File size: 15,656 Bytes
a9dc537 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 |
"""
MatchmakingAgent for Patent Wake-Up Scenario
Matches patents with potential licensees, partners, and investors:
- Semantic search in stakeholder database
- Multi-dimensional match scoring
- Geographic alignment (EU-Canada focus)
- Generates match rationale and collaboration opportunities
"""
from typing import List, Optional
from loguru import logger
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.messages import HumanMessage
from ..base_agent import BaseAgent, Task
from ...llm.langchain_ollama_client import LangChainOllamaClient
from ...workflow.langgraph_state import (
PatentAnalysis,
MarketAnalysis,
StakeholderMatch
)
class MatchmakingAgent(BaseAgent):
"""
Specialized agent for stakeholder matching.
Uses semantic search and LLM reasoning to find best-fit partners.
"""
def __init__(self, llm_client: LangChainOllamaClient, memory_agent):
"""
Initialize MatchmakingAgent.
Args:
llm_client: LangChain Ollama client
memory_agent: Memory agent (required for stakeholder search)
"""
# Note: MatchmakingAgent uses LangChain directly
self.name = "MatchmakingAgent"
self.description = "Stakeholder matching and partner identification"
self.llm_client = llm_client
self.memory_agent = memory_agent # Required
if not memory_agent:
raise ValueError("MatchmakingAgent requires memory_agent for stakeholder database")
# Use complex reasoning for matching
self.llm = llm_client.get_llm('complex') # qwen2.5:14b
# Scoring chain
self.scoring_chain = self._create_scoring_chain()
# Ensure sample stakeholders exist
self._stakeholders_initialized = False
logger.info("Initialized MatchmakingAgent")
def _create_scoring_chain(self):
"""Create chain for match scoring"""
prompt = ChatPromptTemplate.from_messages([
("system", "You are an expert in technology transfer and business development."),
("human", """
Evaluate the match quality between this patent and stakeholder:
PATENT:
- Title: {patent_title}
- Technical Domains: {technical_domains}
- Key Innovations: {key_innovations}
- TRL: {trl_level}
- Target Markets: {target_markets}
STAKEHOLDER:
- Name: {stakeholder_name}
- Type: {stakeholder_type}
- Expertise: {stakeholder_expertise}
- Focus Sectors: {stakeholder_sectors}
- Location: {stakeholder_location}
Provide match assessment in JSON format:
{{
"technical_fit": 0.85,
"market_fit": 0.90,
"geographic_fit": 1.0,
"strategic_fit": 0.80,
"overall_fit_score": 0.88,
"match_rationale": "Detailed explanation of why this is a strong match",
"collaboration_opportunities": ["Licensing", "Joint development", "Co-marketing"],
"potential_value": "High/Medium/Low",
"recommended_approach": "How to approach this stakeholder",
"talking_points": ["Point 1", "Point 2", "Point 3"]
}}
Scoring guidelines:
- technical_fit: Does stakeholder have expertise in this technology?
- market_fit: Does stakeholder operate in target markets?
- geographic_fit: Geographic alignment (EU/Canada priority)
- strategic_fit: Overall strategic alignment
- overall_fit_score: Weighted average (0-1)
Return ONLY valid JSON.
""")
])
parser = JsonOutputParser()
return prompt | self.llm | parser
async def find_matches(
self,
patent_analysis: PatentAnalysis,
market_analysis: MarketAnalysis,
max_matches: int = 10
) -> List[StakeholderMatch]:
"""
Find best-fit stakeholders for patent commercialization.
Args:
patent_analysis: Patent technical details
market_analysis: Market opportunities
max_matches: Maximum number of matches to return
Returns:
List of StakeholderMatch objects ranked by fit score
"""
logger.info(f"🤝 Finding matches for: {patent_analysis.title}")
# Ensure stakeholders are initialized
if not self._stakeholders_initialized:
await self._ensure_stakeholders()
# Create search query from patent and market analysis
query = self._create_search_query(patent_analysis, market_analysis)
# Search stakeholder profiles in memory
logger.info("Searching stakeholder database...")
