aaron
3f7fba7e0e
Moves 28 experiment scripts to scripts/experiments/ (E1, E1.4, E1.6, E2, base_class, cascade, cost_test, briefing, consistency, token series). Moves 2 dissolved-layer scripts to scripts/deprecated/ (consolidator_v0_1.py, tier1_migration.py — under the bespoke decision both target retired substrate work). Removes 19 .bak* files from disk (gitignored, never tracked; git history is the durable record of every prior version). The 11 production scripts remain in scripts/. All systemd ExecStart paths, api.py subprocess calls, and cron jobs continue to resolve correctly — verified by grep against /etc/systemd/system/aaronai-*.service, scripts/ references in api.py, and the user crontab. Track 1 inventory cross-cutting finding: scripts/ mixed 11 production files with 32 experimental scripts and ~20 .bak files. After this commit a clean-room reader can identify the live workers from a directory listing alone. Found by Track 1 inventory 2026-05-02. See ~/aaronai/docs/scripts-reorg-plan-2026-05-02.md for full reasoning. After commit, run: 1. git log --oneline -3 — show the new commit on top 2. git status — confirm clean working tree (modulo the docs/ untracked files which are intentional)
486 lines
18 KiB
Python
486 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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Cascade Test — Nodes-vs-Edges Experiment
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Tests whether splitting graph extraction into "local drafts entity candidates,
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API verifies + draws edges" reduces total API cost vs single-shot full
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extraction, while producing a comparable graph.
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Two conditions per document:
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A — Baseline: single Claude Haiku call, full extraction
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B — Cascade: Mistral lists entity candidates, then Haiku does verify+edges
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Both conditions:
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- See the full document (parity-respecting)
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- Use open entity type vocabulary (no fixed schema)
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- Use natural-language predicates (no constrained relations)
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- Same target output schema, same temperature
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Sample: 20 docs from briefing_test_v2_results.json, stratified by char length.
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Reports API cost only. Local Mistral time is recorded but not monetized
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(ran on the VPS, no per-token API charge).
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Outputs: ~/aaronai/experiments/cascade_test_results.json
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"""
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import json
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import os
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import re
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import statistics
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import sys
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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import anthropic
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import psycopg2
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import requests
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from dotenv import load_dotenv
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load_dotenv(Path.home() / "aaronai" / ".env")
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V2_FILE = Path.home() / "aaronai" / "briefing_test_v2_results.json"
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OUTPUT_FILE = Path.home() / "aaronai" / "experiments" / "cascade_test_results.json"
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HAIKU_MODEL = "claude-haiku-4-5-20251001"
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HAIKU_MAX_TOKENS = 4096
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HAIKU_TEMPERATURE = 0.0
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OLLAMA_URL = "http://localhost:11434/api/generate"
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LOCAL_MODEL = "mistral"
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LOCAL_TIMEOUT = 120
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MAX_DOC_CHARS = 8000
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# Verified pricing 2026-04-28 against Anthropic docs
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HAIKU_IN_PER_M = 1.0
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HAIKU_OUT_PER_M = 5.0
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CONDITION_A_PROMPT = """Extract a knowledge graph from the document below.
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Return ONLY valid JSON with this exact schema:
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{
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"entities": [
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{"name": string, "type": string}
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],
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"edges": [
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{"subject": string, "predicate": string, "object": string}
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]
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}
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Entity types: use whatever fits the entity. Do not constrain yourself to a fixed list.
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Edge predicates: natural language phrases that capture the actual relationship the document states or implies.
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Extract every entity and every relationship the document states or strongly implies. Both subject and object in every edge must appear in entities. JSON only, no commentary, no markdown fences.
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DOCUMENT:
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"""
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LOCAL_PROMPT = """List every named entity that appears in the document below — every person, organization, place, project, document, material, technique, date, event, or other named thing.
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Return ONLY valid JSON:
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{
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"candidates": [string]
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}
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Just names. No types, no relationships. JSON only.
