rag-architecture

RAG Architecture for Modern Chatbots

1. Introduction

Retrieval Augmented Generation (RAG) is an advanced architecture in natural language processing (NLP) that combines the capabilities of retrieval-based methods with generative models to improve question answering systems. We'll delve into the components of the RAG architecture, focusing on embeddings in NLP, semantic context, vector databases, semantic search, and building a RAG pipeline using Langchain and OpenAI embeddings. Additionally, we'll demonstrate how to create a chatbot that answers questions based on a document and develop a Streamlit application for interacting with documents.

2. Embeddings in NLP and Semantic Context

Embeddings in NLP refer to vector representations of words, phrases, or documents in a continuous semantic space. These embeddings capture the semantic meaning of words and their contextual relationships, enabling NLP models to understand language semantics better. You can see notebooks folder for more information.

3. Vector Databases and Semantic Search

Vector databases store embeddings generated from textual data, enabling efficient semantic search and retrieval. These databases organize embeddings in a high-dimensional vector space, allowing for similarity-based search queries. Semantic search utilizes vector similarity measures to retrieve documents or passages relevant to a user's query, considering semantic context rather than just keyword matching. You can see notebooks folder for more information.

Here an example with Movies Search and Recommendation based on a description as query:

git clone https://github.com/jolual2747/rag-architecture.git
cd rag-architecture
poetry install --no-root
make build_gradio_app

4. Building a RAG Pipeline

To build a RAG pipeline, we integrate retrieval-based methods with generative models to enhance question answering systems. Langchain is a framework that facilitates the integration of language models with vector databases for retrieval-based question answering. Combining Langchain with OpenAI embeddings enables us to create a powerful RAG pipeline capable of providing accurate and contextually relevant answers to user queries.

5. Chatbot for Document-based Question Answering

We'll develop a chatbot using the RAG architecture to answer questions based on a document. The chatbot will utilize Langchain and OpenAI embeddings to retrieve relevant information from a vector database and generate responses tailored to the user's queries. This chatbot can be deployed in various applications, including customer support, information retrieval systems, and virtual assistants.

6. Streamlit Application

In addition to the chatbot, we'll create a Streamlit application that allows users to upload documents and interact with them. The application will provide a user-friendly interface for adding documents, asking questions, and receiving answers based on the document contents. By leveraging Streamlit's capabilities, we can create an intuitive and accessible platform for document-based question answering.

How to use it?

git clone https://github.com/jolual2747/rag-architecture.git
cd rag-architecture
poetry install --no-root
make build_streamlit_app

It works as next:

7. Conclusion

The RAG architecture offers a powerful approach to enhancing question answering systems by combining retrieval-based methods with generative models. By leveraging embeddings, vector databases, and semantic search techniques, RAG enables more accurate and contextually relevant responses to user queries. Through the development of a chatbot and a Streamlit application, we demonstrate the practical applications of the RAG architecture in real-world scenarios.