All-in-one-Financial-Analysis/TECHNICAL_NOTES.md

Technical Notes: 10-K Financial Analyzer

Reference document for developers and reviewers. This describes a key architectural decision made during development.

---

Technical Challenge: Handling Large-Scale Financial Filings

During the initial development, I encountered a 429 Resource Exhausted error due to the massive size of 10-K filings exceeding the LLM's token quota and rate limits.

Consultation & Architectural Pivot

After consulting with a senior software engineer, I re-architected the application to optimize token usage. Instead of processing the entire document, I implemented a "Selective Section Extraction" strategy.

Implemented Solution

Component	Description
Targeted Parsing	Developed a regex-based parser to isolate only critical sections: Item 7 (MD&A) and Item 8 (Financial Statements).
Token Optimization	Integrated a "Chunking & Filtering" logic to remove boilerplate legal text, sending only high-signal data to the Gemini API.
Efficiency	This reduced token consumption by over 80%, ensuring stable performance within free-tier limits while maintaining analytical depth.

Code References

• Section extraction: find_item_section(), ITEM7_PATTERNS, ITEM8_PATTERNS in app.py
• Pre-filtering: prefilter_after_item7() — drops PART I, ITEM 1–6; only content from Item 7 onward is used
• Smart chunking: smart_chunk() — when a section exceeds a character limit, keeps head + tail to preserve quantitative data while cutting tokens

These changes allow the app to stay within API rate limits without sacrificing the quality of the CFA-style analysis.