238 lines
10 KiB
Python
238 lines
10 KiB
Python
"""
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Medium Blog Generator Service
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Handles generation of medium-length blogs (≤1000 words) using structured AI calls.
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"""
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import time
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import json
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from typing import Dict, Any, List
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from loguru import logger
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from models.blog_models import (
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MediumBlogGenerateRequest,
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MediumBlogGenerateResult,
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MediumGeneratedSection,
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ResearchSource,
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)
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from services.llm_providers.gemini_provider import gemini_structured_json_response
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from services.cache.persistent_content_cache import persistent_content_cache
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class MediumBlogGenerator:
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"""Service for generating medium-length blog content using structured AI calls."""
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def __init__(self):
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self.cache = persistent_content_cache
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async def generate_medium_blog_with_progress(self, req: MediumBlogGenerateRequest, task_id: str) -> MediumBlogGenerateResult:
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"""Use Gemini structured JSON to generate a medium-length blog in one call."""
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import time
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start = time.time()
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# Prepare sections data for cache key generation
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sections_for_cache = []
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for s in req.sections:
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sections_for_cache.append({
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"id": s.id,
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"heading": s.heading,
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"keyPoints": getattr(s, "key_points", []) or getattr(s, "keyPoints", []),
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"subheadings": getattr(s, "subheadings", []),
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"keywords": getattr(s, "keywords", []),
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"targetWords": getattr(s, "target_words", None) or getattr(s, "targetWords", None),
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})
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# Check cache first
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cached_result = self.cache.get_cached_content(
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keywords=req.researchKeywords or [],
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sections=sections_for_cache,
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global_target_words=req.globalTargetWords or 1000,
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persona_data=req.persona.dict() if req.persona else None,
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tone=req.tone,
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audience=req.audience
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)
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if cached_result:
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logger.info(f"Using cached content for keywords: {req.researchKeywords} (saved expensive generation)")
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# Add cache hit marker to distinguish from fresh generation
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cached_result['generation_time_ms'] = 0 # Mark as cache hit
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cached_result['cache_hit'] = True
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return MediumBlogGenerateResult(**cached_result)
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# Cache miss - proceed with AI generation
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logger.info(f"Cache miss - generating new content for keywords: {req.researchKeywords}")
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# Build schema expected from the model
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schema = {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"sections": {
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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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"id": {"type": "string"},
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"heading": {"type": "string"},
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"content": {"type": "string"},
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"wordCount": {"type": "number"},
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"sources": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {"title": {"type": "string"}, "url": {"type": "string"}},
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},
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},
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},
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},
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},
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},
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}
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# Compose prompt
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def section_block(s):
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return {
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"id": s.id,
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"heading": s.heading,
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"outline": {
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"keyPoints": getattr(s, "key_points", []) or getattr(s, "keyPoints", []),
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"subheadings": getattr(s, "subheadings", []),
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"keywords": getattr(s, "keywords", []),
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"targetWords": getattr(s, "target_words", None) or getattr(s, "targetWords", None),
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"references": [
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{"title": r.title, "url": r.url} for r in getattr(s, "references", [])
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],
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},
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}
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payload = {
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"title": req.title,
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"globalTargetWords": req.globalTargetWords or 1000,
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"persona": req.persona.dict() if req.persona else None,
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"tone": req.tone,
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"audience": req.audience,
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"sections": [section_block(s) for s in req.sections],
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}
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# Build persona-aware system prompt
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persona_context = ""
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if req.persona:
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persona_context = f"""
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PERSONA GUIDELINES:
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- Industry: {req.persona.industry or 'General'}
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- Tone: {req.persona.tone or 'Professional'}
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- Audience: {req.persona.audience or 'General readers'}
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- Persona ID: {req.persona.persona_id or 'Default'}
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Write content that reflects this persona's expertise and communication style.
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Use industry-specific terminology and examples where appropriate.
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Maintain consistent voice and authority throughout all sections.
