287 lines
9.3 KiB
Python
287 lines
9.3 KiB
Python
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#!/usr/bin/env python3
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"""Deep Dive Intelligence Pipeline - Reference Implementation Scaffold
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This is ARCHITECTURE PROOF code — not production-ready but demonstrates
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component contracts and data flow. Use as integration target.
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"""
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import asyncio
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import logging
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import List, Optional, Dict, Any
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# Phase 1: Aggregation
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@dataclass
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class FeedItem:
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title: str
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summary: str
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url: str
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source: str
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published: datetime
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raw: Dict[str, Any]
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class RSSAggregator:
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"""Fetch and normalize RSS feeds."""
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def __init__(self, cache_dir: Path = None):
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self.cache_dir = cache_dir or Path.home() / ".cache" / "deepdive"
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self.cache_dir.mkdir(parents=True, exist_ok=True)
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self.logger = logging.getLogger(__name__)
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async def fetch_feed(self, url: str, etag: str = None) -> List[FeedItem]:
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"""Fetch single feed with caching. Returns normalized items."""
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# TODO: Implement with httpx, feedparser
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# TODO: Respect ETag/Last-Modified for incremental fetch
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raise NotImplementedError("Phase 1: Implement RSS fetch")
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async def fetch_all(
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self,
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sources: List[Dict[str, str]],
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since: datetime
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) -> List[FeedItem]:
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"""Fetch all configured sources since cutoff time."""
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all_items = []
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# TODO: asyncio.gather() parallel fetches
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# TODO: Filter items.published >= since
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raise NotImplementedError("Phase 1: Implement parallel fetch")
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# Phase 2: Relevance
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@dataclass
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class ScoredItem:
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item: FeedItem
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score: float
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keywords_matched: List[str]
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class RelevanceScorer:
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"""Score items by relevance to Hermes/Timmy work."""
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KEYWORDS = [
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"LLM agent", "agent system", "tool use", "function calling",
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"reinforcement learning", "RLHF", "GRPO", "PPO",
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"transformer", "attention", "inference optimization",
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"local LLM", "llama.cpp", "ollama", "vLLM",
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"Hermes", "LM Studio", "open source AI"
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]
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def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
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self.model_name = model_name
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self.model = None # Lazy load sentence-transformers
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self.keyword_embeddings = None
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def _load_model(self):
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from sentence_transformers import SentenceTransformer
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import numpy as np
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self.model = SentenceTransformer(self.model_name)
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self.keyword_embeddings = self.model.encode(self.KEYWORDS)
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def score(self, item: FeedItem) -> ScoredItem:
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"""Calculate relevance score for item."""
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if self.model is None:
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self._load_model()
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# TODO: Encode title + summary
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# TODO: Cosine similarity to keyword embeddings
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# TODO: Calculate centroid match score
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# TODO: Boost for high-signal terms in title
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raise NotImplementedError("Phase 2: Implement scoring")
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def rank(self, items: List[FeedItem], top_n: int = 10) -> List[ScoredItem]:
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"""Score all items, return top N."""
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# TODO: Parallel scoring, cutoff at min_score
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raise NotImplementedError("Phase 2: Implement ranking")
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# Phase 3: Synthesis
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@dataclass
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class Briefing:
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date: datetime
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headline_items: List[ScoredItem]
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deep_dive: ScoredItem
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summary_text: str
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action_items: List[str]
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class SynthesisEngine:
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"""Generate briefing text via local LLM."""
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def __init__(self, model_endpoint: str = "http://localhost:4000/v1"):
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self.endpoint = model_endpoint
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self.system_prompt = """You are an intelligence analyst for the Timmy Foundation.
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Produce concise daily briefings on AI/ML developments relevant to:
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- LLM agent systems and architecture
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- Reinforcement learning for LLMs
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- Local inference and optimization
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- Open-source AI tooling
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Tone: Professional, tight, actionable."""
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async def synthesize(self, items: List[ScoredItem]) -> Briefing:
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"""Generate structured briefing from top items."""
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# TODO: Format prompt with item data
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# TODO: Call local LLM via OpenAI-compatible API
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# TODO: Parse response into structured briefing
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raise NotImplementedError("Phase 3: Implement synthesis")
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# Phase 4: TTS
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@dataclass
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class AudioResult:
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path: Path
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duration_seconds: float
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word_count: int
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class TTSGenerator:
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"""Generate audio via local Piper TTS."""
