🤖 Local AI Agent with Python (Ollama, LangChain & RAG)

A locally hosted AI agent built using Python, Ollama, LangChain, and RAG (Retrieval-Augmented Generation).
This project enables offline LLM inference, semantic search, and contextual question answering using a vector database.

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🚀 Features

• 🧠 Local language model integration via Ollama
• 🔍 RAG-based context retrieval for enhanced responses
• 🗂️ ChromaDB as a vector store for embedding management
• ⚡ LangChain pipeline for modular orchestration
• 🔊 Seamless local and offline LLM usage
• 💾 Persistent and queryable data storage
• 🔐 Privacy-friendly — no external API calls required

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🏗️ Tech Stack

• Python 3.10+
• Ollama – Local LLM runtime
• LangChain – Framework for chaining LLM logic
• ChromaDB – Vector database for document retrieval
• FAISS / SentenceTransformers – For embedding generation
• VS Code / Jupyter / Colab – Development environment

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/rathod-0007/LocalAIAgentWithRAG.git
cd LocalAIAgentWithRAG

2️⃣ Create and Activate Virtual Environment

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

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Application

python main.py

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🧩 Architecture Overview

User Query → LangChain → Retriever (ChromaDB) → Ollama Model → Response

• LangChain: Handles query flow and RAG orchestration
• Ollama: Runs local LLMs (e.g., Llama3, Mistral)
• ChromaDB: Manages embeddings for semantic retrieval

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📁 Project Structure

LocalAIAgentWithRAG/
│
├── main.py               # Entry point of the application
├── retriever.py          # Vector retrieval and embedding logic
├── vector/               # Database and embedding utilities
├── data/                 # Local dataset/documents
├── requirements.txt      # Dependencies
└── README.md             # Project documentation

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🧠 Example Use Cases

• Chat with local documents or datasets
• Build knowledge-grounded assistants
• Perform semantic search and retrieval
• Run LLMs fully offline

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💡 Future Enhancements

• 🧩 Integration with advanced embedding models (e.g., E5, OpenAI)
• 🌐 Web-based UI with Streamlit
• 📊 Conversation analytics dashboard
• 💬 Multi-agent reasoning system

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🪪 License

This project is licensed under the MIT License.
Feel free to use, modify, and distribute it with attribution.

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👤 Author

Rathod Pavan Kumar Naik
GitHub | LinkedIn