Building Personalized Recommendation Systems Using RAG Techniques with FastAPI, Redis, and Weaviate: A Step-by-Step Guide

Recommendation systems are at the heart of personalized user experiences, playing a pivotal role in industries like e-commerce, streaming, and content delivery. Traditionally, recommendation systems rely heavily on collaborative filtering or deep learning models. However, Retrieval-Augmented Generation (RAG) techniques have emerged as a powerful alternative, combining retrieval-based methods with generative AI to create robust and more adaptive systems.

In this guide, we’ll walk through building a personalized recommendation system using RAG techniques with **FastAPI**, **Redis**, and **Weaviate**, providing sample code snippets throughout.

**Retrieval-Augmented Generation (RAG)** is a technique that incorporates external knowledge into generative AI models to improve their responses. In the case of recommendation systems, RAG pairs **retrieval** components (e.g., Redis and Weaviate) with generative models (e.g., GPT-like models) to offer adaptive and personalized recommendations.

1. FastAPI: A modern and fast Python web framework for building APIs.
2. Redis: An in-memory data store for caching and fast retrieval.
3. Weaviate: A vector database that enables semantic searches and storage of embeddings.
4. Python: Used for implementing the backend logic.

Before building the system, ensure you have Python installed. Then install the required libraries:

“`bash pip install fastapi uvicorn redis weaviate-client python-dotenv “`

We’ll use Redis as a caching layer and Weaviate as the vector database to store embeddings. Here’s how to set up Redis and connect to Weaviate.

Redis will store frequently accessed recommendations for quick retrieval. Below is a sample Redis connection:

import redis

# Connect to Redis
redis_client = redis.Redis(host='localhost', port=6379, decode_responses=True)

# Example: Storing and retrieving data
redis_client.set("user:123", "['item1', 'item2', 'item3']")
recommendations = redis_client.get("user:123")
print(recommendations)  # Output: ['item1', 'item2', 'item3']

Weaviate will store embeddings for semantic retrieval. Below is how to connect and store data in Weaviate:

from weaviate import Client

# Connect to Weaviate instance
client = Client("http://localhost:8080")

# Define schema for recommendations
schema = {
    "class": "Recommendation",
    "properties": [
        {"name": "item_id", "dataType": ["string"]},
        {"name": "embedding", "dataType": ["vector"]}
    ]
}
client.schema.create(schema)

# Example: Adding a recommendation
recommendation = {
    "item_id": "item1",
    "embedding": [0.1, 0.2, 0.3]  # Example embedding vector
}
client.data_object.create(recommendation, class_name="Recommendation")

Now, we’ll build a FastAPI application to handle user requests and provide personalized recommendations.

Below is the complete code for the FastAPI backend:

from fastapi import FastAPI, HTTPException
import redis
from weaviate import Client

app = FastAPI()

# Connect to Redis
redis_client = redis.Redis(host='localhost', port=6379, decode_responses=True)

# Connect to Weaviate
weaviate_client = Client("http://localhost:8080")

@app.get("/recommendations/{user_id}")
async def get_recommendations(user_id: str):
    # Retrieve cached recommendations from Redis
    cached_data = redis_client.get(f"user:{user_id}")
    if cached_data:
        return {"recommendations": cached_data}
    
    # If no cached data, retrieve from Weaviate
    query_result = weaviate_client.query.get("Recommendation", ["item_id"]).with_where({
        "path": ["item_id"],
        "operator": "Equal",
        "valueString": user_id
    }).do()
    
    if not query_result:
        raise HTTPException(status_code=404, detail="Recommendations not found")
    
    recommendations = [obj["item_id"] for obj in query_result["data"]["Get"]["Recommendation"]]
    
    # Cache the recommendations in Redis
    redis_client.set(f"user:{user_id}", str(recommendations))
    return {"recommendations": recommendations}

Start the FastAPI server by running:

“`bash uvicorn main:app –reload “`

Then, test the `/recommendations/{user_id}` endpoint using tools like **Postman** or **curl**.

Example request:

“`bash curl -X GET “http://127.0.0.1:8000/recommendations/user123” “`

To incorporate a generative AI model (e.g., OpenAI’s GPT), you can integrate the model to refine or personalize recommendations based on user input.

Example integration with OpenAI:

import openai

openai.api_key = "your_openai_api_key"

def refine_recommendations(recommendations, user_query):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Based on the user's query '{user_query}', refine these recommendations: {recommendations}",
        max_tokens=50
    )
    return response.choices[0].text.strip()

Once the application is tested locally, deploy it using cloud platforms like **AWS** or **Azure**. Both Redis and Weaviate can be hosted on cloud instances for scalability.

By combining **FastAPI**, **Redis**, and **Weaviate**, you can build a powerful personalized recommendation system using RAG techniques. This architecture enables efficient retrieval, caching, and semantic search, while generative AI models provide personalized experiences.

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