# Generative-AI-with-LangChain-and-HuggingFace

Welcome to **Generative-AI-with-LangChain-and-HuggingFace**, a comprehensive repository where I explore and implement cutting-edge techniques in Generative AI using **LangChain**, **HuggingFace**, and various AI tools. This repository serves as a hub for learning, experimentation, and building real-world applications.

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## Objectives

- Master LangChain and HuggingFace frameworks for Generative AI.
- Explore advanced topics like RAG (Retrieval-Augmented Generation), vector databases, graph databases, and tool-based AI agents.
- Implement end-to-end applications for chatbots, summarization, search engines, and more.

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## Repository Contents

### **Core Sections**
1. **LangChain 101**  
   - Introduction to LangChain concepts: chains, prompts, and memory.

2. **Exploring ChromaDB**  
   - Understanding and implementing vector databases for efficient similarity search.

3. **ML for NLP**  
   - Code snippets for foundational NLP tasks and integration with ML models.

4. **Building LLMs with LCEL**  
   - Techniques for fine-tuning and deploying Large Language Models (LLMs).

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### **Application Development**

1. **Chatbots with Message History**
   - Section 26-28: Implement chatbots capable of maintaining conversation history using RAG.

2. **End-to-End Generative AI Apps**
   - Section 29: Build robust generative AI apps with OpenAI APIs.

3. **Document Q&A with RAG**
   - Section 30: Develop Q&A systems integrating tools and agents for document retrieval.

4. **Conversation Q&A with Chat History**
   - Section 31: Enhance conversational systems with memory capabilities.

5. **Search Engine with LangChain**
   - Section 32: Create end-to-end tools and agents for search engine functionality.

6. **Chat with SQL Database**
   - Section 33: Implement chat systems that query SQL databases using LangChain’s SQL toolkit and agents.

7. **Text Summarization**
   - Section 34: Explore methods like stuff, map-reduce, and refine chains for summarizing text.
   - Section 35: Summarize content from YouTube videos and website URLs.

8. **Text-to-Math Problem Solver**
   - Section 36: Develop tools for solving math problems from text inputs using **Gemma2**.

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### **Advanced Topics**
- Integration of HuggingFace Transformers for fine-tuned generative models.
- Graph databases and their applications in AI pipelines.
- Vector database exploration and similarity search applications.
- Developing RAG-based Q&A systems and AI tools.

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## Tools and Frameworks

- **LangChain**: For building AI pipelines with memory, tools, and chains.  
- **HuggingFace**: For model fine-tuning and deployment.  
- **Vector Databases**: ChromaDB, FAISS, Pinecone.  
- **Graph Databases**: For advanced AI applications.  
- **Libraries**: Transformers, PyTorch, NumPy, scikit-learn.  
- **Development Tools**: Jupyter Notebook, Python, VS Code.

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## Future Topics

- Fine-tuning LLMs for specific domains with HuggingFace.  
- Advanced RAG implementations.  
- Multi-modal applications with image, text, and video inputs.  
- Building scalable AI solutions with LangChain and vector databases.  
- Deployment of generative AI apps on cloud platforms.

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## How to Use This Repository

1. Clone this repository:  
   ```bash
   git clone https://github.com/your-username/Generative-AI-with-Langchain-and-Huggingface.git
   ```

2. Navigate to the project directory:  
   ```bash
   cd Generative-AI-with-Langchain-and-Huggingface
   ```

3. Install dependencies:  
   ```bash
   pip install -r requirements.txt
   ```

4. Explore the structured sections and start implementing projects.

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## Progress Tracking

I will keep this repository updated with new learnings, projects, and advanced implementations. Stay tuned for exciting updates! Contributions and feedback are always welcome.

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## Connect and Collaborate

If you are passionate about Generative AI, LangChain, or HuggingFace, feel free to collaborate, share insights, or suggest improvements. Let’s build the future of AI together!
