854 lines
39 KiB
Python
854 lines
39 KiB
Python
"""
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YouTube Video Planner Service
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Generates video plans, outlines, and insights using AI with persona integration.
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Supports optional Exa research for enhanced, data-driven plans.
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"""
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from typing import Dict, Any, Optional, List
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from loguru import logger
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from fastapi import HTTPException
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import os
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from services.llm_providers.main_text_generation import llm_text_gen
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from utils.logger_utils import get_service_logger
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logger = get_service_logger("youtube.planner")
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# Video type configurations for optimization
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VIDEO_TYPE_CONFIGS = {
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"tutorial": {
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"hook_strategy": "Problem statement or quick preview of solution",
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"structure": "Problem → Steps → Result → Key Takeaways",
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"visual_style": "Clean, instructional, screen-recordings or clear demonstrations",
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"tone": "Clear, patient, instructional",
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"optimal_scenes": "2-6 scenes showing sequential steps",
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"avatar_style": "Approachable instructor, professional yet friendly",
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"cta_focus": "Subscribe for more tutorials, try it yourself"
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},
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"review": {
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"hook_strategy": "Product reveal or strong opinion statement",
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"structure": "Hook → Overview → Pros/Cons → Verdict → CTA",
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"visual_style": "Product-focused, close-ups, comparison shots",
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"tone": "Honest, engaging, opinionated but fair",
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"optimal_scenes": "4-8 scenes covering different aspects",
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"avatar_style": "Trustworthy reviewer, confident, credible",
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"cta_focus": "Check links in description, subscribe for reviews"
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},
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"educational": {
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"hook_strategy": "Intriguing question or surprising fact",
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"structure": "Question → Explanation → Examples → Conclusion",
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"visual_style": "Illustrative, concept visualization, animations",
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"tone": "Authoritative yet accessible, engaging",
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"optimal_scenes": "3-10 scenes breaking down concepts",
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"avatar_style": "Knowledgeable educator, professional, warm",
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"cta_focus": "Learn more, subscribe for educational content"
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},
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"entertainment": {
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"hook_strategy": "Grab attention immediately with energy/humor",
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"structure": "Hook → Setup → Payoff → Share/Subscribe",
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"visual_style": "Dynamic, energetic, varied angles, transitions",
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"tone": "High energy, funny, engaging, personality-driven",
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"optimal_scenes": "3-8 scenes with varied pacing",
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"avatar_style": "Energetic creator, expressive, relatable",
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"cta_focus": "Like, share, subscribe for more fun content"
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},
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"vlog": {
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"hook_strategy": "Preview of day/event or personal moment",
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"structure": "Introduction → Journey/Experience → Reflection → CTA",
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"visual_style": "Natural, personal, authentic moments",
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"tone": "Conversational, authentic, relatable",
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"optimal_scenes": "5-15 scenes following narrative",
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"avatar_style": "Authentic person, approachable, real",
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"cta_focus": "Follow my journey, subscribe for daily updates"
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},
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"product_demo": {
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"hook_strategy": "Product benefit or transformation",
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"structure": "Benefit → Features → Use Cases → CTA",
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"visual_style": "Product-focused, polished, commercial quality",
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"tone": "Enthusiastic, persuasive, benefit-focused",
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"optimal_scenes": "3-7 scenes highlighting features",
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"avatar_style": "Professional presenter, polished, confident",
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"cta_focus": "Get it now, learn more, special offer"
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},
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"reaction": {
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"hook_strategy": "Preview of reaction or content being reacted to",
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"structure": "Setup → Reaction → Commentary → CTA",
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"visual_style": "Split-screen or picture-in-picture, expressive",
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"tone": "Authentic reactions, engaging commentary",
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"optimal_scenes": "4-10 scenes with reactions",
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"avatar_style": "Expressive creator, authentic reactions",
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"cta_focus": "Watch full video, subscribe for reactions"
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},
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"storytelling": {
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"hook_strategy": "Intriguing opening or compelling question",
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"structure": "Hook → Setup → Conflict → Resolution → CTA",
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"visual_style": "Cinematic, narrative-driven, emotional",
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"tone": "Engaging, immersive, story-focused",
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"optimal_scenes": "6-15 scenes following narrative arc",
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"avatar_style": "Storyteller, warm, engaging narrator",
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"cta_focus": "Subscribe for more stories, share your thoughts"
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}
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}
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class YouTubePlannerService:
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"""Service for planning YouTube videos with AI assistance."""
