LangChain Experiments

This repository contains hands-on experiments and examples using LangChain for Retrieval-Augmented Generation (RAG) and Conversational Agents with memory and tool use.

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🚀 Examples

1. Retrieval-Augmented Generation (RAG)

File: RAG/1a_rag_basics.py

• Loads a text file (odyssey.txt).
• Splits it into manageable chunks.
• Converts each chunk into embeddings using HuggingFace models.
• Stores embeddings in a Chroma vector database.
• Supports semantic search over the stored vectors.

How to run:

python RAG/1a_rag_basics.py

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2. Conversational Agent with Tools & Memory

File: agent_conversational.py

• Loads your Google Gemini API key from .env.
• Initializes a Gemini LLM with LangChain.
• Adds conversational memory (remembers chat history).
• Loads tools (e.g., math tool, LLM tool).
• Uses a prompt template and LLMChain for flexible queries.
• Demonstrates multi-turn conversation and tool use.

How to run:

python agent_conversational.py

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🛠️ Setup

1. Clone the repository:

   git clone https://github.com/ali3dev/LangChain.git
   cd LangChain

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your .env file:

   GOOGLE_API_KEY=your-google-api-key-here
   

4. Add your data:

   • Place your text files (e.g., odyssey.txt) in the RAG/books/ folder.

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📦 Requirements

• Python 3.8+
• LangChain
• ChromaDB
• sentence-transformers
• langchain-google-genai
• python-dotenv

See requirements.txt for the full list.

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🤖 Credits

• LangChain
• ChromaDB
• Google Gemini

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📄 License

This project is for educational and experimental purposes.

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