314 lines
12 KiB
Python
314 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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BirdAI Briefing Generator Test
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===============================
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Tests the local LLM as a document briefing generator.
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The local model produces a structured roadmap for the API —
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cleaning, structure detection, signal flagging — without semantic judgment.
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Results written to ~/aaronai/briefing_test_results.json
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"""
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import json
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import os
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import urllib.request
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import urllib.error
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import psycopg2
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import psycopg2.extras
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import hashlib
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import time
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from datetime import datetime, timedelta
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from dotenv import load_dotenv
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load_dotenv(os.path.expanduser("~/aaronai/.env"))
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PG_DSN = os.getenv("PG_DSN")
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RESULTS_FILE = os.path.expanduser("~/aaronai/briefing_test_results.json")
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MODEL = "mistral"
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SAMPLE_SIZE = 50
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OLLAMA_URL = "http://localhost:11434/api/generate"
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VALID_DOC_TYPES = {
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"academic_pdf", "technical_doc", "chat_log", "code",
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"presentation", "book_excerpt", "form", "syllabus",
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"email", "notes", "unknown"
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}
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VALID_DENSITIES = {"high", "medium", "low"}
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VALID_PRIORITIES = {"full", "partial", "skip"}
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BRIEFING_PROMPT = """Analyze this document and return a JSON briefing. No explanation, no prose, JSON only.
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Return exactly this structure:
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{
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"document_type": "one of: academic_pdf, technical_doc, chat_log, code, presentation, book_excerpt, form, syllabus, email, notes, unknown",
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"primary_language": "language code e.g. en, fr, de",
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"density": "one of: high, medium, low",
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"has_proper_nouns": true or false,
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"has_dates": true or false,
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"has_numeric_data": true or false,
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"has_institutional_language": true or false,
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"has_technical_terms": true or false,
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"likely_has_named_entities": true or false,
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"structure_signals": [],
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"noise_signals": [],
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"extraction_priority": "one of: full, partial, skip"
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}
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Rules:
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- document_type: identify from formatting patterns and vocabulary, not meaning
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- density: high=information dense technical or academic text, medium=mixed, low=narrative/literary/sparse
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- has_proper_nouns: true if you see capitalized words that are not sentence starts
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- has_dates: true if you see date patterns (numbers with months, years, slashes)
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- has_numeric_data: true if you see measurements, percentages, statistics
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- has_institutional_language: true if you see words like university, department, policy, committee, grant
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- has_technical_terms: true if you see domain-specific jargon or acronyms
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- likely_has_named_entities: true if has_proper_nouns is true
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- structure_signals: list any structural markers you see e.g. ["headings", "bullet_lists", "numbered_lists", "tables", "code_blocks", "citations"]
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- noise_signals: list any noise patterns you see e.g. ["repeated_headers", "page_numbers", "formatting_artifacts", "boilerplate"]
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- extraction_priority: full if density=high and likely_has_named_entities=true, skip if density=low and likely_has_named_entities=false, partial otherwise
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Document:
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"""
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def get_sample_documents():
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if not PG_DSN:
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raise RuntimeError("PG_DSN not found in .env — cannot connect to database")
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conn = psycopg2.connect(PG_DSN)
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cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
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cur.execute("""
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SELECT DISTINCT ON (source) id, document, source, created_at
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FROM embeddings
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WHERE length(document) > 100
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AND length(document) < 3000
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ORDER BY source, random()
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LIMIT %s
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""", (SAMPLE_SIZE,))
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docs = cur.fetchall()
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cur.close()
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conn.close()
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return docs
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def run_briefing(text):
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prompt = BRIEFING_PROMPT + text[:1500]
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payload = json.dumps({
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"model": MODEL,
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"prompt": prompt,
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"stream": False
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}).encode()
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raw = ""
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try:
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req = urllib.request.Request(
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OLLAMA_URL,
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data=payload,
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headers={"Content-Type": "application/json"}
