List all available namespaces in Moorcheh

Create a new namespace for document storage in Moorcheh

Delete a namespace and all its contents from Moorcheh

Upload text documents to a namespace in Moorcheh

Upload vector data to a namespace in Moorcheh

Delete specific data items from a namespace in Moorcheh

Get specific data items by ID from a text namespace in Moorcheh

List text and summary chunks from a text-type namespace via GET /documents/fetch-text-data. Returns up to 100 items per request with statistics (text vs summary counts, source_counts). Only text namespaces are supported; not for vector namespaces. See Moorcheh API docs.

Upload a file to a text namespace using pre-signed URL flow. The tool requests an upload URL, uploads the file directly to storage, then the file is queued for processing and indexing.

List file objects stored in document storage (S3) for a namespace: file_name, size (bytes), last_modified. This is raw storage listing (e.g. after upload-url uploads), not indexed text documents. GET only; no body.

Permanently delete file(s) from document storage (S3) for a namespace. Use snake_case: file_name (one file) and/or file_names (array). At least one is required. This deletes storage objects, not indexed documents by pipeline ID (use delete-data for documents/vectors by id).

Search for data in a namespace using semantic search or vector similarity. This tool provides powerful search capabilities across your namespaces, supporting both text-based semantic search and vector-based similarity search. For text search, you can use natural language queries to find relevant documents based on meaning rather than just keywords. For vector search, you can find similar content by comparing vector embeddings. The tool supports advanced features like result filtering, similarity thresholds, metadata filters, keyword filters, and kiosk mode for production environments. This is ideal for building intelligent search interfaces, recommendation systems, or content discovery features.

Filtering Capabilities:

• Metadata Filters: Use #key:value format (e.g., #category:tech, #priority:high)

• Keyword Filters: Use #keyword format (e.g., #important, #urgent)

• Filters only apply to text search and metadata must be manually uploaded with documents

Get AI-generated answers based on data in a namespace using text queries. This tool provides intelligent, context-aware responses by searching through your stored text documents and generating comprehensive answers using advanced language models. Supports two modes: Search Mode (with namespace) and Direct AI Mode (empty namespace).