Evaluation Pipeline

# Lead Intelligence Engine System Prompt

You are an expert business analyst and lead qualification assistant.

## Objective
Extract the business name, type, and select ONE primary service and ONE 
optional secondary service from the provided list that best fits the 
business's needs.

## Analysis Rules
1. **Business Name**: Extract the official name from the website.
2. **Business Type**: Identify what the business does.
3. **Maturity Check & Industry Exclusions**:
   - Marketing agencies: NEVER suggest Marketing Services
   - Dev shops: NEVER suggest Technology Services
4. **Primary Service Selection**: Match based on ideal_for and use_case_signals
5. **Fit Score**: 0-100 based on service-business alignment
6. **Reasoning**: Max 2 sentences explaining the match
7. **Outreach Angle**: Strategic entry point for conversation

services = self._load_services()
system_prompt = self._load_prompt()

# Inject service catalog into system prompt
formatted_system_prompt = system_prompt.replace(
    "[SERVICES_JSON]", 
    json.dumps(services, indent=2)
)

# Build user message with RAG context
user_content = f"Analyze this website content and provide the evaluation in JSON:\n\n{content}"
if rag_context:
    rag_text = "\n\n".join(rag_context)
    user_content = f"Additional Advisory Context (RAG):\n{rag_text}\n\n---\n\n{user_content}"

completion = self.client.chat.completions.create(
    model=self.model,  # llama-3.3-70b-versatile
    messages=[
        {"role": "system", "content": formatted_system_prompt},
        {"role": "user", "content": user_content}
    ],
    temperature=0.1,  # Low temperature for consistency
    response_format={"type": "json_object"}  # Enforce JSON output
)

content_str = completion.choices[0].message.content

# Handle markdown code blocks
if "```json" in content_str:
    content_str = content_str.split("```json")[1].split("```")[0].strip()
# Extract JSON object
elif "{" in content_str:
    content_str = content_str[content_str.find("{"):content_str.rfind("}")+1]

result = json.loads(content_str)

valid_service_names = self._get_all_service_names(services)

# Validate Primary
if result.get("primary_service") not in valid_service_names:
    raise ValueError(f"Selected primary service '{result.get('primary_service')}' is not in the approved list.")

# Validate Secondary (if present)
secondary = result.get("secondary_service")
if secondary and secondary not in valid_service_names:
    raise ValueError(f"Selected secondary service '{secondary}' is not in the approved list.")

def _get_all_service_names(self, services_obj):
    """Recursively extracts all 'name' fields."""
    names = []
    if isinstance(services_obj, dict):
        if "name" in services_obj:
            names.append(services_obj["name"])
        for value in services_obj.values():
            names.extend(self._get_all_service_names(value))
    elif isinstance(services_obj, list):
        for item in services_obj:
            names.extend(self._get_all_service_names(item))
    return names

usage = completion.usage
result["_usage"] = {
    "prompt_tokens": usage.prompt_tokens,
    "completion_tokens": usage.completion_tokens,
    "total_tokens": usage.total_tokens
}

# Track cumulative totals (class-level)
self.total_usage["prompt_tokens"] += usage.prompt_tokens
self.total_usage["completion_tokens"] += usage.completion_tokens
self.total_usage["total_tokens"] += usage.total_tokens

attempts = 0
while attempts <= retry_count:  # retry_count=1 by default
    try:
        # ... LLM call ...
        return result
    except Exception as e:
        attempts += 1
        if attempts > retry_count:
            raise e
        logger.warning(f"Retry {attempts}/{retry_count} due to: {e}")

if "rate_limit" in str(e).lower() or "quota" in str(e).lower():
    Evaluator.quota_ok = False
    Evaluator.status = "Rate Limited / Quota Reached"

{
  "name": "Foundation Package",
  "ideal_for": [
    "SMEs",
    "Personal Brands",
    "Startups",
    "Local manual businesses"
  ]
}