Liquid AI Introduces LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: Dense Bi-Encoder and Late-Interaction — 2026-06-19

## Short Segments Amazon Bedrock AgentCore now offers real-time web search, bridging the gap between static AI knowledge and dynamic information needs. This new feature allows AI agents to access up-to-date web data without the hassle of managing infrastructure. Coming up, we'll explore how Salesforce's CodeGen enhances Python function generation with safety checks and unit tests. Later, we'll dive into Liquid AI's latest multilingual search models, promising faster and more accurate retrieval across 11 languages. Amazon Bedrock AgentCore introduces a game-changing web search capability. AI agents often struggle with outdated information, but Amazon's new web search feature on Bedrock AgentCore changes that. Now generally available, this tool allows agents to access current web data seamlessly, without the need for complex infrastructure management. It integrates with the AgentCore Gateway, enabling agents to discover and use it like any other tool. The web index, maintained by Amazon, spans tens of billions of documents and updates continuously, ensuring that agents have access to the latest information. This development means AI agents can now provide more accurate and timely responses, enhancing their utility in dynamic environments. Salesforce CodeGen tutorial showcases advanced Python function generation. Salesforce's CodeGen model, available on Hugging Face, is not just for code completion. A new tutorial demonstrates its capabilities in generating Python functions from natural-language prompts, complete with syntax checking, static safety checks, and unit-test-based validation. The workflow includes best-of-N candidate reranking and multi-step program synthesis, making it a comprehensive tool for developers. This structured pipeline ensures that generated code is not only functional but also safe and reliable, streamlining the development process and enhancing productivity. Adobe Marketing Agent for Amazon Quick accelerates campaign insights. Marketing teams can now access campaign insights faster with the integration of Adobe Marketing Agent into Amazon Quick. This collaboration allows marketers to ask natural language questions about campaign performance and receive immediate insights. Amazon Quick handles the chat experience, while Adobe provides the marketing-domain analysis. The integration supports audience rankings, loyalty segment summaries, and conflict recommendations, enabling marketers to make informed decisions quickly. This seamless workflow enhances the efficiency of marketing campaigns by providing strategic insights in real-time. ## Feature Story Liquid AI's new multilingual search models promise faster, more accurate retrieval. This week, Liquid AI unveiled two new retrieval models: LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M. Both models, with 350 million parameters, are designed for fast and reliable multilingual search across 11 languages. The LFM2.5-Embedding-350M is a dense bi-encoder, ideal for scenarios where speed and storage efficiency are paramount. In contrast, the LFM2.5-ColBERT-350M, a late-interaction model, offers higher accuracy by matching queries word-by-word, albeit with a larger index. These models are particularly suited for short-context searches, such as product catalogs and FAQ knowledge bases, and can serve as drop-in replacements in existing retrieval-augmented generation pipelines. Available on Hugging Face under the LFM Open License v1.0, these models are accessible to developers looking to enhance their search capabilities. The introduction of these models marks a significant step in multilingual search technology, offering a balance between speed and accuracy. As organizations increasingly operate in multilingual environments, the ability to perform fast and accurate searches across languages becomes crucial. These models provide a practical solution, enabling businesses to improve their search functionalities without significant infrastructure changes. Looking ahead, the impact of these models on multilingual search efficiency and accuracy will be an area to watch.