Add Step 5 flat context and txtai file tools for agents
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ي
@@ -13,12 +13,13 @@ from sqlalchemy import select, desc
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import json
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from services.database import get_session_for_user
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from models.onboarding import WebsiteAnalysis, OnboardingSession, CompetitorAnalysis
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from models.onboarding import WebsiteAnalysis, OnboardingSession, CompetitorAnalysis, ResearchPreferences, PersonaData
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# Import existing SIF components
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from .txtai_service import TxtaiIntelligenceService
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from .semantic_cache import semantic_cache_manager, SemanticCacheStats
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from services.intelligence.harvester import SemanticHarvesterService
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from services.intelligence.agent_flat_context import AgentFlatContextStore
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class SIFIntegrationService:
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@@ -61,6 +62,284 @@ class SIFIntegrationService:
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)
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return self.trend_surfer_agent
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async def get_step2_website_context(self) -> Dict[str, Any]:
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"""
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Retrieve onboarding step 2 website context with a strict fallback chain:
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flat file -> database -> SIF semantic index.
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"""
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# 1) Fastest: flat-file agent context
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try:
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flat_doc = AgentFlatContextStore(self.user_id).load_step2_context_document()
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if flat_doc:
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return {
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"source": "flat_file",
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"data": flat_doc.get("data") or {},
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"agent_summary": flat_doc.get("agent_summary") or {},
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"document_context": flat_doc.get("document_context") or {},
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"meta": flat_doc.get("meta") or {},
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"updated_at": flat_doc.get("updated_at"),
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}
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except Exception as e:
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logger.warning(f"Flat context lookup failed for user {self.user_id}: {e}")
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# 2) Database fallback
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db = None
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try:
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db = get_session_for_user(self.user_id)
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if db:
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stmt = (
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select(WebsiteAnalysis)
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.join(OnboardingSession, WebsiteAnalysis.session_id == OnboardingSession.id)
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.where(OnboardingSession.user_id == self.user_id)
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.order_by(desc(WebsiteAnalysis.updated_at))
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)
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row = db.execute(stmt).scalars().first()
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if row:
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payload = row.to_dict() if hasattr(row, "to_dict") else {}
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return {
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"source": "database",
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"data": payload,
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"agent_summary": {
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"quick_facts": {
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"website_url": payload.get("website_url"),
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"brand_voice": (payload.get("brand_analysis") or {}).get("brand_voice") if isinstance(payload.get("brand_analysis"), dict) else "",
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}
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},
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}
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except Exception as e:
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logger.warning(f"Database fallback failed for user {self.user_id}: {e}")
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finally:
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if db:
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db.close()
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# 3) Semantic fallback
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try:
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results = await self.intelligence_service.search("website analysis brand voice style", limit=1)
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if results:
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top = results[0]
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metadata = top.get("object") if isinstance(top, dict) else None
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if isinstance(metadata, str):
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try:
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metadata = json.loads(metadata)
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except Exception:
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metadata = {}
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if isinstance(metadata, dict):
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report = metadata.get("full_report") if isinstance(metadata.get("full_report"), dict) else metadata
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return {
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"source": "sif_semantic",
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"data": report,
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"agent_summary": {
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"quick_facts": {
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"website_url": report.get("website_url") if isinstance(report, dict) else None,
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}
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},
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}
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except Exception as e:
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logger.warning(f"SIF semantic fallback failed for user {self.user_id}: {e}")
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return {"source": "none", "data": {}}
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async def get_step3_research_context(self) -> Dict[str, Any]:
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"""
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Retrieve onboarding step 3 research context with fallback chain:
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flat file -> database -> SIF semantic index.
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"""
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try:
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flat_doc = AgentFlatContextStore(self.user_id).load_step3_context_document()
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if flat_doc:
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return {
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"source": "flat_file",
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"data": flat_doc.get("data") or {},
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"agent_summary": flat_doc.get("agent_summary") or {},
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"document_context": flat_doc.get("document_context") or {},
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"meta": flat_doc.get("meta") or {},
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"updated_at": flat_doc.get("updated_at"),
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}
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except Exception as e:
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logger.warning(f"Step 3 flat context lookup failed for user {self.user_id}: {e}")
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db = None
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try:
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db = get_session_for_user(self.user_id)
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if db:
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stmt = (
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select(ResearchPreferences)
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.join(OnboardingSession, ResearchPreferences.session_id == OnboardingSession.id)
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.where(OnboardingSession.user_id == self.user_id)
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.order_by(desc(ResearchPreferences.updated_at))
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)
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prefs = db.execute(stmt).scalars().first()
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if prefs:
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payload = prefs.to_dict() if hasattr(prefs, "to_dict") else {}
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return {
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"source": "database",
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"data": payload,
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"agent_summary": {
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"quick_facts": {
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"research_depth": payload.get("research_depth"),
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"content_types_count": len(payload.get("content_types") or []),
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}
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},
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}
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except Exception as e:
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logger.warning(f"Step 3 database fallback failed for user {self.user_id}: {e}")
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finally:
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if db:
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db.close()
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try:
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results = await self.intelligence_service.search("research preferences competitors onboarding step 3", limit=1)
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if results:
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top = results[0]
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metadata = top.get("object") if isinstance(top, dict) else None
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if isinstance(metadata, str):
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try:
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metadata = json.loads(metadata)
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except Exception:
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metadata = {}
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report = metadata.get("full_report") if isinstance(metadata, dict) and isinstance(metadata.get("full_report"), dict) else (metadata if isinstance(metadata, dict) else {})
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return {
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"source": "sif_semantic",
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"data": report,
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"agent_summary": {
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"quick_facts": {
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"research_depth": report.get("research_depth") if isinstance(report, dict) else None,
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}
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},
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}
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except Exception as e:
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logger.warning(f"Step 3 semantic fallback failed for user {self.user_id}: {e}")
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return {"source": "none", "data": {}}
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async def get_step4_persona_context(self) -> Dict[str, Any]:
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"""
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Retrieve onboarding step 4 persona context with fallback chain:
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flat file -> database -> SIF semantic index.
