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RAG & KNOWLEDGE BASE
Ask a question. Get an answer with sources.
A knowledge base that cites its work. Every answer traced back to the source.
SEARCH
HOW IT WORKS
Under the hood
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1
Embed the query Your question is converted to a 1536-dimension vector using text-embedding-3-small.
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2
Vector search The query vector is matched against all document chunks using cosine similarity.
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3
Retrieve top-k chunks The 3 most relevant passages are retrieved with their document source and chunk position.
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4
Rerank A cross-encoder reranker orders the passages by relevance to the specific question.
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5
Generate with grounding Claude generates an answer using only the retrieved passages: no hallucination, full citation.
BUILT WITH
bge-base-en-v1.5 embeddingsCosine similarity searchLlama 3.1 8B20-document HR knowledge baseCloudflare Workers AI
You just asked a question and got a cited answer from 25 documents. In production, this makes your entire knowledge base searchable by anyone on your team.
Try Chatbots to see this retrieval engine powering a real conversation.