https://github.com/kauxtubh/pinecone
Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, and better understand what is possible with Pinecone.
https://github.com/kauxtubh/pinecone
chatgpt embeddings langchain-js langchain-python language llamaindex neondb openai-api overlay overlay-network rag streamlit vercel wifi-security
Last synced: 2 months ago
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Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, and better understand what is possible with Pinecone.
- Host: GitHub
- URL: https://github.com/kauxtubh/pinecone
- Owner: Kauxtubh
- License: mit
- Created: 2025-05-03T13:48:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-03T15:59:20.000Z (about 1 year ago)
- Last Synced: 2025-05-03T16:51:57.433Z (about 1 year ago)
- Topics: chatgpt, embeddings, langchain-js, langchain-python, language, llamaindex, neondb, openai-api, overlay, overlay-network, rag, streamlit, vercel, wifi-security
- Language: Python
- Size: 80.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pinecone 🌲
Welcome to the Pinecone repository! This project focuses on experimenting with Pinecone, a vector database that plays a crucial role in AI-native systems. Here, we explore its core capabilities and aim to understand the possibilities that Pinecone offers for embedding and retrieval tasks.
## Table of Contents
- [Introduction](#introduction)
- [Project Goals](#project-goals)
- [Installation](#installation)
- [Usage](#usage)
- [Core Features](#core-features)
- [Contributing](#contributing)
- [License](#license)
- [Releases](#releases)
- [Contact](#contact)
## Introduction
As artificial intelligence continues to evolve, the need for efficient data retrieval and storage solutions becomes paramount. Pinecone offers a unique approach to managing vector data, allowing for seamless integration with various AI models. This repository serves as a playground for developers and researchers to dive deep into Pinecone's functionalities.
## Project Goals
1. **Understand Vector Embeddings**: Gain insights into how vector embeddings work and their significance in AI.
2. **Explore Retrieval-Augmented Generation**: Investigate how Pinecone can enhance retrieval-augmented generation tasks.
3. **Experiment with Similarity Search**: Test and measure the performance of similarity searches using various embedding models.
4. **Develop Practical Applications**: Create sample applications that demonstrate the power of Pinecone in real-world scenarios.
## Installation
To get started with Pinecone, follow these simple steps:
1. Clone the repository:
```bash
git clone https://github.com/Kauxtubh/pinecone.git
cd pinecone
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Set up your Pinecone account and API key. You can sign up [here](https://www.pinecone.io/start/).
4. Configure your environment:
Create a `.env` file in the root directory and add your Pinecone API key:
```plaintext
PINECONE_API_KEY=your_api_key_here
```
## Usage
Once you have everything set up, you can start experimenting with Pinecone. Here are some basic commands to get you started:
1. **Initialize Pinecone**:
```python
import pinecone
pinecone.init(api_key="your_api_key_here", environment="us-west1-gcp")
```
2. **Create a new index**:
```python
pinecone.create_index("example-index", dimension=128)
```
3. **Insert data**:
```python
index = pinecone.Index("example-index")
index.upsert([(id, vector)])
```
4. **Query for similar items**:
```python
results = index.query(queries=[query_vector], top_k=5)
```
## Core Features
### Vector Database
Pinecone acts as a fully managed vector database, enabling you to store and retrieve high-dimensional vectors efficiently. This feature is essential for applications that rely on embeddings.
### Embedding Models
Explore various embedding models, such as BERT, GPT, and others. The repository includes examples demonstrating how to generate embeddings from text and images.
### Similarity Search
Utilize Pinecone's fast similarity search capabilities to find items that are most similar to a given vector. This is particularly useful in recommendation systems and search applications.
### Scalability
Pinecone automatically scales to handle millions of vectors, making it suitable for both small and large datasets.
### Real-time Updates
Insert, update, and delete vectors in real-time, allowing for dynamic data management.
## Contributing
We welcome contributions from the community! If you would like to contribute to the Pinecone project, please follow these steps:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature/YourFeature
```
3. Make your changes and commit them:
```bash
git commit -m "Add your message here"
```
4. Push to the branch:
```bash
git push origin feature/YourFeature
```
5. Open a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Releases
For the latest updates and releases, visit the [Releases](https://github.com/Kauxtubh/pinecone/releases) section. Here, you can download and execute the latest versions of the project.
## Contact
For questions or feedback, feel free to reach out:
- **Email**: your-email@example.com
- **Twitter**: [@yourhandle](https://twitter.com/yourhandle)
Thank you for checking out Pinecone! We hope you find it useful for your projects. Happy coding!