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DOCUMENT:
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"""
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CONDITION_B_API_PROMPT_WITH_DRAFT = """Extract a knowledge graph from the document below.
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A local model has identified entity candidates that may help orient your reading. Treat the candidates as a hint, not as truth — verify each candidate appears in the document, ignore any that do not, and add any entities the candidates missed.
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Return ONLY valid JSON with this exact schema:
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{
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"entities": [
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{"name": string, "type": string}
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],
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"edges": [
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{"subject": string, "predicate": string, "object": string}
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]
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}
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Entity types: use whatever fits. Edge predicates: natural language phrases capturing the actual relationship. Both subject and object in every edge must appear in entities. Extract every entity and every relationship the document states or strongly implies. JSON only, no commentary, no markdown fences.
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ENTITY CANDIDATES FROM LOCAL MODEL:
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{local_draft}
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DOCUMENT:
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"""
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def strip_json_fences(text):
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if not text:
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return ""
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t = text.strip()
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t = re.sub(r"^```(?:json)?\s*", "", t)
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t = re.sub(r"\s*```$", "", t)
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return t.strip()
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def fetch_document_text(pg_conn, source):
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cur = pg_conn.cursor()
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cur.execute(
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"SELECT document FROM embeddings WHERE source = %s ORDER BY id",
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(source,),
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)
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rows = cur.fetchall()
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cur.close()
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if not rows:
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return None, 0
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full = "\n\n".join(r[0] for r in rows)
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return full[:MAX_DOC_CHARS], len(full)
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def call_haiku(client, prompt_text):
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t0 = time.time()
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resp = client.messages.create(
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model=HAIKU_MODEL,
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max_tokens=HAIKU_MAX_TOKENS,
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temperature=HAIKU_TEMPERATURE,
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messages=[{"role": "user", "content": prompt_text}],
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)
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return {
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"input_tokens": resp.usage.input_tokens,
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"output_tokens": resp.usage.output_tokens,
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"latency_s": round(time.time() - t0, 2),
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"response_text": resp.content[0].text if resp.content else "",
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"stop_reason": resp.stop_reason,
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}
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def call_local(document_text):
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t0 = time.time()
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try:
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resp = requests.post(
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OLLAMA_URL,
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json={
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"model": LOCAL_MODEL,
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"prompt": LOCAL_PROMPT + document_text,
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"stream": False,
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"format": "json",
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"options": {"num_predict": 1024, "temperature": 0, "num_ctx": 8192},
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},
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timeout=LOCAL_TIMEOUT,
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)
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resp.raise_for_status()
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return {
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"response": resp.json().get("response", ""),
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"latency_s": round(time.time() - t0, 2),
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}
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except Exception as e:
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return {"error": str(e), "latency_s": round(time.time() - t0, 2)}
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def parse_graph(raw):
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cleaned = strip_json_fences(raw)
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if not cleaned:
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return None, None
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try:
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data = json.loads(cleaned)
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except json.JSONDecodeError:
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return None, None
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if not isinstance(data, dict):
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return None, None
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ents = data.get("entities")
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edges = data.get("edges")
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if isinstance(ents, list) and isinstance(edges, list):
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return len(ents), len(edges)
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return None, None
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def parse_candidates(raw):
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cleaned = strip_json_fences(raw)
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if not cleaned:
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return None
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try:
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data = json.loads(cleaned)
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except json.JSONDecodeError:
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return None
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if not isinstance(data, dict):
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return None
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cands = data.get("candidates")
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if isinstance(cands, list):
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return [str(c).strip() for c in cands if c]
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return None
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def stratify(docs):
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"""Pick 5 small / 10 medium / 5 large by character length, in file order."""
