In this article, you will learn the step-by-step process to implement an Advanced Q & A chatbot with Multiple Data Sources using Langchain.
The idea behind this project is we have multiple outside data sources and these sources are used as a toolkit these will go through prompts and LLM and the result will be by agents.
 |
I will explain how to implement this project and this implementation code will be available on my GitHub account.
First, we have arxiv which is a website where all the research papers are available, Wikipedia which covers all the topics and we have the pdf file. these are called toolkits which are available on langchain documentation.
Second, we loaded the pdf and then split this pdf into small chunks and converted into vectors this will go to vector databases to store these chunks and retriever which is used as an interface to provide results seamlessly and then will go through this retriever and get a relevant response.
Third, we have created a retriever tool to get responses from the toolkit. and we initialize tools to execute tools one by one it means that if one tool has no result then this will go to find the result for another tool and so on.
Fourth these tools will pass through LLM, prompts which are available on the langchain hub, and these three components will go to agents and pass to the agent executer which is responsible for giving responses.