kdeps-tools-vector_store
v1.0.0Run one vector store operation against an external vector database: add_documents to upsert documents, or similarity_search to query them. Call it through the vector_store component. Requires: collection, operation, embedModel (except bedrock). Optional: provider, url, apiKey, documents, query, topK, embedBackend, embedBaseURL.
set tools / shelf tools
LLM context: ~430 tokens when loaded (prompt or tool definition)
Where it runs
Part of the tools set, used in workflow and agent mode. What the model is told about each built-in tool: its description, usage notes and parameters. kdeps runs the tool itself.
- the agent loop builds the request
- the tool definitions are sent to the modeltools/vector_store
- the model calls a tool
- kdeps runs it
- the result goes back to the model
Loaded by: kdeps (agent loop), /update, /settings, kdeps run, kdeps install, kdeps list, kdeps uninstall, kdeps update
In the code: kdeps (agent loop)agent.RegisterBuiltinToolsagent.definedAs
Install
A kdeps asset: installed with the first-run profile that includes its set, or the first time a command needs it, and updated without a new release.
Pin a version with kdeps install tools/vector_store@<version>.
Contents
Run one vector store operation against an external vector database: add_documents to upsert documents, or similarity_search to query them. Call it through the vector_store component. Requires: collection, operation, embedModel (except bedrock). Optional: provider, url, apiKey, documents, query, topK, embedBackend, embedBaseURL.
| Parameter | Type | Description |
|---|---|---|
| apiKey | string | Auth token. For mongodb the database name (default kdeps); for opensearch/elasticsearch user:pass |
| collection * | string | Collection, index, or table name in the store |
| documents | array | Documents to upsert for add_documents, each an object with content and optional metadata |
| embedBackend | string | Embedding provider: openai, ollama, google, or system (follow KDEPS_DEFAULT_BACKEND) |
| embedBaseURL | string | Custom base URL for an OpenAI-compatible embedding backend |
| embedModel | string | Embedding model used to vectorize documents and queries, e.g. text-embedding-3-small |
| operation * | string | add_documents or similarity_search. |
| provider | string | Vector store backend: qdrant (default), chroma, pinecone, azureaisearch, opensearch, elasticsearch, weaviate, pgvector, postgres, alloydb, cloudsql, mariadb, dolt, mysql, mongodb, redis, bedrock. |
| query | string | Natural language search query (operation similarity_search) |
| topK | number | Number of results to return for similarity_search (default 5) |
| url | string | Endpoint or DSN for the store |
version: 1.0.0
description: 'Run one vector store operation against an external vector database: add_documents to upsert documents, or similarity_search to query them. Call it through the vector_store component. Requires: collection, operation, embedModel (except bedrock). Optional: provider, url, apiKey, documents, query, topK, embedBackend, embedBaseURL.'
parameters:
apiKey:
type: string
description: 'Auth token. For mongodb the database name (default kdeps); for opensearch/elasticsearch user:pass'
collection:
type: string
description: Collection, index, or table name in the store
required: true
documents:
type: array
description: Documents to upsert for add_documents, each an object with content and optional metadata
embedBackend:
type: string
description: 'Embedding provider: openai, ollama, google, or system (follow KDEPS_DEFAULT_BACKEND)'
embedBaseURL:
type: string
description: Custom base URL for an OpenAI-compatible embedding backend
embedModel:
type: string
description: Embedding model used to vectorize documents and queries, e.g. text-embedding-3-small
operation:
type: string
description: 'add_documents or similarity_search.'
required: true
provider:
type: string
description: 'Vector store backend: qdrant (default), chroma, pinecone, azureaisearch, opensearch, elasticsearch, weaviate, pgvector, postgres, alloydb, cloudsql, mariadb, dolt, mysql, mongodb, redis, bedrock.'
query:
type: string
description: Natural language search query (operation similarity_search)
topK:
type: number
description: Number of results to return for similarity_search (default 5)
url:
type: string
description: Endpoint or DSN for the store
Versions
| Version | Published | Status |
|---|---|---|
| 1.0.0 | 10/21/2026 | active |
Details
- Author
- kdeps
- License
- Apache-2.0
- Latest Version
- 1.0.0
- Published
- 10/21/2026