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"""
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system = (
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"You are a professional blog writer with deep expertise in your field. "
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"Generate high-quality, persona-driven content for each section based on the provided outline. "
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"Write engaging, informative content that follows the section's key points and target word count. "
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"Ensure the content flows naturally and maintains consistent voice and authority. "
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"Format content with proper paragraph breaks using double line breaks (\\n\\n) between paragraphs. "
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"Structure content with clear paragraphs - aim for 2-4 sentences per paragraph. "
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f"{persona_context}"
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"Return ONLY valid JSON with no markdown formatting or explanations."
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)
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# Build persona-specific content instructions
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persona_instructions = ""
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if req.persona:
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industry = req.persona.industry or 'General'
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tone = req.persona.tone or 'Professional'
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audience = req.persona.audience or 'General readers'
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persona_instructions = f"""
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PERSONA-DRIVEN CONTENT REQUIREMENTS:
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- Write as an expert in {industry} industry
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- Use {tone} tone appropriate for {audience}
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- Include industry-specific examples and terminology
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- Demonstrate authority and expertise in the field
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- Use language that resonates with {audience}
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- Maintain consistent voice that reflects this persona's expertise
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"""
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prompt = (
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f"Write blog content for the following sections. Each section should be {req.globalTargetWords or 1000} words total, distributed across all sections.\n\n"
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f"Blog Title: {req.title}\n\n"
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"For each section, write engaging content that:\n"
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"- Follows the key points provided\n"
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"- Uses the suggested keywords naturally\n"
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"- Meets the target word count\n"
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"- Maintains professional tone\n"
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"- References the provided sources when relevant\n"
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"- Breaks content into clear paragraphs (2-4 sentences each)\n"
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"- Uses double line breaks (\\n\\n) between paragraphs for proper formatting\n"
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"- Starts with an engaging opening paragraph\n"
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"- Ends with a strong concluding paragraph\n"
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f"{persona_instructions}\n"
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"IMPORTANT: Format the 'content' field with proper paragraph breaks using \\n\\n between paragraphs.\n\n"
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"Return a JSON object with 'title' and 'sections' array. Each section should have 'id', 'heading', 'content', and 'wordCount'.\n\n"
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f"Sections to write:\n{json.dumps(payload, ensure_ascii=False, indent=2)}"
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)
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ai_resp = gemini_structured_json_response(
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prompt=prompt,
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schema=schema,
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temperature=0.2,
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max_tokens=8192,
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system_prompt=system,
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)
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# Check for errors in AI response
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if not ai_resp or ai_resp.get("error"):
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error_msg = ai_resp.get("error", "Empty generation result from model") if ai_resp else "No response from model"
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logger.error(f"AI generation failed: {error_msg}")
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raise Exception(f"AI generation failed: {error_msg}")
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# Normalize output
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title = ai_resp.get("title") or req.title
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out_sections = []
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for s in ai_resp.get("sections", []) or []:
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out_sections.append(
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MediumGeneratedSection(
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id=str(s.get("id")),
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heading=s.get("heading") or "",
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content=s.get("content") or "",
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wordCount=int(s.get("wordCount") or 0),
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sources=[
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# map to ResearchSource shape if possible; keep minimal
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ResearchSource(title=src.get("title", ""), url=src.get("url", ""))
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for src in (s.get("sources") or [])
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] or None,
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)
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)
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duration_ms = int((time.time() - start) * 1000)
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result = MediumBlogGenerateResult(
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success=True,
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title=title,
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sections=out_sections,
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model="gemini-2.5-flash",
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generation_time_ms=duration_ms,
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safety_flags=None,
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)
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# Cache the result for future use
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try:
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self.cache.cache_content(
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keywords=req.researchKeywords or [],
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sections=sections_for_cache,
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global_target_words=req.globalTargetWords or 1000,
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persona_data=req.persona.dict() if req.persona else None,
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tone=req.tone or "professional",
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audience=req.audience or "general",
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result=result.dict()
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)
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logger.info(f"Cached content result for keywords: {req.researchKeywords}")
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except Exception as cache_error:
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logger.warning(f"Failed to cache content result: {cache_error}")
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# Don't fail the entire operation if caching fails
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return result
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