Vector Store Comparison Demo

A simple demonstration comparing Pinecone and Weaviate vector stores using OpenAI embeddings.

Project Structure

.
├── data/
│   ├── cat_facts.txt          # Sample dataset of cat facts
│   ├── passages.json          # Additional text passages for vector store
│   └── question_answer.json   # Q&A pairs for testing
├── notebooks/
│   ├── agents/                # Agent-based implementations
│   │   ├── langchain.ipynb    # LangChain agent with routing
│   │   └── claude.ipynb       # Claude-based intelligent agent
│   ├── chatbot/               # Chatbot implementations
│   │   ├── vector_store.ipynb # Document vectorization
│   │   ├── agent.ipynb        # RAG agent with Claude
│   │   ├── OpenAI/            # OpenAI text for DB
│   │   └── Anthropic/         # Anthropic text for DB
│   └── vector_stores/         # Vector store implementations
│       ├── pinecone.ipynb     # Pinecone vector store demo
│       └── weaviate.ipynb     # Weaviate vector store demo
└── requirements.txt           # Project dependencies

Features

Vector Stores (notebooks/vector_stores/)

• Implementations using both Pinecone and Weaviate
• Vector similarity search demonstrations
• OpenAI embeddings integration
• Basic RAG (Retrieval-Augmented Generation) examples

Intelligent Agents (notebooks/agents/)

• LangChain-based routing system
• Claude integration for advanced reasoning
• Hybrid search capabilities
• Tool-based interaction patterns

Chatbot (notebooks/chatbot/)

• Document chunking and vectorization
• RAG together with docuemnt retrieval implementation with Claude
• Tool-use capabilities
• Provider-specific examples for OpenAI and Anthropic

Setup

1. Create and activate virtual environment:

python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip3 install -r requirements.txt

2. Create a .env file with your API keys:

OPENAI_API_KEY=your_openai_key
PINECONE_API_KEY=your_pinecone_key
WCD_URL=your_weaviate_cloud_url
WEAVIATE_API_KEY=your_weaviate_key
...