Fix GTM Architect: Robust prompt syntax, Gemini API migration, Docker logging
This commit is contained in:
@@ -1,259 +1,431 @@
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import os
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import sys
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import json
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import argparse
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from pathlib import Path
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import json
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import logging
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import re
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import sys
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import os
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import requests
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from bs4 import BeautifulSoup
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from datetime import datetime
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from config import Config
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# Add project root to Python path
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project_root = Path(__file__).resolve().parents[1]
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sys.path.append(str(project_root))
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# Append the current directory to sys.path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from helpers import call_openai_chat
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import market_db_manager
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from helpers import call_gemini_flash
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print("--- PYTHON ORCHESTRATOR V_20260101_1200_FINAL ---", file=sys.stderr)
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# Configure logging to file
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LOG_DIR = "Log_from_docker"
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if not os.path.exists(LOG_DIR):
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os.makedirs(LOG_DIR)
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__version__ = "1.4.0_UltimatePromptFix"
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timestamp = datetime.now().strftime("%Y-%m-%d")
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log_file = os.path.join(LOG_DIR, f"{timestamp}_gtm_architect.log")
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# Ensure DB is ready
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market_db_manager.init_db()
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logging.basicConfig(
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level=logging.DEBUG,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(log_file, mode='a', encoding='utf-8'),
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logging.StreamHandler(sys.stderr)
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]
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)
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def log_to_stderr(msg):
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sys.stderr.write(f"[GTM-ORCHESTRATOR] {msg}\n")
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sys.stderr.flush()
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# --- SCRAPING HELPER ---
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def get_text_from_url(url):
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try:
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log_to_stderr(f"Scraping URL: {url}")
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headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
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response = requests.get(url, headers=headers, timeout=15)
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response.raise_for_status()
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# Using html.parser
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soup = BeautifulSoup(response.content, 'html.parser')
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# Remove noise
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for element in soup(['script', 'style', 'noscript', 'iframe', 'svg', 'header', 'footer', 'nav', 'aside']):
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element.decompose()
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# Get text
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text = soup.get_text(separator=' ', strip=True)
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log_to_stderr(f"Scraping success. Length: {len(text)}")
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return text[:30000] # Limit length
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except Exception as e:
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log_to_stderr(f"Scraping failed: {e}")
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logging.warning(f"Could not scrape URL {url}: {e}")
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return ""
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# --- SYSTEM PROMPTS (Constructed reliably) ---
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def get_system_instruction(lang):
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if lang == 'de':
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return """
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# IDENTITY & PURPOSE
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Du bist die "GTM Architect Engine" für Roboplanet. Deine Aufgabe ist es, für neue technische Produkte (Roboter) eine präzise Go-to-Market-Strategie zu entwickeln.
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Du handelst nicht als kreativer Werbetexter, sondern als strategischer Analyst. Dein oberstes Ziel ist Product-Market-Fit und operative Umsetzbarkeit.
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Antworte IMMER auf DEUTSCH.
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# CONTEXT: THE PARENT COMPANY (WACKLER)
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Wir sind Teil der Wackler Group, einem großen Facility-Management-Dienstleister.
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Unsere Strategie ist NICHT "Roboter ersetzen Menschen", sondern "Hybrid-Reinigung":
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- 80% der Arbeit (monotone Flächenleistung) = Roboter.
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- 20% der Arbeit (Edge Cases, Winterdienst, Treppen, Grobschmutz) = Manuelle Reinigung durch Wackler.
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# STRICT ANALYSIS RULES (MUST FOLLOW):
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1. TECHNICAL FACT-CHECK (Keine Halluzinationen):
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- Analysiere technische Daten extrem konservativ.
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- Vakuumsystem = Kein "Winterdienst" (Schnee) und keine "Schwerindustrie" (Metallspäne), außer explizit genannt.
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- Erfinde keine Features, nur um eine Zielgruppe passend zu machen.
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2. REGULATORY LOGIC (StVO-Check):
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- Wenn Vmax < 20 km/h: Schließe "Öffentliche Städte/Kommunen/Straßenreinigung" kategorisch aus (Verkehrshindernis).
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- Fokusänderung: Konzentriere dich stattdessen ausschließlich auf "Große, zusammenhängende Privatflächen" (Gated Areas).
