Building a Local AI Agent System with RAG Capabilities: A Comprehensive Guide Using Docker, Ollama, Qwen, FAISS, Redis, SQLite, and Tauri

Building a Local AI Agent System with RAG Capabilities Using Docker, Ollama, Qwen 2.5, FAISS, Redis, SQLite, and a Fast Desktop Chat Interface

In this blog, we will discuss how to build a local AI agent system with RAG (retrieval-augmented generation) capabilities using various powerful technologies like Docker, Ollama, Qwen 2.5, FAISS, Redis, SQLite, and a fast desktop chat interface. The system will automatically vectorize and upload vectors from a specified folder when updated, ensuring high performance and efficiency. We'll be utilizing JavaScript, Python, and Rust for implementation, along with some advanced features like permissions management, system cleanup, and optimized performance through good system design.

Key Features:
- RAG Capabilities: Integrating retrieval-augmented generation for enhanced AI responses.
- Automatic Vectorization: Auto-vectorization of documents and content from a specific folder.
- Fast Desktop Chat Interface: Real-time chat interface for interacting with the AI agent.
- Efficient System Design: Using technologies like Kafka, GraphQL, and TRPC for optimal performance and data flow.
- Memory Management: Daily cleanup of in-memory databases to maintain system health.
Let’s walk through the steps of building this system, from setting up the environment to implementing advanced features.

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1. System Design Overview

Before diving into the implementation, let's break down the core components of the system:

- RAG Model: The system will use Qwen 2.5 (a language model) and Ollama (an AI agent framework) for generating responses. The responses will be augmented with retrieved data from a local vector store.
 
- Vector Store: FAISS (Facebook AI Similarity Search) will be used to store and search document vectors. Redis will be used for caching and fast retrieval of vector data.

- Data Storage: SQLite will be used for local data storage of metadata, such as indexing data, logs, and user interactions.

- Automatic Vectorization: The system will monitor a specific folder for updates (e.g., new documents). When the folder is updated, the system will automatically vectorize the contents and upload the vectors to FAISS.

- Chat Interface: A fast desktop chat interface will be built using Electron (for the frontend), allowing users to interact with the AI agent.

- Permissions Management: Each time the system accesses sensitive data or performs a critical operation, the agent will ask for user permissions.
- Memory Cleanup: The system will perform a daily cleanup of in-memory databases (Redis and FAISS) to ensure it remains lightweight and efficient.

- Performance Optimization: We'll ensure the system performs efficiently by utilizing Kafka for handling large-scale messaging and GraphQL/ TRPC for optimized queries and API management.

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2. Setting Up the Environment with Docker

2.1 Dockerizing the System

Docker is essential for ensuring that all dependencies are correctly isolated and the environment is reproducible. We’ll create multiple Docker containers for different components of the system, including the AI model, vector store, chat interface, and database.

bash
Create a Dockerfile for the Python environment
FROM python:3.9-slim

Set the working directory
WORKDIR /app

Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

Expose the application port
EXPOSE 5000

Set the entry point for the app
CMD ["python", "app.py"]


This Dockerfile sets up the Python environment for the backend (e.g., vectorization, communication with FAISS and Redis).

2.2 Docker Compose

To orchestrate all the containers (Python, Redis, SQLite, and Kafka), we will use Docker Compose.

yaml
version: '3.8'
services:
  ai-agent:
build: .
    volumes:
      - .:/app
    ports:
      - "5000:5000"
    depends_on:
      - redis
      - faiss

  redis:
    image: redis:latest
    ports:
      - "6379:6379"

  faiss:
    image: facebook/faiss:latest
    ports:
      - "8000:8000"

  sqlite:
    image: nouchka/sqlite:latest
    volumes:
      - ./data:/data
    ports:
      - "3306:3306"

  kafka:
    image: wurstmeister/kafka
    ports:
      - "9092:9092"
    depends_on:
      - zookeeper

  zookeeper:
    image: wurstmeister/zookeeper
    ports:
      - "2181:2181"


In this setup:
- The AI agent is running in a Docker container, which will communicate with other services like Redis, FAISS, SQLite, and Kafka.
- Redis is used for caching and quick vector retrieval.
- FAISS stores the document vectors and allows for efficient similarity search.
- SQLite is used for storing metadata and logs.
- Kafka helps with message passing and asynchronous communication between services.