stakeholder_docs = await self.memory_agent.retrieve_relevant_context(
query=query,
context_type="stakeholders",
top_k=max_matches * 2 # Get more for filtering
)
logger.info(f"Found {len(stakeholder_docs)} potential stakeholders")
# Score and rank matches
matches = []
for doc in stakeholder_docs:
try:
stakeholder = self._parse_stakeholder(doc)
match = await self._score_match(
patent_analysis,
market_analysis,
stakeholder
)
matches.append(match)
except Exception as e:
logger.warning(f"Failed to score match: {e}")
continue
# Sort by fit score and return top matches
matches.sort(key=lambda x: x.overall_fit_score, reverse=True)
logger.success(f"✅ Found {len(matches)} matches, returning top {max_matches}")
return matches[:max_matches]
def _create_search_query(
self,
patent: PatentAnalysis,
market: MarketAnalysis
) -> str:
"""Create search query for stakeholder matching"""
query_parts = []
# Add technical domains
query_parts.extend(patent.technical_domains)
# Add top market sectors
query_parts.extend(market.top_sectors)
# Add key innovations (first few words only)
for innovation in patent.key_innovations[:2]:
query_parts.append(innovation.split('.')[0])
return " ".join(query_parts)
def _parse_stakeholder(self, doc) -> dict:
"""Parse stakeholder document into dict"""
import json
# Extract profile from metadata
profile_json = doc.metadata.get('profile', '{}')
profile = json.loads(profile_json)
# Add page content for additional context
profile['search_match_text'] = doc.page_content
return profile
async def _score_match(
self,
patent: PatentAnalysis,
market: MarketAnalysis,
stakeholder: dict
) -> StakeholderMatch:
"""
Score match quality using LLM reasoning.
Args:
patent: Patent analysis
market: Market analysis
stakeholder: Stakeholder profile dict
Returns:
StakeholderMatch with scores and rationale
"""
# Invoke scoring chain
scoring = await self.scoring_chain.ainvoke({
"patent_title": patent.title,
"technical_domains": ", ".join(patent.technical_domains),
"key_innovations": ", ".join(patent.key_innovations[:3]),
"trl_level": patent.trl_level,
"target_markets": ", ".join(market.top_sectors),
"stakeholder_name": stakeholder.get('name', 'Unknown'),
"stakeholder_type": stakeholder.get('type', 'Unknown'),
"stakeholder_expertise": ", ".join(stakeholder.get('expertise', [])),
"stakeholder_sectors": ", ".join(stakeholder.get('focus_sectors', [])),
"stakeholder_location": stakeholder.get('location', 'Unknown')
})
# Build StakeholderMatch
return StakeholderMatch(
stakeholder_name=stakeholder.get('name', 'Unknown'),
stakeholder_type=stakeholder.get('type', 'Unknown'),
location=stakeholder.get('location', 'Unknown'),
contact_info=stakeholder.get('contact_info'),
overall_fit_score=scoring.get('overall_fit_score', 0.5),
technical_fit=scoring.get('technical_fit', 0.5),
market_fit=scoring.get('market_fit', 0.5),
geographic_fit=scoring.get('geographic_fit', 0.5),
strategic_fit=scoring.get('strategic_fit', 0.5),
match_rationale=scoring.get('match_rationale', 'Match assessment'),
collaboration_opportunities=scoring.get('collaboration_opportunities', []),
potential_value=scoring.get('potential_value', 'Medium'),
recommended_approach=scoring.get('recommended_approach', 'Professional outreach'),
talking_points=scoring.get('talking_points', [])
)
async def _ensure_stakeholders(self):
"""Ensure sample stakeholders exist in database"""
# Check if stakeholders exist
stats = self.memory_agent.get_collection_stats()
if stats.get('stakeholders_count', 0) < 5:
logger.info("Populating sample stakeholder database...")
await self._populate_sample_stakeholders()
self._stakeholders_initialized = True
async def _populate_sample_stakeholders(self):
"""
Create sample stakeholder profiles for demonstration.
In production, this would be populated from real databases.
