We built an AI Chat Agent that is built off your data with Langchain, Langflow, and Huggingface.

By OmniAI | omniai | 25 Mar 2025

Load your data for chat context with Retrieval Augmented Generation.

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How to Build an AI Chatbot with Retrieval-Augmented Generation (RAG)

Introduction

If you’ve ever wanted to build an AI chatbot that can intelligently retrieve and respond based on your data, then you’re in the right place. In this guide, I’ll walk you through how we built an advanced chatbot using Retrieval-Augmented Generation (RAG), vector stores, and Langflow. This bot is powered by OpenAI and Astra DB, allowing it to provide more context-aware responses by leveraging a vector search database.

What is Retrieval-Augmented Generation (RAG)?

RAG is a technique that combines the power of Large Language Models (LLMs) with external data retrieval. Instead of relying only on pre-trained knowledge, a RAG chatbot can query external data sources to improve accuracy and relevance.

At the core of RAG is a vector store, a specialized database that stores embeddings (numerical representations) of text data. This enables efficient semantic search, allowing the chatbot to retrieve the most relevant information when responding to queries.

Tools We Used

To build this chatbot, we used:

OpenAI API for natural language processing.
Astra DB as our vector database for storing embeddings.
Langflow for easy flow-based development.

Now, let’s dive into the step-by-step process.

Step 1: Set Up Prerequisites

Before getting started, make sure you have:

An OpenAI API key
An Astra DB account with:

A database and collection for storing vector embeddings.
An Astra DB application token with read/write permissions.

Step 2: Create a New Langflow Project

Open Langflow and start a new project.
From the Langflow dashboard, click New Flow.
Select Vector Store RAG.
The Vector Store RAG flow will be created automatically.

Step 3: Build the Vector Store RAG Flow

The RAG chatbot consists of two main flows:

1. Load Data Flow (Indexing Your Data)

This flow takes local files, processes them into smaller chunks, generates embeddings, and stores them in Astra DB.

Load Data: Ingests files.
Chunking: Splits text into small segments.
Embedding Generation: Converts text chunks into numerical vectors.
Vector Store Indexing: Stores vectors in Astra DB.

2. Retriever Flow (Querying Your Data)

This flow processes user queries and finds relevant data from Astra DB.

User Input: Captures the query.
Embedding Conversion: Converts the query into a vector.
Vector Search: Finds similar embeddings from Astra DB.
Contextual Prompting: Merges retrieved data with the query.
OpenAI Generation: Uses the refined prompt to generate responses.
Chat Output: Displays the response.

Step 4: Configure OpenAI & Astra DB

Configuring OpenAI API

In the OpenAI component, click the Globe button.
Add a new variable with:

Variable Name: openai_api_key
Value: Paste your OpenAI API Key (sk-...).

Click Save Variable.

Configuring Astra DB

In the Astra DB component, add your application token.
Choose your database (or create a new one).
Select or create a collection to store vector data.

If using Astra’s Vectorize service, embeddings will be auto-generated. Otherwise, select Bring Your Own to use OpenAI’s embedding model.

Step 5: Run the RAG Chatbot

Click the Playground button in Langflow.
Type a query like: “What topics do you know about?”
The chatbot will retrieve and generate a response based on your embedded data.

Conclusion

By leveraging RAG, Astra DB, and OpenAI, you can build a chatbot that is contextually aware and continuously learn from new data. This method is powerful for AI assistants, research tools, and customer support chatbots.

Want to see our chatbot in action? Check out DisputeAI.xyz—our AI-powered legal assistant helping consumers analyze credit reports and generate dispute letters based on FCRA & FDCPA laws.