pathwaycom/llm-app
Ready-to-run cloud templates for building real-time RAG, AI pipelines, and enterprise search applications that synchronize with various live data sources.
Awesome Infra for AI › Vector Databases & Retrieval Infrastructure
jina-ai/vectordb
`vectordb` is a Python-native vector database designed for managing and querying embeddings with a focus on simplicity and scalability. It provides a comprehensive set of CRUD (Create, Read, Update, Delete) operations and supports advanced features like sharding and replication for robust scaling. The project is deployable across various environments, from local development to on-premise and cloud deployments, including integration with Jina AI Cloud. The database capitalizes on the DocArray library for its powerful retrieval logic, serving as the algorithmic engine for vector search, and Jina for its capabilities in efficient and scalable index serving. This combination creates a user-friendly yet powerful vector database that allows developers to maintain control over the vector search algorithms while benefiting from a scalable infrastructure. `vectordb` supports multiple communication protocols for serving, including gRPC, HTTP, and WebSocket, making it versatile for different client-server architectures. It provides clear examples for local setup, deploying as a service, and integration with Jina AI Cloud, demonstrating its flexibility for various deployment scenarios. The project emphasizes avoiding over-engineering, aiming to deliver essential vector database functionalities with a lean and effective Pythonic design.
https://github.com/jina-ai/vectordb
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