Document Retrieval 3

Open-source RAG infrastructure for building production-ready AI chat and search applications. Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed to help developers build production-grade AI chat and search applications. It handles the complex aspects of RAG, including document extraction, chunking, hybrid search, and agentic reasoning, ensuring that AI apps remain reliable even with large-scale data and high user traffic. The platform is model-agnostic, allowing users to choose their own LLMs, vector databases, and embedding models. It supports over 22 file formats and multimodal data like images and tables, providing cited answers out of the box for sectors like medical AI, legal tech, and enterprise search.

Vellum AI: A platform for developing, evaluating, and deploying AI products. Vellum AI is a platform designed to help AI product developers take their ideas from early-stage concepts to production-grade features. It provides tooling for experimentation, evaluation, deployment, monitoring, and collaboration, all in one place. Vellum offers features like a visual workflow builder, an SDK, prompt engineering tools, evaluation metrics, retrieval UIs and APIs, one-click deployment, and observability tools to track AI decisions.

Create GPT-4 chatbots by combining chat completions and document retrievals. Chatmate.dev allows you to easily make gpt4 based chatbots by combining multiple chat completions and/or document retrievals. You can chain different components together or run them in parallel and then use their responses in future prompts/components. With chatmate.dev you can combine multiple gpt4 chatbots with vector document retrievals to create complex multi-chain chatbots. You can share your chatbots and discover new ones from the community.