saksham
1a4c80958f
FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring, source collection, LLM-based change analysis, enrichment, and account security (Turnstile, escalating lockout, email verification).
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
"""Task B: fact and signal extraction. Pulls discrete, evidence-anchored
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signals (hiring, partnerships, leadership, financial, etc.) out of a single
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document - the atomic units Task C (synthesis) and Task D (report
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generation) later combine."""
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from __future__ import annotations
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from pydantic import BaseModel, Field
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from app.analysis.llm.base import LLMProvider
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from app.prompts.base import build_user_prompt
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SYSTEM_PROMPT = (
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"You are a competitive intelligence analyst extracting discrete factual signals from a "
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"single document. Extract only what the text actually states or clearly implies - never "
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"add outside knowledge. Every signal must include the exact passage that supports it."
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)
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_SIGNAL_TYPES = (
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"event, entity, date, location, product, leadership, partnership, hiring, investment, "
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"manufacturing, technology, financial, regulatory, sentiment"
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)
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class ExtractedSignal(BaseModel):
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signal_type: str = Field(description=f"One of: {_SIGNAL_TYPES}")
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description: str
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supporting_passage: str = Field(description="The exact quote from the document backing this")
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date: str | None = None
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entities: list[str] = Field(default_factory=list)
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class ExtractionResult(BaseModel):
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signals: list[ExtractedSignal] = Field(default_factory=list)
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async def extract_signals(
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llm: LLMProvider,
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*,
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document_title: str | None,
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document_url: str,
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document_text: str,
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) -> ExtractionResult:
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evidence = {
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"document_title": document_title,
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"document_url": document_url,
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"document_text": document_text[:6000],
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}
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user_prompt = build_user_prompt(
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"Extract every discrete factual signal from this document, each anchored to its "
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"exact supporting passage.",
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evidence,
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)
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return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, ExtractionResult)
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