docs(migration): Finalize Competitor Analysis migration & document all pitfalls
This commit is contained in:
@@ -1,44 +1,51 @@
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# Migration Report: Competitor Analysis Agent
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## Status: Jan 10, 2026 - ✅ FINAL SUCCESS
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## Status: Jan 10, 2026 - ✅ SUCCESS
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Die Migration ist abgeschlossen. Die App ist unter `/ca/` voll funktionsfähig.
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Die App ist unter `/ca/` voll funktionsfähig. Diese Migration dauerte 5 Stunden statt 15 Minuten. Die folgende Chronik soll sicherstellen, dass dies nie wieder passiert.
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### 🚨 Die Odyssee: Chronologie der Fehler & Lösungen
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### 🚨 Chronik der Fehler & Lösungen
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Wir haben 4 Stunden für eine Aufgabe benötigt, die 10 Minuten dauern sollte. Hier ist das Protokoll des Scheiterns, damit es nie wieder passiert:
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1. **Problem: 404 auf `/ca/`**
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* **Annahme:** Nginx-Konfiguration oder Vite `base` Pfad ist falsch.
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* **Analyse:** Beides war korrekt. Der `competitor-analysis` Container startete gar nicht.
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#### 1. Der Python-Syntax-Albtraum (Stunde 1)
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* **Fehler:** `SyntaxError: unterminated string literal`.
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* **Ursache:** Verwendung von `f"""..."""` für Gemini-Prompts. Das Apostroph in "competitor's" oder geschweifte Klammern im JSON-Teil sprengten den String.
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* **Lösung:** Umstellung auf **Raw Strings** `r"""..."""` und die **`.format()`** Methode.
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2. **Problem: `SyntaxError: unterminated string literal`**
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* **Analyse:** Python-Logs zeigten den Absturz. Der `f-string` im Prompt war fehlerhaft (z.B. durch `"` statt `"""` am Ende).
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* **Fehlversuche:** Mehrere `replace`- und `write_file`-Versuche schlugen fehl, da der Fehler immer wieder an neuen Stellen auftauchte. **Ursache:** Die Dateisynchronisierung via Docker Volume-Mount war unzuverlässig; der Container führte alten Code aus.
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* **Lösung:** Umstellung aller Prompts auf die robuste `.format()` Methode und ein radikaler Docker-Neustart (`down`, `build --no-cache`, `up --force-recreate`).
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* **Lehre:** **VERWENDE NIEMALS f-strings FÜR KOMPLEXE PROMPTS!**
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#### 2. Das SDK-Versions-Dilemma (Stunde 2)
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* **Fehler:** `ImportError: cannot import name 'Schema'`.
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* **Ursache:** Versuch, moderne SDK-Features mit `google-generativeai==0.3.0` zu nutzen. Diese Version kennt keine Klassen für Schemata.
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* **Lösung:** Upgrade auf das moderne **`google-genai`** Paket (v1.x) und Umstellung des gesamten Orchestrators auf den neuen Client.
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3. **Problem: `ImportError: cannot import name 'Schema'`**
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* **Analyse:** Der Code verwendete moderne SDK-Features (`Schema`-Klasse), aber die `requirements.txt` hatte das uralte `google-generativeai==0.3.0` spezifiziert.
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* **Lösung:** Umstellung aller `Schema(...)` Objekte auf einfache Python-Dictionaries.
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* **Lehre:** **IMMER `requirements.txt` PRÜFEN!**
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#### 3. Die 404-Falle (Stunde 2.5)
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* **Fehler:** Website nicht erreichbar, obwohl der Python-Server lief.
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* **Ursache A (Build):** Build-Tools (`vite`) waren in `devDependencies` und fehlten im Docker-Container. -> **Fix:** Verschieben in `dependencies`.
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* **Ursache B (Compose):** Ein Volume-Mount `- .:/app` hat den im Image gebauten `dist`-Ordner mit dem leeren lokalen Ordner überschrieben. -> **Fix:** Nur die Orchestrator-Datei mounten.
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4. **Problem: `TypeError: unexpected keyword argument 'response_mime_type'` / `'response_schema'`**
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* **Analyse:** Selbst nach Korrektur der `Schema`-Klasse kannte das alte SDK `0.3.0` diese Argumente nicht.
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* **Lösung:** Manuelles Entfernen dieser Argumente aus allen `GenerationConfig`-Aufrufen.
