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- The need for answering questions from arbitrary data sources

- The benefits of a multi-document answering system

- Building a chatbot to answer questions and summarize documents

- Breaking up text into chunks for indexing

- Building an index

- Converting unicode to ascii to prevent gpt3 errors

- Saving data as a JSON file

- Building the index

- Building a Knowledge Base

- Building the index

- Searching for memories

- Using GPT-3 to answer questions about a text

- The majority's decision to allow states to ban abortion

- Answering a question with a superintelligence

- Generating answers to questions with GPT-3

- Joining answers into one big block

- Creating a detailed summary of chunks

- Trying to fix a broken search

- Fixing the bug in the gpt3 completion function

- GPT3's difficulty with complex questions

- The gpt3 log

- The Supreme Court's decision on abortion

- The Supreme Court overturns a lower ruling banning abortion

- The final result of the superintelligence question

- The Supreme Court overturns Roe v. Wade

Description:

Learn how to build a system that answers complex questions from large document sets using vector search and GPT-3 in this comprehensive tutorial video. Explore techniques for breaking text into indexable chunks, building a knowledge base, and leveraging GPT-3 to generate detailed summaries and answers. Discover methods for handling unicode conversion, JSON file storage, and troubleshooting common issues. Apply these concepts to analyze real-world examples, including the Supreme Court's decision on Roe v. Wade. Gain practical insights into creating an advanced question-answering system capable of processing arbitrary data sources and providing intelligent responses.

Answer Complex Questions From an Arbitrarily Large Set of Documents With Vector Search and GPT-3

David Shapiro ~ AI

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#Computer Science #Artificial Intelligence #Natural Language Processing (NLP) #LLM (Large Language Model) #GPT-3 #ChatGPT #Machine Learning #Vector Search