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def __init__(self):
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"""Initialize the planner service."""
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logger.info("[YouTubePlanner] Service initialized")
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async def generate_video_plan(
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self,
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user_idea: str,
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duration_type: str, # "shorts", "medium", "long"
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video_type: Optional[str] = None, # "tutorial", "review", etc.
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target_audience: Optional[str] = None,
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video_goal: Optional[str] = None,
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brand_style: Optional[str] = None,
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persona_data: Optional[Dict[str, Any]] = None,
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reference_image_description: Optional[str] = None,
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source_content_id: Optional[str] = None, # For blog/story conversion
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source_content_type: Optional[str] = None, # "blog", "story"
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user_id: str = None,
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include_scenes: bool = False, # For shorts: combine plan + scenes in one call
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enable_research: bool = True, # Always enable research by default for enhanced plans
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) -> Dict[str, Any]:
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"""
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Generate a comprehensive video plan from user input.
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Args:
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user_idea: User's video idea or topic
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duration_type: "shorts" (≤60s), "medium" (1-4min), "long" (4-10min)
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video_type: Optional video format type (tutorial, review, etc.)
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target_audience: Optional target audience description
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video_goal: Optional primary goal of the video
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brand_style: Optional brand aesthetic preferences
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persona_data: Optional persona data for tone/style
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reference_image_description: Optional description of reference image
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source_content_id: Optional ID of source content (blog/story)
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source_content_type: Type of source content
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user_id: Clerk user ID for subscription checking
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Returns:
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Dictionary with video plan, outline, insights, and metadata
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"""
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try:
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logger.info(
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f"[YouTubePlanner] Generating plan: idea={user_idea[:50]}..., "
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f"duration={duration_type}, video_type={video_type}, user={user_id}"
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)
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# Get video type config
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video_type_config = {}
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if video_type and video_type in VIDEO_TYPE_CONFIGS:
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video_type_config = VIDEO_TYPE_CONFIGS[video_type]
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# Build persona context
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persona_context = self._build_persona_context(persona_data)
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# Build duration context
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duration_context = self._get_duration_context(duration_type)
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# Build source content context if provided
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source_context = ""
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if source_content_id and source_content_type:
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source_context = f"""
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**Source Content:**
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- Type: {source_content_type}
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- ID: {source_content_id}
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- Note: This video should be based on the existing {source_content_type} content.
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"""
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# Build reference image context
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image_context = ""
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if reference_image_description:
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image_context = f"""
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**Reference Image:**
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{reference_image_description}
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- Use this as visual inspiration for the video
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"""
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# Generate smart defaults based on video type if selected
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# When video_type is selected, use its config for defaults; otherwise use user inputs or generic defaults
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if video_type_config:
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default_tone = video_type_config.get('tone', 'Professional and engaging')
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default_visual_style = video_type_config.get('visual_style', 'Professional and engaging')
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default_goal = video_goal or f"Create engaging {video_type} content"
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default_audience = target_audience or f"Viewers interested in {video_type} content"
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else:
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# No video type selected - use user inputs or generic defaults
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default_tone = 'Professional and engaging'
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default_visual_style = 'Professional and engaging'
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default_goal = video_goal or 'Engage and inform viewers'
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default_audience = target_audience or 'General YouTube audience'
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# Perform Exa research if enabled (after defaults are set)
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research_context = ""
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research_sources = []
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research_enabled = False
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if enable_research:
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logger.info(f"[YouTubePlanner] 🔍 Starting Exa research for plan generation (idea: {user_idea[:50]}...)")
