Kikiola

Kikiola is a high-performance vector database written in Go. It efficiently stores, indexes, and searches for vectors, making it suitable for similarity search, recommendation systems, artificial intelligence, and machine learning applications.

Features

• Tensor Compression
• Support multilingual embedding
• Support for high-dimensional vectors
• Handles concurrency and multiple writes
• Simple and intuitive API for easy integration
• Indexing techniques for fast similarity search
• Embedding Reranking with a relevance score
• Fast and efficient vector storage and retrieval
• Text embedding support for text-based queries
• Scalable architecture for handling large datasets
• Distributed Storage: multiple nodes or shards for scalability
• Objects (e.g., document, image, audio, video, or any other file type)

Run

To run Kikiola, ensure that you have Go installed on your system. Then, follow these steps:

1. Clone the Kikiola repository:

git clone https://github.com/0xnu/kikiola.git

2. Navigate to the project directory:

cd kikiola

3. Build the project:

go build ./...

4. Run the Kikiola server:

go run cmd/main.go

The Kikiola server will start running on http://localhost:3400.

Test

To test Kikiola, ensure that you have Go installed on your system. Then, follow these steps:

go test ./...

Usage, Use Cases, and Benchmark

• Usage
• Docker
• Benchmark
• Quay
• JFrog
• GitLab
• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• Generate and Store Embeddings - Documents and Images
• Generate and Store Embeddings - Genome Sequence
• Generate and Store Embeddings - Multilingual
• Generate and Store Embeddings - Hugging Face 🤗
• Generate and Store Embeddings - SEC Form 10-K - Be a responsible Human and use the EDGAR API. 😎
• Generate and Store Embeddings - Videos
• Generate and Store Embeddings - Audios

License

This project is licensed under the MIT License.

Copyright

(c) 2024 Finbarrs Oketunji.