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.

Granite Retrieval Agent provides developers with a flexible platform to build retrieval-augmented generative AI agents that combine semantic search and large language models. Users can ingest documents from diverse sources, create vector embeddings, and configure Azure Cognitive Search indexes or alternative vector stores. When a query arrives, the agent retrieves the most relevant passages, constructs context windows, and calls LLM APIs for precise answers or summaries. It supports memory management, chain-of-thought orchestration, and custom plugins for pre- and post-processing. Deployable with Docker or directly via Python, Granite Retrieval Agent accelerates the creation of knowledge-driven chatbots, enterprise assistants, and Q&A systems with reduced hallucinations and enhanced factual accuracy.

Agentic-RAG-DeepSeek combines agentic orchestration with RAG techniques to enable advanced conversational and research applications. It first processes document corpora, generating embeddings using LLMs and storing them in DeepSeek's vector database. At runtime, an AI agent retrieves relevant passages, constructs context-aware prompts, and leverages LLMs to synthesize accurate, concise responses. The framework supports iterative, multi-step reasoning workflows, tool-based operations, and customizable policies for flexible agent behavior. Developers can extend components, integrate additional APIs or tools, and monitor agent performance. Whether building dynamic Q&A systems, automated research assistants, or domain-specific chatbots, Agentic-RAG-DeepSeek provides a scalable, modular platform for retrieval-driven AI solutions.

Hebbia is a cutting-edge AI platform built to revolutionize the way knowledge work is performed. By leveraging advanced AI technology, Hebbia enables users to synthesize public information effortlessly, analyze financial transactions and bidding dynamics instantly, and create comprehensive profiles. The platform is designed for industries such as finance, law, government, and pharmaceuticals, providing specialized tools that help in extracting and managing relevant data, ultimately enhancing decision-making processes and productivity.