94 lines
4.1 KiB
Python
94 lines
4.1 KiB
Python
"""
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EnhancedContentGenerator - thin orchestrator for section generation.
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Provider parity:
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- Uses main_text_generation.llm_text_gen to respect GPT_PROVIDER (Gemini/HF)
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- No direct provider coupling here; Google grounding remains in research only
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"""
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from typing import Any, Dict
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from services.llm_providers.main_text_generation import llm_text_gen
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from .source_url_manager import SourceURLManager
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from .context_memory import ContextMemory
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from .transition_generator import TransitionGenerator
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from .flow_analyzer import FlowAnalyzer
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class EnhancedContentGenerator:
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def __init__(self):
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self.url_manager = SourceURLManager()
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self.memory = ContextMemory(max_entries=12)
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self.transitioner = TransitionGenerator()
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self.flow = FlowAnalyzer()
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async def generate_section(self, section: Any, research: Any, mode: str = "polished", user_id: str = None) -> Dict[str, Any]:
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prev_summary = self.memory.build_previous_sections_summary(limit=2)
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urls = self.url_manager.pick_relevant_urls(section, research)
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prompt = self._build_prompt(section, research, prev_summary, urls)
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# Provider-agnostic text generation (respect GPT_PROVIDER & circuit-breaker)
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content_text: str = ""
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try:
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ai_resp = llm_text_gen(
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prompt=prompt,
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json_struct=None,
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system_prompt=None,
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user_id=user_id
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)
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if isinstance(ai_resp, dict) and ai_resp.get("text"):
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content_text = ai_resp.get("text", "")
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elif isinstance(ai_resp, str):
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content_text = ai_resp
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else:
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# Fallback best-effort extraction
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content_text = str(ai_resp or "")
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except Exception as e:
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content_text = ""
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result = {
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"content": content_text,
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"sources": [{"title": u.get("title", ""), "url": u.get("url", "")} for u in urls] if urls else [],
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}
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# Generate transition and compute intelligent flow metrics
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previous_text = prev_summary
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current_text = result.get("content", "")
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transition = self.transitioner.generate_transition(previous_text, getattr(section, 'heading', 'This section'), use_llm=True)
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metrics = self.flow.assess_flow(previous_text, current_text, use_llm=True)
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# Update memory for subsequent sections and store continuity snapshot
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if current_text:
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self.memory.update_with_section(getattr(section, 'id', 'unknown'), current_text, use_llm=True)
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# Return enriched result
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result["transition"] = transition
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result["continuity_metrics"] = metrics
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# Persist a lightweight continuity snapshot for API access
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try:
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sid = getattr(section, 'id', 'unknown')
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if not hasattr(self, "_last_continuity"):
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self._last_continuity = {}
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self._last_continuity[sid] = metrics
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except Exception:
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pass
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return result
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def _build_prompt(self, section: Any, research: Any, prev_summary: str, urls: list) -> str:
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heading = getattr(section, 'heading', 'Section')
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key_points = getattr(section, 'key_points', [])
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keywords = getattr(section, 'keywords', [])
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target_words = getattr(section, 'target_words', 300)
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url_block = "\n".join([f"- {u.get('title','')} ({u.get('url','')})" for u in urls]) if urls else "(no specific URLs provided)"
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return (
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f"You are writing the blog section '{heading}'.\n\n"
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f"Context summary (previous sections): {prev_summary}\n\n"
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f"Authoring requirements:\n"
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f"- Target word count: ~{target_words}\n"
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f"- Use the following key points: {', '.join(key_points)}\n"
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f"- Include these keywords naturally: {', '.join(keywords)}\n"
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f"- Cite insights from these sources when relevant (do not output raw URLs):\n{url_block}\n\n"
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"Write engaging, well-structured markdown with clear paragraphs (2-4 sentences each) separated by double line breaks."
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)
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