SEC AI
Wouldn’t it be great if you didn’t have to manually sift through 100-page annual reports for your investments?
Just kidding, you should probably still do that.
But if you want a reading companion that can help you find the answers to quick questions, track trends over many years, or compare multiple competitors in an industry, then check out SEC AI. It’s a simple Q&A chatbot that lets you search for companies by ticker, load one or more SEC filings, and then ask questions of the loaded docs.
For example, let’s get a breakdown of Berkshire Hathaway’s revenue for 2023. First we search by ticker and select Add Filing for the latest 10-k filing:
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Next, click Load Docs to selected filing(s) into a local in-memory vector store (powered by Chroma). Finally we ask our question and get a response:
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This currently requires an OpenAI API key, however the next version will include model selection as an option with an open-source LLM option to avoid having to provision your own key. I’m especially excited to test this given the recent news that an open-source model surpassed GPT-4 in performance
Next Steps
In addition to testing open-source models, I’d also like to add the following:
- Including investor call transcripts in context
- Generating summary-level reports to focus on specific metrics and financials
- Automating reports based on company-specific prompts via email
Reflections on ChatGPT for Coding
While I’ve built some RAG-based tools in Python using frameworks like LangChain, I had zero upfront knowledge of Streamlit. So I also used this as an opportunity to see just how effective I could be with a brand new framework using mostly ChatGPT and Cursor. While I needed to hit the documentation in a few spots, overall these coding tools got me most of the way there. While this may not be production-ready per se, it certainly provides quick iterative feedback on ideas and gives me a feel for how the app should flow.
Suggestions?
Let me know what you think and if you have any suggestions. Thanks!