import os import json from dotenv import load_dotenv from google import genai from google.genai import types from typing import List, Dict, Any, Optional # 載入環境變數 load_dotenv() class GeminiService: def __init__(self): api_key = os.getenv("GEMINI_API_KEY") if not api_key: print("警告：找不到 GEMINI_API_KEY") self.client = genai.Client(api_key=api_key) if api_key else None self.model_id = os.getenv("GEMINI_MODEL_ID", "gemini-2.0-flash") def _check_client(self): if not self.client: raise ValueError("API Key 未設定，請檢查 .env 或 Hugging Face Secrets") # ========================== # 🎓 教授搜尋相關功能 # ========================== def search_professors(self, query: str, exclude_names: List[str] = []) -> List[Dict]: self._check_client() exclusion_prompt = "" if exclude_names: exclusion_prompt = f"IMPORTANT: Do not include: {', '.join(exclude_names)}." # Phase 1: Search search_prompt = f""" Using Google Search, find 10 prominent professors in universities across Taiwan who are experts in the field of "{query}". CRITICAL: FACT CHECK they are current faculty. RELEVANCE must be high. {exclusion_prompt} List them (Name - University - Department) in Traditional Chinese. """ search_response = self.client.models.generate_content( model=self.model_id, contents=search_prompt, config=types.GenerateContentConfig(tools=[types.Tool(google_search=types.GoogleSearch())]) ) # Phase 2: Extract JSON extract_prompt = f""" From the text below, extract professor names, universities, and departments. Calculate a Relevance Score (0-100) based on query: "{query}". Return ONLY a JSON array: [{{"name": "...", "university": "...", "department": "...", "relevanceScore": 85}}] Text: --- {search_response.text} --- """ extract_response = self.client.models.generate_content( model=self.model_id, contents=extract_prompt, config=types.GenerateContentConfig(response_mime_type='application/json') ) try: return json.loads(extract_response.text) except: return [] def get_professor_details(self, professor: Dict) -> Dict: self._check_client() name, uni, dept = professor.get('name'), professor.get('university'), professor.get('department') prompt = f""" Act as an academic consultant. Investigate Professor {name} from {dept} at {uni}. Find "Combat Experience": 1. **Key Publications (Last 5 Years)**: Find 2-3 top papers with Citation Counts. 2. **Alumni Directions**: Where do their graduates work? 3. **Industry Collaboration**: Any industry projects? Format output in Markdown (Traditional Chinese). """ response = self.client.models.generate_content( model=self.model_id, contents=prompt, config=types.GenerateContentConfig(tools=[types.Tool(google_search=types.GoogleSearch())]) ) return self._format_response_with_sources(response) # ========================== # 🏢 公司搜尋相關功能 # ========================== def search_companies(self, query: str, exclude_names: List[str] = []) -> List[Dict]: self._check_client() exclusion_prompt = "" if exclude_names: exclusion_prompt = f"IMPORTANT: Do not include: {', '.join(exclude_names)}." # Phase 1: Search search_prompt = f""" Using Google Search, find 5 to 10 prominent companies in Taiwan related to: "{query}". Instructions: 1. If "{query}" is an industry (e.g. AI), list representative Taiwanese companies. 2. If "{query}" is a name, list the company and competitors. {exclusion_prompt} List them (Full Name - Industry/Main Product) in Traditional Chinese. """ search_response = self.client.models.generate_content( model=self.model_id, contents=search_prompt, config=types.GenerateContentConfig(tools=[types.Tool(google_search=types.GoogleSearch())]) ) # Phase 2: Extract JSON extract_prompt = f""" From text, extract company names and industry. Calculate Relevance Score (0-100) for query: "{query}". Return ONLY JSON array: [{{"name": "...", "industry": "...", "relevanceScore": 85}}] Text: --- {search_response.text} --- """ extract_response = self.client.models.generate_content( model=self.model_id, contents=extract_prompt, config=types.GenerateContentConfig(response_mime_type='application/json') ) try: return json.loads(extract_response.text) except: return [] def get_company_details(self, company: Dict) -> Dict: self._check_client() name = company.get('name') prompt = f""" Act as a "Business Analyst". Investigate Taiwanese company: "{name}". Targets: 1. **Overview**: Tax ID (統編), Capital (資本額), Representative. 2. **Workforce & Culture**: Employee count, Reviews from PTT(Tech_Job)/Dcard/Qollie (Pros & Cons). 3. **Legal & Risks**: Search for "{name} 勞資糾紛", "{name} 判決", "{name} 違反勞基法". Format in Markdown (Traditional Chinese). Be objective. """ response = self.client.models.generate_content( model=self.model_id, contents=prompt, config=types.GenerateContentConfig(tools=[types.Tool(google_search=types.GoogleSearch())]) ) return self._format_response_with_sources(response) # ========================== # 共用功能 # ========================== def _format_response_with_sources(self, response): sources = [] if response.candidates[0].grounding_metadata and response.candidates[0].grounding_metadata.grounding_chunks: for chunk in response.candidates[0].grounding_metadata.grounding_chunks: if chunk.web and chunk.web.uri and chunk.web.title: sources.append({"title": chunk.web.title, "uri": chunk.web.uri}) unique_sources = {v['uri']: v for v in sources}.values() return {"text": response.text, "sources": list(unique_sources)} def chat_with_ai(self, history: List[Dict], new_message: str, context: str, role_instruction: str = "Source of truth") -> str: self._check_client() system_instruction = f"{role_instruction}:\n{context}" chat_history = [] for h in history: role = "user" if h["role"] == "user" else "model" chat_history.append(types.Content(role=role, parts=[types.Part(text=h["content"])])) chat = self.client.chats.create( model=self.model_id, history=chat_history, config=types.GenerateContentConfig(system_instruction=system_instruction) ) response = chat.send_message(new_message) return response.text