LLMs, RAG, & the missing storage layer for AI
Connect external data
to LLMs , no matter the source.
The universal retrieval engine for LLMs to access unstructured data from any source.
to LLMs , no matter the source.
The universal retrieval engine for LLMs to access unstructured data from any source.
Carbon | Data Connectors for LLMs
SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system.
The core features of SGLang include:
The core features of SGLang include:
- A Flexible Front-End Language : This allows for easy programming of LLM applications with multiple ch
sgl-project • GitHub - sgl-project/sglang: SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.
DocArray as our in-memory vector storage. DocArray provides various features like advanced indexing, comprehensive serialization protocols, a unified Pythonic interface, and more. Further, it offers efficient and intuitive handling of multimodal data for tasks such as natural language processing, computer vision, and audio processing.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Creative AI Lab
creative-ai.org