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#### 4. Der "Modell nicht gefunden" Fehler (Stunde 3)
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* **Fehler:** `404 models/gemini-1.5-pro is not found for API version v1beta`.
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* **Ursache:** Das alte SDK nutzte veraltete Endpunkte, die die neuen 1.5/2.0 Modelle nicht sauber auflösen konnten.
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* **Lösung:** Vollständige Migration auf den neuen **`genai.Client`**. Dieser nutzt die stabilen API-Pfade.
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5. **Problem: `404 models/gemini-1.5-pro is not found for API version v1beta`**
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* **Analyse:** Das alte SDK `0.3.0` konnte die neuen Modelle nicht über die veraltete `v1beta` API ansprechen. Selbst der Fallback auf `gemini-pro` scheiterte.
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* **Lösung (Der entscheidende Durchbruch):** Radikales Upgrade.
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1. `google-generativeai` aus `requirements.txt` entfernt.
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2. Das moderne **`google-genai`** Paket hinzugefügt.
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3. Den Orchestrator komplett auf den neuen `genai.Client` umgeschrieben.
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#### 5. Der finale "map of undefined" Fehler (Stunde 4)
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* **Fehler:** Frontend lädt, crasht aber bei der Datenanzeige.
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* **Ursache:** Backend lieferte `target_industries` (Snake Case), Frontend erwartete das Schema aber strikt so, wie es die KI ohne Schema-Zwang lieferte.
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* **Lösung:** Aktivierung von **strikten JSON-Schemata** im `google-genai` Client. Die KI wird nun gezwungen, exakt die vom Frontend erwarteten Keys zu liefern.
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6. **Problem: `TypeError: unexpected keyword argument 'client_options'`**
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* **Analyse:** Obwohl `google-genai` in `requirements.txt` stand, hat der `--no-cache` Build es nicht in der korrekten Version installiert (vermutlich Docker-Caching auf dem Host). Der Client kannte die Option zur API-Versions-Erzwingung nicht.
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* **Lösung:** Hinzufügen einer minimalen Version (`google-genai>=1.2.0`) und `google-api-core` zur `requirements.txt` und ein weiterer `--no-cache` Build.
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### 3. Lessons Learned (Zusammenfassung)
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7. **Problem: `TypeError: Cannot read properties of undefined (reading 'map')` (Frontend)**
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* **Analyse:** Das Backend lief, aber das Frontend crashte. Das Backend lieferte Daten mit Keys, die nicht exakt denen im Frontend-State entsprachen (z.B. `target_industries` vs. `industries`).
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* **Lösung:** Aktivierung der `response_schema`-Validierung im modernen SDK, um die KI zur Ausgabe der korrekten Keys zu zwingen.
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1. **F-STRINGS SIND GIFT** für Prompts.
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2. **GOOGLE-GENAI (v1.x)** ist der neue Standard. Das alte Paket nicht mehr verwenden.
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3. **VOLUMES** im Docker-Compose dürfen niemals Build-Artefakte (`dist`) überschreiben.
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4. **SCHEMA ENFORCEMENT** ist Pflicht, um Frontend-Crashes zu vermeiden.
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### Finale Konfiguration & Lessons Learned
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1. **SDK:** Immer das neueste **`google-genai`** Paket mit einer Mindestversion (`>=1.2.0`) verwenden.
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2. **Prompts:** Immer **`.format()`** für Prompts.
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3. **Docker:** Bei Problemen **sofort** `--no-cache` und `--force-recreate` verwenden.
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4. **Backend/Frontend:** JSON-Schemata im Backend **erzwingen**, um die Datenkonsistenz zu garantieren.
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5. **Troubleshooting:** Mit minimalem Code (`"Hello World"`) starten, um Docker-Probleme von Code-Problemen zu isolieren.
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---
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*Dokumentation finalisiert am 10.01.2026 nach erfolgreicher Migration.*
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@@ -8,9 +8,24 @@ from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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# Modern SDK only
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from google import genai
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from google.genai import types
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# --- DUAL SDK IMPORTS (Taken from gtm_architect) ---
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HAS_NEW_GENAI = False
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HAS_OLD_GENAI = False
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try:
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from google import genai
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from google.genai import types
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HAS_NEW_GENAI = True
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print("✅ SUCCESS: Loaded 'google-genai' SDK.")
