Chaining RAG Systems for Advanced LLM Pipelines

This tutorial will guide you through the process of chaining two Retrieval-Augmented Generation (RAG) systems together. The first system will use MongoDB for data retrieval and ChatGPT for generation. The second system will use Weaviate for data retrieval and LLaMA-2 for generation. Each step is detailed for clarity.

Why Chain RAGs?

Chaining Retrieval-Augmented Generation (RAG) systems offers a powerful approach to leveraging the strengths of different data sources and AI models to create more context-aware, accurate, and relevant responses. In many real-world scenarios, the data needed for generating informed AI responses is distributed across various silos. By chaining RAG systems, you can combine diverse data repositories with state-of-the-art language models, enhancing the quality and applicability of the generated content.

Use Cases:

  1. Customer Support Automation: In a scenario where customer queries are complex and require information from various internal databases, a chained RAG system can be invaluable. For instance, initial queries can be processed using data from a MongoDB database containing product information, followed by a secondary query in a Weaviate database with customer interaction histories, to provide comprehensive and personalized support.
  2. Medical Research and Diagnosis Assistance: In medical applications, a RAG system can first retrieve medical research and case studies from a structured MongoDB database. Then, it can use Weaviate to access patient histories or genomic data, assisting healthcare professionals in making informed decisions or in research for novel treatments.
  3. Financial Market Analysis and Forecasting: For financial analysis, a chained RAG system can initially extract historical financial data and market trends from MongoDB. Subsequently, it can use Weaviate to access real-time market news or social media sentiment analysis. This approach can aid in creating more accurate market predictions or investment strategies.


Ensure you have the necessary libraries and services running:

  • MongoDB
  • ChatGPT API
  • Weaviate
  • LLaMA-2

Setup MongoDB Connection

from pymongo import MongoClient

# Connect to your MongoDB instance
mongo_client = MongoClient("mongodb://localhost:27017/")
mongo_db = mongo_client["your_database"]  # Replace with your database name
mongo_collection = mongo_db["your_collection"]  # Replace with your collection name

Retrieve Data from MongoDB and Generate Response with ChatGPT

def retrieve_data_mongo(query):
    # Retrieve data from MongoDB
    search_results = mongo_collection.find(
    	{"$text": {"$search": query}}
    return [result for result in search_results]

def generate_with_chatgpt(prompt):
    # Use ChatGPT API to generate a response (pseudo-code)
    response = chatgpt_api.generate(prompt)
    return response

# Example usage
mongo_data = retrieve_data_mongo("your_search_query")
chatgpt_response = generate_with_chatgpt(mongo_data)

Setup Weaviate Connection

from weaviate import Client

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

Retrieve Data from Weaviate and Generate Response with LLaMA-2

def retrieve_data_weaviate(query):
    # Retrieve data from Weaviate
    search_results = weaviate_client.query.get("YourClassName", properties=["your_property"]).with_q(query).do()
    return [result for result in search_results["data"]["Get"]["YourClassName"]]

def generate_with_llama2(prompt):
    # Use LLaMA-2 API to generate a response (pseudo-code)
    response = llama2_api.generate(prompt)
    return response

# Example usage
weaviate_data = retrieve_data_weaviate("your_search_query")
llama2_response = generate_with_llama2(weaviate_data)

Chain RAG Systems

Now, let's chain these two RAG systems. We'll use the output of the first RAG (MongoDB + ChatGPT) as the input for the second RAG (Weaviate + LLaMA-2).

# Chaining RAGs
first_rag_output = generate_with_chatgpt(mongo_data)
second_rag_input = first_rag_output
second_rag_output = generate_with_llama2(second_rag_input)

print("Final output:", second_rag_output)

Challenges in Testing, Prototyping, and Productionizing RAG Systems

When developing and deploying a chained RAG system like the one described in this tutorial, several challenges can arise:

  1. Glue Code Complexity: Bridging different systems (MongoDB with ChatGPT and Weaviate with LLaMA-2) requires custom 'glue code' to manage data formats and API interactions. This increases complexity and maintenance effort.
  2. Error Handling: Robust error handling is crucial, especially when dealing with multiple external services. Handling timeouts, incorrect data formats, or unavailable services is essential for a stable system.
  3. Network Latency and Performance: Network latency can significantly impact the response time, especially when querying large datasets or when services are hosted on different networks.
  4. Data Consistency and Security: Ensuring data consistency across different systems and maintaining high standards of security, especially with sensitive data, is a complex but necessary task.
  5. Scalability: As demand grows, scaling each component of the system (databases, AI models, and middleware) can become a challenging task.

NUX: Your Managed AI Backend

To address these challenges, consider NUX – a completely managed AI backend as a service. NUX revolutionizes the way you work with AI by allowing you to convert your AI workbooks into a fully-functional AI backend with just one line of code.

Benefits of NUX:

  • Simplified Integration: Seamlessly integrates various AI models and databases, reducing the complexity of writing and maintaining glue code.
  • Robust Error Handling: Built-in robust error handling mechanisms ensure your applications are stable and reliable.
  • Optimized Performance: Manages network latency and performance issues, providing faster and more efficient responses.
  • Enhanced Security and Data Consistency: Ensures high standards of security and maintains data consistency across different data sources.
  • Scalability: Effortlessly scales to meet growing demands, handling increased traffic and data without the need for manual intervention.
  • Rapid Prototyping and Deployment: Accelerates the process from prototyping to production, allowing for faster experimentation and deployment of AI-powered applications.

NUX contains everything you need to prototype, experiment, and productionize AI-powered applications. It's an ideal solution for businesses and developers looking to leverage the power of AI without the overhead of managing complex backend systems. With NUX, you focus on what you do best – creating innovative AI solutions – while we handle the rest.

Incorporating NUX into your AI strategy can significantly reduce development time, increase efficiency, and ensure your AI applications are scalable, secure, and robust. Say goodbye to the complexities of backend management and hello to streamlined AI development with NUX.

What will you build?

Explore workbook templates or customize your own.

Start Building