Browse

kdeps tool: email_ops

Run one embedding operation: index, search, upsert or delete in the local SQLite embedding database, vectorize or embed_query through an embedding backend, or rerank documents with Cohere. Call it through the embedding component. Requires: operation. Optional: text, collection, dbPath, limit, inputs, model, backend, baseURL, rerankQuery, rerankDocuments, rerankTopN.

Search for documents semantically similar to a query in the local embedding database. Returns ranked results with similarity scores. Requires: query (natural language search query), collection (name of the document collection). Optional: limit (max results, default 5).

Convert text into vector embeddings using an embedding model. Returns the embedding vectors. Use for indexing documents into the local embedding database or computing semantic similarity. Requires: texts (list of strings to embed). Optional: model, backend.

Search the web using Exa (formerly Metaphor) neural search. Finds highly relevant URLs and content using AI-powered link prediction. Best for research, finding authoritative sources, and content discovery. Requires EXA_API_KEY.

Run a shell command and return its structured result: success, exitCode, stdout, stderr, command, result and timedOut. Use through the exec component for pipeline steps that shell out. The command runs with sh -c (cmd /C on Windows) unless args are given. Optional: args, env, workingDir, timeout.