Nostr RAG (Retrieval-Augmented Generation)¶

This module provides classes for building Retrieval-Augmented Generation (RAG) agents that can store and retrieve information from the Nostr network.

Usage¶

import asyncio
from agentstr import NostrRAG
# Note: To use NostrRAG, you must install the required dependencies:
# pip install "agentstr-sdk[rag]"
# You will also need an LLM API key

# Create a RAG agent, connecting to Nostr relays and using an LLM.
rag_agent = NostrRAG()

async def main():
    # Ask a question. The agent will build a knowledge base from
    # recent Nostr posts related to the query and generate an answer.
    question = "What's new with AI?"
    answer = await rag_agent.query(question, limit=5)
    print(f"Question: {question}")
    print(f"Answer: {answer}")

if __name__ == "__main__":
    asyncio.run(main())

Note

For a complete, working example, check out the Nostr RAG example.

Environment Variables¶

NostrRAG uses the following environment variables by default through its underlying components:

NOSTR_RELAYS: A comma-separated list of relay URLs to connect to. If not provided as a parameter, the agent will use this environment variable.
NOSTR_NSEC: The Nostr private key in ‘nsec’ format for authenticated operations. If not provided as a parameter, the agent will look for this environment variable.
LLM_BASE_URL: The base URL for the LLM API endpoint. If not provided as a parameter, the agent will use this environment variable.
LLM_API_KEY: The API key for accessing the LLM service. If not provided as a parameter, the agent will use this environment variable.
LLM_MODEL_NAME: The name of the LLM model to use for chat interactions. If not provided as a parameter, the agent will use this environment variable.

Note

You can override these environment variables by passing explicit parameters to the NostrRAG constructor, such as relays, private_key, llm_base_url, llm_api_key, or llm_model_name.

Nostr RAG (Retrieval-Augmented Generation)¶

Usage¶

Environment Variables¶

Reference¶

See Also¶