feat: podcast demo mode with ALWRITY_ENABLED_FEATURES support
- Add ALWRITY_ENABLED_FEATURES env var for feature gating - Podcast-only mode: skip LLM bootstrap, scheduler, persona services - Enhance video generation prompt with scene context, analysis, narration - Add voice cloning support via custom_voice_id in WaveSpeed - Add text-to-speech for research results (browser speechSynthesis) - Fix render queue to sync images from script phase - Add WaveSpeed LLM pricing (gpt-oss-120b) - Fix podcast bible generation error handling - Refactor RouterManager for feature-based router loading
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ajaysi
@@ -8,6 +8,7 @@ from fastapi import APIRouter, Depends, HTTPException
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from typing import Dict, Any, List
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from types import SimpleNamespace
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import json
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import re
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from middleware.auth_middleware import get_current_user
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from api.story_writer.utils.auth import require_authenticated_user
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@@ -36,10 +37,16 @@ async def podcast_research_exa(
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Uses Podcast Bible and Analysis context for hyper-personalization.
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"""
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user_id = require_authenticated_user(current_user)
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logger.warning(f"[Podcast Research] ========== REQUEST START ==========")
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logger.warning(f"[Podcast Research] User: {user_id}, Topic: {request.topic[:80]}...")
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logger.warning(f"[Podcast Research] Queries count: {len(request.queries) if request.queries else 0}")
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queries = [q.strip() for q in request.queries if q and q.strip()]
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if not queries:
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raise HTTPException(status_code=400, detail="At least one query is required for research.")
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logger.warning(f"[Podcast Research] EXACT queries being sent to Exa: {queries}")
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exa_cfg = request.exa_config or PodcastExaConfig()
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cfg = SimpleNamespace(
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@@ -52,6 +59,7 @@ async def podcast_research_exa(
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)
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provider = ExaResearchProvider()
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logger.warning(f"[Podcast Research] Provider initialized, starting Exa search...")
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# --- Context Building ---
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bible_service = PodcastBibleService()
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@@ -68,9 +76,16 @@ async def podcast_research_exa(
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if request.analysis:
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analysis_context = f"""
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PODCAST ANALYSIS CONTEXT:
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Audience: {request.analysis.get('audience', 'General')}
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========================
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Topic: {request.topic}
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Target Audience: {request.analysis.get('audience', 'General')}
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Content Type: {request.analysis.get('content_type', 'Informative')}
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Top Keywords: {', '.join(request.analysis.get('top_keywords', []))}
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Episode Hook (Intro): {request.analysis.get('episode_hook', 'N/A')}
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Key Takeaways: {', '.join(request.analysis.get('key_takeaways', [])) or 'N/A'}
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Guest Talking Points: {', '.join(request.analysis.get('guest_talking_points', [])) or 'N/A'}
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Listener CTA: {request.analysis.get('listener_cta', 'N/A')}
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"""
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# Exa search params
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@@ -84,6 +99,7 @@ Top Keywords: {', '.join(request.analysis.get('top_keywords', []))}
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try:
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# 1. RUN EXA SEARCH
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logger.warning(f"[Podcast Research] Calling Exa search with topic: {request.topic[:100]}...")
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result = await provider.search(
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prompt=request.topic,
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topic=request.topic,
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@@ -92,8 +108,9 @@ Top Keywords: {', '.join(request.analysis.get('top_keywords', []))}
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config=cfg,
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user_id=user_id,
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)
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logger.warning(f"[Podcast Research] Exa search completed, got {len(result.get('sources', []))} sources")
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except Exception as exc:
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logger.error(f"[Podcast Exa Research] Search failed for user {user_id}: {exc}")
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logger.error(f"[Podcast Exa Research] Search failed for user {user_id}: {exc}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"Exa research failed: {exc}")
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# 2. EXTRACT INSIGHTS VIA LLM
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@@ -104,46 +121,77 @@ Top Keywords: {', '.join(request.analysis.get('top_keywords', []))}
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key_insights = []
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if raw_content and sources:
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logger.info(f"[Podcast Research] Extracting insights from {len(sources)} sources for user {user_id}")
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logger.warning(f"[Podcast Research] Extracting insights from {len(sources)} sources for user {user_id}")
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# Build list of research queries used for this search
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queries_used = ", ".join([f"Query {i+1}: {q}" for i, q in enumerate(queries)]) if queries else "No specific queries"
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prompt = f"""
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You are an expert research analyst for a high-end podcast production team.
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Your task is to analyze the following research data and extract deep, actionable insights for a podcast episode.
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You are an expert research analyst and content strategist for a high-end podcast production team.
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Your task is to analyze the research data and extract deep, podcast-ready insights.
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PODCAST CONTEXT:
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Topic: {request.topic}
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================
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Main Topic: {request.topic}
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RESEARCH QUERIES USED:
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=====================
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{queries_used}
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PODCAST BIBLE & BRAND CONTEXT:
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==============================
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{bible_context}
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PODCAST ANALYSIS (from AI Analysis phase):
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==========================================
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{analysis_context}
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RESEARCH DATA (from {len(sources)} sources):
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============================================
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{raw_content}
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TASK:
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1. Provide a comprehensive summary (2-3 paragraphs) of the most important findings. Use Markdown for formatting (bolding, lists).
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2. Extract 3-5 "Key Insights". Each insight should have a title and a detailed explanation.
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3. For each insight, identify which source indices (e.g. 1, 2) it was derived from.
