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Installable packages, the resource types built into kdeps, and the versioned assets kdeps runs on, in one catalog. The colored badge on each entry says which kind it is.
404 entries
kdeps command: efficiency
kdeps event: efficiency
kdeps event: efficiency-actions
kdeps event: efficiency-bash
kdeps event: efficiency-code
kdeps event: efficiency-failures
kdeps event: efficiency-file
kdeps event: efficiency-reads
kdeps event: efficiency-stops
kdeps event: efficiency-tighten
kdeps event: efficiency-verbose
kdeps event: efficiency-web
kdeps theme: emacs
kdeps kdeps-component: email
AI email client — triage, summarize, draft, reorganize labels, and unsubscribe, with a web UI
kdeps tool: email_ops
kdeps kdeps-component: embedding
The `embedding` component runs one embedding operation and returns a structured result: a SQLite keyword store for `index`, `search`, `upsert`, and `delete`, an embedding backend for `vectorize` and `embed_query`, and Cohere for `rerank`. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `embedding` tool, which runs the native embedding executor. A workflow that uses `embedding:` directly - primary or in a `before:`/`after:` step - is rejected.
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.
kdeps kdeps-component: embedding_search
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).
kdeps kdeps-component: embedding_vectorize
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.
kdeps provider: ernie
kdeps harness: errors
kdeps kdeps-component: exa_search
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.
kdeps kdeps-component: exec
The `exec` component runs a shell command and returns a structured result: `{success, exitCode, stdout, stderr, command, result, timedOut}`. It is a kdeps component from the `components` asset set: call it with a `component:` resource, no install step. Its call is the kdeps API's `exec` tool, which runs the native exec executor. A workflow that uses `exec:` directly - primary or in a `before:`/`after:` step - is rejected.
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.