Awesome

Pitch Book using LLM

This sample demonstrates building a pitch book from public, private and paid data sources.

Updates

• 4/12/2024 - Upgrade the packages (langchain, azure-search, pinecone, redis, etc) to the latest versions
• 2/7/2024 - Add capability to suggest questions for Earning Calls & SEC Filings
• 1/28/2024 - Additional Details on all Cognitive search Index used
  • pibec - Index to store the earning calls raw content
  • pibpr - Index to store the Press Releases raw content (PR Date, Title, Content)
  • pibecvector - Index to store the earning calls vector content (Only latest earning call transcript data)
  • pibsummaries - Index to store the summaries of Pre-defined or Custom Topics for earning calls and SEC Filings
  • pibsec - Index to store the SEC Filings raw content (Itemized by sections, content and additional metadata)
  • pibsecvector - Index to store the sec data vector content (Only latest sec filing data - Not the itemized vector content, but the entire document vector. For now missing additional metadata too)
  • pibdata - Index to store the "Cached" data from the above indexes. This is the index that is used for the search results
• 1/27/2024 - Initial Version

Architecture

Resources

• Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and Cognitive Search
• Azure Cognitive Search
• Azure OpenAI Service
• Redis Search
• Pinecone
• Cognitive Search Vector Store

Contributions

We are open to contributions, whether it is in the form of new feature, update existing functionality or better documentation. Please create a pull request and we will review and merge it.

Note

Adapted from the repo at OpenAI-CogSearch, Call Center Analytics, Auto Evaluator and Edgar Crawler