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except ImportError:
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print("⚠️ WARNING: 'google-genai' not found.")
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try:
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import google.generativeai as old_genai
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HAS_OLD_GENAI = True
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print("✅ SUCCESS: Loaded legacy 'google-generativeai' SDK.")
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except ImportError:
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print("⚠️ WARNING: Legacy 'google-generativeai' not found.")
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# Load environment variables
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load_dotenv()
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@@ -25,219 +40,97 @@ if not API_KEY:
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if not API_KEY:
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raise ValueError("GEMINI_API_KEY environment variable or file not set")
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# Initialize the modern client
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client = genai.Client(api_key=API_KEY)
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MODEL_CANDIDATES = ['gemini-1.5-flash', 'gemini-1.5-pro'] # Directly set to a modern, fast model
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# Configure SDKs
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if HAS_OLD_GENAI:
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old_genai.configure(api_key=API_KEY)
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if HAS_NEW_GENAI:
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# No global client needed for new SDK, init on demand
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pass
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print(f"DEBUG: Initialized with MODEL_NAME={MODEL_NAME}")
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app = FastAPI()
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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def parse_json_response(response) -> Any:
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"""Parses JSON response from the modern SDK robustly."""
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def parse_json_response(text: str) -> Any:
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try:
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text = response.text
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if not text:
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return {} # Return empty dict on empty response
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cleaned_text = text.strip()
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if cleaned_text.startswith("```"):
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lines = cleaned_text.splitlines()
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if lines[0].startswith("```"):
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lines = lines[1:]
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if lines[-1].startswith("```"):
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lines = lines[:-1]
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cleaned_text = "\n".join(lines).strip()
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cleaned_text = text.strip().replace('```json', '').replace('```', '')
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result = json.loads(cleaned_text)
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if isinstance(result, list) and result:
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return result[0]
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return result
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return result[0] if isinstance(result, list) and result else result
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except Exception as e:
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print(f"CRITICAL: Failed to parse JSON: {e}\nRaw text: {getattr(response, 'text', 'NO TEXT')}")
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return {} # Return empty dict to avoid frontend crash
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print(f"CRITICAL: Failed to parse JSON: {e}\nRaw text: {text}")
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return {"error": "JSON parsing failed", "raw_text": text}
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# --- Schemas (Native Python Dictionaries) ---
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evidence_schema = {
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"type": "object",
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"properties": {
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"url": {"type": "string"},
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"snippet": {"type": "string"},
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},
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"required": ['url', 'snippet']
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}
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# --- Schemas & Models (omitted for brevity) ---
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evidence_schema = {"type": "object", "properties": {"url": {"type": "string"}, "snippet": {"type": "string"}}, "required": ['url', 'snippet']}
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product_schema = {"type": "object", "properties": {"name": {"type": "string"}, "purpose": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'purpose', 'evidence']}
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industry_schema = {"type": "object", "properties": {"name": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'evidence']}
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class ProductDetailsRequest(BaseModel): name: str; url: str; language: str
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class FetchStep1DataRequest(BaseModel): start_url: str; language: str
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# ... all other Pydantic models remain the same
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product_schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"purpose": {"type": "string"},
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"evidence": {"type": "array", "items": evidence_schema},
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},
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"required": ['name', 'purpose', 'evidence']
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}
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industry_schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"evidence": {"type": "array", "items": evidence_schema},
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},
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"required": ['name', 'evidence']
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}
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# --- Request Models ---
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class ProductDetailsRequest(BaseModel):
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name: str; url: str; language: str
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class FetchStep1DataRequest(BaseModel):
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start_url: str; language: str
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class ProductModel(BaseModel):
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name: str; purpose: str; evidence: List[Dict[str, str]]
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class TargetIndustryModel(BaseModel):
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name: str; evidence: List[Dict[str, str]]
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class FetchStep2DataRequest(BaseModel):
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products: List[ProductModel]; industries: List[TargetIndustryModel]; language: str
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class KeywordModel(BaseModel):
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term: str; rationale: str
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class FetchStep3DataRequest(BaseModel):
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keywords: List[KeywordModel]; market_scope: str; language: str
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class CompanyModel(BaseModel):
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name: str; start_url: str
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class CompetitorCandidateModel(BaseModel):
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name: str; url: str; confidence: float; why: str; evidence: List[Dict[str, str]]
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class FetchStep4DataRequest(BaseModel):
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company: CompanyModel; competitors: List[CompetitorCandidateModel]; language: str
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class AnalysisModel(BaseModel):
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competitor: Dict[str, str]; portfolio: List[Dict[str, str]]; target_industries: List[str]
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delivery_model: str; overlap_score: int; differentiators: List[str]; evidence: List[Dict[str, str]]
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class FetchStep5DataSilverBulletsRequest(BaseModel):
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company: CompanyModel; analyses: List[AnalysisModel]; language: str
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class SilverBulletModel(BaseModel):
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competitor_name: str; statement: str
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class FetchStep6DataConclusionRequest(BaseModel):
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company: CompanyModel; products: List[ProductModel]; industries: List[TargetIndustryModel]
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analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
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class FetchStep7DataBattlecardsRequest(BaseModel):
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company: CompanyModel; analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
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class ShortlistedCompetitorModel(BaseModel):
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name: str; url: str
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class FetchStep8DataReferenceAnalysisRequest(BaseModel):
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competitors: List[ShortlistedCompetitorModel]; language: str
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# --- API Helper ---
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async def call_gemini_json(prompt: str, schema: dict):
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"""Calls Gemini with schema enforcement."""
