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

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

David Shapiro ~ AI via YouTube Direct link

- Building a Knowledge Base

9 of 27

9 of 27

- Building a Knowledge Base

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

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  1. 1 - The need for answering questions from arbitrary data sources
  2. 2 - The benefits of a multi-document answering system
  3. 3 - Building a chatbot to answer questions and summarize documents
  4. 4 - Breaking up text into chunks for indexing
  5. 5 - Building an index
  6. 6 - Converting unicode to ascii to prevent gpt3 errors
  7. 7 - Saving data as a JSON file
  8. 8 - Building the index
  9. 9 - Building a Knowledge Base
  10. 10 - Building the index
  11. 11 - Searching for memories
  12. 12 - Using GPT-3 to answer questions about a text
  13. 13 - The majority's decision to allow states to ban abortion
  14. 14 - Answering a question with a superintelligence
  15. 15 - Generating answers to questions with GPT-3
  16. 16 - Joining answers into one big block
  17. 17 - Creating a detailed summary of chunks
  18. 18 - Trying to fix a broken search
  19. 19 - Fixing the bug in the gpt3 completion function
  20. 20 - GPT3's difficulty with complex questions
  21. 21 - The gpt3 log
  22. 22 - The Supreme Court's decision on abortion
  23. 23 - The Supreme Court's decision on abortion
  24. 24 - The Supreme Court overturns a lower ruling banning abortion
  25. 25 - The final result of the superintelligence question
  26. 26 - The Supreme Court overturns Roe v. Wade
  27. 27 - The Supreme Court overturns Roe v. Wade

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