Open Source Release of Anthropic’s Contextual RAG: Introducing Open Contextual RAG
Anthropic's Contextual RAG is now open source! Discover Together Computing's GitHub release, Open Contextual RAG, boosting retrieval accuracy by 67%!
Previously, I introduced a method developed by the Anthropic research team that significantly enhances RAG performance—Contextual RAG (Anthropic's Contextual Retrieval advances RAG, dramatically reducing retrieval failure rates). Although there was a detailed introduction, the full implementation code wasn’t disclosed.
The New RAG Method in Claude 3.5: Contextual Retrieval
To make AI models work effectively in specific environments, they often need to access background knowledge.
However, this gap has been filled by the Together Computing team, who released an open-source reference implementation of this technology on GitHub—Open Contextual RAG.
What is Contextual RAG?
Contextual RAG is an advanced chunk enhancement technique that cleverly uses LLMs, such as Claude, to enrich each document fragment with additional context.
Imagine if our brain could not only recall an event but also automatically bring up its causes and effects—this is the capability Contextual RAG aims to bring to LLMs.
This approach significantly improves document retrieval accuracy, achieving up to a 67% boost when combined with reranking.
