https://github.com/264gaurav/rag-app
RAG and basic chatbot application using langchain , langgraph and langsmith frameworks/tool.
https://github.com/264gaurav/rag-app
7b ai aimodel chatbot deepseek-r1 gemini langchian langgr langsmith langsmithapi ollama python27 rag rag-chatbot streamlit
Last synced: 2 months ago
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RAG and basic chatbot application using langchain , langgraph and langsmith frameworks/tool.
- Host: GitHub
- URL: https://github.com/264gaurav/rag-app
- Owner: 264Gaurav
- License: mit
- Created: 2025-06-19T14:47:39.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-19T15:16:53.000Z (12 months ago)
- Last Synced: 2025-06-19T15:44:33.560Z (12 months ago)
- Topics: 7b, ai, aimodel, chatbot, deepseek-r1, gemini, langchian, langgr, langsmith, langsmithapi, ollama, python27, rag, rag-chatbot, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RAG-app
RAG (Retrieval-Augmented Generation) and basic chatbot application built using the LangChain, LangGraph, and LangSmith frameworks/tools.
## Features
- **Retrieval-Augmented Generation (RAG):** Combines retrieval techniques with generative AI to provide accurate and context-aware responses.
- **Chatbot Functionality:** Interactive chatbot powered by LangChain and LangGraph for seamless communication.
- **Modular Frameworks:** Utilizes LangSmith for advanced debugging and monitoring of AI workflows.
- **Streamed Responses:** Supports streaming responses for real-time interaction.
## Technologies Used
- **LangChain:** Framework for building applications with LLMs (Large Language Models).
- **LangGraph:** Tool for managing and executing complex workflows with AI agents.
- **LangSmith:** Framework for debugging and monitoring AI workflows.
- **Python:** Core programming language for the application.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/RAG-app.git
cd RAG-app
```
2. Create a virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Ensure the Ollama server is running and the required models (e.g., deepseek-coder) are available.
## Usage
1. Start the chatbot application:
```bash
python chatbot.py
```
2. Interact with the chatbot by typing your queries in the terminal.
3. To exit the chatbot, type quit.
---
# Environment Variables
## Ensure the following environment variables are set.
1. create .env file and insert these important key-values in that:
OPENAI_API_KEY=sk-your-openai-key-here
GOOGLE_API_KEY=your-gemini-api-key
### add any other secrets below
LANGSMITH_API_KEY=your-langsmith-key
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=your-langsmith-key
### (If you also use Ollama/OpenAI, set their keys as well)
export OLLAMA_API_KEY="your_ollama_api_key"