[deep-dive] Complete #830 implementation scaffold
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Phase 3 (Synthesis): - deepdive_synthesis.py: LLM-powered briefing generation - Supports OpenAI (gpt-4o-mini) and Anthropic (claude-3-haiku) - Fallback to keyword summary if LLM unavailable - Intelligence briefing format: Headlines, Deep Dive, Bottom Line Phase 4 (TTS): - TTS integration in orchestrator - Converts markdown to speech-friendly text - Configurable provider (openai/elevenlabs/piper) Phase 5 (Delivery): - Enhanced delivery.py with --text and --chat-id/--bot-token overrides - Supports text-only and audio+text delivery - Full Telegram Bot API integration Orchestrator: - Complete 5-phase pipeline - --dry-run mode for testing - State management in ~/the-nexus/deepdive_state/ - Error handling with fallbacks Progresses #830 to implementation-ready status
This commit is contained in:
Ezra (Archivist)
@@ -112,50 +112,74 @@ def load_config():
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def main():
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parser = argparse.ArgumentParser(description="Deep Dive Delivery Pipeline")
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parser.add_argument("--audio", "-a", required=True, help="Path to audio file (MP3)")
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parser.add_argument("--audio", "-a", help="Path to audio file (MP3)")
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parser.add_argument("--text", "-t", help="Text message to send")
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parser.add_argument("--caption", "-c", help="Caption for voice message")
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parser.add_argument("--preview-text", help="Optional text preview sent before voice")
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parser.add_argument("--bot-token", help="Telegram bot token (overrides env)")
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parser.add_argument("--chat-id", help="Telegram chat ID (overrides env)")
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parser.add_argument("--dry-run", action="store_true", help="Validate config without sending")
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args = parser.parse_args()
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# Load config
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try:
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token, chat_id = load_config()
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if args.bot_token and args.chat_id:
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token, chat_id = args.bot_token, args.chat_id
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else:
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token, chat_id = load_config()
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except RuntimeError as e:
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print(f"[ERROR] {e}", file=sys.stderr)
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sys.exit(1)
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audio_path = Path(args.audio)
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if not audio_path.exists():
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print(f"[ERROR] Audio file not found: {audio_path}", file=sys.stderr)
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# Validate input
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if not args.audio and not args.text:
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print("[ERROR] Either --audio or --text required", file=sys.stderr)
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sys.exit(1)
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if args.dry_run:
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print(f"[DRY RUN] Config valid")
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print(f" Bot: {token[:10]}...")
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print(f" Chat: {chat_id}")
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print(f" Audio: {audio_path} ({audio_path.stat().st_size} bytes)")
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if args.audio:
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audio_path = Path(args.audio)
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print(f" Audio: {audio_path} ({audio_path.stat().st_size} bytes)")
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if args.text:
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print(f" Text: {args.text[:100]}...")
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sys.exit(0)
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# Deliver
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adapter = TelegramDeliveryAdapter(token, chat_id)
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if args.preview_text:
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print("[DELIVERY] Sending text preview...")
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adapter.send_text_preview(args.preview_text)
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# Send text if provided
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if args.text:
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print("[DELIVERY] Sending text message...")
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result = adapter.send_text_preview(args.text)
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message_id = result["result"]["message_id"]
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print(f"[DELIVERY] Text sent! Message ID: {message_id}")
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print(f"[DELIVERY] Sending voice message: {audio_path}...")
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result = adapter.send_voice(audio_path, args.caption)
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message_id = result["result"]["message_id"]
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print(f"[DELIVERY] Sent! Message ID: {message_id}")
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print(json.dumps({
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"success": True,
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"message_id": message_id,
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"chat_id": chat_id,
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"audio_size_bytes": audio_path.stat().st_size
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}))
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# Send audio if provided
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if args.audio:
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audio_path = Path(args.audio)
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if not audio_path.exists():
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print(f"[ERROR] Audio file not found: {audio_path}", file=sys.stderr)
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sys.exit(1)
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if args.preview_text:
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print("[DELIVERY] Sending text preview...")
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adapter.send_text_preview(args.preview_text)
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print(f"[DELIVERY] Sending voice message: {audio_path}...")
