Moorcheh

Moorcheh- Universal Memory Layer Operations

This skill provides comprehensive access to the Moorcheh platform including namespace management, data operations, semantic search with ITS scoring, and AI-powered answer generation.

Moorcheh Account

If the user does not have an account yet, direct them to the console to register and create a free account.

Create a Moorcheh account at console.moorcheh.ai.

Environment Variables

export MOORCHEH_API_KEY="your-api-key-here"

For full environment setup, see Environment Requirements.

Script Index

Namespace Management

• Create Namespace: Use to create a new text or vector namespace for organizing data. Text namespaces handle automatic embedding; vector namespaces require pre-computed embeddings.
• List Namespaces: Use to discover what namespaces exist in the account. This should be the first step before any operation.
• Delete Namespace: Use to permanently remove a namespace and all its data. This action is irreversible.

Data Operations

• Upload Text Data: Use to upload text documents with metadata to a text namespace. Documents are automatically embedded and indexed for semantic search.
• Get Documents: Use to fetch indexed text documents by ID (up to 100 IDs per call). For listing chunks without IDs, use Fetch Text Data; for similarity search, use Search.
• Upload File: Use to upload files (PDF, DOCX, XLSX, TXT, MD, CSV, JSON) to a text namespace via the pre-signed S3 URL flow (up to 5GB), or use documents.upload_file in the SDK which performs that flow for you. Prefer this over Upload Text when the user has a file on disk- no manual extraction or huge JSON payloads.
• List Files: Use to list raw file objects in document storage (S3) for a namespace (file_name, size, last_modified). Not the same as indexed text chunks- use Fetch Text Data or Search for pipeline-backed content.
• Delete Files: Use to delete storage file objects by name (S3-backed uploads). Not the same as Delete Data (remove indexed documents/vectors by ID).
• Upload Vectors: Use to upload pre-computed vector embeddings to a vector namespace. Best when you have your own embedding pipeline.
• Fetch Text Data: Use to list text and summary chunks in a text namespace (up to 100 per call) for export, UI, or inspection. Not a semantic search- use Search for queries.
• Delete Data: Use to remove specific documents or vectors from a namespace.
• Create Example Data: Use to create sample data for demos and testing when no data is available.

Search & AI

• Semantic Search: Primary search operation. Performs semantic search across one or more namespaces using ITS scoring. Supports text queries, metadata filters, keyword filters, and relevance thresholds.
• Generate AI Answer: Use to generate AI-powered answers from your data (RAG). Searches relevant context and synthesizes a natural-language answer. Supports chat history, custom prompts, and structured output.

Recommendations

• Always run List Namespaces first to discover available data before searching or uploading.
• For text data, prefer text namespaces- Moorcheh handles embedding automatically.
• Use ITS scoring thresholds (0.0–1.0) to control result quality. Higher = stricter matching.
• The Generate Answer endpoint is the primary RAG capability- use it for Q&A over documents.

Output Formats

• Search results include id, score, label (relevance category), text, and metadata.
• AI answers include answer, model, context_count, and optional structured_data (plus used_context when structured output is enabled).

Error Handling

• 401 Unauthorized: Verify MOORCHEH_API_KEY is set and valid
• 404 Namespace not found: Create the namespace first or check spelling (case-sensitive)
• 400 Vector dimension mismatch: Ensure vectors match the namespace's configured dimension
• 429 Too Many Requests: Implement exponential backoff

Code Generation Rules

Follow these rules when generating Python code that uses the Moorcheh SDK:

No Unicode Emoji in Output

Do not use emoji characters (e.g. ✅ ❌ 📁 ⏳ 🎉) in print() statements or log messages. On Windows with cp1252 encoding, these cause UnicodeEncodeError and mask actual success/failure output. Use plain ASCII prefixes instead:

Use snake_case (REST and Python)

As of platform 1.5.10, Moorcheh accepts and returns snake_case only for the answer API (and related JSON). Legacy camelCase field names were removed. Use the same names in curl, TypeScript/Java backends, and Python (moorcheh_sdk kwargs match JSON keys).

Common fields: ai_model, chat_history, header_prompt, footer_prompt, structured_response, kiosk_mode, top_k, context_count, structured_data, follow_up_questions (inside structured_data when using the default schema).

Do not send camelCase; validation may reject the request.