finsum 0.1.0

Summarizes financial information for stock tickers

SEC 10-K RAG Pipeline

Summarize SEC 10-K filings with one function call.

Usage

from facade import summarize

summary = summarize(
    ticker="AAPL",
    item="1a",
    query="What are the main risk factors?",
    llm_api_key="your-mistral-api-key",
)
print(summary)

With Reranking

summary = summarize(
    ticker="AAPL",
    item="7",
    query="What drove revenue growth?",
    llm_api_key="your-mistral-key",
    reranker_api_key="your-zeroentropy-key",
)

Parameters

Parameter	Required	Description
ticker	Yes	Company ticker symbol (e.g., "AAPL")
item	Yes	Item section to analyze (e.g., "1a", "7")
query	Yes	Question or topic to summarize
llm_api_key	Yes	Mistral API key
reranker_api_key	No	Zero Entropy API key for reranking
top_k	No	Number of chunks for context (default: 10)

Item Sections

Item	Section Name
1	Business
1a	Risk Factors
1b	Unresolved Staff Comments
2	Properties
3	Legal Proceedings
4	Mine Safety Disclosures
5	Market for Common Equity
6	Reserved
7	Management's Discussion and Analysis (MD&A)
7a	Quantitative and Qualitative Disclosures About Market Risk
8	Financial Statements
9	Changes in and Disagreements with Accountants
9a	Controls and Procedures
9b	Other Information
10	Directors and Executive Officers
11	Executive Compensation
12	Security Ownership
13	Certain Relationships
14	Principal Accountant Fees
15	Exhibits and Financial Statement Schedules

Installation

pip install -r requirements.txt

Configuration

Settings in config/settings.json:

{
  "chunking": {
    "chunk_size_tokens": 512,
    "chunk_overlap_tokens": 50,
    "min_chunk_chars": 50,
    "max_chunk_chars": 2200
  },
  "llm": {
    "model": "mistral-small-latest",
    "temperature": 0.3,
    "max_tokens": 1024
  },
  "retrieval": {
    "embedding_model": "BAAI/bge-small-en-v1.5",
    "vector_dim": 384,
    "top_k": 10,
    "min_score": 0.0,
    "rerank_top_k": 20
  }
}

Architecture

summarize(ticker, item, query, llm_api_key, reranker_api_key)
│
├─ 1. ingestion  → Download 10-K via datamule
├─ 2. chunking  → Split item section into chunks
├─ 3. storage        → Store chunks with embeddings
├─ 4. retrieval         → Vector search(sentence-transformers) and Optional reranking (Zero Entropy API)
└─ 5. inference         → Generate summary (Mistral API)