Build a RAG FAQ System with LangChain and Supabase

Introduction

Customer support is expensive, and most SaaS teams answer the same questions repeatedly. In this tutorial, you will build a Retrieval-Augmented Generation (RAG) FAQ system that automatically answers user questions by retrieving relevant content from your knowledge base and generating accurate, context-aware responses.

You will use LangChain to orchestrate the RAG pipeline and Supabase as a vector database to store and search FAQ embeddings. By the end, you will have a production-ready FAQ system that reduces support ticket volume and improves customer experience.

What You'll Learn

• How to set up Supabase as a vector store for FAQ embeddings
• How to load, chunk, and embed FAQ documents using LangChain
• How to build a RAG query pipeline that retrieves context and generates answers
• How to expose the system as a simple API endpoint

Prerequisites

Before starting, make sure you have the following ready:

• Python 3.9+ installed on your machine
• A Supabase account (free tier works fine)
• An OpenAI API key for embeddings and LLM responses
• Basic familiarity with Python and REST APIs
• A set of FAQ documents (plain text, Markdown, or JSON)

Install the required Python packages by running this command:

pip install langchain langchain-openai langchain-community supabase vecs tiktoken python-dotenv

Create a .env file in your project root to store your credentials securely:

OPENAI_API_KEY=sk-your-openai-key-here
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-role-key-here

Setting Up Supabase as a Vector Store

Supabase is an open-source Firebase alternative that includes a PostgreSQL database with the pgvector extension. This extension enables similarity search over high-dimensional vectors, making it ideal for RAG applications.

Log in to your Supabase dashboard and navigate to the SQL Editor. Run the following SQL to enable the vector extension and create the documents table:

-- Enable the pgvector extension
create extension if not exists vector with schema extensions;

-- Create the documents table for storing FAQ embeddings
create table faq_documents (
  id bigserial primary key,
  content text not null,
  metadata jsonb,
  embedding vector(1536)
);

-- Create an index for fast similarity search
create index on faq_documents using ivfflat (embedding vector_cosine_ops)
  with (lists = 100);

The vector(1536) column stores OpenAI's text-embedding-ada-002 embeddings, which have 1536 dimensions. The IVFFlat index speeds up cosine similarity searches dramatically.

Creating the Match Function

Supabase uses a PostgreSQL function to perform similarity searches. Create this function in the SQL Editor:

sql
create or replace function match_faq_documents (
  query_embedding vector(1536),
  match_count int default 5,
  filter jsonb default '{}'
)
returns table (
  id bigint,
  content text,
  meta

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