This repository contains a Python project that demonstrates how to use the LangChain framework to create a chatbot that can answer questions about documents. The project includes multiple notebooks that explain vector databases like Pinecone and Qdrant, and how to use LangChain with OpenAI to create semantic search in a vector database.
To get started, you will need to install the following dependencies:
pip install -r requirements.txt
Once you have installed the dependencies, you can run the notebooks.
The notebooks are as follows:
chunking.ipynb: This notebook explains how to chunk documents.vectors.ipynb: This notebook shows how to use chunked documents into vectors and store them in vector DB.LangChain.ipynb: This notebook shows how to use the LangChain library.Conversational Agent.ipynb: This notebook shows how to use the semantic search functionality to answer questions about documents.Qdrant Vector DB.ipynb: This notebook shows how to use the Qdrant Vector DB to store vectors.Azure OpenAI docs.ipynb: This notebook shows how to use LangChain with OpenAI to create a semantic search in a vector database.