# ElizaOS Feature

* **Core Functionality:** A conversational AI framework designed for natural language understanding and adaptive dialogue.
* **Custom Enhancements:** Optimized modules for medical and biological terminology.
* **Real-time Adaptation:** Adjusts recommendations based on ongoing research and user feedback.

#### Implementation DRAFT

```sql
import arweave
import llama_index
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
from eliza_framework import ElizaAgent

# --- Configuration Variables ---
ARWEAVE_GATEWAY = 'https://arweave.net'
DATABASE_TRANSACTION_ID = '<your-arweave-database-transaction-id>'
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."

# --- Main Function ---
def main():
    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 is ready. Type 'exit' to quit.")
        while True:
            query = input("You: ")
            if query.lower() == 'exit':
                break
            response = agent.respond(query)
            print(f"Agent: {response}")
    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/elizaos-feature.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.