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)
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with urllib.request.urlopen(req, timeout=180) as resp:
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result = json.loads(resp.read().decode())
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raw = result.get("response", "").strip()
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start = raw.find("{")
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end = raw.rfind("}") + 1
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if start == -1 or end == 0:
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return None, f"NO_JSON: {raw[:200]}"
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json_str = raw[start:end]
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parsed = json.loads(json_str)
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if not isinstance(parsed, dict):
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return None, f"NOT_DICT: {raw[:100]}"
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return parsed, raw
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except urllib.error.URLError as e:
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return None, f"URL_ERROR: {e}"
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except TimeoutError:
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return None, "TIMEOUT"
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except json.JSONDecodeError as e:
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return None, f"JSON_ERROR: {e} | raw: {raw[:200]}"
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except Exception as e:
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return None, f"ERROR: {type(e).__name__}: {e}"
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def sanitize_briefing(briefing):
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safe = {}
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dt = str(briefing.get("document_type", "unknown")).lower().strip()
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safe["document_type"] = dt if dt in VALID_DOC_TYPES else "unknown"
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safe["primary_language"] = str(briefing.get("primary_language", "en")).lower().strip()[:10]
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density = str(briefing.get("density", "medium")).lower().strip()
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safe["density"] = density if density in VALID_DENSITIES else "medium"
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for field in ["has_proper_nouns", "has_dates", "has_numeric_data",
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"has_institutional_language", "has_technical_terms",
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"likely_has_named_entities"]:
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val = briefing.get(field, False)
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if isinstance(val, bool):
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safe[field] = val
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elif isinstance(val, str):
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safe[field] = val.lower() in ("true", "yes", "1")
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else:
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safe[field] = bool(val)
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for field in ["structure_signals", "noise_signals"]:
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val = briefing.get(field, [])
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if isinstance(val, list):
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safe[field] = [str(v) for v in val if v]
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elif isinstance(val, str):
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safe[field] = [val] if val else []
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else:
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safe[field] = []
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priority = str(briefing.get("extraction_priority", "partial")).lower().strip()
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safe["extraction_priority"] = priority if priority in VALID_PRIORITIES else "partial"
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return safe
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def estimate_token_reduction(original_text, briefing):
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original_tokens = max(len(original_text) / 4, 1)
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orientation_saved = 200
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if briefing.get("extraction_priority") == "skip":
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return {
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"original_tokens_approx": round(original_tokens),
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"orientation_tokens_saved": round(original_tokens + 200),
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"noise_reduction_pct": 100.0,
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"total_reduction_pct": 100.0,
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"note": "skip — no API call"
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}
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noise_count = len(briefing.get("noise_signals", []))
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noise_reduction_pct = min(noise_count * 0.05, 0.40)
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noise_tokens_saved = original_tokens * noise_reduction_pct
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total_saved = orientation_saved + noise_tokens_saved
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total_cost = original_tokens + 200
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reduction_pct = min((total_saved / total_cost) * 100, 99.0)
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return {
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"original_tokens_approx": round(original_tokens),
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"orientation_tokens_saved": orientation_saved,
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"noise_tokens_saved": round(noise_tokens_saved),
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"noise_reduction_pct": round(noise_reduction_pct * 100, 1),
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"total_reduction_pct": round(reduction_pct, 1)
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}
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def format_eta(elapsed_times, completed, total):
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if completed == 0:
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return "ETA: --:--"
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avg = sum(elapsed_times) / completed
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remaining = (total - completed) * avg
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eta = timedelta(seconds=int(remaining))
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return f"ETA: {str(eta)}"
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def content_hash(text):
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return hashlib.md5(text.encode()).hexdigest()[:8]
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def main():
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test_start = time.time()
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print(f"\nBirdAI Briefing Generator Test")
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print(f"Model: {MODEL} | Sample: {SAMPLE_SIZE} docs (distinct sources)")
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print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print(f"Results: {RESULTS_FILE}")
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print("-" * 75)
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docs = get_sample_documents()
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print(f"Loaded {len(docs)} distinct source documents from pgvector\n")
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results = {
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"meta": {
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"model": MODEL,