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def __init__(
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self,
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model_path: Path,
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voice: str = "en_US-amy-medium"
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):
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self.model_path = model_path
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self.voice = voice
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async def generate(self, text: str, output_dir: Path) -> AudioResult:
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"""Generate audio file from briefing text."""
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# TODO: Split long text into chunks if needed
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# TODO: Call Piper subprocess
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# TODO: Optionally combine chunks
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# TODO: Return path and metadata
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raise NotImplementedError("Phase 4: Implement TTS")
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# Phase 5: Delivery
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class TelegramDelivery:
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"""Deliver briefing via Hermes Telegram gateway."""
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def __init__(self, bot_token: str, chat_id: str):
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self.bot_token = bot_token
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self.chat_id = chat_id
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async def deliver_voice(self, audio_path: Path) -> bool:
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"""Send voice message to Telegram."""
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# TODO: Use python-telegram-bot or Hermes gateway
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raise NotImplementedError("Phase 5: Implement voice delivery")
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async def deliver_text(self, text: str) -> bool:
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"""Send text summary to Telegram."""
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# TODO: Truncate if > 4096 chars
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raise NotImplementedError("Phase 5: Implement text delivery")
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# Orchestration
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@dataclass
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class PipelineResult:
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success: bool
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items_considered: int
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items_selected: int
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briefing: Optional[Briefing]
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audio: Optional[AudioResult]
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errors: List[str]
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class DeepDivePipeline:
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"""Full pipeline orchestrator."""
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def __init__(self, config: Dict[str, Any]):
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self.config = config
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self.aggregator = RSSAggregator()
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self.scorer = RelevanceScorer()
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self.synthesis = SynthesisEngine()
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self.tts = TTSGenerator(
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model_path=Path(config["tts"]["model_path"])
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)
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self.delivery = TelegramDelivery(
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bot_token=config["delivery"]["bot_token"],
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chat_id=config["delivery"]["chat_id"]
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)
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async def run(
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self,
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since: Optional[datetime] = None,
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deliver: bool = True
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) -> PipelineResult:
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"""Execute full pipeline."""
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errors = []
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# Phase 1: Aggregate
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try:
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items = await self.aggregator.fetch_all(
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self.config["sources"],
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since or datetime.now() - timedelta(days=1)
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)
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except Exception as e:
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return PipelineResult(False, 0, 0, None, None, [f"Aggregation failed: {e}"])
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# Phase 2: Score and rank
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try:
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top_items = self.scorer.rank(
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items,
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top_n=self.config.get("top_n", 10)
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)
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except Exception as e:
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return PipelineResult(False, len(items), 0, None, None, [f"Scoring failed: {e}"])
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# Phase 3: Synthesize
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try:
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briefing = await self.synthesis.synthesize(top_items)
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except Exception as e:
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return PipelineResult(False, len(items), len(top_items), None, None, [f"Synthesis failed: {e}"])
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# Phase 4: Generate audio
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try:
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audio = await self.tts.generate(
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briefing.summary_text,
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Path(self.config.get("output_dir", "/tmp/deepdive"))
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)
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except Exception as e:
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return PipelineResult(False, len(items), len(top_items), briefing, None, [f"TTS failed: {e}"])
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# Phase 5: Deliver
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if deliver:
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try:
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await self.delivery.deliver_voice(audio.path)
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await self.delivery.deliver_text(briefing.summary_text[:4000])
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except Exception as e:
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errors.append(f"Delivery failed: {e}")
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return PipelineResult(
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success=len(errors) == 0,
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items_considered=len(items),
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items_selected=len(top_items),
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briefing=briefing,
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audio=audio,
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errors=errors
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)
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# CLI entry point
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async def main():
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import argparse
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import yaml
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", default="config.yaml")
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parser.add_argument("--since", type=lambda s: datetime.fromisoformat(s))
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parser.add_argument("--no-deliver", action="store_true")
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parser.add_argument("--dry-run", action="store_true")
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args = parser.parse_args()
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with open(args.config) as f:
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config = yaml.safe_load(f)
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pipeline = DeepDivePipeline(config)
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result = await pipeline.run(
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since=args.since,
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deliver=not args.no_deliver
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)
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print(f"Success: {result.success}")
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print(f"Items: {result.items_selected}/{result.items_considered}")
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if result.briefing:
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print(f"Briefing length: {len(result.briefing.summary_text)} chars")
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if result.audio:
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print(f"Audio: {result.audio.duration_seconds}s at {result.audio.path}")
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if result.errors:
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print(f"Errors: {result.errors}")
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if __name__ == "__main__":
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asyncio.run(main())
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