Gaia - Decentralized AI

• Decentralization: Secure and transparent access to a peer-reviewed research database.

• Scalability: Supports massive parallel processing for data analysis.

• Data Integrity: Immutable storage ensures trustworthiness of recommendations.

Implementation DRAFT

import arweave
import llama_index
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
from eliza_framework import ElizaAgent
from flask import Flask, request, jsonify
import os

# --- Configuration Variables ---
ARWEAVE_GATEWAY = os.getenv('ARWEAVE_GATEWAY', 'https://arweave.net')
DATABASE_TRANSACTION_ID = os.getenv('DATABASE_TRANSACTION_ID', '<your-arweave-database-transaction-id>')
LLAMA_MODEL = os.getenv('LLAMA_MODEL', 'llama-3.2')

# --- Initialize Arweave Connection ---
def fetch_arweave_data(transaction_id):
    try:
        client = arweave.Client(gateway=ARWEAVE_GATEWAY)
        transaction_data = client.transactions.get_data(transaction_id)
        return transaction_data.decode('utf-8')
    except Exception as e:
        print(f"Error fetching data from ArWeave: {e}")
        return None

# --- Load Data into LlamaIndex ---
def load_data_to_index(data):
    with open('arweave_data.txt', 'w') as file:
        file.write(data)
    documents = SimpleDirectoryReader(input_dir='.', required_exts=['.txt']).load_data()
    index = GPTVectorStoreIndex.from_documents(documents, service_context=llama_index.ServiceContext.from_defaults(model=LLAMA_MODEL))
    return index

# --- Define AI Agent with Eliza Framework ---
class ArweaveAgent(ElizaAgent):
    def __init__(self, index):
        super().__init__(name='ArWeave Query Agent')
        self.index = index

    def respond(self, query):
        retriever = self.index.as_retriever()
        response = retriever.retrieve(query)
        return response[0].node.get_text() if response else "No relevant data found."

# --- API Setup ---
app = Flask(__name__)
agent = None

@app.route('/query', methods=['POST'])
def query_agent():
    if not agent:
        return jsonify({"error": "Agent not initialized"}), 500
    data = request.json
    query = data.get('query')
    if not query:
        return jsonify({"error": "No query provided"}), 400
    response = agent.respond(query)
    return jsonify({"response": response})

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({"status": "running"})

# --- Main Function ---
def main():
    global agent
    arweave_data = fetch_arweave_data(DATABASE_TRANSACTION_ID)
    if arweave_data:
        index = load_data_to_index(arweave_data)
        agent = ArweaveAgent(index)
        print("ArWeave AI Agent API is running on Gaia AI.")
        app.run(host='0.0.0.0', port=5000)
    else:
        print("Failed to retrieve ArWeave data.")

if __name__ == '__main__':
    main()