"""
sample_stakeholders = [
{
"name": "BioVentures Capital (Toronto)",
"type": "Investor",
"expertise": ["AI", "Machine Learning", "Drug Discovery", "Healthcare"],
"focus_sectors": ["Pharmaceuticals", "Biotechnology", "Healthcare AI"],
"location": "Toronto, Canada",
"investment_stage": ["Seed", "Series A"],
"description": "Early-stage deep tech venture capital focusing on AI-driven healthcare innovation"
},
{
"name": "EuroTech Licensing GmbH",
"type": "Licensing Organization",
"expertise": ["Materials Science", "Nanotechnology", "Energy", "Manufacturing"],
"focus_sectors": ["Renewable Energy", "Advanced Materials", "Industrial IoT"],
"location": "Munich, Germany",
"description": "Technology licensing and commercialization across European markets"
},
{
"name": "McGill University Technology Transfer",
"type": "University TTO",
"expertise": ["Biomedical Engineering", "Software", "Clean Tech", "AI"],
"focus_sectors": ["Healthcare", "Environmental Tech", "AI Applications"],
"location": "Montreal, Canada",
"description": "Academic technology transfer and industry partnerships"
},
{
"name": "PharmaTech Solutions Inc.",
"type": "Company",
"expertise": ["Drug Discovery", "Clinical Trials", "Regulatory Affairs"],
"focus_sectors": ["Pharmaceuticals", "Biotechnology"],
"location": "Basel, Switzerland",
"description": "Pharmaceutical development and commercialization services"
},
{
"name": "Nordic Innovation Partners",
"type": "Investor",
"expertise": ["Clean Tech", "Sustainability", "Energy", "Manufacturing"],
"focus_sectors": ["Renewable Energy", "Circular Economy", "Green Tech"],
"location": "Stockholm, Sweden",
"investment_stage": ["Series A", "Series B"],
"description": "Impact investment in sustainable technologies"
},
{
"name": "Canadian AI Consortium",
"type": "Industry Consortium",
"expertise": ["AI", "Machine Learning", "Computer Vision", "NLP"],
"focus_sectors": ["AI Applications", "Software", "Healthcare AI"],
"location": "Vancouver, Canada",
"description": "Collaborative AI research and commercialization network"
},
{
"name": "MedTech Innovators (Amsterdam)",
"type": "Company",
"expertise": ["Medical Devices", "Digital Health", "AI Diagnostics"],
"focus_sectors": ["Healthcare", "Medical Technology"],
"location": "Amsterdam, Netherlands",
"description": "Medical technology development and distribution"
},
{
"name": "Quebec Pension Fund Technology",
"type": "Investor",
"expertise": ["Technology", "Healthcare", "Clean Tech", "AI"],
"focus_sectors": ["Healthcare", "Clean Energy", "AI", "Manufacturing"],
"location": "Montreal, Canada",
"investment_stage": ["Series B", "Growth"],
"description": "Large-scale technology investment fund"
},
{
"name": "European Patent Office Services",
"type": "IP Services",
"expertise": ["Patent Strategy", "IP Licensing", "Technology Transfer"],
"focus_sectors": ["All Technology Sectors"],
"location": "Munich, Germany",
"description": "Patent commercialization and licensing support"
},
{
"name": "CleanTech Accelerator Berlin",
"type": "Accelerator",
"expertise": ["Clean Tech", "Sustainability", "Energy", "Materials"],
"focus_sectors": ["Renewable Energy", "Environmental Tech", "Circular Economy"],
"location": "Berlin, Germany",
"description": "Accelerator program for sustainable technology startups"
}
]
# Store in memory
for stakeholder in sample_stakeholders:
try:
await self.memory_agent.store_stakeholder_profile(
name=stakeholder["name"],
profile=stakeholder,
categories=[stakeholder["type"]] + stakeholder["expertise"][:3]
)
logger.debug(f"Stored stakeholder: {stakeholder['name']}")
except Exception as e:
logger.warning(f"Failed to store stakeholder {stakeholder['name']}: {e}")
logger.success(f"✅ Populated {len(sample_stakeholders)} sample stakeholders")
async def process_task(self, task: Task) -> Task:
"""
Process task using agent interface.
Args:
task: Task with patent_analysis and market_analysis in metadata
Returns:
Task with list of StakeholderMatch results
"""
task.status = "in_progress"
try:
patent_dict = task.metadata.get('patent_analysis')
market_dict = task.metadata.get('market_analysis')
if not patent_dict or not market_dict:
raise ValueError("Both patent_analysis and market_analysis required")
# Convert dicts to objects
patent_analysis = PatentAnalysis(**patent_dict)
market_analysis = MarketAnalysis(**market_dict)
matches = await self.find_matches(patent_analysis, market_analysis)
task.result = [m.model_dump() for m in matches]
task.status = "completed"
except Exception as e:
logger.error(f"Matchmaking failed: {e}")
task.status = "failed"
task.error = str(e)
return task
|