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3. STRATEGIC TARGETING (Use-Case-Logik):
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- Priorisiere Cluster A (Efficiency): Logistikzentren & Industrie-Hubs (24/7 Betrieb, Sicherheit).
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- Priorisiere Cluster B (Experience): Shopping Center, Outlets & Freizeitparks (Sauberkeit als Visitenkarte).
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- Entferne reine E-Commerce-Händler ohne physische Kundenfläche.
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4. THE "HYBRID SERVICE" LOGIC (RULE 5):
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Wann immer du ein "Hartes Constraint" oder eine technische Limitierung identifizierst (z.B. "Kein Winterdienst" oder "Kommt nicht in Ecken"), darfst du dies niemals als reines "Nein" stehen lassen.
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Wende stattdessen die **"Yes, and..." Logik** an:
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1. **Identifiziere die Lücke:** (z.B. "Roboter kann bei Schnee nicht fahren").
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2. **Fülle die Lücke mit Service:** Schlage explizit vor, diesen Teil durch "Wackler Human Manpower" abzudecken.
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3. **Formuliere den USP:** Positioniere das Gesamtpaket als "100% Coverage" (Roboter + Mensch aus einer Hand).
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# THE PRODUCT MATRIX (CONTEXT)
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Behalte immer im Hinterkopf, dass wir bereits folgende Produkte im Portfolio haben:
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1. "Indoor Scrubber 50": Innenreinigung, Hartboden, Fokus: Supermärkte. Message: "Sauberkeit im laufenden Betrieb."
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2. "Service Bot Bella": Service/Gastro, Indoor. Fokus: Restaurants. Message: "Entlastung für Servicekräfte."
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"""
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return "\n".join([
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"# IDENTITY & PURPOSE",
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'Du bist die "GTM Architect Engine" für Roboplanet. Deine Aufgabe ist es, für neue technische Produkte (Roboter) eine präzise Go-to-Market-Strategie zu entwickeln.',
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"Du handelst nicht als kreativer Werbetexter, sondern als strategischer Analyst. Dein oberstes Ziel ist Product-Market-Fit und operative Umsetzbarkeit.",
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"Antworte IMMER auf DEUTSCH.",
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"",
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"# CONTEXT: THE PARENT COMPANY (WACKLER)",
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"Wir sind Teil der Wackler Group, einem großen Facility-Management-Dienstleister.",
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'Unsere Strategie ist NICHT "Roboter ersetzen Menschen", sondern "Hybrid-Reinigung":',
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"- 80% der Arbeit (monotone Flächenleistung) = Roboter.",
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"- 20% der Arbeit (Edge Cases, Winterdienst, Treppen, Grobschmutz) = Manuelle Reinigung durch Wackler.",
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"",
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"# STRICT ANALYSIS RULES (MUST FOLLOW):",
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"1. TECHNICAL FACT-CHECK (Keine Halluzinationen):",
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" - Analysiere technische Daten extrem konservativ.",
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' - Vakuumsystem = Kein "Winterdienst" (Schnee) und keine "Schwerindustrie" (Metallspäne), außer explizit genannt.',
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" - Erfinde keine Features, nur um eine Zielgruppe passend zu machen.",
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" ",
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"2. REGULATORY LOGIC (StVO-Check):",
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' - Wenn Vmax < 20 km/h: Schließe "Öffentliche Städte/Kommunen/Straßenreinigung" kategorisch aus (Verkehrshindernis).',
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' - Fokusänderung: Konzentriere dich stattdessen ausschließlich auf "Große, zusammenhängende Privatflächen" (Gated Areas).',
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"",
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"3. STRATEGIC TARGETING (Use-Case-Logik):",
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" - Priorisiere Cluster A (Efficiency): Logistikzentren & Industrie-Hubs (24/7 Betrieb, Sicherheit).",
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" - Priorisiere Cluster B (Experience): Shopping Center, Outlets & Freizeitparks (Sauberkeit als Visitenkarte).",
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" - Entferne reine E-Commerce-Händler ohne physische Kundenfläche.",
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"",
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'4. THE "HYBRID SERVICE" LOGIC (RULE 5):',
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'Wann immer du ein "Hartes Constraint" oder eine technische Limitierung identifizierst (z.B. "Kein Winterdienst" oder "Kommt nicht in Ecken"), darfst du dies niemals als reines "Nein" stehen lassen.',
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'Wende stattdessen die **"Yes, and..." Logik** an:',
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' 1. **Identifiziere die Lücke:** (z.B. "Roboter kann bei Schnee nicht fahren").',
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' 2. **Fülle die Lücke mit Service:** Schlage explizit vor, diesen Teil durch "Wackler Human Manpower" abzudecken.',
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' 3. **Formuliere den USP:** Positioniere das Gesamtpaket als "100% Coverage" (Roboter + Mensch aus einer Hand).'
