🚀 Generative AI with LangChain & Ollama

A comprehensive collection of my learning journey, code implementations, experiments, and projects built while studying Generative AI, LangChain, RAG, AI Agents, and Ollama.

This repository contains hands-on implementations of concepts covered throughout the LangChain and Ollama learning path, ranging from prompt engineering to building end-to-end AI agents.

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📚 Topics Covered

1. Introduction to LangChain

• What is LangChain?
• Why LangChain for LLM Applications?
• LangChain Architecture
• Core Building Blocks

2. LangChain Components

• Models
• Prompts
• Chains
• Tools
• Agents
• Memory
• Retrievers

3. Working with Models

• OpenAI Models
• Groq Models
• Local LLMs
• Chat Models
• Embedding Models

4. Prompt Engineering

• Prompt Templates
• Chat Prompt Templates
• Dynamic Prompt Construction
• Prompt Chaining

5. Structured Outputs

• JSON Output Generation
• Pydantic Output Parsing
• Schema Validation

6. Output Parsers

• String Parsers
• JSON Parsers
• Custom Output Parsers

7. LangChain Chains

• LLM Chains
• Sequential Chains
• Runnable Chains
• Chain Composition

8. LangChain Runnables

• RunnableSequence
• RunnableParallel
• RunnableLambda
• Advanced Runnable Workflows

9. Document Processing

• Document Loaders
• PDF Loading
• Web Page Loading
• CSV Processing
• Text Extraction

10. Text Splitting

• Character Text Splitter
• Recursive Text Splitter
• Token-Based Splitting
• Chunking Strategies

11. Vector Databases

• Embeddings
• Similarity Search
• Vector Storage
• Semantic Search

12. Retrievers

• Basic Retrieval
• Context Retrieval
• Similarity Search Retrieval
• Advanced Retrieval Techniques

13. Retrieval-Augmented Generation (RAG)

• RAG Fundamentals
• Knowledge Retrieval
• Context Injection
• End-to-End RAG Pipelines

14. RAG Projects

• YouTube Chatbot
• PDF Question Answering
• Document Search Systems
• Knowledge Base Assistants

15. Tools and Tool Calling

• Custom Tools
• Built-in LangChain Tools
• Search Tools
• API Integration
• Tool Calling Workflows

16. AI Agents

• ReAct Agents
• Tool-Using Agents
• Agent Executors
• Multi-Step Reasoning
• End-to-End Agent Development

17. Ollama

• Running Local LLMs
• Model Management
• Local AI Development
• LangChain + Ollama Integration

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🛠️ Tech Stack

• Python
• LangChain
• Ollama
• OpenAI API
• Groq API
• ChromaDB
• FAISS
• HuggingFace Embeddings
• Pydantic
• Streamlit
• Jupyter Notebook

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📂 Repository Structure

├── 01_Introduction/
├── 02_LangChain_Components/
├── 03_Models/
├── 04_Prompts/
├── 05_Structured_Output/
├── 06_Output_Parsers/
├── 07_Chains/
├── 08_Runnables/
├── 09_Document_Loaders/
├── 10_Text_Splitters/
├── 11_Vector_Stores/
├── 12_Retrievers/
├── 13_RAG/
├── 14_Projects/
├── 15_Tools/
├── 16_Tool_Calling/
├── 17_AI_Agents/
├── 18_Ollama/
└── README.md

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⚙️ Setup

Clone Repository

git clone https://github.com/your-username/repository-name.git
cd repository-name

Create Virtual Environment

python -m venv venv

Activate Environment

Windows:

venv\Scripts\activate

Linux/Mac:

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file:

OPENAI_API_KEY=your_api_key
GROQ_API_KEY=your_api_key

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🚨 Security Notice

The .env file is ignored using .gitignore and should never be pushed to GitHub.

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🎯 Learning Outcomes

By completing the implementations in this repository, I learned:

• Prompt Engineering
• LLM Application Development
• Retrieval-Augmented Generation (RAG)
• AI Agent Design
• Tool Calling
• Vector Databases
• Semantic Search
• Local LLM Deployment using Ollama
• Production-Oriented LangChain Workflows

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🙏 Credits

Learning resources and inspiration from the excellent Generative AI and LangChain tutorials by CampusX.

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⭐ Support

If you find this repository useful, consider giving it a star.