Backfill sparse report sections, add enrichment section refresh, and bootstrap first-admin
Reports: the LLM reliably used company_enrichment for prose fields but inconsistently populated the parallel Finding-list/string-list fields from the same evidence, even with progressively more explicit prompting. Add a code-level backfill (products, recent developments, financial signals, strategic initiatives, regulatory signals, risks/opportunities mirrored from SWOT, unknowns, monitoring recommendations) that only ever fills in what the model left empty, never overwrites what it produced. Enrichment tab: reorder sections (Products/Recent updates before Customers/Competitors) and add a per-section "Refresh" button that re-fetches just one of NinjaPear's six independent per-company endpoints when it came back empty - confirmed live that a data-coverage gap (e.g. Amazon returning no products) is real provider behavior, not a bug. Auth: the first account registered on a deployment with zero existing admins is now auto-promoted to admin, closing the chicken-and-egg gap where the only path to admin access was direct DB access. Self-heals if the last admin ever deletes their account. Also bumps nginx's proxy_read_timeout for api.ciagent.org to cover the enrichment refresh's synchronous funding-endpoint call (up to 5 minutes per NinjaPear's docs). Co-Authored-By: Claude Sonnet 5 <[email protected]>
This commit is contained in:
Claude Sonnet 5
@@ -23,7 +23,8 @@ from app.models.enums import EnrichmentStatus, ReportType, SourceStatus
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from app.models.report import Report
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from app.models.source import Source
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from app.models.source_document import SourceDocument
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from app.prompts.report_generation import generate_report
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from app.prompts.report_generation import ReportContent, generate_report
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from app.prompts.schemas import ConfidenceLabel, EvidenceRef, Finding
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from app.repositories.company_enrichment_repository import CompanyEnrichmentRepository
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from app.repositories.report_repository import ReportRepository
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from app.services import company_service
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@@ -31,6 +32,210 @@ from app.services.report_markdown import render_report_markdown
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_MAX_DOCUMENTS = 40
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_MAX_CHANGES = 20
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_MAX_BACKFILLED_PRODUCTS = 30
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_MAX_BACKFILLED_UPDATES = 15
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_MAX_BACKFILLED_FUNDING_ROUNDS = 15
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def _format_amount(amount: object) -> str | None:
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if amount is None:
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return None
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text = str(amount)
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return f"${int(text):,}" if text.isdigit() else text
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def _backfill_from_enrichment(content: ReportContent, enrichment: dict | None) -> None:
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"""company_enrichment.products/recent_updates/funding map onto
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products_and_services/recent_developments/financial_signals with no
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inference required - a direct transcription, not a judgment call.
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Confirmed live that the LLM is nonetheless inconsistent about
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populating these Finding-list fields from enrichment alone (it
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reliably uses the same data for prose fields like company_overview,
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but repeated real API calls with increasingly explicit instructions
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still came back with these lists empty). Rather than keep fighting
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prompt compliance for a purely mechanical transform, backfill directly
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from the source data whenever the model leaves a field empty despite
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the data being available - this only ever *adds* real, non-fabricated
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content the model chose not to surface, never overwrites what the
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model did produce."""
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if not enrichment:
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return
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if not content.products_and_services:
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content.products_and_services = [
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Finding(
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headline=product["name"],
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summary=product.get("description") or product.get("category") or product["name"],
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category=product.get("category"),
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confidence=ConfidenceLabel.CONFIRMED,
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)
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for product in enrichment.get("products", [])[:_MAX_BACKFILLED_PRODUCTS]
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if product.get("name")
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]
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if not content.recent_developments:
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content.recent_developments = [
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Finding(
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headline=update["text"],
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summary=f"{update.get('type', 'update').capitalize()} published by the company.",
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date=update.get("date"),
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confidence=ConfidenceLabel.CONFIRMED,
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evidence=(
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[EvidenceRef(url=update["url"], description="Company-published update.")]
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if update.get("url")
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else []
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),
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)
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for update in enrichment.get("recent_updates", [])[:_MAX_BACKFILLED_UPDATES]
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if update.get("text")
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]
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if not content.financial_signals:
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funding = enrichment.get("funding") or {}
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findings = []
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total_raised = _format_amount(funding.get("total_raised"))
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if total_raised:
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findings.append(
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Finding(
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headline=f"Total funding raised: {total_raised}",
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summary="Cumulative funding raised across all rounds, per company_enrichment.",
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confidence=ConfidenceLabel.CONFIRMED,
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)
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)
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for round_ in funding.get("rounds", [])[:_MAX_BACKFILLED_FUNDING_ROUNDS]:
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name = round_.get("round_name")
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if not name:
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continue
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amount = _format_amount(round_.get("amount"))
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investors = round_.get("investors") or []
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headline = name.replace("_", " ").title() + (f" - {amount}" if amount else "")
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findings.append(
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Finding(
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headline=headline,
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summary=(
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f"Investors: {', '.join(investors)}."
