langchain-learning 0.1.0

A structured learning project for LangChain and vector databases

LangChain Learning Project

A structured learning project for mastering LangChain and vector databases (Milvus).

Project Structure

langchain-learning/
├── src/                    # Source code
│   ├── core/              # Core functionality modules
│   ├── chains/            # LangChain chains
│   ├── agents/            # LangChain agents
│   └── utils/             # Utility functions
├── examples/              # Example scripts (migrated from LangChain-*.py)
├── tests/                 # Test code
├── docs/                  # Documentation
├── data/                  # Data files
└── config/                # Configuration files

Getting Started

Prerequisites

• Python 3.8+
• Milvus server (for vector database examples)
• API keys for AI services (DeepSeek, OpenAI, etc.)

Installation

1. Clone the repository:

git clone <repository-url>
cd langchain-learning

2. Create and activate virtual environment:

python -m venv .venv
# On Windows
.venv\Scripts\activate
# On Linux/Mac
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys

Examples

The examples/ directory contains learning scripts:

• LangChain-1.py to LangChain-22.py: Step-by-step tutorials
• Each script demonstrates a specific LangChain/Milvus concept

Development

Code Quality Tools

• Black: Code formatting
• isort: Import sorting
• mypy: Static type checking
• pytest: Testing framework
• flake8: Linting

Running Tests

pytest

Code Formatting

black src tests
isort src tests

Learning Path

1. Basics: Embeddings, vector stores, document loaders
2. Chains: Sequential chains, transformation chains
3. Agents: Tool-using agents, reasoning agents
4. Memory: Conversation memory, entity memory
5. Advanced: Retrieval-augmented generation (RAG), multi-modal chains

Resources

• LangChain Documentation
• Milvus Documentation
• Pydantic Documentation

License

This project is for educational purposes.

#cd examples #python demo_embeddings.py