Trieve offers advanced AI-powered search and discovery solutions, ensuring companies have a competitive edge. Features include semantic vector search, full-text search with BM25 and SPLADE models, and hybrid search capabilities. Trieve also provides relevance tuning, sub-sentence highlighting, and robust API integrations for easy data management. Companies can manage ingestion, embeddings, and analytics effortlessly, leveraging private open-source models for maximum data security. Set up industry-leading search experiences quickly and efficiently.

Azure AI Foundry Agents Samples provides developers with a rich set of example scenarios that illustrate how to leverage Azure AI Foundry SDKs and services. It includes conversational agents with long-term memory, planner agents that break down complex tasks, tool-enabled agents that call external APIs, and multimodal agents combining text, vision, and speech. Each sample is preconfigured with environment setups, LLM orchestration, vector search, and telemetry to accelerate prototyping and deployment of robust AI solutions on Azure.

Local RAG Researcher Deepseek combines Deepseek’s powerful file crawling and indexing capabilities with vector-based semantic search and local LLM inference to create a standalone retrieval-augmented generation (RAG) agent. Users configure a directory to index various document formats—including PDF, Markdown, text, and more—while custom embedding models integrate via FAISS or other vector stores. Queries are processed through local open-source models (e.g., GPT4All, Llama) or remote APIs, returning concise answers or summaries based on the indexed content. With an intuitive CLI interface, customizable prompt templates, and support for incremental updates, the tool ensures data privacy and offline accessibility for researchers, developers, and knowledge workers.

Clinical Agent is designed to streamline clinical workflows by combining the power of retrieval-augmented generation and vector search. It ingests electronic medical record data, indexes documents using a vector database, and uses LLMs to answer clinical queries, generate discharge summaries, and create structured notes. Developers can customize prompts, integrate additional data sources, and extend modules. The framework supports modular pipelines for data ingestion, semantic search, question answering, and summarization, enabling hospitals and research teams to rapidly deploy AI-driven clinical assistants.