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if investors
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else "No investor detail provided."
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),
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date=round_.get("date"),
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confidence=ConfidenceLabel.CONFIRMED,
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)
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)
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content.financial_signals = findings
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def _mirror_swot_into_flat_lists(content: ReportContent) -> None:
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"""risks/opportunities are meant to be the same analysis as
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swot.threats/swot.opportunities, just in a flat top-level list rather
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than nested under swot - not a second, independent judgment call.
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Confirmed live: the model reliably populates the SWOT fields but is
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inconsistent about also populating these parallel flat fields with the
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same content, even though nothing about them requires different
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evidence. Mirror rather than re-derive, since the model already did
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the real analytical work once."""
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if not content.risks and content.swot.threats:
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content.risks = list(content.swot.threats)
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if not content.opportunities and content.swot.opportunities:
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content.opportunities = list(content.swot.opportunities)
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def _backfill_reflective_sections(
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content: ReportContent,
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*,
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enrichment: dict | None,
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documents: list[dict],
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changes: list[dict],
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company_name: str,
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) -> None:
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"""strategic_initiatives, regulatory_and_legal_signals,
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unknowns_and_missing_data, and monitoring_recommendations are the
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fields the LLM was most persistently reluctant to populate even after
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two rounds of explicit prompt strengthening (confirmed live - see
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SYSTEM_PROMPT in report_generation.py). Unlike products/recent_updates,
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these don't have a single obvious mechanical source, but each still
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has SOMETHING honest and non-fabricated to fall back on:
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- strategic_initiatives: company_enrichment.specialties names the
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company's own stated focus areas - a real, low-confidence signal,
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not invented.
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- regulatory_and_legal_signals: when truly nothing applies, a single
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insufficient_evidence Finding is what the prompt already asks the
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model to emit in this situation instead of leaving the list empty -
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the model just isn't doing it reliably, so this fills the same gap.
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- unknowns_and_missing_data / monitoring_recommendations: these are
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meta-analysis of the evidence set itself, not claims about the
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company, so they can be derived honestly from what evidence this
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pipeline actually did or didn't collect for this company."""
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enrichment = enrichment or {}
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if not content.strategic_initiatives:
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specialties = enrichment.get("specialties") or []
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content.strategic_initiatives = [
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Finding(
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headline=f"Focus area: {specialty}",
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summary=(
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f"{company_name} lists \"{specialty}\" among its specialties, "
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"indicating an area of strategic focus."
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),
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confidence=ConfidenceLabel.POSSIBLE,
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)
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for specialty in specialties
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if specialty
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]
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if not content.regulatory_and_legal_signals:
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content.regulatory_and_legal_signals = [
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Finding(
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headline="No regulatory or legal signals identified",
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summary=(
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"No licensing, compliance, jurisdictional, or legal-structure "
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"information was present in the available evidence for "
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f"{company_name}."
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),
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confidence=ConfidenceLabel.INSUFFICIENT_EVIDENCE,
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)
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]
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if not content.unknowns_and_missing_data:
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unknowns = []
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if not enrichment:
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unknowns.append(
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"No third-party company enrichment data was available for this company."
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)
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else:
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if not enrichment.get("funding", {}).get("total_raised") and not enrichment.get(
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"funding", {}
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).get("rounds"):
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unknowns.append(
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"No detailed financial statements, revenue, or funding figures were found."
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)
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if not enrichment.get("leadership_team"):
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unknowns.append("No leadership or executive team data was found.")
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if not enrichment.get("customers"):
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unknowns.append("No named customers or case studies were found.")
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if not documents:
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unknowns.append(
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"No source documents have been collected yet from ongoing monitoring, so "
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"hiring, technology, patent, and manufacturing signals are not yet available."
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)
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if not changes:
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unknowns.append(
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"No changes have been detected yet between monitoring runs for this company."
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)
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content.unknowns_and_missing_data = unknowns
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if not content.monitoring_recommendations:
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recommendations = [
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f"Monitor company_enrichment for {company_name} on its next refresh for changes "
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"to products, leadership, or funding.",
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"Track newly published company updates for announcements of new initiatives "
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"or partnerships.",
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]
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if documents:
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recommendations.append(
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"Continue reviewing newly collected source documents for signals not yet "
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"reflected in company_enrichment."
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)
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content.monitoring_recommendations = recommendations
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async def _gather_documents(db: AsyncSession, company_id: uuid.UUID) -> list[dict]:
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@@ -130,6 +335,15 @@ async def generate_and_persist_report(
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public_identifiers=company.public_identifiers,
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enrichment=enrichment,
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)
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_backfill_from_enrichment(content, enrichment)
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_mirror_swot_into_flat_lists(content)
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_backfill_reflective_sections(
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content,
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enrichment=enrichment,
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documents=documents,
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changes=changes,
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company_name=company.name,
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)
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generated_at = datetime.now(UTC)
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model_name = _model_name(settings, llm)
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