https://github.com/sebaskhe/rag-chatbot-with-langchain
This project leverages advanced natural language processing techniques to build a Question-Answering (QA) bot that extracts and answers user queries from PDF documents. Using LangChain and Large Language Models (LLM)
https://github.com/sebaskhe/rag-chatbot-with-langchain
bot langchain llms qa-automation
Last synced: about 1 month ago
JSON representation
This project leverages advanced natural language processing techniques to build a Question-Answering (QA) bot that extracts and answers user queries from PDF documents. Using LangChain and Large Language Models (LLM)
- Host: GitHub
- URL: https://github.com/sebaskhe/rag-chatbot-with-langchain
- Owner: SebasKHE
- Created: 2024-12-23T14:00:37.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-30T02:54:45.000Z (6 months ago)
- Last Synced: 2025-02-17T21:46:27.724Z (4 months ago)
- Topics: bot, langchain, llms, qa-automation
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RAG-Chatbot-with-Langchain
Question-Answering Bot for PDF Documents
This project combines advanced natural language processing techniques to create a Question-Answering (QA) bot that answers user queries based on content extracted from PDF documents. Built using LangChain, a Large Language Model (LLM), and additional tools, this bot automates the process of retrieving and understanding information from extensive document libraries, such as legal files, technical manuals, or business reports.
Features
Document Loading: Seamlessly load and preprocess PDF documents.
Text Splitting: Efficiently handle large documents by splitting text into manageable chunks.
Embeddings and Vector Databases: Use embedding models to store document data in a vectorized format for fast and accurate retrieval.
Retrievers: Enable precise question-answering by finding the most relevant information.
Gradio Interface: Provide a user-friendly front-end for interacting with the QA bot.
Use Case
Imagine having a digital assistant that can instantly search through and summarize content from large document collections. This bot is particularly useful for industries like law, engineering, or customer support, where quick access to specific information is crucial.
Learning Outcomes
This project demonstrates how to:
Integrate document loaders, text splitters, embedding models, and vector databases to build a fully functional QA system.
Utilize LangChain and LLMs to address complex information retrieval challenges.
Develop an intuitive user interface using Gradio.
Technologies Used
LangChain
Large Language Models (LLM)
Gradio for the front-end interface
PDF Document Loaders
Vector Databases and Retrieval Systems
---
## Installation Guide
### Step 1: Clone the Repository
Clone the repository to your local machine:
```bash
git clone https://github.com/sebaskhe/rag-chatbot-with-langchain.git
cd rag-chatbot-with-langchain
```
### Step 2: Set Up a Virtual Environment
Setting up a virtual environment helps manage dependencies for this project.
1. Install `virtualenv` (if not already installed):
```bash
pip install virtualenv
```
2. Create a virtual environment named `my_env`:
```bash
virtualenv my_env
```
3. Activate the virtual environment:
- On Linux/macOS:
```bash
source my_env/bin/activate
```
- On Windows:
```bash
my_env\Scripts\activate
```
### Step 3: Install Required Libraries
Install the necessary dependencies in the virtual environment:
```bash
python3.11 -m pip install \
gradio==4.44.0 \
ibm-watsonx-ai==1.1.2 \
langchain==0.2.11 \
langchain-community==0.2.10 \
langchain-ibm==0.1.11 \
chromadb==0.4.24 \
pypdf==4.3.1 \
pydantic==2.9.1
```
### Step 4: Run the Application
With the environment set up and dependencies installed, you're ready to run the application.
1. Ensure the virtual environment is active.
2. Execute the main script:
```bash
python3.11 qabot.py
```
### Step 5: Access the Application
Open the URL provided in the terminal to access the Gradio interface and start interacting with the Question-Answering Bot.
---