Graph and vector usage for automated QA system

This system automatically answers user questions by first breaking them down. It then queries a network of facts, where each fact is specifically tied to text from a document, to find relevant information. It also searches a database of text chunks, which are converted into numerical representations by an AI model, to locate specific content for generating an answer.

Why it matters: The rapid advancement and accessibility of large language models since 2023 have significantly improved the capabilities for query decomposition, text embedding, and especially the final answer generation step described. This makes building a highly effective version of this system much more feasible now.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedAI models, knowledge graph, vector database

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