GenAI-LangChain

This repository is a comprehensive collection of implementations to learn and practice Generative AI concepts using LangChain . It covers all the essential building blocks — from loading documents to embeddings, retrievers, chains, and complete applications like YouTube bots.

📂 Project Structure 🔹 Core Components

ChatModels/ – Examples of using chat-based Large Language Models (LLMs) with LangChain.

LLM's/ – Direct LLM usage (text generation, completions, etc.).

embedding_models/ & EmbeddedModels/ – Implementation of embeddings for converting text into vector space.

🔹 Data Handling

Document LangChain_Loaders_/ – Loading documents from various sources (PDFs, web pages, APIs, etc.).

Text_Splitter/ – Splitting long documents into smaller chunks for processing.

Vector_store/ – Storing embeddings in vector databases for efficient similarity search.

🔹 Retrieval & Processing

Retrievers/ – Building retrieval mechanisms over vector stores.

Chains/ – Chaining together different components like LLMs, retrievers, and prompts to build workflows.

Runnables/ – Using the LangChain runnable interface for modular pipelines.

Structured_output/ – Generating structured outputs from LLMs (JSON, key-value pairs, etc.).

🔹 Applications

prompts/ – Different prompt engineering techniques and templates.

Yt-bot_/ – YouTube bot example built with LangChain (loading transcripts, embeddings, Q&A).

• Features & Learning Goals

Understand document loaders and how to bring external data into LangChain.

Learn prompt engineering and building reusable templates.

Work with LLMs and ChatModels (OpenAI, Hugging Face, etc.).

Explore embeddings and store them in vector databases.

Implement retrievers for question-answering systems.

Chain components together to build GenAI-powered applications.

Build end-to-end projects like a YouTube Transcript Q&A Bot.

• Getting Started

Clone the repository first Install dependencies

pip install -r requirements.txt

Run examples Explore any folder (e.g., retrievers):

python Retrievers/retriever_example.py

📚 Learning Path

If you’re new to LangChain, follow this order for best learning experience:

Document LangChain_Loaders_/ → Load data

Text_Splitter/ → Split into chunks

embedding_models/ → Create embeddings

Vector_store/ → Store & search

Retrievers/ → Build retrievers

LLM's/ & ChatModels/ → Interact with LLMs

prompts/ → Improve outputs

Chains/ & Runnables/ → Connect everything

Structured_output/ → Get clean structured responses

Yt-bot_/ → Full project implementation

• Contributing

This repo is mainly for learning & practice. Feel free to fork, improve, and experiment with new LangChain modules.

📌 Notes

Requires API keys (OpenAI / HuggingFace / etc.) depending on examples.

Code is modular → you can pick and run specific folders.

Best suited for hands-on learners exploring GenAI step-by-step.