Query Your Books and Documents with Natural Language - Offline

Ever wished you could just ask natural language questions to your own data and get direct answers? This talk showcases a system that allows exactly that - running locally and offline, you can query things like books you've read, meeting transcripts, and other personal data using natural language. #NaturalLanguageProcessing #QuestionAnswering #OfflineAI
The demo combines an open-source retrieval augmented generation (RAG) #RAG system with open-domain language models fine-tuned specifically for question answering over documents. This unique approach provides accurate extracts from the ingested data, avoiding hallucinations common when general #LanguageModels go beyond the given context.
Two key use cases are demonstrated: 1) A "book assistant" that retrieves relevant information from PDFs to answer questions, serving as a reference aid for previously read material. 2) Querying public meeting transcripts from an AI non-profit organization. #BookAssistant #MeetingTranscripts
Rather than attempting open-ended conversation, this extractive #QA system focuses on understanding the user's intent and returning a concise, relevant answer pulled from the source data. Example queries illustrate how it cites sources while providing the requested information.
The speaker explains the benefits of this data-constrained approach compared to conventional conversational #AIAssistants. They also discuss the goals of allowing natural queries while avoiding issues like hallucination that arise when language models stray too far from their training data. #HallucinationPrevention
See how this innovative system can unlock your own data's potential by making it straightforwardly queryable using natural language! #InnovativeAI #OfflineDataQuerying #NLPDemoCut
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