Advanced RAG 01 - Self Querying Retrieval

Introduction

This tutorial provides a step-by-step guide on advanced retrieval-augmented generation (RAG) techniques, specifically focusing on self-querying retrieval using tools like LangChain and OpenAI. By the end of this guide, you will understand how to implement self-querying mechanisms to enhance your application's data retrieval capabilities.

Step 1: Understand the Self Querying Concept

Begin by familiarizing yourself with the self-querying retrieval mechanism:

• Definition: Self-querying involves the system autonomously generating queries based on input data to retrieve relevant information.
• Diagram Review: Visualize the self-querying process through the diagram presented in the video, which outlines the flow of data and the interaction between different components.

Step 2: Set Up Your Environment

Before diving into coding, prepare your development environment:

1. Colab Setup:

   • Open Google Colab to write and execute your code.
   • Access the provided Colab link: Colab Notebook.

2. Install Required Libraries:

   • Ensure you have the necessary libraries installed. Use the following commands in a Colab cell:

     !pip install langchain openai
     

Step 3: Implementing Self Querying Retrieval

Now, let’s move on to the actual coding part:

1. Import Necessary Libraries: Start your code with the required imports:

   from langchain import OpenAI, RetrievalQA
   

2. Initialize the Model: Set up the OpenAI model to handle queries:

   llm = OpenAI(api_key='YOUR_API_KEY')
   

3. Create the Retrieval Function: Define a retrieval function that generates queries based on input:

   def self_query(input_text):
       query = f"Retrieve information related to: {input_text}"
       results = llm(query)
       return results
   

4. Test the Function: Run your retrieval function with a sample input to verify it works:

   response = self_query("What are the benefits of RAG?")
   print(response)
   

Step 4: Integrate with LangChain

To enhance your application’s capabilities, integrate your self-querying function with LangChain:

1. Set Up LangChain Retrieval: Use LangChain's RetrievalQA for structured querying:

   qa = RetrievalQA(llm=llm)
   

2. Query the Model: Execute queries through the LangChain interface:

   answer = qa.run("Explain self-querying retrieval")
   print(answer)
   

Conclusion

In this tutorial, you learned how to set up a self-querying retrieval system using LangChain and OpenAI. You explored the concept, set up your environment, implemented the necessary code, and integrated it into a structured retrieval system.

Next Steps

• Experiment with different queries and inputs to see how the model responds.
• Explore additional features and optimizations within LangChain for enhanced performance.
• Consider sharing your findings or projects on platforms like GitHub to engage with the community.