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])
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else:
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return """
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# IDENTITY & PURPOSE
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You are the "GTM Architect Engine" for Roboplanet. Your task is to develop a precise Go-to-Market strategy for new technical products (robots).
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You do not act as a creative copywriter, but as a strategic analyst. Your top goal is product-market fit and operational feasibility.
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ALWAYS respond in ENGLISH.
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return "\n".join([
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"# IDENTITY & PURPOSE",
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'You are the "GTM Architect Engine" for Roboplanet. Your task is to develop a precise Go-to-Market strategy for new technical products (robots).',
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"You do not act as a creative copywriter, but as a strategic analyst. Your top goal is product-market fit and operational feasibility.",
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"ALWAYS respond in ENGLISH.",
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"",
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"# CONTEXT: THE PARENT COMPANY (WACKLER)",
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"We are part of the Wackler Group, a major facility management service provider.",
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'Our strategy is NOT "Robots replace humans", but "Hybrid Cleaning":',
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"- 80% of work (monotonous area coverage) = Robots.",
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"- 20% of work (Edge cases, winter service, stairs, heavy debris) = Manual cleaning by Wackler.",
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"",
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"# STRICT ANALYSIS RULES (MUST FOLLOW):",
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"1. TECHNICAL FACT-CHECK (No Hallucinations):",
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" - Analyze technical data extremely conservatively.",
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' - Vacuum System = No "Winter Service" (snow) and no "Heavy Industry" (metal shavings), unless explicitly stated.',
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" - Do not invent features just to fit a target audience.",
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"",
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"2. REGULATORY LOGIC (Traffic Regs):",
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' - If Vmax < 20 km/h: Categorically exclude "Public Cities/Streets" (traffic obstruction).',
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' - Change Focus: Concentrate exclusively on "Large, contiguous private areas" (Gated Areas).',
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"",
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"3. STRATEGIC TARGETING (Use Case Logic):",
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" - Prioritize Cluster A (Efficiency): Logistics Centers & Industrial Hubs (24/7 ops, safety).",
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" - Prioritize Cluster B (Experience): Shopping Centers, Outlets & Theme Parks (Cleanliness as a calling card).",
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" - Remove pure E-commerce retailers without physical customer areas.",
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"",
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'4. THE "HYBRID SERVICE" LOGIC (RULE 5):',
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'Whenever you identify a "Hard Constraint" or technical limitation (e.g., "No winter service" or "Cannot reach corners"), never let this stand as a simple "No".',
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'Instead, apply the **"Yes, and..." logic**:',
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' 1. **Identify the gap:** (e.g., "Robot cannot operate in snow").',
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' 2. **Fill the gap with service:** Explicitly suggest covering this part with "Wackler Human Manpower".',
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' 3. **Formulate the USP:** Position the total package as "100% Coverage" (Robot + Human from a single source).'
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])
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# CONTEXT: THE PARENT COMPANY (WACKLER)
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We are part of the Wackler Group, a major facility management service provider.
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Our strategy is NOT "Robots replace humans", but "Hybrid Cleaning":
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- 80% of work (monotonous area coverage) = Robots.
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- 20% of work (Edge cases, winter service, stairs, heavy debris) = Manual cleaning by Wackler.
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# --- ORCHESTRATOR LOGIC ---
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# STRICT ANALYSIS RULES (MUST FOLLOW):
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1. TECHNICAL FACT-CHECK (No Hallucinations):
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- Analyze technical data extremely conservatively.
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- Vacuum System = No "Winter Service" (snow) and no "Heavy Industry" (metal shavings), unless explicitly stated.
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- Do not invent features just to fit a target audience.
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def analyze_product(product_input, lang):
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# 1. Scraping if URL
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content = product_input
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if re.match(r'^https?://', product_input.strip()):
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logging.info(f"Detected URL: {product_input}. Scraping...")
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scraped_text = get_text_from_url(product_input.strip())
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if scraped_text:
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content = scraped_text
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logging.info(f"Scraped {len(content)} chars.")
