https://github.com/aasherkamal216/devsinc-rag-chatbot

A streamlit app that uses Langchain and LangGraph to explore different RAG techniques.
https://github.com/aasherkamal216/devsinc-rag-chatbot

ai chatbot groq langchain langgraph llama3 rag

Last synced: about 1 year ago
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A streamlit app that uses Langchain and LangGraph to explore different RAG techniques.

• Host: GitHub
• URL: https://github.com/aasherkamal216/devsinc-rag-chatbot
• Owner: aasherkamal216
• Created: 2024-09-06T02:21:33.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-11-11T16:34:59.000Z (over 1 year ago)
• Last Synced: 2025-04-06T09:12:34.788Z (over 1 year ago)
• Topics: ai, chatbot, groq, langchain, langgraph, llama3, rag
• Language: Python
• Homepage: https://rag-techniques-chatbot.streamlit.app/
• Size: 2.3 MB
• Stars: 2
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Devsinc RAG Chatbot

This Streamlit application demonstrates various Retrieval-Augmented Generation (RAG) techniques using the Devsinc AI Assistant.

It provides an interactive interface to explore different RAG methods and their applications in question-answering tasks.

![Image](assets/devsinc.jpg)

## ⚡Features

- Interactive chat interface with AI assistant

- Multiple RAG techniques and types implemented:

  - Naive RAG

  - Multi-Query Perspective

  - Hypothetical Document Embeddings (HyDE)

  - Reranker

  - Reciprocal Rank Fusion

  - Self-RAG

  - Corrective RAG

  - Agentic RAG

  - Adaptive RAG

- LLM selection option

- Vector store integration using FAISS

## Getting Started

1. **Clone the repository:**

   ```

   git clone https://github.com/aasherkamal216/Devsinc-RAG-Chatbot.git

   cd Devsinc-RAG-Chatbot

   ```

2. **Set Up a Virtual Environment:**

```

python -m venv venv

venv\Scripts\activate

```

3. **Install the required dependencies:**

   ```

   pip install -r requirements.txt

   ```

4. **Get your API key:**

   - [Groq API key](https://console.groq.com/keys)

   - [Cohere API key](https://dashboard.cohere.com/api-keys)

   

5. **Set up environment variables:**

   Create a `.env` file in the root directory and add the following:

   ```

   COHERE_API_KEY=your_cohere_api_key

   GROQ_API_KEY=your_groq_api_key

   ```

6. **Running the App**

Run the Streamlit app:

```

streamlit run app.py

```

Your app should now be running at `http://localhost:8501`