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research_enabled = True
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try:
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research_context, research_sources = await self._perform_exa_research(
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user_idea=user_idea,
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video_type=video_type,
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target_audience=default_audience,
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user_id=user_id
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)
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if research_sources:
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logger.info(
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f"[YouTubePlanner] ✅ Exa research completed successfully: "
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f"{len(research_sources)} sources found. Research context length: {len(research_context)} chars"
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)
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else:
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logger.warning(f"[YouTubePlanner] ⚠️ Exa research completed but no sources returned")
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except HTTPException as http_ex:
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# Subscription limit exceeded or other HTTP errors
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error_detail = http_ex.detail
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if isinstance(error_detail, dict):
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error_msg = error_detail.get("message", error_detail.get("error", str(http_ex)))
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else:
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error_msg = str(error_detail)
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logger.warning(
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f"[YouTubePlanner] ⚠️ Exa research skipped due to subscription limits or error: {error_msg} "
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f"(status={http_ex.status_code}). Continuing without research."
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)
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# Continue without research - non-critical failure
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except Exception as e:
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error_msg = str(e)
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logger.warning(
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f"[YouTubePlanner] ⚠️ Exa research failed (non-critical): {error_msg}. "
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f"Continuing without research."
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)
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# Continue without research - non-critical failure
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else:
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logger.info(f"[YouTubePlanner] ℹ️ Exa research disabled for this plan generation")
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# Generate comprehensive video plan
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video_type_context = ""
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if video_type_config:
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video_type_context = f"""
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**Video Type: {video_type}**
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Follow these guidelines:
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- Structure: {video_type_config.get('structure', '')}
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- Hook: {video_type_config.get('hook_strategy', '')}
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- Visual: {video_type_config.get('visual_style', '')}
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- Tone: {video_type_config.get('tone', '')}
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- CTA: {video_type_config.get('cta_focus', '')}
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"""
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planning_prompt = f"""Create a YouTube video plan for: "{user_idea}"
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**Video Format:** {video_type or 'General'} | **Duration:** {duration_type} ({duration_context['target_seconds']}s target)
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**Audience:** {default_audience}
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**Goal:** {default_goal}
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**Style:** {brand_style or default_visual_style}
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{video_type_context}
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**Constraints:**
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- Duration: {duration_context['target_seconds']}s (Hook: {duration_context['hook_seconds']}s, Main: {duration_context['main_seconds']}s, CTA: {duration_context['cta_seconds']}s)
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- Max scenes: {duration_context['max_scenes']}
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{persona_context if persona_data else ""}
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{source_context if source_content_id else ""}
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{image_context if reference_image_description else ""}
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{research_context if research_context else ""}
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**Generate a plan with:**
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1. **Video Summary**: 2-3 sentences capturing the essence
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2. **Target Audience**: {f"Match: {target_audience}" if target_audience else f"Infer from video idea and {video_type or 'content type'}"}
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3. **Video Goal**: {f"Align with: {video_goal}" if video_goal else f"Infer appropriate goal for {video_type or 'this'} content"}
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4. **Key Message**: Single memorable takeaway
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5. **Hook Strategy**: Engaging opening for first {duration_context['hook_seconds']}s{f" ({video_type_config.get('hook_strategy', '')})" if video_type_config else ""}
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6. **Content Outline**: 3-5 sections totaling {duration_context['target_seconds']}s{f" following: {video_type_config.get('structure', '')}" if video_type_config else ""}
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7. **Call-to-Action**: Actionable CTA{f" ({video_type_config.get('cta_focus', '')})" if video_type_config else ""}
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8. **Visual Style**: Match {brand_style or default_visual_style}
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9. **Tone**: {default_tone}
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10. **SEO Keywords**: 5-7 relevant terms based on video idea
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11. **Avatar Recommendations**: {f"{video_type_config.get('avatar_style', '')} " if video_type_config else ""}matching audience and style
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**Response Format (JSON):**
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{{
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"video_summary": "...",
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"target_audience": "...",
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"video_goal": "...",
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"key_message": "...",
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"hook_strategy": "...",
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"content_outline": [
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{{"section": "...", "description": "...", "duration_estimate": 30}},
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{{"section": "...", "description": "...", "duration_estimate": 45}}
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],
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"call_to_action": "...",
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"visual_style": "...",
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"tone": "...",
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"seo_keywords": ["keyword1", "keyword2", ...],
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"avatar_recommendations": {{
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"description": "...",
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"style": "...",
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"energy": "..."
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}}
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}}
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**Critical:** Content outline durations must sum to {duration_context['target_seconds']}s (±20%).
