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).
136 lines
6.2 KiB
Python
136 lines
6.2 KiB
Python
"""Task D: report generation. Turns accumulated evidence (source documents +
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detected changes) for a company into the structured CI report (spec section
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17). The LLM only ever sees evidence this pipeline collected - it is
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explicitly instructed not to introduce outside knowledge, and every
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non-trivial claim must carry a confidence label and evidence references.
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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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from app.prompts.schemas import ConfidenceLabel, EvidenceRef, Finding
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SYSTEM_PROMPT = (
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"You are a competitive intelligence analyst producing a structured report about a "
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"company, using ONLY the evidence provided - the company_profile block (discovered once "
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"at onboarding from the company's real website and search results, not your own "
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"knowledge), the company_enrichment block (when present - fetched once from a paid "
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"third-party data provider at onboarding, also real evidence, not your own knowledge), "
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"stored source documents, and previously detected changes. Never introduce "
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"facts from outside knowledge, even if you recognize the company - if it isn't in the "
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"evidence block, it doesn't go in the report. The company_profile and company_enrichment "
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"fields ARE real evidence and should ground company_overview/market_positioning/"
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"financial_signals/leadership_changes/products_and_services/competitor_comparison/"
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"customer_sentiment/etc even when source_documents and detected_changes are sparse or "
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"empty - do not say 'insufficient evidence' for a field company_profile or "
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"company_enrichment already answers. Every finding must be "
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"traceable to the evidence given and must carry an honest confidence label: confirmed "
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"(the source states it directly), strongly_indicated, likely, possible, unconfirmed, or "
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"insufficient_evidence. When evidence is thin or missing for a section, say so explicitly "
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"in that section rather than inventing content. Distinguish clearly between what a source "
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"states and what you are inferring."
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)
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class InferredProject(BaseModel):
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project_name: str
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status: ConfidenceLabel
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summary: str
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confidence: float = Field(ge=0.0, le=1.0)
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evidence: list[EvidenceRef] = Field(default_factory=list)
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signal_types: list[str] = Field(default_factory=list)
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alternative_explanations: list[str] = Field(default_factory=list)
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class SwotAnalysis(BaseModel):
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strengths: list[str] = Field(default_factory=list)
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weaknesses: list[str] = Field(default_factory=list)
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opportunities: list[str] = Field(default_factory=list)
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threats: list[str] = Field(default_factory=list)
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class ReportContent(BaseModel):
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executive_summary: str
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company_overview: str
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products_and_services: list[Finding] = Field(default_factory=list)
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market_positioning: str
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recent_developments: list[Finding] = Field(default_factory=list)
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strategic_initiatives: list[Finding] = Field(default_factory=list)
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key_inferred_projects: list[InferredProject] = Field(default_factory=list)
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leadership_changes: list[Finding] = Field(default_factory=list)
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hiring_signals: list[Finding] = Field(default_factory=list)
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technology_signals: list[Finding] = Field(default_factory=list)
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patent_signals: list[Finding] = Field(default_factory=list)
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manufacturing_and_expansion_signals: list[Finding] = Field(default_factory=list)
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partnerships_and_acquisitions: list[Finding] = Field(default_factory=list)
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financial_signals: list[Finding] = Field(default_factory=list)
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regulatory_and_legal_signals: list[Finding] = Field(default_factory=list)
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customer_sentiment: str
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competitor_comparison: str
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swot: SwotAnalysis
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risks: list[str] = Field(default_factory=list)
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opportunities: list[str] = Field(default_factory=list)
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unknowns_and_missing_data: list[str] = Field(default_factory=list)
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monitoring_recommendations: list[str] = Field(default_factory=list)
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methodology: str
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limitations: str
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async def generate_report(
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llm: LLMProvider,
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*,
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company_name: str,
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company_aliases: list[str],
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competitors: list[str],
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monitoring_focus: str | None,
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industry: str | None,
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documents: list[dict],
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detected_changes: list[dict],
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sources_failed: list[str],
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description: str | None = None,
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official_website: str | None = None,
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headquarters: str | None = None,
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country: str | None = None,
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region: str | None = None,
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public_identifiers: dict[str, str] | None = None,
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enrichment: dict | None = None,
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) -> ReportContent:
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evidence = {
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# Discovered once at onboarding (app/services/discovery_service.py)
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# from the company's real website + search results - genuine
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# evidence, not the LLM's own background knowledge, and the only
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# evidence available before any monitoring run has collected
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# source_documents/detected_changes.
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"company_profile": {
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"name": company_name,
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"description": description,
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"official_website": official_website,
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"aliases": company_aliases,
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"competitors": competitors,
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"monitoring_focus": monitoring_focus,
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"industry": industry,
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"headquarters": headquarters,
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"country": country,
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"region": region,
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"public_identifiers": public_identifiers or {},
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},
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# Fetched once, at onboarding, from a paid third-party provider
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# (NinjaPear) - see app/services/enrichment_service.py. Absent for
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# any company created before this feature existed, or whose
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# enrichment never completed successfully - never fabricated.
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"company_enrichment": enrichment or {},
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"source_documents": documents,
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"detected_changes": detected_changes,
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"sources_that_failed_to_collect": sources_failed,
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}
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user_prompt = build_user_prompt(
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"Produce a full structured competitive intelligence report from this evidence. "
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"If a section has no supporting evidence, say so explicitly instead of leaving it "
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"generic or inventing content.",
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evidence,
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)
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return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, ReportContent)
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