Improving RAG: Strategies
Overall, RAG and RALMs overcome the limits of language models’ memory by grounding responses in external information.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
You (Kind of) Don't Need RAG - ChatGPT Finetuning Technique
cosine.sh
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
arXiv:2405.02048v1 [cs.IR] 3 May 2024
A Beginner's Guide to Website Chunking and Embedding for Your RAG Applications - Zilliz Learn
Ruben Winastwanzilliz.com
To replace indexes with a single, consolidated model, it must be possible for the model itself to have knowledge about the universe of document identifiers, in the same way that traditional indexes do. One way to accomplish this is to move away from traditional LMs and towards corpus models that jointly model term-term, term-document, and document-... See more