Devansh968/Chat-with-Pdf-using-Qdrant-vector-database

An AI-app that allows you to upload a PDF and ask questions about it. It uses OpenAI's LLMs to generate a response.

PDF Chat Solution with Langchain and Qdrant

This repository contains the code and documentation for a PDF Chat solution developed using Python. The solution addresses the client's requirements for interactive PDF-based querying, allowing users to ask questions and receive answers directly from PDF documents. The solution leverages Qdrant vector database for efficient embedding management and integrates persistent cloud storage. Additionally, a Langchain RetrievalQA chain has been orchestrated to provide seamless question-answering capabilities. The user interface is built using Streamlit, offering an intuitive web-based GUI for real-time visualization of the PDF Chat through Langchain.

Features

Interactive PDF-based querying. Utilizes Qdrant vector database for efficient embedding management. Integrates persistent cloud storage for embeddings. Langchain RetrievalQA chain for question-answering capabilities. User-friendly Streamlit web GUI for easy navigation and real-time PDF Chat visualization. Prerequisites

Before running the project, make sure you have the following prerequisites:

Python 3.10.11

qdrant-client==1.3.1

Langchain 0.0.239

Streamlit 1.25.0

openai==0.27.8

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