
LLMs, RAG, & the missing storage layer for AI

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.
pg_vectorize: a VectorDB for Postgres
A Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls.
This project relies heavily on the work by pgvector f... See more
A Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls.
This project relies heavily on the work by pgvector f... See more
GitHub - tembo-io/pg_vectorize: The simplest way to orchestrate vector search on Postgres
You’ve got a vector database that has all the right database fundamentals you require, has the right incremental indexing strategy for your use case, has a good story around your metadata filtering needs, and will keep its index up-to-date with latencies you can tolerate. Awesome.
Your ML team (or maybe OpenAI) comes out with a new version of their... See more
Your ML team (or maybe OpenAI) comes out with a new version of their... See more
6 Hard Problems Scaling Vector Search
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.