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"""
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system_prompt = (
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"You are an expert YouTube content strategist. Create clear, actionable video plans "
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"that are optimized for the specified video type and audience. Focus on accuracy and "
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"specificity - these plans will be used to generate actual video content."
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)
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# For shorts, combine plan + scenes in one call to save API calls
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if include_scenes and duration_type == "shorts":
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planning_prompt += f"""
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**IMPORTANT: Since this is a SHORTS video, also generate the complete scene breakdown in the same response.**
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**Additional Task - Generate Detailed Scenes:**
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Create detailed scenes (up to {duration_context['max_scenes']} scenes) that include:
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1. Scene number and title
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2. Narration text (what will be spoken) - keep it concise for shorts
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3. Visual description (what viewers will see)
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4. Duration estimate (2-8 seconds each)
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5. Emphasis tags (hook, main_content, transition, cta)
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**Scene Format:**
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Each scene should be detailed enough for video generation. Total duration must fit within {duration_context['target_seconds']} seconds.
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**Update JSON structure to include "scenes" array and "avatar_recommendations":**
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Add a "scenes" field with the complete scene breakdown, and include "avatar_recommendations" with ideal presenter appearance, style, and energy.
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"""
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json_struct = {
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"type": "object",
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"properties": {
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"video_summary": {"type": "string"},
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"target_audience": {"type": "string"},
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"video_goal": {"type": "string"},
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"key_message": {"type": "string"},
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"hook_strategy": {"type": "string"},
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"content_outline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"section": {"type": "string"},
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"description": {"type": "string"},
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"duration_estimate": {"type": "number"}
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}
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}
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},
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"call_to_action": {"type": "string"},
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"visual_style": {"type": "string"},
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"tone": {"type": "string"},
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"seo_keywords": {
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"type": "array",
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"items": {"type": "string"}
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},
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"scenes": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"scene_number": {"type": "number"},
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"title": {"type": "string"},
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"narration": {"type": "string"},
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"visual_description": {"type": "string"},
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"duration_estimate": {"type": "number"},
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"emphasis": {"type": "string"},
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"visual_cues": {
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"type": "array",
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"items": {"type": "string"}
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}
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},
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"required": [
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"scene_number", "title", "narration", "visual_description",
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"duration_estimate", "emphasis"
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]
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}
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},
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"avatar_recommendations": {
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"type": "object",
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"properties": {
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"description": {"type": "string"},
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"style": {"type": "string"},
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"energy": {"type": "string"}
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}
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}
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},
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"required": [
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"video_summary", "target_audience", "video_goal", "key_message",
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"hook_strategy", "content_outline", "call_to_action",
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"visual_style", "tone", "seo_keywords", "scenes", "avatar_recommendations"
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]
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}
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else:
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json_struct = {
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"type": "object",
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"properties": {
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"video_summary": {"type": "string"},
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"target_audience": {"type": "string"},
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"video_goal": {"type": "string"},
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"key_message": {"type": "string"},
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"hook_strategy": {"type": "string"},
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"content_outline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"section": {"type": "string"},
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"description": {"type": "string"},
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"duration_estimate": {"type": "number"}
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}
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}
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},
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"call_to_action": {"type": "string"},
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"visual_style": {"type": "string"},
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"tone": {"type": "string"},
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"seo_keywords": {
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"type": "array",
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"items": {"type": "string"}
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},
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"avatar_recommendations": {
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"type": "object",
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"properties": {
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"description": {"type": "string"},
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"style": {"type": "string"},
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"energy": {"type": "string"}
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}
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}
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},
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"required": [
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"video_summary", "target_audience", "video_goal", "key_message",
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"hook_strategy", "content_outline", "call_to_action",
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"visual_style", "tone", "seo_keywords", "avatar_recommendations"
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]
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}
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# Generate plan using LLM with structured JSON response
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# llm_text_gen handles subscription checks and provider selection automatically
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# json_struct ensures deterministic structured response (returns dict, not string)
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response = llm_text_gen(
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prompt=planning_prompt,
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system_prompt=system_prompt,
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user_id=user_id,
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json_struct=json_struct
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)
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# Parse response (structured responses return dict, text responses return string)
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if isinstance(response, dict):
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plan_data = response
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else:
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import json
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try:
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plan_data = json.loads(response)
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except json.JSONDecodeError as e:
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logger.error(f"[YouTubePlanner] Failed to parse JSON response: {e}")
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logger.debug(f"[YouTubePlanner] Raw response: {response[:500]}")
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raise HTTPException(
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status_code=500,
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detail="Failed to parse video plan response. Please try again."