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result = adapter.send_voice(audio_path, args.caption)
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message_id = result["result"]["message_id"]
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print(f"[DELIVERY] Voice sent! Message ID: {message_id}")
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print(json.dumps({
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"success": True,
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"message_id": message_id,
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"chat_id": chat_id,
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"audio_size_bytes": audio_path.stat().st_size
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}))
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if __name__ == "__main__":
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@@ -3,6 +3,7 @@
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import argparse
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import json
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import os
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import subprocess
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import sys
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from datetime import datetime
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@@ -11,33 +12,42 @@ from pathlib import Path
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DEFAULT_CONFIG = {
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"sources": ["arxiv_cs_ai", "arxiv_cs_cl", "arxiv_cs_lg"],
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"max_items": 10,
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"tts_enabled": False,
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"tts_enabled": True,
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"tts_provider": "openai",
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}
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class Orchestrator:
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def __init__(self, date: str = None):
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def __init__(self, date: str = None, dry_run: bool = False):
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self.date = date or datetime.now().strftime("%Y-%m-%d")
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self.dry_run = dry_run
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self.state_dir = Path("~/the-nexus/deepdive_state").expanduser() / self.date
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self.state_dir.mkdir(parents=True, exist_ok=True)
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self.script_dir = Path(__file__).parent
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def phase1(self, sources):
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def phase1_aggregate(self, sources):
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"""Aggregate from sources."""
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print("[PHASE 1] Aggregating...")
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print("[PHASE 1] Aggregating from sources...")
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output_file = self.state_dir / "raw_items.json"
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if self.dry_run:
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print(f" [DRY RUN] Would aggregate from: {sources}")
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return {"items": [], "metadata": {"count": 0}}
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subprocess.run([
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sys.executable, Path(__file__).parent / "deepdive_aggregator.py",
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sys.executable, self.script_dir / "deepdive_aggregator.py",
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"--sources", ",".join(sources), "--output", str(output_file)
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])
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], check=True)
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return json.loads(output_file.read_text())
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def phase2(self, raw_items, max_items):
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def phase2_filter(self, raw_items, max_items):
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"""Filter by keywords."""
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print("[PHASE 2] Filtering...")
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keywords = ["agent", "llm", "tool use", "rlhf", "alignment", "finetuning"]
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print("[PHASE 2] Filtering by relevance...")
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keywords = ["agent", "llm", "tool use", "rlhf", "alignment", "finetuning",
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"reasoning", "chain-of-thought", "mcp", "hermes"]
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scored = []
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for item in raw_items["items"]:
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for item in raw_items.get("items", []):
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content = f"{item.get('title','')} {item.get('content','')}".lower()
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score = sum(1 for kw in keywords if kw in content)
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scored.append({**item, "score": score})
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@@ -47,49 +57,204 @@ class Orchestrator:
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output_file = self.state_dir / "ranked.json"
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output_file.write_text(json.dumps({"items": top}, indent=2))
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print(f" Selected top {len(top)} items")
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return top
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def phase3(self, ranked_items):
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"""Synthesize briefing."""
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print("[PHASE 3] Synthesizing (MVP: structured text)...")
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md = f"# Deep Dive — {self.date}\n\n"
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for i, item in enumerate(ranked_items[:3], 1):
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md += f"{i}. [{item['title']}]({item['url']}) — Score: {item['score']}\n\n"
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def phase3_synthesize(self, ranked_items):
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"""Synthesize briefing with LLM."""
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print("[PHASE 3] Synthesizing intelligence briefing...")
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if self.dry_run:
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print(" [DRY RUN] Would synthesize briefing")
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briefing_file = self.state_dir / "briefing.md"
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briefing_file.write_text(f"# Deep Dive — {self.date}\n\n[Dry run - no LLM call]\n")
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return str(briefing_file)
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# Write ranked items for synthesis script
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ranked_file = self.state_dir / "ranked.json"
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ranked_file.write_text(json.dumps({"items": ranked_items}, indent=2))
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briefing_file = self.state_dir / "briefing.md"
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briefing_file.write_text(md)
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result = subprocess.run([
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sys.executable, self.script_dir / "deepdive_synthesis.py",
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"--input", str(ranked_file),
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"--output", str(briefing_file),
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"--date", self.date
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])
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if result.returncode != 0:
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print(" [WARN] Synthesis failed, using fallback")
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fallback = self._fallback_briefing(ranked_items)
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briefing_file.write_text(fallback)
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return str(briefing_file)
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def phase4(self, briefing_file):
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def phase4_tts(self, briefing_file):
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"""Generate audio."""
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print("[PHASE 4] ⚠ TTS decision needed — skipping")
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return None
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print("[PHASE 4] Generating audio...")