---

3. AI Agent with RAG Capabilities

3.1 Integrating Ollama and Qwen 2.5

We’ll use Ollama as the AI framework for handling user interactions and Qwen 2.5 as the underlying model to generate responses. To use these tools, you need to install their Python SDKs and integrate them into the backend.

bash
pip install ollama qwen


3.2 Building the RAG System

The AI agent will first retrieve relevant documents from FAISS based on the user query, then pass the retrieved documents to Qwen 2.5 to generate a response. Here’s how the interaction works:

- User Query: The user sends a query through the chat interface.
- Vectorization: The query is vectorized using the Qwen 2.5 model.
- FAISS Search: The vector is compared against stored vectors in FAISS to retrieve the most relevant documents.
- Response Generation: The retrieved documents are passed to the Qwen 2.5 model, which generates a response augmented with the retrieved information.

```python
from ollama import Ollama
from faiss import FAISS
import numpy as np

Initialize Ollama and FAISS
ollama_model = Ollama.load("qwen-2.5")
faiss_index = FAISS.load("path_to_faiss_index")

Function to process query and retrieve results
def get_response(query):
    # Vectorize the query
    query_vector = ollama_model.vectorize(query)
    
    # Search FAISS for the most similar document
    result = faiss_index.search(query_vector, k=5)
    
    # Generate response with augmented information
    augmented_query = f"{query}\n\nRelevant Information:result['docs']"
    response = ollama_model.ask(augmented_query)
return response


3.3 *Automatic Vectorization and Upload*

You’ll need a system to watch a folder for updates (e.g., new documents) and automatically vectorize and upload them to *FAISS*.

python
import os
import time
from faiss import FAISS
from ollama import Ollama

Initialize Ollama and FAISS
ollama_model = Ollama.load("qwen-2.5")
faiss_index = FAISS.load("path_to_faiss_index")

Watch folder for changes
folder_to_watch = "/path/to/folder"
while True:
    for filename in os.listdir(folder_to_watch):
        if filename.endswith(".txt"):
            with open(os.path.join(folder_to_watch, filename), "r") as file:
                text = file.read()
                vector = ollama_model.vectorize(text)
                faiss_index.add(np.array([vector]))
    time.sleep(60)  # Check for changes every 60 seconds


This script automatically monitors a folder, vectorizes new documents, and uploads them to FAISS.

---

4. *Permissions Management*

Each time the system accesses sensitive data or performs a critical operation, it will ask for user permissions. This ensures the system operates securely and is in line with privacy concerns.

python
def ask_permission(action):
    response = input(f"Do you allow the system to {action}? (yes/no): ")
return response.lower() == 'yes'

if ask_permission("access the document storage"):
    # Proceed with the action
    pass
else:
    print("Permission denied.")


---

5. *Memory and Performance Optimization*

5.1 *System Cleanup*

To keep the system running smoothly, we’ll perform a daily cleanup of in-memory databases like *Redis* and *FAISS* to remove unnecessary data and free up resources.

python
import redis
import faiss

Initialize Redis and FAISS
redis_client = redis.Redis(host='localhost', port=6379, db=0)
faiss_index = faiss.read_index("path_to_faiss_index")

Cleanup Redis and FAISS every day
def cleanup_system():
    redis_client.flushdb()  # Clear Redis cache
    faiss_index.reset()     # Reset FAISS index
    print("System cleaned up.")

Run cleanup every 24 hours
from time import sleep
while True:
    sleep(86400)  # Sleep for 24 hours
    cleanup_system()
```

5.2 Optimized System Design with Kafka, GraphQL, and TRPC

For performance efficiency, we’ll use Kafka for messaging, GraphQL for querying, and TRPC for API communication between frontend and backend services.

- Kafka helps with message passing and asynchronous communication.
- GraphQL ensures efficient data retrieval from the backend.
- TRPC provides a type-safe API layer, allowing smooth interaction between the frontend and backend.

---

6. Building the Desktop Chat Interface

6.1 Frontend with Electron

We'll use Electron to build a fast desktop chat interface. The interface will communicate with the backend through APIs (GraphQL or TRPC) and allow users to chat with the AI agent in real time.

bash
Set up Electron for the frontend
npm install electron


Create a simple Electron window that communicates with the backend:

javascript
const { app, BrowserWindow } = require('electron')

let win

function createWindow() {
  win = new BrowserWindow({
    width: 800,
    height: 600,
    webPreferences: {
      nodeIntegration: true
    }
  })

  win.loadURL('http://localhost:5000')  // Backend URL
}

app.whenReady().then(createWindow)


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Conclusion

In this blog, we’ve walked through how to build a local AI agent system with RAG capabilities using Docker, Ollama, Qwen 2.5, FAISS, Redis, SQLite, and a fast desktop chat interface. The system automatically vectorizes new documents, asks for permissions before accessing critical data, and ensures high performance with tools like Kafka, GraphQL, and TRPC.
By following this approach, you can create a highly efficient, scalable, and secure AI agent system capable of handling large-scale data with real-time interaction and intelligent responses