How to Build a RAG-Powered LLM Chat App with ChromaDB and Python

Generative AI is revolutionizing technology with its ability to create contextually relevant content, ushering in a new era of AI possibilities. At its core is retrieval augmented generation (RAG), merging information retrieval with large language models (LLMs) to produce intelligent, informed responses from external documents.

This tutorial explains how to build a RAG-powered LLM application using ChromaDB, an AI-native, open source embedding database known for its efficient handling of large data sets. I’ll guide you through each step, demonstrating RAG’s real-world applicability in creating advanced LLM applications.

What You’ll Need

To start building your LLM application, you’ll need Python (downloadable from Python’s official website), an OpenAI API key (available on OpenAI’s platform) and a basic understanding of Python and web APIs. These technologies will help ensure a smooth experience in following this tutorial and developing your generative AI-powered chat application.

Set up the Project

Once you’ve downloaded the apps and technology you’ll need, begin to set up your project environment.

1. Create and navigate to the project directory: In your terminal, create a new directory:

Change your working directory to the project folder:

2. Create your virtual environment: This is a crucial step for dependency management. You can create one with the following command:

and activate it.

For Mac or Linux:

For Windows:

3. Install the required packages: Install essential libraries for your project using the command:

Ensure that all the necessary dependencies are in the requirements.txt file.

After completing these steps, your environment is ready and you’re set to begin building a state-of-the-art RAG chat application with ChromaDB.

Load and Process Documents

This LLM application adeptly handles various document formats including PDF, DOCX and TXT using LangChain loaders. This is crucial for enabling external data accessibility, ensuring efficient data processing and maintaining uniform data readiness for subsequent stages. This snippet illustrates the process:

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Chunking data — grouping different bits of information into more manageable or meaningful chunks — eases processing and embedding and enables efficient context retention and information retrieval. The following code snippet demonstrates this vital process:

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Create Embeddings with OpenAI and ChromaDB

In this app, RAG uses OpenAI’s language models to create embeddings — essential vector representations of text for efficient data understanding. These embeddings are pivotal for RAG’s retrieval, allowing access to relevant external data. Stored efficiently in ChromaDB, they enable swift information retrieval, as highlighted in the code snippet below. This process enhances the application’s AI capabilities significantly.

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Build the Chat Interface with Streamlit

Streamlit is an app that turns data scripts into shareable web apps in minutes. This RAG LLM application links user inputs to backend processing. With Streamlit’s initialization and layout design, users can upload documents and manage data. The backend processes these inputs and returns responses directly in the Streamlit interface, displaying a seamless integration of frontend and backend operations.

The code below shows how to create a text input field in Streamlit and handle user inputs.

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With this setup complete, users can interact with the AI application seamlessly and intuitively.

Retrieve Answers and Enhance User Interaction

This RAG chat application leverages LangChain’s RetrievalQA and ChromaDB to efficiently respond to user queries with relevant, accurate information extracted from ChromaDB’s embedded data, exemplifying advanced generative AI capabilities.

The code snippet below demonstrates the practical implementation of this process in the Streamlit application:

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This code integrates user inputs and response generation in Streamlit. Using ChromaDB’s vector data, it fetches accurate answers, enhancing the chat application’s interactivity and providing informative AI dialogues.

Conclusion

This tutorial explored the intricacies of building an LLM application using OpenAI, ChromaDB and Streamlit. It explained setting up the environment, processing documents, creating and storing embeddings, and building a user-friendly chat interface, highlighting the powerful combination of RAG and ChromaDB in generative AI.

This GitHub repo covers the process. To run the application, execute the following command in your terminal:

You can now test the application by navigating to http://localhost:8501.

I encourage you to experiment with this application, make it your own and share your experiences with others!

Oladimeji Sowole is an Andela community member.  A Data Scientist and Data Analyst with more than 6 years of professional experience building data visualizations with different tools and predictive models for actionable insights, he has hands-on expertise in implementing technologies such as Python, R, and SQL to develop solutions that drive client satisfaction. A collaborative team player, he has a great passion for solving problems.

This article first appeared in The New Stack.

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