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if not DEFAULT_CONFIG["tts_enabled"]:
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print(" [SKIP] TTS disabled in config")
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return None
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if self.dry_run:
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print(" [DRY RUN] Would generate audio")
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return str(self.state_dir / "briefing.mp3")
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audio_file = self.state_dir / "briefing.mp3"
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# Read briefing and convert to speech-suitable text
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briefing_text = Path(briefing_file).read_text()
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# Remove markdown formatting for TTS
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clean_text = self._markdown_to_speech(briefing_text)
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# Write temp text file for TTS
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text_file = self.state_dir / "briefing.txt"
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text_file.write_text(clean_text)
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result = subprocess.run([
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sys.executable, self.script_dir / "deepdive_tts.py",
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"--input", str(text_file),
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"--output", str(audio_file),
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"--provider", DEFAULT_CONFIG["tts_provider"]
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])
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if result.returncode != 0:
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print(" [WARN] TTS generation failed")
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return None
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print(f" Audio: {audio_file}")
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return str(audio_file)
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def phase5(self, briefing_file, audio_file):
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"""Deliver."""
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print("[PHASE 5] ⚠ Telegram delivery not integrated")
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text = Path(briefing_file).read_text()
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print(text[:500])
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return {"status": "logged"}
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def phase5_deliver(self, briefing_file, audio_file):
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"""Deliver to Telegram."""
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print("[PHASE 5] Delivering to Telegram...")
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if self.dry_run:
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print(" [DRY RUN] Would deliver briefing")
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briefing_text = Path(briefing_file).read_text()
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print("\n--- BRIEFING PREVIEW ---")
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print(briefing_text[:800] + "..." if len(briefing_text) > 800 else briefing_text)
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print("--- END PREVIEW ---\n")
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return {"status": "dry_run"}
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# Delivery configuration
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bot_token = os.environ.get("DEEPDIVE_TELEGRAM_BOT_TOKEN") or os.environ.get("TELEGRAM_BOT_TOKEN")
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chat_id = os.environ.get("DEEPDIVE_TELEGRAM_CHAT_ID") or os.environ.get("TELEGRAM_CHAT_ID")
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if not bot_token or not chat_id:
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print(" [ERROR] Telegram credentials not configured")
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print(" Set DEEPDIVE_TELEGRAM_BOT_TOKEN and DEEPDIVE_TELEGRAM_CHAT_ID")
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return {"status": "error", "reason": "missing_credentials"}
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# Send text summary
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briefing_text = Path(briefing_file).read_text()
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summary = self._extract_summary(briefing_text)
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result = subprocess.run([
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sys.executable, self.script_dir / "deepdive_delivery.py",
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"--text", summary,
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"--chat-id", chat_id,
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"--bot-token", bot_token
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])
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if result.returncode != 0:
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print(" [WARN] Text delivery failed")
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# Send audio if available
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if audio_file and Path(audio_file).exists():
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print(" Sending audio briefing...")
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subprocess.run([
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sys.executable, self.script_dir / "deepdive_delivery.py",
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"--audio", audio_file,
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"--caption", f"🎙️ Deep Dive — {self.date}",
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"--chat-id", chat_id,
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"--bot-token", bot_token
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])
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return {"status": "delivered"}
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def _fallback_briefing(self, items):
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"""Generate basic briefing without LLM."""
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lines = [
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f"# Deep Dive Intelligence Brief — {self.date}",
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"",
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"## Headlines",
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""
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]
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for i, item in enumerate(items[:5], 1):
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lines.append(f"{i}. [{item.get('title', 'Untitled')}]({item.get('url', '')})")
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lines.append(f" Score: {item.get('score', 0)}")
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lines.append("")
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return "\n".join(lines)
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def _markdown_to_speech(self, text: str) -> str:
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"""Convert markdown to speech-friendly text."""
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import re
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# Remove markdown links but keep text
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text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
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# Remove other markdown
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text = re.sub(r'[#*_`]', '', text)
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# Clean up whitespace
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text = re.sub(r'\n+', '\n', text)
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return text.strip()
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def _extract_summary(self, text: str) -> str:
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"""Extract first section for text delivery."""
