ZK RAG Bot - Zero-Knowledge Contextual Retrieval-Augmented Generation

🛡️ ZK Contextual RAG Bot

A Production-Grade Retrieval-Augmented Generation Chatbot for Zero-Knowledge Proofs, Noir, and Tornado Cash

📋 Overview

ZK Contextual RAG Bot is a production-grade Retrieval-Augmented Generation (RAG) chatbot that combines cutting-edge LLM technology with vector-based document retrieval. It provides instant, accurate answers to questions about Zero-Knowledge Proofs, Noir programming language, and Tornado Cash privacy protocols.

The bot uses LangChain Expression Language (LCEL) with modern imports and ChromaDB for efficient semantic search, making it ideal for knowledge-intensive applications.

✨ Key Features

🎯 Intelligent RAG Pipeline: Combines LLM reasoning with document retrieval for accurate, contextual answers
⚡ Production-Ready LCEL: Modern LangChain Expression Language with simplified, stable imports
🔐 Privacy-Focused Content: Specialized knowledge base on ZKPs, Noir, and Tornado Cash
💾 Vector Database: ChromaDB for fast semantic similarity search
💬 Conversational Memory: Maintains chat history for coherent multi-turn conversations
🌐 Cloud-Ready: Easily deployable on Google Colab with ngrok tunneling
🎨 User-Friendly UI: Clean Streamlit interface with real-time feedback
📊 Efficient Retrieval: Top-3 document retrieval with configurable parameters

🏗️ Architecture

┌─────────────────────────────────────────────────────┐
│              User Query (Streamlit UI)               │
└────────────────────┬────────────────────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  Query Processing & Context │
        │  (with Chat History)        │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  Generate Embeddings       │
        │  (OpenAI Embeddings)       │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  Vector Similarity Search  │
        │  (ChromaDB Retrieval)      │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  LCEL Chain Execution      │
        │  (RunnablePassthrough)     │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  Format Prompt with Context│
        │  + Chat History            │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  GPT-4 Response Generation │
        └────────────┬────────────────┘
                     │
                     ▼
        ┌────────────────────────────┐
        │  Display Response (UI)     │
        │  Store in Memory           │
        └────────────────────────────┘

🚀 Quick Start

Prerequisites

Python 3.8 or higher
OpenAI API Key
ngrok token (for cloud deployment)
Google Colab account (optional, for cloud hosting)

Installation (Local)

# Clone the repository
git clone https://github.com/solo938/ZKWhisper.git
cd ZKWhisper

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create .env file
echo "OPENAI_API_KEY=your_api_key_here" > .env

# Run the application
streamlit run app.py

Installation (Google Colab)

Cell 1: Install Dependencies

!pip install streamlit langchain langchain-community langchain-openai chromadb openai tiktoken pyngrok pysqlite3-binary -q

Cell 2: Create Knowledge Base

knowledge_base_content = """
# Zero-Knowledge Proofs, Noir, and Tornado Cash Knowledge Base
# [Your knowledge base content here]
"""

with open("knowledge_base.md", "w") as f:
    f.write(knowledge_base_content)

print("✅ Knowledge base created successfully!")

Cell 3: Run the App

!streamlit run app.py &

Cell 4: Setup ngrok Tunnel

from pyngrok import ngrok

ngrok.set_auth_token("YOUR_NGROK_TOKEN")
ngrok.kill()

public_url = ngrok.connect(8501)
print(f"🚀 View your RAG Bot here: {public_url}")

!streamlit run app.py &

🔧 Configuration

Environment Variables (.env)

# OpenAI Configuration
OPENAI_API_KEY=sk-your-api-key-here

# Database Configuration
CHROMA_DB_PATH=./chroma_db

# Application Settings
MODEL_NAME=gpt-4
TEMPERATURE=0
MAX_TOKENS=2048

# Retrieval Settings
TOP_K_RESULTS=3
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

Configuration File (config.py)

import os
from dotenv import load_dotenv

load_dotenv()

class Config:
    # API Keys
    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
    
    # Database
    CHROMA_DB_PATH = os.getenv("CHROMA_DB_PATH", "./chroma_db")
    
    # LLM Settings
    MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4")
    TEMPERATURE = float(os.getenv("TEMPERATURE", 0))
    MAX_TOKENS = int(os.getenv("MAX_TOKENS", 2048))
    
    # Retrieval Settings
    TOP_K_RESULTS = int(os.getenv("TOP_K_RESULTS", 3))
    CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", 1000))
    CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", 200))

💻 Core Components

1. Document Loading & Processing

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

loader = TextLoader("knowledge_base.md")
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
splits = splitter.split_documents(docs)

2. Vector Embeddings & Storage

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    splits,
    embeddings,
    persist_directory="./chroma_db"
)
vectorstore.persist()

3. Modern LCEL Chain (Fixed Imports)

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model_name="gpt-4", temperature=0)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

rag_chain = (
    RunnablePassthrough.assign(
        context=lambda x: format_docs(retriever.get_relevant_documents(x["input"]))
    )
    | qa_prompt
    | llm
)