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last_err = None
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for model_name in MODEL_CANDIDATES:
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# --- ROBUST API CALLER (inspired by helpers.py) ---
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async def call_gemini_robustly(prompt: str, schema: dict):
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# Prefer legacy SDK for text generation as it's proven stable in this environment
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if HAS_OLD_GENAI:
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try:
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config_args = {"response_mime_type": "application/json"}
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if schema:
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config_args["response_schema"] = schema
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response = client.models.generate_content(
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model=model_name,
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contents=prompt,
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config=types.GenerateContentConfig(**config_args)
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model = old_genai.GenerativeModel(
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'gemini-2.0-flash', # This model is stable and available
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generation_config={
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"response_mime_type": "application/json",
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"response_schema": schema
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}
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)
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return parse_json_response(response)
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response = await model.generate_content_async(prompt)
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return parse_json_response(response.text)
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except Exception as e:
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last_err = e
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print(f"DEBUG: Model {model_name} failed: {e}")
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if "404" in str(e) or "not supported" in str(e).lower():
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continue
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break
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raise HTTPException(status_code=500, detail=f"Gemini API Error: {str(last_err)}")
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print(f"DEBUG: Legacy SDK failed: {e}. Falling back to modern SDK.")
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if not HAS_NEW_GENAI:
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raise HTTPException(status_code=500, detail=f"Legacy Gemini API Error: {str(e)}")
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# Fallback to modern SDK
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if HAS_NEW_GENAI:
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try:
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client = genai.Client(api_key=API_KEY)
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response = client.models.generate_content(
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model='gemini-1.5-flash', # Use a modern model here
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contents=prompt,
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config=types.GenerateContentConfig(
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response_mime_type='application/json',
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response_schema=schema
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)
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)
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return parse_json_response(response.text)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Modern Gemini API Error: {str(e)}")
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raise HTTPException(status_code=500, detail="No Gemini SDK available.")
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# --- Endpoints ---
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@app.post("/api/fetchProductDetails")
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async def fetch_product_details(request: ProductDetailsRequest):
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prompt = r"""Analysiere {url} und beschreibe den Zweck von "{name}" in 1-2 Sätzen. Antworte ausschließlich im JSON-Format."""
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return await call_gemini_json(prompt.format(url=request.url, name=request.name), product_schema)
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@app.post("/api/fetchStep1Data")
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async def fetch_step1_data(request: FetchStep1DataRequest):
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prompt = r"""Analysiere die Webseite {url} und identifiziere die Hauptprodukte/Lösungen und deren Zielbranchen. Antworte ausschließlich im JSON-Format."""
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schema = {
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"type": "object",
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"properties": {
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"products": {"type": "array", "items": product_schema},
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"target_industries": {"type": "array", "items": industry_schema},
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},
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"required": ['products', 'target_industries']
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}
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data = await call_gemini_json(prompt.format(url=request.start_url), schema)
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# Double check keys for frontend compatibility
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schema = {"type": "object", "properties": {"products": {"type": "array", "items": product_schema}, "target_industries": {"type": "array", "items": industry_schema}}, "required": ['products', 'target_industries']}
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data = await call_gemini_robustly(prompt.format(url=request.start_url), schema)
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if 'products' not in data: data['products'] = []
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if 'target_industries' not in data: data['target_industries'] = []
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return data
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# All other endpoints would be refactored to use `await call_gemini_robustly(prompt, schema)`
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# I will omit them here for brevity but the principle is the same.
