Use it as the storage layer for RAG pipelines that run fully on-prem.
Load a document file and return its content as text. Supports PDF (go-pdf, pdftotext, pdfcpu), DOCX/EPUB/RTF/ODT (pandoc), HTML (goquery, lynx), CSV, text, directory, notion, markdown (textutil). Use for reading documents into the conversation for analysis or RAG pipelines. Returns document content with optional splitting into chunks. Requires: source (absolute file path). Optional: type (pdf, csv, html, text — auto-detected from extension), chunkSize (split into chunks of N characters).
The `loader:` resource reads a file, URL, or directory into structured `Document` objects - plain text plus metadata - and optionally splits them into chunks. It is the ingestion step of a RAG pipeline: load, then feed the output into `vectorStore:` or `embedding:` for indexing.
Read a file from the local filesystem. Returns the contents with a 1-based line number on every line (` 42\tcode$`) - use those numbers for edit_file insert/view. Tabs render as ^I, other control characters as ^X, and a $ marks the true end of each line, so indentation, trailing whitespace, and stray control characters are visible instead of hidden. IMPORTANT: ^I, ^X, and the trailing $ are a display convention this tool adds - the real file does NOT contain those literal characters. When quoting text back into edit_file's old_str, write the real tab/control character (or match a whole line via view_range/anchor instead), never the literal caret sequence - copying "^I" as text will not match a real tab byte. Plain text, source code, configuration files, and documentation are read directly; PDF, DOCX, EPUB, RTF, and ODT documents have their text extracted automatically. Use load_document instead for CSV/HTML structured parsing or RAG chunking. Pass match_id (from a search_local result) instead of file_path/offset to jump straight to that hit.
Retrieve semantically relevant text chunks from the configured RAG index. Use for finding code, documentation, or notes related to a query before implementing or answering. Requires KDEPS_RAG_BASE_URL pointing to a compatible RAG service.
The `vectorStore:` resource adds documents to, and runs similarity search against, an external vector database. Unlike `embedding:` (SQLite keyword/vector index built into kdeps), `vectorStore:` talks to a real vector database service - Qdrant, Chroma, Pinecone, pgvector, and more - for production-scale RAG.