LangChain Basics 🦜️🔗

A practical introduction to LangChain framework for developing applications powered by language models.

Overview

This repository contains Jupyter notebooks and code samples demonstrating fundamental LangChain concepts. Perfect for developers starting their journey in LLM-powered application development.

What is LangChain?

LangChain is a framework that simplifies:

• Building context-aware applications
• Connecting language models to data sources
• Creating powerful pipelines (chains) for text processing

Repository Contents

🧠 Foundational Concepts

0.0 - OpenAI Basics

• Prompt template fundamentals
• Chain input/output parsing
• Basic LLM interaction patterns

0.0 - LangChain Chatbot using Ollama

• Build mini-chatbot using Gemma + Ollama
• Local LLM integration
• Conversation memory basics

📊 Data Pipeline Implementation

1. Data Ingestion Techniques

• Document loaders (PDF, HTML, Markdown)
• Web scraping integration
• Structured/unstructured data loading

2. Data Transformation

• Text splitting strategies
• Chunk size optimization
• Overlap techniques for context preservation

3. Embeddings & Vectorization

• OpenAI embeddings implementation
• Hugging Face sentence transformers
• Ollama local embedding models
• Embedding comparison/analysis

4. Vector Databases

• ChromaDB integration
• FAISS similarity search
• Vector store persistence
• Retrieval-augmented generation (RAG) setup

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/EniolaAdemola/langchain-basics.git
   

2. Navigate to the project directory:

   cd langchain-basics

3. Install the required dependencies:

(Recommended: make sure to create your virtual environment, activate it and run the command below)

pip install -r requirements.txt

🎥 Watch the tutorial here: Step by step video on how to create virtual environment using conda and python