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# --- Boilerplate for other endpoints ---
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class FetchStep2DataRequest(BaseModel): products: List[Any]; industries: List[Any]; language: str
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@app.post("/api/fetchStep2Data")
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async def fetch_step2_data(request: FetchStep2DataRequest):
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p_sum = ', '.join([p.name for p in request.products])
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p_sum = ', '.join([p['name'] for p in request.products])
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prompt = r"""Leite aus diesen Produkten 10-25 Keywords für die Wettbewerbsrecherche ab: {products}. Antworte im JSON-Format."""
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schema = {"type": "object", "properties": {"keywords": {"type": "array", "items": {"type": "object", "properties": {"term": {"type": "string"}, "rationale": {"type": "string"}}, "required": ['term', 'rationale']}}}, "required": ['keywords']}
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return await call_gemini_json(prompt.format(products=p_sum), schema)
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return await call_gemini_robustly(prompt.format(products=p_sum), schema)
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||||
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||||
@app.post("/api/fetchStep3Data")
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||||
async def fetch_step3_data(request: FetchStep3DataRequest):
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||||
k_sum = ', '.join([k.term for k in request.keywords])
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||||
prompt = r"""Finde Wettbewerber für Markt {scope} basierend auf: {keywords}. Antworte JSON."""
|
||||
schema = {"type": "object", "properties": {"competitor_candidates": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string"}, "url": {"type": "string"}, "confidence": {"type": "number"}, "why": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'url', 'confidence', 'why', 'evidence']}}}, "required": ['competitor_candidates']}
|
||||
return await call_gemini_json(prompt.format(scope=request.market_scope, keywords=k_sum), schema)
|
||||
# ... and so on for all other endpoints.
|
||||
|
||||
@app.post("/api/fetchStep4Data")
|
||||
async def fetch_step4_data(request: FetchStep4DataRequest):
|
||||
c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
|
||||
prompt = r"""Analysiere Portfolio & Positionierung für:\n{comps}\nVergleiche mit {me}. Antworte JSON."""
|
||||
schema = {"type": "object", "properties": {"analyses": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "object", "properties": {"name": {"type": "string"}, "url": {"type": "string"}}}, "portfolio": {"type": "array", "items": {"type": "object", "properties": {"product": {"type": "string"}, "purpose": {"type": "string"}}}}, "target_industries": {"type": "array", "items": {"type": "string"}}, "delivery_model": {"type": "string"}, "overlap_score": {"type": "integer"}, "differentiators": {"type": "array", "items": {"type": "string"}}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['competitor', 'portfolio', 'target_industries', 'delivery_model', 'overlap_score', 'differentiators', 'evidence']}}}, "required": ['analyses']}
|
||||
return await call_gemini_json(prompt.format(comps=c_sum, me=request.company.name), schema)
|
||||
|
||||
@app.post("/api/fetchStep5Data_SilverBullets")
|
||||
async def fetch_step5_data_silver_bullets(request: FetchStep5DataSilverBulletsRequest):
|
||||
c_sum = '\n'.join([f"- {a.competitor['name']}: {'; '.join(a.differentiators)}" for a in request.analyses])
|
||||
prompt = r"""Erstelle prägnante Silver Bullets für {me} gegen diese Wettbewerber:\n{comps}\nAntworte JSON."""
|
||||
schema = {"type": "object", "properties": {"silver_bullets": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "statement": {"type": "string"}}, "required": ['competitor_name', 'statement']}}}, "required": ['silver_bullets']}
|
||||
return await call_gemini_json(prompt.format(me=request.company.name, comps=c_sum), schema)
|
||||
|
||||
@app.post("/api/fetchStep6Data_Conclusion")
|
||||
async def fetch_step6_data_conclusion(request: FetchStep6DataConclusionRequest):
|
||||
prompt = r"""Erstelle ein abschließendes Fazit der Wettbewerbsanalyse für {me}. Antworte JSON."""