4. Conversational Memory

from langchain_core.messages import HumanMessage, AIMessage

formatted_chat_history = []
for msg in st.session_state.messages[:-1]:
    if msg["role"] == "user":
        formatted_chat_history.append(HumanMessage(content=msg["content"]))
    elif msg["role"] == "assistant":
        formatted_chat_history.append(AIMessage(content=msg["content"]))

🐍 Usage Examples

Basic Query

# User asks a question
question = "What are Zero-Knowledge Proofs?"

# The bot retrieves relevant documents and generates a response
response = rag_chain.invoke({
    "input": question,
    "chat_history": chat_history
})

Multi-turn Conversation

# Question 1
user_input_1 = "Explain Noir programming language"
response_1 = rag_chain.invoke({
    "input": user_input_1,
    "chat_history": []
})

# Question 2 (with context from previous)
user_input_2 = "How is it used for ZKPs?"
response_2 = rag_chain.invoke({
    "input": user_input_2,
    "chat_history": [
        HumanMessage(content=user_input_1),
        AIMessage(content=response_1.content)
    ]
})

🧪 Testing

# Run all tests
pytest tests/

# Run specific test file
pytest tests/test_rag.py -v

# Run with coverage
pytest tests/ --cov=. --cov-report=html

# Run integration tests
pytest tests/test_integration.py -v

🌐 Deployment Options

Option 1: Google Colab (Free, Recommended for Getting Started)

Uses ngrok for public tunneling
No infrastructure setup needed
Free tier available
See Colab section above

Option 2: Streamlit Cloud (Free Tier Available)

# Push to GitHub
git push origin main

# Connect repo to Streamlit Cloud
# Visit: https://share.streamlit.io

Option 3: Docker (Production)

# Build Docker image
docker build -t zkwhisper:latest .

# Run container
docker run -p 8501:8501 \
  -e OPENAI_API_KEY=your_key \
  zkwhisper:latest

Option 4: AWS/GCP/Azure

Deploy via cloud provider's container or serverless services.

📊 Performance & Optimization

Metric	Value	Notes
Document Load Time	~500ms	Cached after first run
Embedding Generation	~1-2s	Per query
Retrieval Time	~100ms	ChromaDB similarity search
LLM Response Time	~5-15s	Depends on GPT-4 load
Total Response Time	~8-20s	End-to-end

Optimization Tips

Cache Results: Use @st.cache_resource for expensive operations
Reduce Chunk Size: Smaller chunks = faster retrieval
Limit Top-K: Reduce TOP_K_RESULTS from 3 to 1-2
Use gpt-3.5-turbo: Faster & cheaper than gpt-4
Batch Requests: Group multiple queries together

🔧 Troubleshooting

Import Error: `ModuleNotFoundError: No module named 'langchain.chains'`

Solution: This project uses modern LCEL with RunnablePassthrough. The old chain imports don't work in newer LangChain versions. Ensure you're using the latest code.

ChromaDB Error or SQLite Issues (Colab)

Solution: The pysqlite3 fix at the top of app.py resolves this:

import pysqlite3
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')

API Key Not Recognized

Solution: Verify your OpenAI API key is valid and has sufficient credits. Check .env file format.

Slow Response Times

Solution:

Reduce CHUNK_SIZE in config
Switch to gpt-3.5-turbo
Reduce TOP_K_RESULTS to 1-2
Enable caching

ngrok Tunnel Not Working

Solution:

Verify ngrok token is correct
Run ngrok.kill() to close existing tunnels
Wait 10-15 seconds for new tunnel to establish

See TROUBLESHOOTING.md for more solutions.

🚧 Roadmap

Support for PDF document uploads
Multi-language support
Fine-tuned models for domain-specific QA
Real-time collaboration features
Advanced filtering and faceted search
Response caching and optimization
Web3 integration (wallet authentication)
Mobile app (React Native)

🤝 Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/AmazingFeature)
Commit changes (git commit -m 'Add AmazingFeature')
Push to branch (git push origin feature/AmazingFeature)
Open a Pull Request

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run code quality checks
black . && flake8 . && isort .

# Run tests before committing
pytest tests/

📚 Documentation

Setup Guide - Detailed installation instructions
Architecture - Technical deep dive
API Reference - Complete API documentation
Troubleshooting - Common issues & solutions
Deployment Guide - Production deployment

📝 License

This project is licensed under the MIT License - see LICENSE file for details.

🙏 Acknowledgments

LangChain - The orchestration framework
Streamlit - The web UI framework
ChromaDB - Vector database
OpenAI - LLM provider
Zero-Knowledge Proof research community

📞 Support & Contact

Issues: GitHub Issues
Discussions: GitHub Discussions
Twitter: @solo938

🎯 Use Cases

✅ Educational: Learn about ZKPs, Noir, and privacy protocols
✅ Research: Quick reference for cryptographic concepts
✅ Development: Q&A assistant for builders
✅ Documentation: Always-available knowledge base
✅ Integration: Embed RAG capabilities in your apps

Name		Name	Last commit message	Last commit date
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README.md		README.md
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