Experimenting with Pinecone as vector data is becoming the standard for AI-native systems. Purpose of the project is learning and experimentation, with the intent of implementing all core Pinecone DB capabilities.

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Table of Contents

• Pinecone Learning & Experimentation
• Features
• Installation
• Examples
• Jupyter Notebooks
• Project Structure
• Future Enhancements
• License

Pinecone Learning & Experimentation

A comprehensive learning project for exploring Pinecone vector database capabilities through hands-on examples and interactive notebooks.

Features

• Complete Pinecone DB operations coverage
• Vector search and similarity matching
• Metadata filtering and namespaces
• Hybrid search (dense + sparse vectors)
• Interactive Jupyter notebooks
• Utility functions and configuration management

Installation

1. Clone the repository:

git clone https://github.com/harehimself/pinecone-lab.git
cd pinecone-lab

2. Create and activate virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Configure API key as environment variable:

# Variable Name: PINECONE_API_KEY
# Variable Value: your-api-key-here

Examples

The src/examples directory contains 8 standalone scripts demonstrating core features:

• Basic Operations: python -m src.examples.01_basic_operations
• Vector Operations: python -m src.examples.02_vector_operations
• Namespaces: python -m src.examples.03_namespaces
• Metadata Filtering: python -m src.examples.04_metadata_filtering
• Sparse Vectors: python -m src.examples.05_sparse_vectors
• Hybrid Search: python -m src.examples.06_hybrid_search
• Integrated Embedding: python -m src.examples.07_integrated_embedding
• Backup & Restore: python -m src.examples.08_backup_restore

Jupyter Notebooks

Interactive notebooks for hands-on exploration:

jupyter notebook notebooks/01_quickstart.ipynb
jupyter notebook notebooks/02_advanced_usage.ipynb

Project Structure

• src/config.py: Configuration and environment variables
• src/utils.py: Utility functions for Pinecone operations
• src/examples/: Individual example scripts
• notebooks/: Interactive learning notebooks

Future Enhancements

• Real embedding models (OpenAI, SentenceTransformers)
• Real-world dataset examples
• Performance benchmarking tools
• Vector space visualization tools

License

MIT License © 2025 HareLabs