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

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.

- 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'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
- The Supreme Court overturns Roe v. Wade