# 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

```sql
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()

```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.drpepe.ai/ai-longevity-agents/gaia-decentralized-ai.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.