Easy RAG with LangChain, Supabase & Ollama (Developer Tutorial)

Science & Technology

Introduction

In the ever-evolving landscape of natural language processing, having the right tools and techniques can make a significant difference in the quality of your model's outputs. One effective approach to enhancing your language model’s performance is the implementation of a Retrieval-Augmented Generation (RAG) system. This tutorial will guide you through the process of building a RAG system using LangChain, Supabase, and the local Ollama LLaMA model.

Understanding RAG Architecture

The architecture of a RAG system involves a few critical components. It starts with a user query directed to LangChain. LangChain interacts with a vector database—in this case, Supabase—to retrieve relevant documents. These documents are then compiled into a cohesive context that is sent to the language model (LLM). Finally, the enriched context allows the LLM to respond accurately and meaningfully to the user.

Use Case Overview

We’ll be developing a utility for code generation using RAG. The process includes ingesting three documents into Supabase, which are:

  1. Architectural Design Guideline
  2. Design Style Guide
  3. Sample User Story

After the ingestion process, LangChain will retrieve the documents separately, compile them into one prompt, and send this prompt to the LLM for generating code.

Tool Installation

Here’s a brief overview of the tools required for this project:

  1. Docker: Download Docker Desktop to manage containers.

  2. Supabase: Install Supabase using command line tools.

  3. Python: Ensure you have the necessary Python packages installed, such as LangChain and the Ollama LLaMA model.

Step-by-Step Process

1. Docker Installation

Begin by downloading Docker Desktop from the official Docker website. Follow the provided instructions for your OS (e.g., macOS, Windows) to complete the installation. Once installed, ensure Docker is running—indicated by a green icon.

2. Supabase Setup

Using the command line interface, install Supabase with:

brew install supabase/tap/supabase

Once installed, initialize Supabase:

supabase init
[supabase start](https://www.topview.ai/blog/detail/Supabase-in-100-Seconds)

After running the above commands, copy the provided service key and URL for future use, as these are essential for accessing Supabase.

3. Configuring Supabase as a Vector Store

Next, you'll need to run SQL scripts to set up Supabase properly. This includes creating a table for documents and defining a function for querying these documents.

4. Document Ingestion

Once your Supabase vector store is set up, you'll write Python scripts to ingest documents. This involves monitoring a specific folder and automatically ingesting any new files you place there. Your script should ensure that no duplicate documents are added to the vector store.

5. Building the RAG Chain

In this step, we will create the logic for retrieving relevant data from Supabase based on user input. This includes defining prompts that will guide the LLM in generating appropriate code. Use the Chat Prompt Template feature in LangChain to create a system prompt and a user prompt—both enriched with the contextual information retrieved from your vector store.

6. Full Invocation with LLM

Finally, run the complete invocation using a Jupyter notebook. Here, you will invoke the LLM with the prompt containing the retrieved context. The result should be a comprehensive piece of code generated based on the specifications laid out in the user story.

Conclusion

By following the steps outlined in this tutorial, you’re able to create a robust RAG system using LangChain, Supabase, and Ollama. This setup not only enhances the responses generated by your LLM but also provides a solid framework for building more complex applications in the future.

Keywords

FAQ

What is RAG (Retrieval-Augmented Generation)?

RAG is a system that combines the powers of information retrieval and language generation to provide more accurate and contextually relevant responses.

What are the prerequisites for building a RAG system?

You need to have basic knowledge of Python, Docker installed on your machine, and familiarity with command-line interfaces.

Can I use models other than LLaMA?

Yes, this RAG setup can work with any compatible LLM, including models from OpenAI like GPT-4 or other available options.

How do I avoid ingesting duplicate documents?

By renaming files after ingestion and implementing a monitoring system, you can ensure that documents are not processed more than once.

What kind of documents can I use for ingestion?

You can use a variety of documents, including architectural guidelines, design style guides, user stories, or anything relevant to your coding needs.