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lines = text.split('\n')
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summary_lines = []
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for line in lines:
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if line.strip().startswith('#') and len(summary_lines) > 5:
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break
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summary_lines.append(line)
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return '\n'.join(summary_lines[:30]) # Limit length
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def run(self, config):
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raw = self.phase1(config["sources"])
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ranked = self.phase2(raw, config["max_items"])
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briefing = self.phase3(ranked)
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audio = self.phase4(briefing)
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return self.phase5(briefing, audio)
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"""Execute full pipeline."""
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print(f"\n{'='*60}")
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print(f" DEEP DIVE — {self.date}")
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print(f"{'='*60}\n")
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raw = self.phase1_aggregate(config["sources"])
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if not raw.get("items"):
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print("[ERROR] No items aggregated")
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return {"status": "error", "phase": 1}
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ranked = self.phase2_filter(raw, config["max_items"])
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if not ranked:
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print("[ERROR] No items after filtering")
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return {"status": "error", "phase": 2}
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briefing = self.phase3_synthesize(ranked)
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audio = self.phase4_tts(briefing)
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result = self.phase5_deliver(briefing, audio)
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print(f"\n{'='*60}")
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print(f" COMPLETE — State: {self.state_dir}")
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print(f"{'='*60}\n")
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return result
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--daily", action="store_true")
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parser.add_argument("--date")
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parser = argparse.ArgumentParser(description="Deep Dive Intelligence Pipeline")
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parser.add_argument("--daily", action="store_true", help="Run daily briefing")
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parser.add_argument("--date", help="Specific date (YYYY-MM-DD)")
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parser.add_argument("--dry-run", action="store_true", help="Preview without sending")
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parser.add_argument("--config", help="Path to config JSON file")
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args = parser.parse_args()
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orch = Orchestrator(date=args.date)
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result = orch.run(DEFAULT_CONFIG)
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print(f"[DONE] State: {orch.state_dir}")
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# Load custom config if provided
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config = DEFAULT_CONFIG.copy()
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if args.config and Path(args.config).exists():
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config.update(json.loads(Path(args.config).read_text()))
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orch = Orchestrator(date=args.date, dry_run=args.dry_run)
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result = orch.run(config)
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return 0 if result.get("status") != "error" else 1
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if __name__ == "__main__":
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main()
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exit(main())
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170
bin/deepdive_synthesis.py
Normal file
170
bin/deepdive_synthesis.py
Normal file
@@ -0,0 +1,170 @@
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#!/usr/bin/env python3
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"""deepdive_synthesis.py — Phase 3: LLM-powered intelligence briefing synthesis. Issue #830."""
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import argparse
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import json
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import os
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from datetime import datetime
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from pathlib import Path
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from typing import List, Dict
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BRIEFING_PROMPT = """You are Deep Dive, an AI intelligence analyst for the Timmy Foundation fleet.
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Your task: Synthesize the following research papers into a tight, actionable intelligence briefing for Alexander Whitestone, founder of Timmy.
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CONTEXT:
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- Timmy Foundation builds autonomous AI agents using the Hermes framework
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- Focus areas: LLM architecture, tool use, RL training, agent systems
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- Alexander prefers: Plain speech, evidence over vibes, concrete implications
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SOURCES:
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{sources}
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OUTPUT FORMAT:
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# Deep Dive Intelligence Brief — {date}
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## Headlines (3 items)
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For each top paper:
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- **Title**: Paper name
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- **Why It Matters**: One sentence on relevance to Hermes/Timmy
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- **Key Insight**: The actionable takeaway
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## Deep Dive (1 item)
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Expand on the most relevant paper:
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- Problem it solves
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- Method/approach
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- Implications for our agent work
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- Suggested follow-up (if any)
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## Bottom Line
|
||||
3 bullets on what to know/do this week
|
||||
|
||||
Write in tight, professional intelligence style. No fluff."""
|
||||
|
||||
|
||||
class SynthesisEngine:
|
||||
def __init__(self, provider: str = None):
|
||||
self.provider = provider or os.environ.get("DEEPDIVE_LLM_PROVIDER", "openai")
|
||||
self.api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("ANTHROPIC_API_KEY")
|
||||
|
||||
def synthesize(self, items: List[Dict], date: str) -> str:
|
||||
"""Generate briefing from ranked items."""
