mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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87 lines
3.6 KiB
Python
87 lines
3.6 KiB
Python
import uuid
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from typing import Dict, Any, List
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# import langgraph primitives when integrated, for now structure the state and logic
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# from langgraph.graph import StateGraph, END
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class PartnershipScoutWorkflow:
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"""
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Partnership Scout Agent (LangGraph-based state machine).
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Reads market signals and generates detailed partnership scorecards.
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"""
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def __init__(self, memory_store, decision_engine):
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self.memory_store = memory_store
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self.decision_engine = decision_engine
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def _fetch_company_data(self, state: dict) -> dict:
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signal = state.get("signal", {})
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target = signal.get("company_name")
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# In a real environment, call clearbit/linkedin/web-scraper here
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state["enriched_data"] = {"name": target, "industry": "SaaS", "revenue_tier": "mid-market"}
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return state
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def _score_partnership_fit(self, state: dict) -> dict:
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data = state.get("enriched_data", {})
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score = 85 if data.get("industry") == "SaaS" else 40
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state["fit_score"] = score
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state["synergy_potential"] = ["Product reselling", "Data API sharing"]
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return state
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def _generate_decision_memo(self, state: dict) -> dict:
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# Generate the structured Decision Memo
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score = state.get("fit_score", 0)
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target = state.get("enriched_data", {}).get("name", "Unknown")
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memo_kwargs = {
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"decision_context": f"Market signal detected interest for {target}",
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"inputs_used": ["LinkedIn API", "Crunchbase Signal"],
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"assumptions": ["Revenue over 10M SAR", "No strict exclusivity clauses"],
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"alternatives_considered": ["Ignore signal", "Direct M&A (ruled out due to size)"],
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"expected_financial_impact": {
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"revenue_upside_sar": 250000.0,
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"cost_downside_sar": 15000.0,
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"capital_at_risk_sar": 0.0
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},
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"risk_register": [
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{"risk": "Brand overlap", "severity": "medium", "mitigation": "Co-branding guidelines"}
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],
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"required_approvals": ["VP Partnerships"],
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"next_best_action": "Send Partnership introductory email to C-level",
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"rollback_plan": "Cease communications and mark as disqualified in CRM",
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"evidence_links": ["https://crm.dealix.local/signals/1"]
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}
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recommendation = f"Initiate Alliance Structuring with {target} (Score: {score})" if score > 70 else "Discard lead."
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memo = self.decision_engine.create_memo(
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agent_id="partnership_scout",
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objective="Evaluate partnership market fit",
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recommendation=recommendation,
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confidence=float(score),
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**memo_kwargs
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)
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state["final_memo"] = memo.to_json()
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return state
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def execute_flow(self, signal: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Simulates the LangGraph execution flow: fetch -> score -> memo.
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"""
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state = {"signal": signal}
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state = self._fetch_company_data(state)
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state = self._score_partnership_fit(state)
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state = self._generate_decision_memo(state)
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# Save to memory backbone
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memo_id = self.memory_store.store_item(
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domain="partners",
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title=f"Partner Scout: {signal.get('company_name', 'Unknown')}",
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memory_type="Partner Evaluation",
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owner="VP Partnerships",
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confidence=int(state.get("fit_score", 0)),
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summary=state["final_memo"]["recommendation_ar"]
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)
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return {"status": "scouted", "memo_id": memo_id, "score": state["fit_score"]}
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