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)
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# Validate and enhance plan quality
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plan_data = self._validate_and_enhance_plan(
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plan_data, duration_context, video_type, video_type_config
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)
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# Add metadata
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plan_data["duration_type"] = duration_type
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plan_data["duration_metadata"] = duration_context
|
||
plan_data["user_idea"] = user_idea
|
||
|
||
# Add research metadata to plan
|
||
plan_data["research_enabled"] = research_enabled
|
||
if research_sources:
|
||
plan_data["research_sources"] = research_sources
|
||
plan_data["research_sources_count"] = len(research_sources)
|
||
else:
|
||
plan_data["research_sources"] = []
|
||
plan_data["research_sources_count"] = 0
|
||
|
||
# Log research status in plan metadata for debugging
|
||
if research_enabled:
|
||
logger.info(
|
||
f"[YouTubePlanner] 📊 Plan metadata: research_enabled=True, "
|
||
f"research_sources_count={plan_data.get('research_sources_count', 0)}, "
|
||
f"research_context_length={len(research_context)} chars"
|
||
)
|
||
|
||
# Validate and process scenes if included (for shorts)
|
||
if include_scenes and duration_type == "shorts":
|
||
if "scenes" in plan_data and plan_data["scenes"]:
|
||
# Validate scenes count and duration
|
||
scenes = plan_data["scenes"]
|
||
scene_count = len(scenes)
|
||
total_scene_duration = sum(
|
||
scene.get("duration_estimate", 0) for scene in scenes
|
||
)
|
||
|
||
max_scenes = duration_context["max_scenes"]
|
||
target_duration = duration_context["target_seconds"]
|
||
|
||
if scene_count > max_scenes:
|
||
logger.warning(
|
||
f"[YouTubePlanner] Scene count ({scene_count}) exceeds max ({max_scenes}). "
|
||
f"Truncating to first {max_scenes} scenes."
|
||
)
|
||
plan_data["scenes"] = scenes[:max_scenes]
|
||
|
||
# Warn if total duration is off
|
||
if abs(total_scene_duration - target_duration) > target_duration * 0.3:
|
||
logger.warning(
|
||
f"[YouTubePlanner] Total scene duration ({total_scene_duration}s) "
|
||
f"differs significantly from target ({target_duration}s)"
|
||
)
|
||
|
||
plan_data["_scenes_included"] = True
|
||
logger.info(
|
||
f"[YouTubePlanner] ✅ Plan + {len(plan_data['scenes'])} scenes "
|
||
f"generated in 1 AI call (optimized for shorts)"
|
||
)
|
||
else:
|
||
# LLM did not return scenes; downstream will regenerate
|
||
plan_data["_scenes_included"] = False
|
||
logger.warning(
|
||
"[YouTubePlanner] Shorts optimization requested but no scenes returned; "
|
||
"scene builder will generate scenes separately."
|
||
)
|
||
|
||
logger.info(f"[YouTubePlanner] ✅ Plan generated successfully")
|
||
|
||
return plan_data
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"[YouTubePlanner] Error generating plan: {e}", exc_info=True)
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=f"Failed to generate video plan: {str(e)}"
|
||
)
|
||
|
||
def _build_persona_context(self, persona_data: Optional[Dict[str, Any]]) -> str:
|
||
"""Build persona context string for prompts."""
|
||
if not persona_data:
|
||
return """
|
||
**Persona Context:**
|
||
- Using default professional tone
|
||
- No specific persona constraints
|
||
"""
|
||
|
||
core_persona = persona_data.get("core_persona", {})
|
||
tone = core_persona.get("tone", "professional")
|
||
voice = core_persona.get("voice_characteristics", {})
|
||
|
||
return f"""
|
||
**Persona Context:**
|
||
- Tone: {tone}
|
||
- Voice Style: {voice.get('style', 'professional')}
|
||
- Communication Style: {voice.get('communication_style', 'clear and direct')}
|
||
- Brand Values: {core_persona.get('core_belief', 'value-driven content')}
|
||
- Use this persona to guide the video's tone, style, and messaging approach.
