Email Style RAG System

[cammclain]

🚀 Develop an Email-Based RAG Application System

Overview

We are developing an Email-Based Retrieval-Augmented Generation (RAG) application system. This system will focus on enhancing asynchronous communication within our team by leveraging local large language models (LLMs) to generate high-quality, contextually relevant responses.

Goals

• Asynchronous Communication: Enable detailed and effective communication without the need for real-time interaction.
• High-Quality AI Responses: Utilize local LLMs to generate and refine responses iteratively.
• Privacy and Security: Ensure robust encryption and secure data handling.
• User-Friendly Interface: Design an intuitive and functional email-style user interface.

System Architecture

Client-Side Components

• Frontend: Svelte-based UI for composing and reading emails.
• Client-Side Decryption: Ensures that all messages are decrypted on the client-side for maximum privacy.

Server-Side Components

• Backend: Asynchronous Python server handling email generation, storage, and retrieval.
• AI Integration: Local LLMs generating and refining email responses.

from fastapi import FastAPI
from pydantic import BaseModel
import asyncio

app = FastAPI()

class EmailRequest(BaseModel):
    prompt: str
    user_id: str

@app.post("/generate_email/")
async def generate_email(request: EmailRequest):
    response = await process_prompt(request.prompt)
    return {"response": response}

async def process_prompt(prompt: str) -> str:
    # Simulate AI processing
    await asyncio.sleep(1)
    return f"Generated email based on: {prompt}"

Requirements

AI Integration

• Local LLMs: Use local models to ensure privacy and control.
• Iterative Drafting: Generate high-quality responses through iterative refinement.

async def refine_reply(prompt: str, iterations: int = 3) -> str:
    current_version = await generate_initial_reply(prompt)
    for _ in range(iterations):
        current_version = await iterate_reply(current_version, prompt)
    return current_version

async def generate_initial_reply(prompt: str) -> str:
    return f"Initial reply to: {prompt}"

async def iterate_reply(current_version: str, prompt: str) -> str:
    await asyncio.sleep(0.5)  # Simulate processing time
    return f"Refined reply to: {prompt} based on: {current_version}"

Privacy and Security

• Client-Side Decryption: Ensure messages are decrypted only on the client side.
• Encryption: Use robust encryption methods to protect data.

from cryptography.hazmat.primitives.asymmetric import ec
from cryptography.hazmat.primitives import serialization, hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF

# Generate a private key for use in the exchange.
private_key = ec.generate_private_key(ec.SECP384R1())

# Load public key of the recipient.
public_key = ec.EllipticCurvePublicKey.from_encoded_point(ec.SECP384R1(), b"...")

# Perform key exchange.
shared_key = private_key.exchange(ec.ECDH(), public_key)

# Derive a key from the shared key.
derived_key = HKDF(
    algorithm=hashes.SHA256(),
    length=32,
    salt=None,
    info=b"handshake data",
).derive(shared_key)

Features

AI-Driven Email Generation

• Iterative Refinement: Continuously improve responses based on previous drafts.
• Context-Aware: Generate responses that are contextually relevant to the user's prompt.

User Interface

• Email-Style Layout: Use a subject line, body content, and attachments.
• Markdown Support: Allow rich text formatting within emails.

<script>
    import { onMount } from 'svelte';
    let emailContent = '';

    async function fetchEmail(prompt) {
        const res = await fetch('/generate_email/', {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
            },
            body: JSON.stringify({ prompt, user_id: '123' }),
        });
        const data = await res.json();
        emailContent = data.response;
    }

    onMount(() => {
        fetchEmail('Initial email prompt');
    });
</script>

<main>
    <textarea bind:value={emailContent} rows="10" cols="30"></textarea>
</main>

AI Integration Workflow

1. User Prompt: User provides an initial prompt.
2. Initial Reply Generation: LLM generates an initial draft.
3. Iterative Refinement: LLM refines the reply through multiple iterations.
4. Final Reply: A high-quality, contextually relevant email is generated.

Conclusion

This proposal outlines the development of a privacy-focused, high-quality, asynchronous Email-Based RAG application system. By leveraging local LLMs and an intuitive email-style interface, we aim to enhance communication within the Conti System, ensuring secure and effective interactions.