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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Mixedbread
Mixedbread is a fully-managed AI search engine that allows users to build production-ready AI search and Retrieval-Augmented Generation (RAG) applications. It offers a complete AI search stack, including vector stores, embedding and reranking models, and document parsing. Users can transform raw data into intelligent search experiences that power AI agents, chatbots, and knowledge systems without the complexity. It integrates with tools like Google Drive, SharePoint, Notion, and Slack. Its vector stores enable users to build production search engines in minutes, supporting over 100 languages. Mixedbread's embedding and reranking models have achieved over 50 million downloads and outperform OpenAI in semantic search and RAG tasks while remaining open-source and cost-effective. The document parser extracts text, tables, and layouts from PDFs, images, and complex documents, providing clean, AI-ready content without manual preprocessing.
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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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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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