|
||
"""
|
||
|
||
def _get_duration_context(self, duration_type: str) -> Dict[str, Any]:
|
||
"""Get duration-specific context and constraints."""
|
||
contexts = {
|
||
"shorts": {
|
||
"description": "YouTube Shorts (15-60 seconds)",
|
||
"target_seconds": 30,
|
||
"hook_seconds": 3,
|
||
"main_seconds": 24,
|
||
"cta_seconds": 3,
|
||
# Keep scenes tight for shorts to control cost and pacing
|
||
"max_scenes": 4,
|
||
"scene_duration_range": (2, 8)
|
||
},
|
||
"medium": {
|
||
"description": "Medium-length video (1-4 minutes)",
|
||
"target_seconds": 150, # 2.5 minutes
|
||
"hook_seconds": 10,
|
||
"main_seconds": 130,
|
||
"cta_seconds": 10,
|
||
"max_scenes": 12,
|
||
"scene_duration_range": (5, 15)
|
||
},
|
||
"long": {
|
||
"description": "Long-form video (4-10 minutes)",
|
||
"target_seconds": 420, # 7 minutes
|
||
"hook_seconds": 15,
|
||
"main_seconds": 380,
|
||
"cta_seconds": 25,
|
||
"max_scenes": 20,
|
||
"scene_duration_range": (10, 30)
|
||
}
|
||
}
|
||
|
||
return contexts.get(duration_type, contexts["medium"])
|
||
|
||
def _validate_and_enhance_plan(
|
||
self,
|
||
plan_data: Dict[str, Any],
|
||
duration_context: Dict[str, Any],
|
||
video_type: Optional[str],
|
||
video_type_config: Dict[str, Any],
|
||
) -> Dict[str, Any]:
|
||
"""
|
||
Validate and enhance plan quality before returning.
|
||
|
||
Performs quality checks:
|
||
- Validates required fields
|
||
- Validates content outline duration matches target
|
||
- Ensures SEO keywords are present
|
||
- Validates avatar recommendations
|
||
- Adds quality metadata
|
||
"""
|
||
# Ensure required fields exist
|
||
required_fields = [
|
||
"video_summary", "target_audience", "video_goal", "key_message",
|
||
"hook_strategy", "content_outline", "call_to_action",
|
||
"visual_style", "tone", "seo_keywords"
|
||
]
|
||
|
||
missing_fields = [field for field in required_fields if not plan_data.get(field)]
|
||
if missing_fields:
|
||
logger.warning(f"[YouTubePlanner] Missing required fields: {missing_fields}")
|
||
# Fill with defaults to prevent errors
|
||
for field in missing_fields:
|
||
if field == "seo_keywords":
|
||
plan_data[field] = []
|
||
elif field == "content_outline":
|
||
plan_data[field] = []
|
||
else:
|
||
plan_data[field] = f"[{field} not generated]"
|
||
|
||
# Validate content outline duration
|
||
if plan_data.get("content_outline"):
|
||
total_duration = sum(
|
||
section.get("duration_estimate", 0)
|
||
for section in plan_data["content_outline"]
|
||
)
|
||
target_duration = duration_context.get("target_seconds", 150)
|
||
|
||
# Allow 20% variance
|
||
tolerance = target_duration * 0.2
|
||
if abs(total_duration - target_duration) > tolerance:
|
||
logger.warning(
|
||
f"[YouTubePlanner] Content outline duration ({total_duration}s) "
|
||
f"doesn't match target ({target_duration}s). Adjusting..."