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sized = [(d, d["content_length"]) for d in docs]
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small = [d for d, n in sized if n < 1000]
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medium = [d for d, n in sized if 1000 <= n < 5000]
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large = [d for d, n in sized if n >= 5000]
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return small[:5] + medium[:10] + large[:5]
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def main():
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api_key = os.environ.get("ANTHROPIC_API_KEY")
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pg_dsn = os.environ.get("PG_DSN")
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if not api_key or not pg_dsn:
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print("ERROR: ANTHROPIC_API_KEY or PG_DSN not set", file=sys.stderr)
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sys.exit(1)
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if not V2_FILE.exists():
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print(f"ERROR: {V2_FILE} not found", file=sys.stderr)
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sys.exit(1)
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with open(V2_FILE) as f:
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v2 = json.load(f)
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docs_meta = [d for d in v2["documents"] if d.get("status") == "SUCCESS"]
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sample = stratify(docs_meta)
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print(f"Sample: {len(sample)} docs (stratified by char length, file order)")
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for d in sample:
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print(f" [{d['content_length']:>6}c] {d['source'][:60]}")
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print(f"Haiku model: {HAIKU_MODEL} temp={HAIKU_TEMPERATURE} max_tokens={HAIKU_MAX_TOKENS}")
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print(f"Local model: {LOCAL_MODEL}")
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print()
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client = anthropic.Anthropic(api_key=api_key)
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pg_conn = psycopg2.connect(pg_dsn)
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results = []
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started_at = datetime.now(timezone.utc).isoformat()
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t_total = time.time()
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for i, doc_meta in enumerate(sample, 1):
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source = doc_meta["source"]
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doc_text, original_len = fetch_document_text(pg_conn, source)
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if not doc_text:
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print(f"[{i:02d}/{len(sample)}] {source[:60]} — SKIP (not in pgvector)")
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results.append({"source": source, "skipped": "not_in_pgvector"})
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continue
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sent_len = len(doc_text)
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truncated = original_len > sent_len
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size_bucket = (
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"small" if sent_len < 1000
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else "medium" if sent_len < 5000
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else "large"
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)
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trunc_marker = "*" if truncated else " "
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print(f"[{i:02d}/{len(sample)}] [{size_bucket:6s}] [{sent_len:>5}c{trunc_marker}] {source[:55]}", flush=True)
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# Condition A
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try:
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a = call_haiku(client, CONDITION_A_PROMPT + doc_text)
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a_ents, a_edges = parse_graph(a["response_text"])
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print(f" A: in={a['input_tokens']} out={a['output_tokens']} "
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f"ents={a_ents} edges={a_edges} stop={a['stop_reason']} t={a['latency_s']}s",
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flush=True)
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except Exception as e:
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print(f" A FAILED: {e}", flush=True)
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a = {"error": str(e)}
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a_ents = a_edges = None
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# Condition B local pass
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local_result = call_local(doc_text)
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if "error" in local_result:
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print(f" B local FAILED: {local_result['error']} — skipping doc", flush=True)
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results.append({
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"source": source,
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"size_bucket": size_bucket,
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"doc_chars_original": original_len,
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"doc_chars_sent": sent_len,
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"truncated": truncated,
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"condition_a": {
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"input_tokens": a.get("input_tokens"),
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"output_tokens": a.get("output_tokens"),
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"latency_s": a.get("latency_s"),
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"entity_count": a_ents,
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"edge_count": a_edges,
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"stop_reason": a.get("stop_reason"),
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"response_text": a.get("response_text", "")[:4000],
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"error": a.get("error"),
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},
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"condition_b": {
