Azure AI Search
Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.
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Jina Reranker
Jina Reranker v2 is a state-of-the-art reranker designed for Agentic Retrieval-Augmented Generation (RAG) systems. It enhances search relevance and RAG accuracy by reordering search results based on deeper semantic understanding. It supports over 100 languages, enabling multilingual retrieval regardless of the query language. It is optimized for function-calling and code search, making it ideal for applications requiring precise function signatures and code snippet retrieval. Jina Reranker v2 also excels in ranking structured data, such as tables, by understanding the downstream intent to query structured databases like MySQL or MongoDB. With a 6x speedup over its predecessor, it offers ultra-fast inference, processing documents in milliseconds. The model is available via Jina's Reranker API and can be integrated into existing applications using platforms like Langchain and LlamaIndex.
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Asimov
Asimov is a foundational AI-search and vector-search platform built for developers to upload content sources (documents, logs, files, etc.), auto-chunk and embed them, and expose them via a single API to power semantic search, filtering, and relevance for AI agents or applications. It removes the burden of managing separate vector-databases, embedding pipelines, or re-ranking systems by handling ingestion, metadata parameterization, usage tracking, and retrieval logic within a unified architecture. With support for adding content via a REST API and performing semantic search queries with custom filtering parameters, Asimov enables teams to build “search-across-everything” functionality with minimal infrastructure. It is designed to handle metadata, automatic chunking, embedding, and storage (e.g., into MongoDB) and provides developer-friendly tools, including a dashboard, usage analytics, and seamless integration.
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Pinecone Rerank v0
Pinecone Rerank V0 is a cross-encoder model optimized for precision in reranking tasks, enhancing enterprise search and retrieval-augmented generation (RAG) systems. It processes queries and documents together to capture fine-grained relevance, assigning a relevance score from 0 to 1 for each query-document pair. The model's maximum context length is set to 512 tokens to preserve ranking quality. Evaluations on the BEIR benchmark demonstrated that Pinecone Rerank V0 achieved the highest average NDCG@10, outperforming other models on 6 out of 12 datasets. For instance, it showed up to a 60% boost on the Fever dataset compared to Google Semantic Ranker and over 40% on the Climate-Fever dataset relative to cohere-v3-multilingual or voyageai-rerank-2. The model is accessible through Pinecone Inference and is available to all users in public preview.
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