|
||||
sources_text = self._format_sources(items)
|
||||
prompt = BRIEFING_PROMPT.format(sources=sources_text, date=date)
|
||||
|
||||
if self.provider == "openai":
|
||||
return self._call_openai(prompt)
|
||||
elif self.provider == "anthropic":
|
||||
return self._call_anthropic(prompt)
|
||||
else:
|
||||
return self._fallback_synthesis(items, date)
|
||||
|
||||
def _format_sources(self, items: List[Dict]) -> str:
|
||||
lines = []
|
||||
for i, item in enumerate(items[:10], 1):
|
||||
lines.append(f"\n{i}. {item.get('title', 'Untitled')}")
|
||||
lines.append(f" URL: {item.get('url', 'N/A')}")
|
||||
lines.append(f" Abstract: {item.get('content', 'No abstract')[:500]}...")
|
||||
lines.append(f" Relevance Score: {item.get('score', 0)}")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _call_openai(self, prompt: str) -> str:
|
||||
"""Call OpenAI API for synthesis."""
|
||||
try:
|
||||
import openai
|
||||
client = openai.OpenAI(api_key=self.api_key)
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model="gpt-4o-mini", # Cost-effective for daily briefings
|
||||
messages=[
|
||||
{"role": "system", "content": "You are an expert AI research analyst. Be concise and actionable."},
|
||||
{"role": "user", "content": prompt}
|
||||
],
|
||||
temperature=0.3,
|
||||
max_tokens=2000
|
||||
)
|
||||
return response.choices[0].message.content
|
||||
except Exception as e:
|
||||
print(f"[WARN] OpenAI synthesis failed: {e}")
|
||||
return self._fallback_synthesis_from_prompt(prompt)
|
||||
|
||||
def _call_anthropic(self, prompt: str) -> str:
|
||||
"""Call Anthropic API for synthesis."""
|
||||
try:
|
||||
import anthropic
|
||||
client = anthropic.Anthropic(api_key=self.api_key)
|
||||
|
||||
response = client.messages.create(
|
||||
model="claude-3-haiku-20240307", # Cost-effective
|
||||
max_tokens=2000,
|
||||
temperature=0.3,
|
||||
system="You are an expert AI research analyst. Be concise and actionable.",
|
||||
messages=[{"role": "user", "content": prompt}]
|
||||
)
|
||||
return response.content[0].text
|
||||
except Exception as e:
|
||||
print(f"[WARN] Anthropic synthesis failed: {e}")
|
||||
return self._fallback_synthesis_from_prompt(prompt)
|
||||
|
||||
def _fallback_synthesis(self, items: List[Dict], date: str) -> str:
|
||||
"""Generate basic briefing without LLM."""
|
||||
lines = [
|
||||
f"# Deep Dive Intelligence Brief — {date}",
|
||||
"",
|
||||
"## Headlines",
|
||||
""
|
||||
]
|
||||
for i, item in enumerate(items[:3], 1):
|
||||
lines.append(f"{i}. [{item.get('title', 'Untitled')}]({item.get('url', '')})")
|
||||
lines.append(f" Relevance Score: {item.get('score', 0)}")
|
||||
lines.append("")
|
||||
|
||||
lines.extend([
|
||||
"## Bottom Line",
|
||||
"",
|
||||
f"- Reviewed {len(items)} papers from arXiv",
|
||||
"- Run with LLM API key for full synthesis"
|
||||
])
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
def _fallback_synthesis_from_prompt(self, prompt: str) -> str:
|
||||
"""Extract items from prompt and do basic synthesis."""
|
||||
# Simple extraction for fallback
|
||||
return "# Deep Dive\n\n[LLM synthesis unavailable - check API key]\n\n" + prompt[:1000]
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--input", required=True, help="Path to ranked.json")
|
||||
parser.add_argument("--output", required=True, help="Path to write briefing.md")
|
||||
parser.add_argument("--date", default=None)
|
||||
parser.add_argument("--provider", default=None)
|
||||
args = parser.parse_args()
|
||||
|
||||
date = args.date or datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
# Load ranked items
|
||||
ranked_data = json.loads(Path(args.input).read_text())
|
||||
items = ranked_data.get("items", [])
|
||||
|
||||
if not items:
|
||||
print("[ERROR] No items to synthesize")
|
||||
return 1
|
||||
|
||||
print(f"[INFO] Synthesizing {len(items)} items...")
|
||||
|
||||
# Generate briefing
|
||||
engine = SynthesisEngine(provider=args.provider)
|
||||
briefing = engine.synthesize(items, date)
|
||||
|
||||
# Write output
|
||||
Path(args.output).write_text(briefing)
|
||||
print(f"[INFO] Briefing written to {args.output}")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
Reference in New Issue
Block a user