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"skipped": "local_model_failed",
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"local_error": local_result["error"],
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"local_latency_s": local_result.get("latency_s"),
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},
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})
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continue
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local_raw = local_result["response"]
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cands = parse_candidates(local_raw)
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local_candidates = cands or []
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print(f" B local: t={local_result['latency_s']}s candidates={len(local_candidates)}",
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flush=True)
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if not local_candidates:
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print(f" B local: empty draft — skipping API call to avoid asymmetric test", flush=True)
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results.append({
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"source": source,
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"size_bucket": size_bucket,
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"doc_chars_original": original_len,
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"doc_chars_sent": sent_len,
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"truncated": truncated,
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"condition_a": {
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"input_tokens": a.get("input_tokens"),
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"output_tokens": a.get("output_tokens"),
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"latency_s": a.get("latency_s"),
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"entity_count": a_ents,
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"edge_count": a_edges,
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"stop_reason": a.get("stop_reason"),
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"response_text": a.get("response_text", "")[:4000],
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"error": a.get("error"),
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},
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"condition_b": {
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"skipped": "local_draft_empty",
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"local_latency_s": local_result.get("latency_s"),
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"local_raw": local_raw[:1000],
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},
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})
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continue
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local_draft_str = "\n".join(f"- {c}" for c in local_candidates)
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b_prompt = CONDITION_B_API_PROMPT_WITH_DRAFT.replace("{local_draft}", local_draft_str) + doc_text
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try:
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b = call_haiku(client, b_prompt)
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b_ents, b_edges = parse_graph(b["response_text"])
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print(f" B api: in={b['input_tokens']} out={b['output_tokens']} "
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f"ents={b_ents} edges={b_edges} stop={b['stop_reason']} t={b['latency_s']}s",
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flush=True)
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except Exception as e:
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print(f" B api FAILED: {e}", flush=True)
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b = {"error": str(e)}
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b_ents = b_edges = None
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if "input_tokens" in a and "input_tokens" in b:
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in_pct = (b["input_tokens"] - a["input_tokens"]) / a["input_tokens"] * 100 if a["input_tokens"] else 0.0
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out_pct = (b["output_tokens"] - a["output_tokens"]) / a["output_tokens"] * 100 if a["output_tokens"] else 0.0
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edge_pct_str = "n/a"
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if a_edges and b_edges is not None and a_edges > 0:
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edge_pct_str = f"{(b_edges - a_edges) / a_edges * 100:+.1f}%"
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print(f" Δ input={in_pct:+.1f}% output={out_pct:+.1f}% edges={edge_pct_str}", flush=True)
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results.append({
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"source": source,
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"size_bucket": size_bucket,
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"doc_chars_original": original_len,
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"doc_chars_sent": sent_len,
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"truncated": truncated,
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"condition_a": {
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"input_tokens": a.get("input_tokens"),
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"output_tokens": a.get("output_tokens"),
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"latency_s": a.get("latency_s"),
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"entity_count": a_ents,
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"edge_count": a_edges,
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"stop_reason": a.get("stop_reason"),
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"response_text": a.get("response_text", "")[:4000],
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"error": a.get("error"),
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},
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"condition_b": {
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"local_latency_s": local_result.get("latency_s"),
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"local_candidates": local_candidates,
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"local_raw": local_raw[:1000],
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"api_input_tokens": b.get("input_tokens"),
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"api_output_tokens": b.get("output_tokens"),
