Search Results for "ai database"

RuVector is part of the broader rUv ecosystem of AI engineering tools and focuses on enabling advanced vector-based processing and intelligent system development within agentic and AI-driven pipelines. The project fits into a larger vision of modular, composable AI infrastructure designed to support autonomous agents, data retrieval, and intelligent automation workflows. It emphasizes extensibility and interoperability with modern AI stacks, allowing developers to integrate vector operations...

PageIndex is an innovative open-source framework that reimagines retrieval-augmented generation (RAG) by eliminating conventional vector similarity search and instead building hierarchical semantic indexes that mirror a document’s natural structure. Rather than chunking text and embedding it into a vector database, PageIndex constructs a tree-structured index — similar to a detailed, AI-enhanced table of contents — that a large language model can traverse to locate the most relevant sections of long documents. This reasoning-driven retrieval aligns more naturally with how humans explore complex texts, improving relevance and traceability, especially in professional domains like financial reports, legal contracts, and technical manuals. ...

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. Faiss contains several methods for similarity search. It...

...The framework supports integration with multiple providers, including OpenAI and other compatible APIs, and includes tools for embeddings, document loading, and vector database interaction. It also provides both low-level async APIs and higher-level chain abstractions, giving developers flexibility in how they structure their applications. With built-in support for retrieval-augmented generation and structured pipelines, LangChain .NET is designed for production-ready AI systems.

Very simple struct storage system, AVL and red-black tree based database library - database engine. You can define indexes over C struct.

The Spring Petclinic is the canonical reference sample application built with Spring Boot and Thymeleaf, demonstrating best practices in building Spring-based web applications; forks also exist showcasing implementations with modern architectures like microservices, reactive stacks, and AI integrations. In its default configuration, Petclinic uses an in-memory database (H2) which gets populated at startup with data. The h2 console is exposed and it is possible to inspect the content of the database using the jdbc:h2:mem:<uuid> URL. The UUID is printed at startup to the console. One of the best parts about working on the Spring Petclinic application is that we have the opportunity to work in direct contact with many Open Source projects.

This project consists out of a windows based designer application and a library (that can run on multiple platforms, including android) together with several demo applications (including an MVC3 chatbot client and an android application). It is probably best compared to a database management system, but for neural networks instead of relational data. As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer, you are able to create different types of decision trees, random forests, n-grams, pattern-matchers, conversational agents and all sorts of AI related algorithms. ...