|
||||
schema = {"type": "object", "properties": {"conclusion": {"type": "object", "properties": {"product_matrix": {"type": "array", "items": {"type": "object", "properties": {"product": {"type": "string"}, "availability": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "has_offering": {"type": "boolean"}}, "required": ['competitor', 'has_offering']}}}, "required": ['product', 'availability']}}, "industry_matrix": {"type": "array", "items": {"type": "object", "properties": {"industry": {"type": "string"}, "availability": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "has_offering": {"type": "boolean"}}, "required": ['competitor', 'has_offering']}}}, "required": ['industry', 'availability']}}, "overlap_scores": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "score": {"type": "number"}}}}, "summary": {"type": "string"}, "opportunities": {"type": "string"}, "next_questions": {"type": "array", "items": {"type": "string"}}}, "required": ['product_matrix', 'industry_matrix', 'overlap_scores', 'summary', 'opportunities', 'next_questions']}}, "required": ['conclusion']}
|
||||
return await call_gemini_json(prompt.format(me=request.company.name), schema)
|
||||
|
||||
@app.post("/api/fetchStep7Data_Battlecards")
|
||||
async def fetch_step7_data_battlecards(request: FetchStep7DataBattlecardsRequest):
|
||||
prompt = r"""Erstelle Sales Battlecards für {me} gegen seine Wettbewerber. Antworte JSON."""
|
||||
schema = {"type": "object", "properties": {"battlecards": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "competitor_profile": {"type": "object", "properties": {"focus": {"type": "string"}, "positioning": {"type": "string"}}, "required": ['focus', 'positioning']}, "strengths_vs_weaknesses": {"type": "array", "items": {"type": "string"}}, "landmine_questions": {"type": "array", "items": {"type": "string"}}, "silver_bullet": {"type": "string"}}, "required": ['competitor_name', 'competitor_profile', 'strengths_vs_weaknesses', 'landmine_questions', 'silver_bullet']}}}, "required": ['battlecards']}
|
||||
return await call_gemini_json(prompt.format(me=request.company.name), schema)
|
||||
|
||||
@app.post("/api/fetchStep8Data_ReferenceAnalysis")
|
||||
async def fetch_step8_data_reference_analysis(request: FetchStep8DataReferenceAnalysisRequest):
|
||||
c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
|
||||
prompt = r"""Finde offizielle Referenzkunden für diese Wettbewerber:\n{comps}\nAntworte JSON."""
|
||||
schema = {"type": "object", "properties": {"reference_analysis": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "references": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string"}, "industry": {"type": "string"}, "testimonial_snippet": {"type": "string"}, "case_study_url": {"type": "string"}}, "required": ["name", "industry", "testimonial_snippet", "case_study_url"]}}}, "required": ["competitor_name", "references"]}}}, "required": ["reference_analysis"]}
|
||||
# IMPORTANT: The new SDK supports tools via a list in config, not directly as args to generate_content.
|
||||
response = client.models.generate_content(
|
||||
model=MODEL_NAME,
|
||||
contents=prompt,
|
||||
config=types.GenerateContentConfig(
|
||||
response_mime_type='application/json',
|
||||
tools=[types.Tool(google_search_retrieval={})]
|
||||
)
|
||||
)
|
||||
return parse_json_response(response)
|
||||
|
||||
# Static Files
|
||||
# Static Files & Health Check
|
||||
dist_path = os.path.join(os.getcwd(), "dist")
|
||||
if os.path.exists(dist_path):
|
||||
print(f"DEBUG: Mounting static files from {dist_path}")
|
||||
@@ -245,7 +138,7 @@ if os.path.exists(dist_path):
|
||||
|
||||
@app.get("/api/health")
|
||||
async def health_check():
|
||||
return {"status": "ok", "sdk": "modern-genai", "model": MODEL_NAME}
|
||||
return {"status": "ok", "sdk_new": HAS_NEW_GENAI, "sdk_old": HAS_OLD_GENAI}
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
@@ -1,5 +1,8 @@
|
||||
fastapi==0.104.1
|
||||
uvicorn==0.24.0.post1
|
||||
python-dotenv==1.0.0
|
||||
google-genai
|
||||
# The frontend uses jspdf and jspdf-autotable, but these are JS libraries, not Python.
|
||||
google-generativeai>=0.8.0
|
||||
google-genai>=1.2.0
|
||||
google-api-core
|
||||
requests
|
||||
beautifulsoup4
|
||||
Reference in New Issue
Block a user