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else:
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logging.warning("Scraping failed, using URL as input.")
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2. REGULATORY LOGIC (Traffic Regs):
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- If Vmax < 20 km/h: Categorically exclude "Public Cities/Streets" (traffic obstruction).
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- Change Focus: Concentrate exclusively on "Large, contiguous private areas" (Gated Areas).
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3. STRATEGIC TARGETING (Use Case Logic):
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- Prioritize Cluster A (Efficiency): Logistics Centers & Industrial Hubs (24/7 ops, safety).
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- Prioritize Cluster B (Experience): Shopping Centers, Outlets & Theme Parks (Cleanliness as a calling card).
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- Remove pure E-commerce retailers without physical customer areas.
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4. THE "HYBRID SERVICE" LOGIC (RULE 5):
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Whenever you identify a "Hard Constraint" or a technical limitation (e.g., "No winter service" or "Cannot reach corners"), never let this stand as a simple "No".
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Instead, apply the **"Yes, and..." logic**:
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1. **Identify the gap:** (e.g., "Robot cannot operate in snow").
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2. **Fill the gap with service:** Explicitly suggest covering this part with "Wackler Human Manpower".
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3. **Formulate the USP:** Position the total package as "100% Coverage" (Robot + Human from a single source).
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# THE PRODUCT MATRIX (CONTEXT)
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Always keep in mind that we already have the following products in our portfolio:
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1. "Indoor Scrubber 50": Indoor cleaning, hard floor, supermarkets.
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2. "Service Bot Bella": Service/Hospitality, indoor. Focus: restaurants. Message: "Relief for service staff."
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"""
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# --- Database Handlers ---
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def save_project_handler(data):
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if 'name' not in data:
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input_text = data.get('productInput', '')
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derived_name = input_text.split('\n')[0][:30] if input_text else "Untitled Strategy"
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data['name'] = derived_name
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result = market_db_manager.save_project(data)
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print(json.dumps(result))
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def list_projects_handler(data):
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projects = market_db_manager.get_all_projects()
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print(json.dumps(projects))
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def load_project_handler(data):
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project_id = data.get('id')
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project = market_db_manager.load_project(project_id)
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if project:
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print(json.dumps(project))
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else:
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print(json.dumps({"error": "Project not found"}))
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def delete_project_handler(data):
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project_id = data.get('id')
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result = market_db_manager.delete_project(project_id)
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print(json.dumps(result))
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# --- AI Handlers ---
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def analyze_product(data):
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product_input = data.get('productInput')
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lang = data.get('language', 'de')
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sys_instr = get_system_instruction(lang)
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# 1. Extraction
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prompt_extract = "\n".join([
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"PHASE 1-A: TECHNICAL EXTRACTION",
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f'Input Product Description: "{content[:25000]}"',
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"",
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"Task:",
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"1. Extract key technical features (specs, capabilities).",
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'2. Derive "Hard Constraints". IMPORTANT: Check Vmax (<20km/h = Private Grounds) and Cleaning Type (Vacuum != Heavy Debris/Snow).',
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"3. Create a short raw analysis summary.",
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"",
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"Output JSON format ONLY:",
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"{",
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' "features": ["feature1", "feature2"],
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' "constraints": ["constraint1", "constraint2"],
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' "rawAnalysis": "summary text"',
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"}"
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])
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log_to_stderr("Starting Phase 1-A: Technical Extraction...")
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raw_response = call_gemini_flash(prompt_extract, system_instruction=sys_instr, json_mode=True)
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try:
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data = json.loads(raw_response)
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except json.JSONDecodeError:
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logging.error(f"Failed to parse Phase 1 JSON: {raw_response}")
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return {"features": [], "constraints": [], "rawAnalysis": "Error parsing AI response."}
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# Prompts als Liste von Strings konstruieren, um Syntax-Fehler zu vermeiden
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if lang == 'en':
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extraction_prompt_parts = [
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"PHASE 1-A: TECHNICAL EXTRACTION",
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f"Input Product Description: \"{product_input[:25000]}\"",
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"",
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"Task:",
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"1) Extract key technical features (specs, capabilities).",
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"2) Derive \"Hard Constraints\". IMPORTANT: Check Vmax (<20km/h = Private Grounds) and Cleaning Type (Vacuum != Heavy Debris/Snow).",
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"3) Create a short raw analysis summary.",
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"",
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"Output JSON format ONLY."