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YOUR TASK:
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==========
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As a podcast research expert, analyze this data and create content that will:
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1. Engage the specific target audience identified above
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2. Support the episode hook and key takeaways already planned
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3. Provide talking points that complement the guest's expertise
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4. Include a compelling call-to-action for listeners
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NOTE: The research data includes "Key Highlights", "Summaries", and "Excerpts" from various sources.
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Pay special attention to the "Key Highlights" sections as they contain the most relevant information extracted by the neural search engine.
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Return JSON structure:
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REQUIRED OUTPUT (JSON):
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=======================
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{{
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"summary": "Detailed markdown summary...",
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"summary": "2-3 paragraph comprehensive summary in Markdown. Start with a hook that matches the episode intro. Include specific data points, expert quotes, and trends.",
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"key_insights": [
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{{
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"title": "Insight Title",
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"content": "Detailed markdown content...",
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"source_indices": [1, 2]
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"title": "Catchy, engaging title for this insight",
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"content": "3-4 sentences with specific facts, quotes, or data. Write in a conversational tone suitable for a podcast host to discuss.",
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"source_indices": [1, 2, 3],
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"podcast_talking_points": ["Point 1 host can expand on", "Counter-point or follow-up", "Question to ask guest"]
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}}
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]
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],
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"expert_quotes": [
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{{
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"quote": "Direct quote from source",
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"source_index": 1,
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"context": "Why this quote matters for the podcast"
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}}
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],
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"listener_cta_suggestions": ["Specific action listener can take", "Resource to share", "Next episode preview"]
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}}
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Requirements:
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- Ensure insights are deep, not just superficial facts. Look for trends, expert opinions, and specific data points.
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- Tone should be professional, insightful, and ready for a podcast host to discuss.
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- Avoid generic filler.
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QUALITY STANDARDS:
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==================
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- INSIGHTS MUST BE DEEP, not superficial - avoid generic statements
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- Include SPECIFIC DATA POINTS, percentages, statistics when available
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- Extract EXPERT QUOTES that hosts can reference
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- Identify GAPS in the research where more depth is needed
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- Make content naturally flow into the planned episode hook and CTA
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- Write in a CONVERSATIONAL tone - how a host would actually speak
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- Flag any CONTROVERSIAL or debatable claims for host to address
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"""
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try:
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logger.warning(f"[Podcast Research] Calling LLM for insight extraction...")
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llm_response = llm_text_gen(
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prompt=prompt,
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user_id=user_id,
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@@ -151,15 +199,45 @@ Requirements:
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preferred_provider=None,
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flow_type="premium_tool",
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)
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logger.warning(f"[Podcast Research] LLM response received, length: {len(llm_response) if llm_response else 0}")
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# Normalize response
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# Normalize response - handle both string and dict responses
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data = None
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if isinstance(llm_response, str):
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data = json.loads(llm_response)
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try:
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# Try to fix common JSON issues
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fixed_response = llm_response.strip()
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# Remove markdown code blocks if present
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if fixed_response.startswith("```"):
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fixed_response = fixed_response.split("```")[1]
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if fixed_response.startswith("json"):
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fixed_response = fixed_response[4:]
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fixed_response = fixed_response.strip()
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data = json.loads(fixed_response)
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except json.JSONDecodeError as json_err:
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logger.warning(f"[Podcast Research] Failed to parse JSON: {json_err}. Response preview: {llm_response[:500]}...")
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# Try to extract JSON from response using regex
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json_match = re.search(r'\{.*\}', llm_response, re.DOTALL)
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if json_match:
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try:
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data = json.loads(json_match.group())
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logger.warning("[Podcast Research] Successfully extracted JSON via regex")
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except:
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pass
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else:
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data = llm_response
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summary = data.get("summary", "")
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key_insights = [PodcastResearchInsight(**insight) for insight in data.get("key_insights", [])]
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if data:
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try:
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summary = data.get("summary", "")
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key_insights = [PodcastResearchInsight(**insight) for insight in data.get("key_insights", [])]
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except Exception as insight_err:
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logger.warning(f"[Podcast Research] Failed to parse insights: {insight_err}. Data keys: {list(data.keys()) if isinstance(data, dict) else 'not a dict'}")
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summary = data.get("summary", "") if isinstance(data, dict) else ""
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key_insights = []
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else:
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summary = ""
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key_insights = []
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except HTTPException:
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raise
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except Exception as exc:
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@@ -183,21 +261,32 @@ Requirements:
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logger.warning(f"[Podcast Exa Research] Failed to track usage: {track_err}")
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sources_payload = []
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seen_urls = set()
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for src in sources:
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url = src.get("url", "")
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# Skip duplicates
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if url and url in seen_urls:
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continue
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if url:
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seen_urls.add(url)
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try:
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sources_payload.append(PodcastExaSource(**src))
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except Exception:
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sources_payload.append(PodcastExaSource(**{
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"title": src.get("title", ""),
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"url": src.get("url", ""),
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"excerpt": src.get("excerpt", ""),
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"url": url,
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"excerpt": src.get("excerpt") or (src.get("highlights")[0] if src.get("highlights") else "") or src.get("summary", ""),
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"published_at": src.get("published_at"),
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"publishedDate": src.get("publishedDate"),
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"highlights": src.get("highlights"),
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"summary": src.get("summary"),
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"source_type": src.get("source_type"),
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"index": src.get("index"),
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"image": src.get("image"),
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"author": src.get("author"),
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"text": src.get("text"),
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"credibility_score": src.get("credibility_score"),
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}))
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return PodcastExaResearchResponse(
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