Overview of the Architecture

This guide will build a Retrieval-Augmented Generation (RAG) system using FastAPI for the backend and React Native for the frontend. The RAG system will allow users to interact with PDF documents by querying relevant information and generating responses using an advanced language model powered by Ollama. This system will also provide citations for the retrieved data, linking it back to the original documents.

Backend Setup: FastAPI for PDF Processing and Query Handling

The backend is built with FastAPI, which is known for its speed and efficiency. The FastAPI server will handle PDF uploads, process text using the Ollama API, and store the results in a vector database for fast retrieval.

Generating Embeddings

For each chunk of text, we generate an embedding using the Ollama API. Embeddings are numerical representations of text that capture semantic meaning, allowing us to efficiently retrieve relevant document chunks when processing user queries.

Handling Input

The input field allows users to type messages. The input is cleared once the message is sent.

Displaying Send Button

The send button triggers the handleSendMessage function, which sends the user’s query to the backend.

Deployment Instructions

Make sure the Docker is installed and running. Run the docker compose command from the root project:

docker compose up --build

Conclusion

In this project, we’ve built a robust RAG system that uses FastAPI for backend processing and React Native for frontend interaction. By combining Ollama’s language models with ChromaDB for vector storage, we’ve enabled efficient retrieval and query processing. The system allows users to upload PDFs, query them, and receive detailed responses with citations for verification.

FAQs

Q: What is Retrieval-Augmented Generation (RAG)?

A: RAG is a system that uses a combination of retrieval and generation techniques to generate responses to user queries.

Q: What is Ollama?

A: Ollama is a language model that generates text based on a given prompt.

Q: What is ChromaDB?

A: ChromaDB is a vector database that stores and retrieves vector representations of text.

Q: Can I use this system for other purposes?

A: Yes, the system can be used for other purposes, such as generating text summaries or answering user questions.

Q: How do I deploy this system?

A: You can deploy this system by running the docker compose command from the root project.