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]
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else:
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extraction_prompt_parts = [
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"PHASE 1-A: TECHNICAL EXTRACTION",
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f"Input Product Description: \"{product_input[:25000]}\"",
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"",
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"Aufgabe:",
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"1) Extrahiere technische Hauptmerkmale (Specs, Fähigkeiten).",
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"2) Leite \"Harte Constraints\" ab. WICHTIG: Prüfe Vmax (<20km/h = Privatgelände) und Reinigungstyp (Vakuum != Grobschmutz/Schnee).",
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"3) Erstelle eine kurze Rohanalyse-Zusammenfassung.",
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"",
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"Output JSON format ONLY."
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]
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extraction_prompt = "\n".join(extraction_prompt_parts)
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# 2. Conflict Check
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prompt_conflict = "\n".join([
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"PHASE 1-B: PORTFOLIO CONFLICT CHECK",
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"",
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f"New Product Features: {json.dumps(data.get('features'))}",
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f"New Product Constraints: {json.dumps(data.get('constraints'))}",
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"",
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"Existing Portfolio:",
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'1. "Indoor Scrubber 50": Indoor cleaning, hard floor, supermarkets.',
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'2. "Service Bot Bella": Service/Gastro, indoor, restaurants.',
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"",
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"Task:",
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"Check if the new product overlaps significantly with existing ones (is it just a clone?).",
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"",
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"Output JSON format ONLY:",
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"{",
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' "conflictCheck": {',
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' "hasConflict": true/false,',
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' "details": "explanation",',
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' "relatedProduct": "name or null"',
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" }
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])
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log_to_stderr("Starting Phase 1-B: Conflict Check...")
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conflict_response = call_gemini_flash(prompt_conflict, system_instruction=sys_instr, json_mode=True)
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try:
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conflict_data = json.loads(conflict_response)
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data.update(conflict_data)
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except:
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pass # Ignore conflict check error
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return data
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full_extraction_prompt = sys_instr + "\n\n" + extraction_prompt
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print(f"DEBUG: Full Extraction Prompt: \n{full_extraction_prompt[:1000]}...\n", file=sys.stderr)
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extraction_response = call_openai_chat(full_extraction_prompt, response_format_json=True)
|
||||
print(f"DEBUG: Raw Extraction Response from API: {extraction_response}", file=sys.stderr)
|
||||
extraction_data = json.loads(extraction_response)
|
||||
def discover_icps(phase1_result, lang):
|
||||
sys_instr = get_system_instruction(lang)
|
||||
prompt = "\n".join([
|
||||
"PHASE 2: ICP DISCOVERY & DATA PROXIES",
|
||||
f"Based on the product features: {json.dumps(phase1_result.get('features'))}",
|
||||
f"And constraints: {json.dumps(phase1_result.get('constraints'))}",
|
||||
"",
|
||||
"Task:",
|
||||
"1. Negative Selection: Which industries are impossible? (Remember Vmax & Vacuum rules!)",
|
||||
"2. High Pain: Identify Cluster A (Logistics/Industry) and Cluster B (Shopping/Outlets).",
|
||||
"3. Data Proxy Generation: How to find them digitally via data traces (e.g. satellite, registries).",
|
||||
"",
|
||||
"Output JSON format ONLY:",
|
||||
"{",
|
||||
' "icps": [',
|
||||
' { "name": "Industry Name", "rationale": "Why this is a good fit" }',
|
||||
" ],
|
||||
' "dataProxies": [',
|
||||
' { "target": "Specific criteria", "method": "How to find" }',
|
||||
" ]
|
||||
])
|
||||
log_to_stderr("Starting Phase 2: ICP Discovery...")
