alphaonedev/rag

rag

Purpose

This skill implements Retrieval-Augmented Generation (RAG) for OpenClaw, enabling AI models to query external knowledge bases and integrate results into responses, enhancing accuracy for tasks like question-answering.

When to Use

Use this skill when your AI needs dynamic access to external data, such as querying a vector database for real-time information in NLP tasks, handling knowledge gaps in models, or augmenting responses in chatbots. Avoid it for purely generative tasks without external dependencies.

Key Capabilities

• Fetches documents from vector databases (e.g., Pinecone, FAISS) using similarity search.
• Integrates retrieved content into AI prompts for generation.
• Supports embedding models for query vectorization (e.g., via Hugging Face transformers).
• Handles chunking of large documents and relevance scoring.
• Configurable via JSON files for custom sources and thresholds.

Usage Patterns

Always set the API key via environment variable: export OPENCLAW_API_KEY=$SERVICE_API_KEY. For CLI, use openclaw rag with required flags. In code, import the skill and call methods like rag.retrieve(). Pattern: Query -> Retrieve -> Augment -> Generate. Ensure queries are under 512 tokens to avoid truncation.

Common Commands/API

• CLI Command: openclaw rag query --db pinecone --index myindex --query "What is RAG?" --top-k 5
  • Flags: --db specifies database (e.g., pinecone, faiss), --index for collection name, --top-k for result count, --query for search string.
• API Endpoint: POST /v1/rag/retrieve with JSON body: {"query": "Explain AI", "db": "pinecone", "top_k": 3}
  • Response: JSON object with keys like "results" (array of documents) and "scores".
• Code Snippet (Python):

  import openclaw
  client = openclaw.Client(api_key=os.environ['OPENCLAW_API_KEY'])
  results = client.rag.retrieve(query="What is NLP?", db="faiss", top_k=4)
  

• Config Format: JSON file (e.g., rag_config.json):

  {
    "db": "pinecone",
    "api_endpoint": "https://api.pinecone.io",
    "embedding_model": "text-embedding-ada-002"
  }
  

  Load it via: openclaw rag config load --file rag_config.json.

Integration Notes

Integrate by wrapping RAG calls around your AI pipeline: First, call rag.retrieve() to get context, then pass it to your model's prompt. For multi-skill workflows, chain with "aiml" skills by piping outputs (e.g., use RAG results as input to a generation skill). Handle asynchronous calls with await client.rag.retrieve_async() in async environments. Test integrations in a sandbox with mock databases to verify data flow.

Error Handling

Check for common errors like authentication failures (e.g., "401 Unauthorized" if $OPENCLAW_API_KEY is invalid) by verifying env vars first. For query errors, catch exceptions like RetrievalError and retry with exponential backoff:

try:
    results = client.rag.retrieve(query=query)
except openclaw.RetrievalError as e:
    if e.status_code == 404:
        print("Database not found; create index first.")
    else:
        raise

Log all errors with details (e.g., error codes, messages) and use --debug flag in CLI for verbose output. Always validate inputs (e.g., ensure query is a string) before calling.

Concrete Usage Examples

1. Example 1: CLI Query for Knowledge Retrieval
   Use to answer user questions: Run openclaw rag query --db faiss --index docs_index --query "Summarize RAG technique" --top-k 3. This retrieves top 3 documents from the "docs_index" database and outputs them. Pipe the result: openclaw rag query ... | openclaw aiml generate --prompt "Use this context:".

2. Example 2: Code Integration for AI Response
   In a Python script, augment a chatbot:

   query = "What is machine learning?"
   context = client.rag.retrieve(query=query, db="pinecone", top_k=2)
   full_prompt = f"Context: {context}\nAnswer: {query}"
   response = client.aiml.generate(prompt=full_prompt)
   print(response)
   

   This fetches relevant context and passes it to the AI for a informed response.

Graph Relationships

• Related to: aiml (for generation integration), nlp (for text processing), vector-db (for data storage dependencies).
• Depends on: embedding skills for vectorization.
• Used by: knowledge-base skills for external data access.