LLMs are coming to Apache Solr, Elastic Rerank model, Gemini 2.0 and much more!

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📰 News

Large Language Models are coming to Apache Solr! The integration of Large Language Models (LLMs) with Apache Solr marks an exciting new chapter for search capabilities, starting with the upcoming Solr 9.8 release. This milestone introduces support for external text-to-vector inference models directly in the textual query parser, enabling seamless semantic search. Users can now input textual queries, which Solr encodes into vectors for efficient vector-based search behind the scenes.

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Introducing Amazon Kendra GenAI Index – Enhanced semantic search and retrieval capabilities

Amazon Kendra GenAI Index is a new index in Amazon Kendra designed for RAG and intelligent search to help enterprises build digital assistants and intelligent search experiences more efficiently and effectively. This index offers high retrieval accuracy, using advanced semantic models and the latest information retrieval technologies. 

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Introducing Pinecone Rerank V0 Pinecone-rerank-v0 — is now available in public preview.

Designed to significantly enhance enterprise search and retrieval augmented generation (RAG) systems, this new model provides a powerful boost in relevance and accuracy, ensuring that your search results and AI-generated content are grounded in the most relevant and contextually precise information.

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Introducing the Elastic Rerank model (in Technical Preview)

“Take your search experiences up to level 11 with our new state-of-the-art cross-encoder Elastic Rerank model (in Tech Preview). Reranking models provide a semantic boost to any search experience, without requiring you to change the schema of your data, giving you room to explore other relevance tools for semantic relevance on your own time and within your budget.”

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Google unveils latest AI model, Gemini 2.0

“Today we’re excited to launch our next era of models built for this new agentic era: introducing Gemini 2.0, our most capable model yet. With new advances in multimodality — like native image and audio output — and native tool use, it will enable us to build new AI agents that bring us closer to our vision of a universal assistant.”

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Introducing Structured Outputs in the API

“Today we’re introducing Structured Outputs in the API, a new feature designed to ensure model-generated outputs will exactly match JSON Schemas provided by developers.”

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🏆 Must-Read Research and Papers:

• Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
• DocFusion: A Unified Framework for Document Parsing Tasks
• HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning

🗓️ Next events:

• NIAI | 25-26 January 2025 - Copenhagen, Denmark
• State of the OpenCon | 4-5 February 2025 - Southbank London (UK)
• NLPI | 15-16 March 2025 - Wien, Austria
• ECIR | 06-10 April 2025 - Lucca (Italy)
• SIGIR | 13-18 July 2025 - Padua (Italy)
• CLEF | 9-12 September 2025 - Madrid (Spain)

In case you missed our talks:

• Blazing-Fast Serverless MapReduce Indexer For Apache Solr
• Hybrid Search With Apache Solr Reciprocal Rank Fusion 
• From Natural Language To Structured Solr Queries Using LLMs

If you know of any AI & Information Retrieval events that are not listed do give us a heads-up so we can add them!

About us

Sease is an Information Retrieval Company based in London, focused on building Search solutions and AI integrations with cutting-edge Machine Learning such as Large Language Models (RAG, Vector-Based search) and Learning To Rank.

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