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"sample_size": len(docs),
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"started": datetime.now().isoformat(),
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"completed": None,
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"total_elapsed_seconds": None,
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"avg_seconds_per_doc": None
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},
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"documents": [],
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"summary": {}
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}
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success_count = 0
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failed_count = 0
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priority_counts = {"full": 0, "partial": 0, "skip": 0}
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total_reduction_pct = 0.0
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elapsed_times = []
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for i, doc in enumerate(docs):
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doc_id = doc["id"]
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content = doc["document"]
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source = doc.get("source", "unknown")
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chash = content_hash(content)
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eta_str = format_eta(elapsed_times, i, len(docs))
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print(f"[{i+1:02d}/{len(docs)}] {source[:38]:<38} {eta_str:<14}", end=" ", flush=True)
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t_start = time.time()
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briefing, raw = run_briefing(content)
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elapsed = round(time.time() - t_start, 1)
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elapsed_times.append(elapsed)
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if briefing is None:
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failed_count += 1
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print(f"→ FAILED {elapsed}s | {raw[:50]}")
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results["documents"].append({
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"id": doc_id, "source": source, "content_hash": chash,
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"content_length": len(content), "status": "FAILED",
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"error": raw, "elapsed_seconds": elapsed
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})
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else:
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briefing = sanitize_briefing(briefing)
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success_count += 1
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priority = briefing["extraction_priority"]
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doc_type = briefing["document_type"]
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density = briefing["density"]
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priority_counts[priority] = priority_counts.get(priority, 0) + 1
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reduction = estimate_token_reduction(content, briefing)
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total_reduction_pct += reduction["total_reduction_pct"]
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print(f"→ {priority.upper():<7} {doc_type:<15} density:{density:<6} -{reduction['total_reduction_pct']:>5.1f}% {elapsed}s")
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results["documents"].append({
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"id": doc_id, "source": source, "content_hash": chash,
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"content_length": len(content), "status": "SUCCESS",
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"elapsed_seconds": elapsed, "briefing": briefing,
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"token_reduction_estimate": reduction
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})
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with open(RESULTS_FILE, "w") as f:
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json.dump(results, f, indent=2, default=str)
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total_elapsed = round(time.time() - test_start, 1)
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avg_per_doc = round(total_elapsed / len(docs), 1) if docs else 0
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completed_at = datetime.now().isoformat()
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results["meta"]["completed"] = completed_at
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results["meta"]["total_elapsed_seconds"] = total_elapsed
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results["meta"]["avg_seconds_per_doc"] = avg_per_doc
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total = len(docs)
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avg_reduction = round(total_reduction_pct / success_count, 1) if success_count else 0
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summary = {
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"total": total,
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"success": success_count,
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"failed": failed_count,
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"success_rate": round(success_count / total * 100, 1),
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"extraction_priority_breakdown": priority_counts,
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"avg_token_reduction_pct": avg_reduction,
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"total_elapsed_seconds": total_elapsed,
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"avg_seconds_per_doc": avg_per_doc,
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"projected_50_doc_minutes": round((avg_per_doc * 50) / 60, 1),
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"approach_viable": success_count / total >= 0.8
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}
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results["summary"] = summary
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with open(RESULTS_FILE, "w") as f:
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json.dump(results, f, indent=2, default=str)
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print("\n" + "=" * 75)
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print(f"RESULTS")
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print(f" Success rate: {success_count}/{total} ({summary['success_rate']}%)")
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print(f" Failed: {failed_count}")
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print(f" Priority — full: {priority_counts.get('full', 0)}")
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print(f" Priority — partial: {priority_counts.get('partial', 0)}")
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print(f" Priority — skip: {priority_counts.get('skip', 0)}")
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print(f" Avg token reduction: {avg_reduction}%")
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print(f" Total elapsed: {total_elapsed}s ({round(total_elapsed/60, 1)} min)")
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print(f" Avg per document: {avg_per_doc}s")
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print(f" Projected 50 docs: {summary['projected_50_doc_minutes']} min")
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print(f" Approach viable: {'YES' if summary['approach_viable'] else 'NO'}")
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print(f" Completed: {completed_at}")
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print(f" Full results: {RESULTS_FILE}")
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print("=" * 75)
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if __name__ == "__main__":
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main()
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