|
||||
response = call_gemini_flash(prompt, system_instruction=sys_instr, json_mode=True)
|
||||
return json.loads(response)
|
||||
|
||||
features_json = json.dumps(extraction_data.get('features'))
|
||||
constraints_json = json.dumps(extraction_data.get('constraints'))
|
||||
def hunt_whales(phase2_result, lang):
|
||||
sys_instr = get_system_instruction(lang)
|
||||
prompt = "\n".join([
|
||||
"PHASE 3: WHALE HUNTING",
|
||||
f"Target ICPs (Industries): {json.dumps(phase2_result.get('icps'))}",
|
||||
"",
|
||||
"Task:",
|
||||
"1. Group 'Whales' (Key Accounts) strictly by the identified ICP industries.",
|
||||
"2. Identify 3-5 concrete top companies in the DACH market per industry.",
|
||||
"3. Define Buying Center Roles.",
|
||||
"",
|
||||
"Output JSON format ONLY:",
|
||||
"{",
|
||||
' "whales": [',
|
||||
' { "industry": "Name of ICP Industry", "accounts": ["Company A", "Company B"] }',
|
||||
" ],
|
||||
' "roles": ["Job Title 1", "Job Title 2"]
|
||||
])
|
||||
log_to_stderr("Starting Phase 3: Whale Hunting...")
|
||||
response = call_gemini_flash(prompt, system_instruction=sys_instr, json_mode=True)
|
||||
return json.loads(response)
|
||||
|
||||
if lang == 'en':
|
||||
conflict_prompt_parts = [
|
||||
"PHASE 1-B: PORTFOLIO CONFLICT CHECK",
|
||||
"",
|
||||
f"New Product Features: {features_json}",
|
||||
f"New Product Constraints: {constraints_json}",
|
||||
"",
|
||||
"Existing Portfolio:",
|
||||
"1. 'Indoor Scrubber 50': Indoor, Hard Floor, Supermarkets.",
|
||||
"2. 'Service Bot Bella': Indoor, Service/Hospitality, Restaurants.",
|
||||
"",
|
||||
"Task: Check for cannibalization. Does the new product make an existing one obsolete? Explain briefly.",
|
||||
"Output JSON format ONLY."
|
||||
]
|
||||
else:
|
||||
conflict_prompt_parts = [
|
||||
"PHASE 1-B: PORTFOLIO-KONFLIKT-CHECK",
|
||||
"",
|
||||
f"Neue Produktmerkmale: {features_json}",
|
||||
f"Neue Produkt-Constraints: {constraints_json}",
|
||||
"",
|
||||
"Bestehendes Portfolio:",
|
||||
"1. 'Indoor Scrubber 50': Innenreinigung, Hartboden, Supermärkte.",
|
||||
"2. 'Service Bot Bella': Indoor, Service/Gastro, Restaurants.",
|
||||
"",
|
||||
"Aufgabe: Prüfe auf Kannibalisierung. Macht das neue Produkt ein bestehendes obsolet? Begründe kurz.",
|
||||
"Output JSON format ONLY."
|
||||
]
|
||||
conflict_prompt = "\n".join(conflict_prompt_parts)
|
||||
def develop_strategy(phase3_result, phase1_result, lang):
|
||||
sys_instr = get_system_instruction(lang)
|
||||
|
||||
all_accounts = []
|
||||
for w in phase3_result.get('whales', []):
|
||||
all_accounts.extend(w.get('accounts', []))
|
||||
|
||||
full_conflict_prompt = sys_instr + "\n\n" + conflict_prompt
|
||||
print(f"DEBUG: Full Conflict Prompt: \n{full_conflict_prompt[:1000]}...\n", file=sys.stderr)
|
||||
conflict_response = call_openai_chat(full_conflict_prompt, response_format_json=True)
|
||||
conflict_data = json.loads(conflict_response)
|
||||
prompt = "\n".join([
|
||||
"PHASE 4: STRATEGY & ANGLE DEVELOPMENT",
|
||||
f"Accounts: {json.dumps(all_accounts)}",
|
||||
f"Product Features: {json.dumps(phase1_result.get('features'))}",
|
||||
"",
|
||||
"Task:",
|
||||
"1. Develop specific 'Angle' per target/industry.",
|
||||
"2. Consistency Check against Product Matrix.",
|
||||
'3. **IMPORTANT:** Apply "Hybrid Service Logic" if technical constraints exist!',
|
||||
"",
|
||||
"Output JSON format ONLY:",
|
||||
"{",
|
||||
' "strategyMatrix": [',
|
||||
" {",
|
||||
' "segment": "Target Segment",',
|
||||
' "painPoint": "Specific Pain",',
|
||||
' "angle": "Our Marketing Angle",',
|
||||
' "differentiation": "How it differs"',
|
||||
" }
|
||||
])
|
||||
log_to_stderr("Starting Phase 4: Strategy...")
