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.

---

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.

---

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).

---

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.

---

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.

---

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.

---

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.

---

How to Use This Repository

1. Clone this repository:

   git clone https://github.com/your-username/Generative-AI-with-Langchain-and-Huggingface.git

2. Navigate to the project directory:

   cd Generative-AI-with-Langchain-and-Huggingface

3. Install dependencies:

   pip install -r requirements.txt

4. Explore the structured sections and start implementing projects.

---

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.

---

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!