|
||
)
|
||
# Normalize durations proportionally
|
||
if total_duration > 0:
|
||
scale_factor = target_duration / total_duration
|
||
for section in plan_data["content_outline"]:
|
||
if "duration_estimate" in section:
|
||
section["duration_estimate"] = round(
|
||
section["duration_estimate"] * scale_factor, 1
|
||
)
|
||
|
||
# Validate SEO keywords
|
||
if not plan_data.get("seo_keywords") or len(plan_data["seo_keywords"]) < 3:
|
||
logger.warning(
|
||
f"[YouTubePlanner] Insufficient SEO keywords ({len(plan_data.get('seo_keywords', []))}). "
|
||
f"Plan may need enhancement."
|
||
)
|
||
|
||
# Validate avatar recommendations
|
||
if not plan_data.get("avatar_recommendations"):
|
||
logger.warning("[YouTubePlanner] Avatar recommendations missing. Generating defaults...")
|
||
plan_data["avatar_recommendations"] = {
|
||
"description": video_type_config.get("avatar_style", "Professional YouTube creator"),
|
||
"style": plan_data.get("visual_style", "Professional"),
|
||
"energy": plan_data.get("tone", "Engaging")
|
||
}
|
||
else:
|
||
# Ensure all avatar recommendation fields exist
|
||
avatar_rec = plan_data["avatar_recommendations"]
|
||
if not avatar_rec.get("description"):
|
||
avatar_rec["description"] = video_type_config.get("avatar_style", "Professional YouTube creator")
|
||
if not avatar_rec.get("style"):
|
||
avatar_rec["style"] = plan_data.get("visual_style", "Professional")
|
||
if not avatar_rec.get("energy"):
|
||
avatar_rec["energy"] = plan_data.get("tone", "Engaging")
|
||
|
||
# Add quality metadata
|
||
plan_data["_quality_checks"] = {
|
||
"content_outline_validated": bool(plan_data.get("content_outline")),
|
||
"seo_keywords_count": len(plan_data.get("seo_keywords", [])),
|
||
"avatar_recommendations_present": bool(plan_data.get("avatar_recommendations")),
|
||
"all_required_fields_present": len(missing_fields) == 0,
|
||
}
|
||
|
||
logger.info(
|
||
f"[YouTubePlanner] Plan quality validated: "
|
||
f"outline_sections={len(plan_data.get('content_outline', []))}, "
|
||
f"seo_keywords={len(plan_data.get('seo_keywords', []))}, "
|
||
f"avatar_recs={'yes' if plan_data.get('avatar_recommendations') else 'no'}"
|
||
)
|
||
|
||
return plan_data
|
||
|
||
async def _perform_exa_research(
|
||
self,
|
||
user_idea: str,
|
||
video_type: Optional[str],
|
||
target_audience: str,
|
||
user_id: str
|
||
) -> tuple[str, List[Dict[str, Any]]]:
|
||
"""
|
||
Perform Exa research directly using ExaResearchProvider (common module).
|
||
Uses the same pattern as podcast research with proper subscription checks.
|
||
|
||
Returns:
|
||
Tuple of (research_context_string, research_sources_list)
|
||
"""
|
||
try:
|
||
# Pre-flight validation for Exa search only (not full blog writer workflow)
|
||
# We only need to validate Exa API calls, not LLM operations
|
||
from services.database import get_db
|
||
from services.subscription import PricingService
|
||
from models.subscription_models import APIProvider
|
||
|
||
db = next(get_db())
|
||
try:
|
||
pricing_service = PricingService(db)
|
||
# Only validate Exa API call, not the full research workflow
|
||
operations_to_validate = [
|
||
{
|
||
'provider': APIProvider.EXA,
|
||
'tokens_requested': 0,
|
||
'actual_provider_name': 'exa',
|
||
'operation_type': 'exa_neural_search'
|
||
}
|
||
]
|
||
|
||
can_proceed, message, error_details = pricing_service.check_comprehensive_limits(
|
||
user_id=user_id,
|
||
operations=operations_to_validate
|
||
)
|
||
|
||
if not can_proceed:
|
||
usage_info = error_details.get('usage_info', {}) if error_details else {}
|
||
logger.warning(
|
||
f"[YouTubePlanner] Exa search blocked for user {user_id}: {message}"
|
||
)
|
||
raise HTTPException(
|
||
status_code=429,
|
||
detail={
|
||
'error': message,
|
||
'message': message,
|
||
'provider': 'exa',
|
||
'usage_info': usage_info if usage_info else error_details
