GitHub - stanford-futuredata/ColBERT: Stanford ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22)

Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more

Welcome to RAGatouille

Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more

Pipeline RobustQA Avg. score Avg. response time (secs) Azure Cognitive Search Retriever + GPT4 + Ada 72.36 >1.0s Canopy (Pinecone) 59.61 >1.0s Langchain + Pinecone + OpenAI 61.42 <0.8s Langchain + Pinecone + Cohere 69.02 <0.6s LlamaIndex + Weaviate Vector Store - Hybrid Search 75.89 <1.0s RAG Google Cloud VertexAI... See more

🥤 Cola [NeurIPS 2023]

Large Language Models are Visual Reasoning Coordinators

Liangyu Chen*,†,♥ Bo Li*,♥ Sheng Shen♣ Jingkang Yang♥

Chunyuan Li♠ Kurt Keutzer♣ Trevor Darrell♣ Ziwei Liu✉,♥

♥S-Lab, Nanyang Technological University

♣University of California, Berkeley ♠Microsoft Research, Redmond

*Equal Contribution †Project Lead ✉Corresponding Author... See more