RAG assistant implementation detailed with verifiable answers

This article details the implementation of a Retrieval-Augmented Generation (RAG) assistant designed to help support teams access operational documentation. The author outlines the architecture, which separates document preparation and indexing from the question-answering process. Key features include the ability to verify answers against their source evidence, a separate 'Evidence preview' mode for inspecting retrieval without model calls, and the use of vector databases like Chroma, Pinecone, and Weaviate. The project's source code and a hosted demo are available, though the current dataset is synthetic and small. AI

IMPACT Provides a practical guide for building and inspecting RAG systems, useful for developers integrating LLMs with custom data.

RANK_REASON Article describes the implementation and usage of a specific AI tool (RAG assistant) with available code and demo.

Read on Towards AI →

• Chroma
• Claude 3
• Faiss
• GPT-4
• LangChain
• LlamaIndex
• Mistral Large
• .openai
• Pinecone
• retrieval-augmented generation
• Weaviate

• product
• infra

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