|
||
}
|
||
)
|
||
|
||
logger.info(f"[YouTubePlanner] Exa search pre-flight validation passed for user {user_id}")
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.warning(f"[YouTubePlanner] Exa search pre-flight validation failed: {e}")
|
||
raise
|
||
finally:
|
||
db.close()
|
||
|
||
# Use ExaResearchProvider directly (common module, same as podcast)
|
||
from services.blog_writer.research.exa_provider import ExaResearchProvider
|
||
from types import SimpleNamespace
|
||
|
||
# Build research query
|
||
query_parts = [user_idea]
|
||
if video_type:
|
||
query_parts.append(f"{video_type} video")
|
||
if target_audience and target_audience != "General YouTube audience":
|
||
query_parts.append(target_audience)
|
||
|
||
research_query = " ".join(query_parts)
|
||
|
||
# Configure Exa research (same pattern as podcast)
|
||
cfg = SimpleNamespace(
|
||
exa_search_type="neural",
|
||
exa_category="web", # Focus on web content for YouTube
|
||
exa_include_domains=[],
|
||
exa_exclude_domains=[],
|
||
max_sources=10, # Limit sources for cost efficiency
|
||
source_types=[],
|
||
)
|
||
|
||
# Perform research
|
||
provider = ExaResearchProvider()
|
||
result = await provider.search(
|
||
prompt=research_query,
|
||
topic=user_idea,
|
||
industry="",
|
||
target_audience=target_audience,
|
||
config=cfg,
|
||
user_id=user_id,
|
||
)
|
||
|
||
# Track usage
|
||
cost_total = 0.0
|
||
if isinstance(result, dict):
|
||
cost_total = result.get("cost", {}).get("total", 0.005) if result.get("cost") else 0.005
|
||
provider.track_exa_usage(user_id, cost_total)
|
||
|
||
# Extract sources and content
|
||
sources = result.get("sources", []) or []
|
||
research_content = result.get("content", "")
|
||
|
||
# Build research context for prompt
|
||
research_context = ""
|
||
if research_content and sources:
|
||
# Limit content to 2000 chars to avoid token bloat
|
||
limited_content = research_content[:2000]
|
||
research_context = f"""
|
||
**Research & Current Information:**
|
||
Based on current web research, here are relevant insights and trends:
|
||
|
||
{limited_content}
|
||
|
||
**Key Research Sources ({len(sources)} sources):**
|
||
"""
|
||
# Add top 5 sources for context
|
||
for idx, source in enumerate(sources[:5], 1):
|
||
title = source.get("title", "Untitled") or "Untitled"
|
||
url = source.get("url", "") or ""
|
||
excerpt = (source.get("excerpt", "") or "")[:200]
|
||
if not excerpt:
|
||
excerpt = (source.get("summary", "") or "")[:200]
|
||
research_context += f"\n{idx}. {title}\n {excerpt}\n Source: {url}\n"
|
||
|
||
research_context += "\n**Use this research to:**\n"
|
||
research_context += "- Identify current trends and popular angles\n"
|
||
research_context += "- Enhance SEO keywords with real search data\n"
|
||
research_context += "- Ensure content is relevant and up-to-date\n"
|
||
research_context += "- Reference credible sources in the plan\n"
|
||
research_context += "- Identify gaps or unique angles not covered by competitors\n"
|
||
|
||
# Format sources for response
|
||
formatted_sources = []
|
||
for source in sources:
|
||
formatted_sources.append({
|
||
"title": source.get("title", "") or "",
|
||
"url": source.get("url", "") or "",
|
||
"excerpt": (source.get("excerpt", "") or "")[:300],
|
||
"published_at": source.get("published_at"),
|
||
"credibility_score": source.get("credibility_score", 0.85) or 0.85,
|
||
})
|
||
|
||
logger.info(f"[YouTubePlanner] Exa research completed: {len(formatted_sources)} sources found")
|
||
return research_context, formatted_sources
|
||
|
||
except HTTPException:
|
||
# Re-raise HTTPException (subscription limits, etc.)
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"[YouTubePlanner] Research error: {e}", exc_info=True)
|
||
# Non-critical failure - return empty research
|
||
return "", []
|
||
|