|
||||
response = call_gemini_flash(prompt, system_instruction=sys_instr, json_mode=True)
|
||||
return json.loads(response)
|
||||
|
||||
# --- Final Response Assembly ---
|
||||
final_response = {
|
||||
"phase1_technical_extraction": extraction_data,
|
||||
"phase2_portfolio_conflict": conflict_data,
|
||||
}
|
||||
print(json.dumps(final_response, indent=2))
|
||||
def generate_assets(phase4_result, phase3_result, phase2_result, phase1_result, lang):
|
||||
sys_instr = get_system_instruction(lang)
|
||||
prompt = "\n".join([
|
||||
"PHASE 5: ASSET GENERATION & FINAL REPORT",
|
||||
"",
|
||||
"CONTEXT DATA:",
|
||||
f"- Technical: {json.dumps(phase1_result)}",
|
||||
f"- ICPs: {json.dumps(phase2_result)}",
|
||||
f"- Targets (Whales): {json.dumps(phase3_result)}",
|
||||
f"- Strategy: {json.dumps(phase4_result)}",
|
||||
"",
|
||||
"TASK:",
|
||||
'1. Create a "GTM STRATEGY REPORT" in Markdown.',
|
||||
"2. Report Structure: Executive Summary, Product Analysis, Target Audience, Target Accounts, Strategy Matrix, Assets.",
|
||||
'3. Hybrid-Check: Ensure "Hybrid Service Logic" is visible.',
|
||||
"",
|
||||
"Output:",
|
||||
'Return strictly MARKDOWN formatted text. Start with "# GTM STRATEGY REPORT".'
|
||||
])
|
||||
# For Phase 5, we expect TEXT (Markdown), not JSON. So json_mode=False.
|
||||
log_to_stderr("Starting Phase 5: Asset Generation...")
|
||||
response = call_gemini_flash(prompt, system_instruction=sys_instr, json_mode=False)
|
||||
# The frontend expects a string here, not a JSON object wrapping it?
|
||||
return response
|
||||
|
||||
def generate_sales_enablement(phase4_result, phase3_result, phase1_result, lang):
|
||||
sys_instr = get_system_instruction(lang)
|
||||
prompt = "\n".join([
|
||||
"PHASE 6: SALES ENABLEMENT & VISUALS",
|
||||
"",
|
||||
"CONTEXT:",
|
||||
f"- Product Features: {json.dumps(phase1_result.get('features'))}",
|
||||
f"- Accounts (Personas): {json.dumps(phase3_result.get('roles'))}",
|
||||
f"- Strategy: {json.dumps(phase4_result.get('strategyMatrix'))}",
|
||||
"",
|
||||
"TASK:",
|
||||
"1. Anticipate Friction & Objections.",
|
||||
"2. Formulate Battlecards.",
|
||||
"3. Create Visual Prompts.",
|
||||
"",
|
||||
"Output JSON format ONLY:",
|
||||
"{",
|
||||
' "battlecards": [',
|
||||
" {",
|
||||
' "persona": "Role",',
|
||||
' "objection": "Objection quote",',
|
||||
' "responseScript": "Response"',
|
||||
" }
|
||||
],
|
||||
' "visualPrompts": [',
|
||||
" {",
|
||||
' "title": "Title",',
|
||||
' "context": "Context",',
|
||||
' "prompt": "Prompt Code"',
|
||||
" }
|
||||
],
|
||||
])
|
||||
log_to_stderr("Starting Phase 6: Sales Enablement...")
|
||||
response = call_gemini_flash(prompt, system_instruction=sys_instr, json_mode=True)
|
||||
return json.loads(response)
|
||||
|
||||
|
||||
# --- Main Execution Logic ---
|
||||
# --- MAIN ---
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='GTM Architect main entry point.')
|
||||
parser.add_argument('--data', help='JSON data for the command.')
|
||||
parser.add_argument('--mode', required=True, help='The command to execute.')
|
||||
|
||||
args = parser.parse_args()
|
||||
log_to_stderr("--- GTM Orchestrator Starting ---")
|
||||
|
||||
command = args.mode
|
||||
|
||||
# Get data from --data arg or stdin
|
||||
if args.data:
|
||||
data = json.loads(args.data)
|
||||
else:
|
||||
# Read from stdin if no data arg provided
|
||||
try:
|
||||
input_data = sys.stdin.read()
|
||||
data = json.loads(input_data) if input_data.strip() else {}
|
||||
except Exception:
|
||||
data = {}
|
||||
# --- CRITICAL FIXES FOR API KEY & SCRAPING ---
|
||||
# 1. Load API keys manually because helpers.py relies on Config class state
|
||||
try:
|
||||
Config.load_api_keys()
|
||||
log_to_stderr("API Keys loaded.")
