QuickDocAssistant - RAG: A Beginner-Friendly Knowledge Retrieval Tool

Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for building accurate, context-aware applications. With this article, I introduce QuickDocAssistant - RAG, a beginner-level project that implements the fundamentals of RAG using Python and FastAPI. This lightweight tool is highly effective in retrieving accurate information and serves as a strong foundation for anyone interested in exploring RAG concepts.

The complete project is open-source and available on my GitHub: https://github.com/adilabbass/QuickDocAssistant-RapidRag

What Is Retrieval-Augmented Generation (RAG)?

RAG is a method of combining retrieval systems with generative language models to produce highly relevant and contextually accurate responses. Instead of relying solely on the model's training data, RAG systems retrieve relevant documents or snippets from an external knowledge base to enhance the quality of responses.

This approach minimizes hallucination, a common problem in generative AI where models generate plausible-sounding but incorrect or irrelevant information. By grounding responses in retrieved knowledge, RAG systems ensure better factual accuracy.

Key Components of QuickDocAssistant - RAG

1. Python

The project is implemented in Python, a versatile and widely-used programming language in AI and machine learning. Its rich ecosystem of libraries and frameworks makes it an excellent choice for building RAG applications.

2. FastAPI

FastAPI is used to build the REST API for QuickDocAssistant. It is a modern web framework for Python, offering:

• High performance with asynchronous support.
• Type hints for better code validation and error handling.
• Easy-to-use API documentation via Swagger UI.

3. LangChain

LangChain serves as the framework for implementing RAG pipelines. It simplifies:

• Creating retrieval-based workflows.
• Integrating external tools like OpenAI models, vector stores, and document loaders.
• Building modular and extensible systems.

4. OpenAI Models

QuickDocAssistant uses OpenAI's GPT-4o-mini, a scaled-down version of GPT-4 optimized for retrieval-based tasks. This model is effective for:

• Understanding complex queries.

• Generating coherent and context-aware responses.

5. SentenceTransformers

For document embeddings, the project uses the all-MiniLM-L6-v2 model from the SentenceTransformers library. Embeddings are numerical representations of text, capturing semantic meaning for efficient similarity searches.

This embedding model is lightweight yet effective, making it ideal for beginner-level projects without compromising retrieval accuracy.

6. FAISS

FAISS (Facebook AI Similarity Search) is employed as the vector store. It enables fast and efficient similarity searches over large datasets of embeddings. Key features include:

• Scalability for large datasets.
• Support for various distance metrics (e.g., cosine similarity).
• Real-time query performance.

How QuickDocAssistant Works

Step 1: Document Upload

The user uploads a .txt file via the /upload endpoint. The contents are processed, and embeddings are generated using SentenceTransformers. These embeddings are stored in FAISS for fast retrieval.

Step 2: Querying the Knowledge Base

When a user sends a query to the /query endpoint, QuickDocAssistant:

1. Retrieves relevant documents from FAISS based on the query embeddings.
2. Passes the retrieved documents along with the query to GPT-4o-mini for response generation.
3. Returns the response to the user.

This process ensures that answers are grounded in the uploaded documents, significantly reducing hallucinations.

Why RAG?

RAG systems are particularly useful for:

• Domain-specific applications: They allow users to query custom knowledge bases, making them ideal for industries like healthcare, legal, and education.
• Minimizing hallucinations: By grounding responses in retrieved documents, RAG improves factual accuracy.
• Scalability: Combining retrieval and generation makes it possible to handle large knowledge bases efficiently.

Upcoming Features

While QuickDocAssistant currently supports .txt files, future versions aim to:

1. Support PDF and other text formats: Users will be able to upload a wider variety of documents.
2. Improve query handling: Adding advanced pre-processing techniques for better query understanding.
3. Enhance scalability: Optimizing the system for larger datasets and higher concurrency.
4. Add multilingual support: Allowing retrieval and queries in multiple languages.

QuickDocAssistant is an excellent starting point for understanding and building Retrieval-Augmented Generation systems. By combining FastAPI, LangChain, OpenAI’s GPT models, SentenceTransformers, and FAISS, it provides a practical example of a scalable and accurate RAG implementation.

Whether you’re new to RAG or looking for a simple framework to extend, this project has you covered. Stay tuned for future updates, where we’ll add support for more formats and advanced features!