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"""
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try:
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flat_doc = AgentFlatContextStore(self.user_id).load_step4_context_document()
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if flat_doc:
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return {
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"source": "flat_file",
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"data": flat_doc.get("data") or {},
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"agent_summary": flat_doc.get("agent_summary") or {},
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"document_context": flat_doc.get("document_context") or {},
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"meta": flat_doc.get("meta") or {},
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"updated_at": flat_doc.get("updated_at"),
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}
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except Exception as e:
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logger.warning(f"Step 4 flat context lookup failed for user {self.user_id}: {e}")
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db = None
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try:
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db = get_session_for_user(self.user_id)
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if db:
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stmt = (
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select(PersonaData)
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.join(OnboardingSession, PersonaData.session_id == OnboardingSession.id)
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.where(OnboardingSession.user_id == self.user_id)
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.order_by(desc(PersonaData.updated_at))
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)
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persona = db.execute(stmt).scalars().first()
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if persona:
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payload = persona.to_dict() if hasattr(persona, "to_dict") else {}
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return {
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"source": "database",
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"data": payload,
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"agent_summary": {
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"quick_facts": {
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"selected_platforms_count": len(payload.get("selected_platforms") or []),
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"has_core_persona": bool(payload.get("core_persona")),
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}
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},
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}
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except Exception as e:
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logger.warning(f"Step 4 database fallback failed for user {self.user_id}: {e}")
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finally:
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if db:
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db.close()
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try:
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results = await self.intelligence_service.search("persona platform personas onboarding step 4", limit=1)
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if results:
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top = results[0]
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metadata = top.get("object") if isinstance(top, dict) else None
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if isinstance(metadata, str):
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try:
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metadata = json.loads(metadata)
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except Exception:
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metadata = {}
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report = metadata.get("full_report") if isinstance(metadata, dict) and isinstance(metadata.get("full_report"), dict) else (metadata if isinstance(metadata, dict) else {})
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return {
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"source": "sif_semantic",
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"data": report,
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"agent_summary": {
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"quick_facts": {
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"has_core_persona": bool(report.get("core_persona")) if isinstance(report, dict) else False,
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}
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},
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}
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except Exception as e:
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logger.warning(f"Step 4 semantic fallback failed for user {self.user_id}: {e}")
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return {"source": "none", "data": {}}
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async def get_step5_integrations_context(self) -> Dict[str, Any]:
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"""
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Retrieve onboarding step 5 integrations context with fallback chain:
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flat file -> SIF semantic index.
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"""
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try:
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flat_doc = AgentFlatContextStore(self.user_id).load_step5_context_document()
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if flat_doc:
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return {
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"source": "flat_file",
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"data": flat_doc.get("data") or {},
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"agent_summary": flat_doc.get("agent_summary") or {},
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"document_context": flat_doc.get("document_context") or {},
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"meta": flat_doc.get("meta") or {},
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"updated_at": flat_doc.get("updated_at"),
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}
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except Exception as e:
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logger.warning(f"Step 5 flat context lookup failed for user {self.user_id}: {e}")
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try:
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results = await self.intelligence_service.search("integrations onboarding step 5 connected providers", limit=1)
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if results:
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top = results[0]
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metadata = top.get("object") if isinstance(top, dict) else None
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if isinstance(metadata, str):
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try:
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metadata = json.loads(metadata)
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except Exception:
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metadata = {}
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report = metadata.get("full_report") if isinstance(metadata, dict) and isinstance(metadata.get("full_report"), dict) else (metadata if isinstance(metadata, dict) else {})
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return {
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"source": "sif_semantic",
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"data": report,
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"agent_summary": {
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"quick_facts": {
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"connected_integrations_count": len((report.get("integrations") or {})) if isinstance(report, dict) and isinstance(report.get("integrations"), dict) else None,
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}
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},
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}
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except Exception as e:
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logger.warning(f"Step 5 semantic fallback failed for user {self.user_id}: {e}")
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return {"source": "none", "data": {}}
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async def get_flat_context_manifest(self) -> Dict[str, Any]:
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"""Return lightweight manifest of available flat context documents for this user."""
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try:
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manifest = AgentFlatContextStore(self.user_id).load_context_manifest()
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if manifest:
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return {"source": "flat_file", "data": manifest}
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except Exception as e:
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logger.warning(f"Failed to load flat context manifest for user {self.user_id}: {e}")
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return {"source": "none", "data": {"documents": []}}
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async def index_market_trends_run(self, trends_result: Dict[str, Any], run_id: str) -> bool:
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try:
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latest_id = f"market_trends_latest:{self.user_id}"
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