{"id":23571653,"url":"https://github.com/sebaskhe/rag-chatbot-with-langchain","last_synced_at":"2026-05-19T14:10:35.431Z","repository":{"id":269434447,"uuid":"907404532","full_name":"SebasKHE/RAG-Chatbot-with-Langchain","owner":"SebasKHE","description":"This project leverages advanced natural language processing techniques to build a Question-Answering (QA) bot that extracts and answers user queries from PDF documents. 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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.\n\nFeatures\nDocument Loading: Seamlessly load and preprocess PDF documents.\nText Splitting: Efficiently handle large documents by splitting text into manageable chunks.\nEmbeddings and Vector Databases: Use embedding models to store document data in a vectorized format for fast and accurate retrieval.\nRetrievers: Enable precise question-answering by finding the most relevant information.\nGradio Interface: Provide a user-friendly front-end for interacting with the QA bot.\nUse Case\nImagine 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.\n\nLearning Outcomes\nThis project demonstrates how to:\n\nIntegrate document loaders, text splitters, embedding models, and vector databases to build a fully functional QA system.\nUtilize LangChain and LLMs to address complex information retrieval challenges.\nDevelop an intuitive user interface using Gradio.\nTechnologies Used\nLangChain\nLarge Language Models (LLM)\nGradio for the front-end interface\nPDF Document Loaders\nVector Databases and Retrieval Systems\n\n---\n\n## Installation Guide  \n\n### Step 1: Clone the Repository  \nClone the repository to your local machine:  \n```bash  \ngit clone https://github.com/sebaskhe/rag-chatbot-with-langchain.git  \ncd rag-chatbot-with-langchain  \n```  \n\n### Step 2: Set Up a Virtual Environment  \nSetting up a virtual environment helps manage dependencies for this project.  \n1. Install `virtualenv` (if not already installed):  \n   ```bash  \n   pip install virtualenv  \n   ```  \n2. Create a virtual environment named `my_env`:  \n   ```bash  \n   virtualenv my_env  \n   ```  \n3. Activate the virtual environment:  \n   - On Linux/macOS:  \n     ```bash  \n     source my_env/bin/activate  \n     ```  \n   - On Windows:  \n     ```bash  \n     my_env\\Scripts\\activate  \n     ```  \n\n### Step 3: Install Required Libraries  \nInstall the necessary dependencies in the virtual environment:  \n```bash  \npython3.11 -m pip install \\  \ngradio==4.44.0 \\  \nibm-watsonx-ai==1.1.2 \\  \nlangchain==0.2.11 \\  \nlangchain-community==0.2.10 \\  \nlangchain-ibm==0.1.11 \\  \nchromadb==0.4.24 \\  \npypdf==4.3.1 \\  \npydantic==2.9.1  \n```  \n\n### Step 4: Run the Application  \nWith the environment set up and dependencies installed, you're ready to run the application.  \n1. Ensure the virtual environment is active.  \n2. Execute the main script:  \n   ```bash  \n   python3.11 qabot.py  \n   ```  \n\n### Step 5: Access the Application  \nOpen the URL provided in the terminal to access the Gradio interface and start interacting with the Question-Answering Bot.  \n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebaskhe%2Frag-chatbot-with-langchain","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsebaskhe%2Frag-chatbot-with-langchain","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsebaskhe%2Frag-chatbot-with-langchain/lists"}