This repository demonstrates how to use Langchain to create a chatbot that is able to query a document and provide sources for its response aswell using OpenAI's Large Language models
LangChain is a powerful framework built around large language models (LLMs), which allows us to create a wide range of natural language processing (NLP) applications. With LangChain, we can create chatbots, generative question-answering systems, summarization models, and much more.
The core idea behind LangChain is that we can "chain" together different components to create more advanced use cases around LLMs. For example, we might use one component to preprocess text data, another to fine-tune a pre-trained LLM on a specific task, and another to generate responses or summaries based on the input.
One of the key benefits of LangChain is its flexibility. It allows us to experiment with different combinations of components and adjust their parameters to optimize performance on specific tasks. Moreover, since it's built around LLMs, we can easily integrate it with other NLP libraries, such as Hugging Face Transformers.
In summary, LangChain is a powerful tool for building advanced NLP applications using LLMs. With its flexible and modular architecture, we can easily create custom pipelines to solve a wide range of NLP tasks.
This notebook aims to guide you to create your own Chatbot that will be able to query a document give answers based on that document and also give sources of how it came about that answer