Analyzing your idea with AI...
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ajaysi
244
_session_backup/research.py
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244
_session_backup/research.py
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"""
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Podcast Research Handlers
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Research endpoints using Exa provider and LLM summarization.
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"""
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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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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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from services.blog_writer.research.exa_provider import ExaResearchProvider
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.podcast_bible_service import PodcastBibleService
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from loguru import logger
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from ..models import (
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PodcastExaResearchRequest,
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PodcastExaResearchResponse,
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PodcastExaSource,
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PodcastExaConfig,
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PodcastResearchInsight,
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)
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router = APIRouter()
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@router.post("/research/exa", response_model=PodcastExaResearchResponse)
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async def podcast_research_exa(
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request: PodcastExaResearchRequest,
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current_user: Dict[str, Any] = Depends(get_current_user),
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):
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"""
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Run podcast research via Exa and then use LLM to extract deep insights.
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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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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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exa_cfg = request.exa_config or PodcastExaConfig()
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cfg = SimpleNamespace(
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exa_search_type=exa_cfg.exa_search_type or "auto",
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exa_category=exa_cfg.exa_category,
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exa_include_domains=exa_cfg.exa_include_domains or [],
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exa_exclude_domains=exa_cfg.exa_exclude_domains or [],
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max_sources=exa_cfg.max_sources or 8,
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source_types=[],
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)
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provider = ExaResearchProvider()
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# --- Context Building ---
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bible_service = PodcastBibleService()
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bible_context = ""
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if request.bible:
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try:
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from models.podcast_bible_models import PodcastBible
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bible_data = PodcastBible(**request.bible)
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bible_context = bible_service.serialize_bible(bible_data)
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except Exception as exc:
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logger.warning(f"[Podcast Research] Failed to serialize bible: {exc}")
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analysis_context = ""
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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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Content Type: {request.analysis.get('content_type', 'Informative')}
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Top Keywords: {', '.join(request.analysis.get('top_keywords', []))}
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"""
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# Exa search params
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industry = request.bible.get("brand", {}).get("industry", "") if request.bible else ""
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target_audience = ""
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if request.bible:
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audience_dna = request.bible.get("audience", {})
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if audience_dna:
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interests = ", ".join(audience_dna.get("interests", []))
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target_audience = f"Expertise: {audience_dna.get('expertise_level', '')}. Interests: {interests}."
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try:
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# 1. RUN EXA SEARCH
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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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industry=industry,
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target_audience=target_audience,
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config=cfg,
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user_id=user_id,
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)
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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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raise HTTPException(status_code=500, detail=f"Exa research failed: {exc}")
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# 2. EXTRACT INSIGHTS VIA LLM
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raw_content = result.get("content", "")
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sources = result.get("sources", [])
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summary = ""
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key_insights = []
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expert_quotes = []
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listener_cta = []
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mapped_angles = []
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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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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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PODCAST CONTEXT:
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Topic: {request.topic}
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{bible_context}
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{analysis_context}
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RESEARCH DATA (from {len(sources)} sources):
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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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4. Extract notable "Expert Quotes" - direct quotes from industry leaders, researchers, or authoritative voices found in the sources.
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5. Suggest 2-4 "Listener CTA" (call-to-action) ideas that the podcast host can use to engage the audience.
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6. Identify 3-5 "Mapped Angles" - unique content angles with rationale for why they matter for this topic.
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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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{{
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"summary": "Detailed markdown summary...",
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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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}}
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],
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"expert_quotes": [
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{{
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"quote": "Exact quote from source...",
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"source_index": 1
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}}
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],
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"listener_cta": [
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"Call-to-action suggestion 1",
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"Call-to-action suggestion 2"
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],
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"mapped_angles": [
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{{
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"title": "Angle Title",
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"why": "Why this angle matters for the audience...",
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"mapped_fact_ids": ["fact_1", "fact_2"]
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}}
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]
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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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- Expert quotes should be exact or near-exact quotes from the sources, with attribution.
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- Listener CTAs should be practical and engaging (e.g., "Share your experience with X on social media").
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- Mapped angles should be unique perspectives that make the episode stand out.
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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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"""
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try:
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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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json_struct=None,
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preferred_provider="huggingface",
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flow_type="premium_tool",
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)
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# Normalize response
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if isinstance(llm_response, str):
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data = json.loads(llm_response)
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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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expert_quotes = data.get("expert_quotes", [])
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listener_cta = data.get("listener_cta", [])
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mapped_angles = data.get("mapped_angles", [])
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except Exception as exc:
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logger.error(f"[Podcast Research] LLM Insight extraction failed: {exc}")
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# Fallback to a basic summary if LLM fails
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summary = f"Research completed for '{request.topic}'. Found {len(sources)} sources."
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# Fallback: if summary is still empty (e.g. LLM returned empty string), use raw content first paragraph or basic text
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if not summary:
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if raw_content:
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summary = raw_content[:2000] # Use first 2000 chars of raw content as summary
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else:
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summary = f"Research completed for '{request.topic}'. Found {len(sources)} sources."
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# 3. TRACK USAGE
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try:
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cost_total = 0.0
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if isinstance(result, dict):
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cost_total = result.get("cost", {}).get("total", 0.005) if result.get("cost") else 0.005
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provider.track_exa_usage(user_id, cost_total)
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except Exception as track_err:
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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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for src in sources:
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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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"published_at": src.get("published_at"),
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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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}))
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return PodcastExaResearchResponse(
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sources=sources_payload,
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search_queries=result.get("search_queries", queries) if isinstance(result, dict) else queries,
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summary=summary,
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key_insights=key_insights,
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expert_quotes=expert_quotes,
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listener_cta=listener_cta,
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mapped_angles=mapped_angles,
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cost=result.get("cost") if isinstance(result, dict) else None,
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search_type=result.get("search_type") if isinstance(result, dict) else None,
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provider=result.get("provider", "exa") if isinstance(result, dict) else "exa",
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content=raw_content,
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)
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