zen-marketing/tools/contentvariant.py

"""Content Variant Generator Tool

Generates multiple variations of marketing content for A/B testing.
Supports testing different hooks, tones, lengths, and psychological angles.
"""

from typing import Optional

from pydantic import Field

from config import TEMPERATURE_HIGHLY_CREATIVE
from systemprompts import CONTENTVARIANT_PROMPT
from tools.models import ToolModelCategory
from tools.shared.base_models import ToolRequest
from tools.simple.base import SimpleTool


class ContentVariantRequest(ToolRequest):
    """Request model for Content Variant Generator"""

    content: str = Field(
        ...,
        description="Base content or topic to create variations from. Can be a draft post, subject line, or content concept.",
    )
    variation_count: int = Field(
        default=10,
        ge=5,
        le=25,
        description="Number of variations to generate (5-25). Default is 10.",
    )
    variation_types: Optional[list[str]] = Field(
        default=None,
        description="Types of variations to explore: 'hook', 'tone', 'length', 'structure', 'cta', 'angle'. Leave empty for mixed approach.",
    )
    platform: Optional[str] = Field(
        default=None,
        description="Target platform for character limits and formatting: 'twitter', 'bluesky', 'linkedin', 'instagram', 'facebook', 'email_subject', 'blog'",
    )
    constraints: Optional[str] = Field(
        default=None,
        description="Additional constraints: character limits, style requirements, brand voice guidelines, prohibited words/phrases",
    )
    testing_angles: Optional[list[str]] = Field(
        default=None,
        description="Specific psychological angles to test: 'curiosity', 'contrarian', 'knowledge_gap', 'urgency', 'insider', 'problem_solution', 'social_proof', 'transformation'",
    )


class ContentVariantTool(SimpleTool):
    """Generate multiple content variations for A/B testing"""

    def get_name(self) -> str:
        return "contentvariant"

    def get_description(self) -> str:
        return (
            "Generate 5-25 variations of marketing content for A/B testing. "
            "Tests different hooks, tones, lengths, and psychological angles. "
            "Ideal for subject lines, social posts, email copy, and ads. "
            "Each variation includes testing rationale and predicted audience response."
        )

    def get_system_prompt(self) -> str:
        return CONTENTVARIANT_PROMPT

    def get_default_temperature(self) -> float:
        return TEMPERATURE_HIGHLY_CREATIVE

    def get_model_category(self) -> ToolModelCategory:
        return ToolModelCategory.FAST_RESPONSE

    def get_request_model(self):
        return ContentVariantRequest

    def get_tool_fields(self) -> dict:
        """
        Tool-specific field definitions for ContentVariant.

        Note: This method isn't used since we override get_input_schema(),
        but it's required by the SimpleTool abstract base class.
        """
        return {
            "content": {
                "type": "string",
                "description": "Base content or topic to create variations from",
            }
        }

    async def prepare_prompt(self, request: ContentVariantRequest) -> str:
        """Prepare the content variant prompt"""
        prompt_parts = [f"Generate {request.variation_count} variations of this content:"]
        prompt_parts.append(f"\n**Base Content:**\n{request.content}")

        if request.platform:
            prompt_parts.append(f"\n**Target Platform:** {request.platform}")

        if request.variation_types:
            prompt_parts.append(f"\n**Variation Types:** {', '.join(request.variation_types)}")

        if request.testing_angles:
            prompt_parts.append(f"\n**Testing Angles:** {', '.join(request.testing_angles)}")

        if request.constraints:
            prompt_parts.append(f"\n**Constraints:** {request.constraints}")

        prompt_parts.append(
            "\nGenerate variations with clear labels, character counts (if platform specified), "
            "testing angles, and predicted audience responses."
        )

        # Return the complete prompt
        return "\n".join(prompt_parts)

    def get_input_schema(self) -> dict:
        """Return the JSON schema for this tool's input"""
        return {
            "type": "object",
            "properties": {
                "content": {
                    "type": "string",
                    "description": "Base content or topic to create variations from",
                },
                "variation_count": {
                    "type": "integer",
                    "description": "Number of variations to generate (5-25, default 10)",
                    "minimum": 5,
                    "maximum": 25,
                    "default": 10,
                },
                "variation_types": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Types: 'hook', 'tone', 'length', 'structure', 'cta', 'angle'",
                },
                "platform": {
                    "type": "string",
                    "description": "Platform: 'twitter', 'bluesky', 'linkedin', 'instagram', 'facebook', 'email_subject', 'blog'",
                },
                "constraints": {
                    "type": "string",
                    "description": "Character limits, style requirements, brand guidelines",
                },
                "testing_angles": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Angles: 'curiosity', 'contrarian', 'knowledge_gap', 'urgency', 'insider', 'problem_solution', 'social_proof', 'transformation'",
                },
                "files": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Optional brand guidelines or style reference files",
                },
                "images": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Optional visual assets for context",
                },
                "continuation_id": {
                    "type": "string",
                    "description": "Thread ID to continue previous conversation",
                },
                "model": {
                    "type": "string",
                    "description": "AI model to use (leave empty for default fast model)",
                },
                "temperature": {
                    "type": "number",
                    "description": "Creativity level 0.0-1.0 (default 0.8 for high variation)",
                    "minimum": 0.0,
                    "maximum": 1.0,
                },
                "thinking_mode": {
                    "type": "string",
                    "description": "Thinking depth: minimal, low, medium, high, max",
                    "enum": ["minimal", "low", "medium", "high", "max"],
                },
                "use_websearch": {
                    "type": "boolean",
                    "description": "Enable web search for current platform best practices",
                    "default": False,
                },
            },
            "required": ["content"],
        }