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
2.0 KiB
Python
55 lines
2.0 KiB
Python
"""Task C: cross-source synthesis. Combines related signals from multiple
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documents/changes into a conclusion (e.g. "hiring battery engineers" +
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"filed a battery patent" + "announced a facility expansion" = "possible
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battery manufacturing initiative"), always with alternative explanations and
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an explicit accounting of what's still unknown.
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"""
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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. Given a set of independently-extracted "
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"signals about a company, identify conclusions that multiple signals point toward "
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"together. Only draw a conclusion when the evidence actually supports it - state your "
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"confidence honestly, list plausible alternative explanations, and note what evidence "
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"would be needed to be more certain. Do not synthesize a conclusion from a single signal."
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)
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class SynthesizedConclusion(BaseModel):
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conclusion: str
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evidence_summary: list[str] = Field(default_factory=list)
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source_count: int = Field(ge=0)
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confidence: float = Field(ge=0.0, le=1.0)
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alternative_explanations: list[str] = Field(default_factory=list)
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missing_information: list[str] = Field(default_factory=list)
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class SynthesisResult(BaseModel):
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conclusions: list[SynthesizedConclusion] = Field(default_factory=list)
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async def synthesize_signals(
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llm: LLMProvider,
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*,
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company_name: str,
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monitoring_focus: str | None,
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signals: list[dict],
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) -> SynthesisResult:
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evidence = {
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"company_name": company_name,
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"monitoring_focus": monitoring_focus,
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"signals": signals,
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}
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user_prompt = build_user_prompt(
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"Identify conclusions supported by two or more of these signals together. Do not "
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"synthesize from a single signal alone.",
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evidence,
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)
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return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, SynthesisResult)
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