Building a Research Chatbot with Python and RAG Framework
In the world of artificial intelligence and machine learning, building a custom chatbot can be a challenging yet rewarding experience. In this blog post, we will delve into the overview and implementation of a research chatbot using Python and the RAG framework.
### Introduction
Not too long ago, I attempted to build a simple custom chatbot that would run entirely on my CPU. However, the results were not as expected, with the application frequently crashing due to the heavy load of a large model.
Learning from my mistakes, I decided to make a more serious attempt at building a research chatbot with an end-to-end project that utilizes AWS to house and provide access to the models required for the application.
### Objective
The aim of this project is to build a question-answer (QA) chatbot using the RAG framework. The chatbot will answer questions using content from PDF documents available on the arXIV repository.
Before delving into the project, it is important to consider the architecture, the tech stack, and the procedure for building the chatbot.
### Chatbot Architecture
The chatbot architecture involves a workflow where a user submits a query, which then gets transformed using an embedding model. The vector database retrieves the most similar embeddings and sends them along with the embedded query to the Language Model (LLM). The LLM uses the provided context to generate an accurate response, which is then displayed to the user on the interface.
### Tech Stack
Building the RAG application with the components shown in the architecture requires several tools. Some of the noteworthy tools include:
– **Amazon Bedrock**: A serverless service that allows users access to models via API
– **Python**: A popular programming language used for implementing the chatbot logic
– **RAG Framework**: A framework for retrieving and generating answers from relevant documents for a given query
### Conclusion
In conclusion, building a research chatbot using Python and the RAG framework can be a challenging but rewarding experience. By leveraging the right tools and frameworks, developers can create high-performant chatbots that can provide valuable insights and information to users. If you are interested in building your own custom chatbot, consider exploring the RAG framework and implementing it with Python for a robust and efficient solution.