Awesome GitHub RepositoriesRelational Vector Engines

Doris is a distributed SQL data warehouse designed for high-performance analytical workloads and real-time data processing. It functions as a unified platform that integrates traditional relational warehousing with lakehouse query capabilities, allowing users to execute analytical operations directly against external data lakes without requiring data migration. The system distinguishes itself through a shared-nothing, massively parallel processing architecture that utilizes vectorized query execution and columnar storage to maintain sub-second latency. It supports dynamic schema evolution, en

Gel is an object-relational database system that models data as a graph of interconnected objects. By utilizing a strongly typed schema, it enables complex relational queries and polymorphic data structures without the need for traditional join tables. The system integrates native vector storage and similarity search operators, allowing it to function as both a relational and a vector database for semantic data retrieval. The platform distinguishes itself through a comprehensive suite of developer-centric automation tools. It features a declarative migration system that tracks and versions sc

Databend is a cloud-native data warehouse and OLAP database designed for large-scale analytics. It functions as a SQL-compliant engine and serverless analytics platform that separates compute from storage to allow for independent scaling. The system integrates vector database capabilities, indexing high-dimensional embeddings to enable semantic, hybrid, and full-text searches across massive datasets. It further distinguishes itself through serverless compute management that automatically scales resources based on demand and shuts them down during idle periods. The platform covers a broad set

GPTCache is a semantic caching layer and response optimizer for large language models. It functions as pluggable middleware for orchestration frameworks, utilizing vector database caching to store and retrieve model responses based on the semantic similarity of prompts rather than exact text matches. The system uses embeddings to determine cache hits by comparing the distance between new queries and stored vectors. It employs a hybrid storage model that persists original prompts in relational databases while maintaining high-dimensional embeddings in vector stores. The project covers a broad

PostgresML is a machine learning database extension for PostgreSQL that integrates model training and inference directly into the database. It functions as an in-database AI platform and vector database, enabling the execution of large language models and natural language processing tasks on stored records without exporting data to external services. The system distinguishes itself by utilizing GPU acceleration to minimize latency during model predictions and employing a hybrid storage engine that maintains relational data alongside high-dimensional vectors. It allows for the building and fin

Olares is a comprehensive suite of self-hosted identity, storage, AI, and orchestration services designed for private infrastructure management. It functions as a Kubernetes home server orchestrator, enabling the deployment of containerized applications, AI models, and GPU resources on local hardware to replace third-party cloud services. The platform distinguishes itself through a combination of self-hosted AI infrastructure for running large language models and image generators, alongside a decentralized identity manager that uses cryptographic keys and OIDC for trustless authentication. It

pgvecto.rs is a database extension that integrates high-dimensional vector search capabilities directly into PostgreSQL. It functions as a specialized engine for storing and retrieving embeddings, allowing relational databases to perform similarity searches alongside traditional structured data queries. The extension distinguishes itself through hardware-aware execution strategies that maximize performance. It performs runtime analysis of the host machine to utilize specific processor instruction sets for accelerated mathematical operations. To manage memory efficiently, it employs quantizati