|
||||
logging.info("Config.load_api_keys() called successfully.")
|
||||
except Exception as e:
|
||||
log_to_stderr(f"CRITICAL: Failed to load API keys: {e}")
|
||||
logging.critical(f"Failed to load API keys: {e}")
|
||||
# ---------------------------------------------
|
||||
|
||||
if not command:
|
||||
print(json.dumps({"error": "No command or mode provided"}))
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--mode', required=True)
|
||||
parser.add_argument('--data', required=True)
|
||||
|
||||
try:
|
||||
args = parser.parse_args()
|
||||
data_in = json.loads(args.data)
|
||||
mode = args.mode
|
||||
lang = data_in.get('language', 'de')
|
||||
|
||||
log_to_stderr(f"Processing mode: {mode} in language: {lang}")
|
||||
logging.info(f"Processing mode: {mode} in language: {lang}")
|
||||
|
||||
result = {}
|
||||
|
||||
if mode == 'analyze_product':
|
||||
product_input = data_in.get('productInput')
|
||||
result = analyze_product(product_input, lang)
|
||||
|
||||
elif mode == 'discover_icps':
|
||||
phase1_result = data_in.get('phase1Result')
|
||||
result = discover_icps(phase1_result, lang)
|
||||
|
||||
elif mode == 'hunt_whales':
|
||||
phase2_result = data_in.get('phase2Result')
|
||||
result = hunt_whales(phase2_result, lang)
|
||||
|
||||
elif mode == 'develop_strategy':
|
||||
phase3_result = data_in.get('phase3Result')
|
||||
phase1_result = data_in.get('phase1Result')
|
||||
result = develop_strategy(phase3_result, phase1_result, lang)
|
||||
|
||||
elif mode == 'generate_assets':
|
||||
phase4_result = data_in.get('phase4Result')
|
||||
phase3_result = data_in.get('phase3Result')
|
||||
phase2_result = data_in.get('phase2Result')
|
||||
phase1_result = data_in.get('phase1Result')
|
||||
# Returns a string (Markdown)
|
||||
markdown_report = generate_assets(phase4_result, phase3_result, phase2_result, phase1_result, lang)
|
||||
print(json.dumps(markdown_report))
|
||||
log_to_stderr("Finished Phase 5. Output sent to stdout.")
|
||||
return
|
||||
|
||||
elif mode == 'generate_sales_enablement':
|
||||
phase4_result = data_in.get('phase4Result')
|
||||
phase3_result = data_in.get('phase3Result')
|
||||
phase1_result = data_in.get('phase1Result')
|
||||
result = generate_sales_enablement(phase4_result, phase3_result, phase1_result, lang)
|
||||
|
||||
else:
|
||||
logging.error(f"Unknown mode: {mode}")
|
||||
result = {"error": f"Unknown mode: {mode}"}
|
||||
|
||||
print(json.dumps(result))
|
||||
log_to_stderr("Finished. Output sent to stdout.")
|
||||
|
||||
except Exception as e:
|
||||
log_to_stderr(f"CRITICAL ERROR: {e}")
|
||||
logging.error(f"Error in orchestrator: {e}", exc_info=True)
|
||||
# Return error as JSON so server.cjs can handle it gracefully
|
||||
print(json.dumps({"error": str(e)}))
|
||||
sys.exit(1)
|
||||
|
||||
command_handlers = {
|
||||
'save': save_project_handler,
|
||||
'list': list_projects_handler,
|
||||
'load': load_project_handler,
|
||||
'delete': delete_project_handler,
|
||||
'analyze': analyze_product,
|
||||
}
|
||||
|
||||
handler = command_handlers.get(command)
|
||||
if handler:
|
||||
handler(data)
|
||||
else:
|
||||
print(json.dumps({"error": f"Unknown command: {command}"}))
|
||||
|
||||
if __name__ == '__main__':
|
||||
print(f"GTM Architect Engine v{__version__}_FORCE_RELOAD_2 running.", file=sys.stderr)
|
||||
main()
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user