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"api_latency_s": b.get("latency_s"),
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"entity_count": b_ents,
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"edge_count": b_edges,
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"stop_reason": b.get("stop_reason"),
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"response_text": b.get("response_text", "")[:4000],
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"error": b.get("error"),
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},
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})
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pg_conn.close()
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total_elapsed = round(time.time() - t_total, 1)
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valid = [r for r in results
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if r.get("condition_a", {}).get("input_tokens") is not None
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and r.get("condition_b", {}).get("api_input_tokens") is not None]
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a_in = sum(r["condition_a"]["input_tokens"] for r in valid)
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a_out = sum(r["condition_a"]["output_tokens"] for r in valid)
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b_in = sum(r["condition_b"]["api_input_tokens"] for r in valid)
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b_out = sum(r["condition_b"]["api_output_tokens"] for r in valid)
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a_cost = (a_in * HAIKU_IN_PER_M + a_out * HAIKU_OUT_PER_M) / 1_000_000
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b_cost = (b_in * HAIKU_IN_PER_M + b_out * HAIKU_OUT_PER_M) / 1_000_000
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by_bucket = {}
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for bucket in ("small", "medium", "large"):
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rows = [r for r in valid if r["size_bucket"] == bucket]
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if not rows:
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by_bucket[bucket] = None
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continue
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ai = sum(r["condition_a"]["input_tokens"] for r in rows)
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ao = sum(r["condition_a"]["output_tokens"] for r in rows)
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bi = sum(r["condition_b"]["api_input_tokens"] for r in rows)
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bo = sum(r["condition_b"]["api_output_tokens"] for r in rows)
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ae = [r["condition_a"]["edge_count"] for r in rows if r["condition_a"]["edge_count"] is not None]
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be = [r["condition_b"]["edge_count"] for r in rows if r["condition_b"]["edge_count"] is not None]
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by_bucket[bucket] = {
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"n": len(rows),
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"a_input_tokens": ai,
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"a_output_tokens": ao,
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"b_input_tokens": bi,
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"b_output_tokens": bo,
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"input_delta_pct": round((bi - ai) / ai * 100, 2) if ai else None,
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"output_delta_pct": round((bo - ao) / ao * 100, 2) if ao else None,
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"a_avg_edges": round(statistics.mean(ae), 1) if ae else None,
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"b_avg_edges": round(statistics.mean(be), 1) if be else None,
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}
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summary = {
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"experiment": "cascade_test",
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"title": "Nodes-vs-Edges Cascade Experiment",
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"started_at": started_at,
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"completed_at": datetime.now(timezone.utc).isoformat(),
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"haiku_model": HAIKU_MODEL,
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"haiku_temperature": HAIKU_TEMPERATURE,
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"haiku_max_tokens": HAIKU_MAX_TOKENS,
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"local_model": LOCAL_MODEL,
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"max_doc_chars": MAX_DOC_CHARS,
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"n_documents": len(sample),
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"n_valid_pairs": len(valid),
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"n_skipped": len(sample) - len(valid),
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"total_elapsed_s": total_elapsed,
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"totals": {
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"a_input_tokens": a_in,
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"a_output_tokens": a_out,
|
|
"b_input_tokens": b_in,
|
|
"b_output_tokens": b_out,
|
|
"a_cost_usd": round(a_cost, 4),
|
|
"b_cost_usd": round(b_cost, 4),
|
|
"cost_delta_usd": round(b_cost - a_cost, 4),
|
|
"cost_delta_pct": round((b_cost - a_cost) / a_cost * 100, 2) if a_cost else None,
|
|
"note": "API cost only — local Mistral runtime on VPS not monetized",
|
|
},
|
|
"by_size_bucket": by_bucket,
|
|
"results": results,
|
|
}
|
|
|
|
OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
|
|
with open(OUTPUT_FILE, "w") as f:
|
|
json.dump(summary, f, indent=2)
|
|
|
|
print()
|
|
print("=" * 60)
|
|
print(f"DONE — {len(valid)}/{len(sample)} valid pairs in {total_elapsed}s")
|
|
print(f"A total cost: ${a_cost:.4f} (in={a_in} out={a_out})")
|
|
print(f"B total cost: ${b_cost:.4f} (in={b_in} out={b_out})")
|
|
delta_pct = summary['totals']['cost_delta_pct']
|
|
if delta_pct is not None:
|
|
verdict = "B cheaper" if delta_pct < 0 else "B more expensive"
|
|
print(f"Cost delta: {delta_pct:+.2f}% ({verdict})")
|
|
print()
|
|
print("By size bucket:")
|
|
for bucket, stats in by_bucket.items():
|
|
if stats:
|
|
print(f" {bucket:6s} (n={stats['n']}): "
|
|
f"in {stats['input_delta_pct']:+.1f}% "
|
|
f"out {stats['output_delta_pct']:+.1f}% "
|
|
f"edges A={stats['a_avg_edges']} B={stats['b_avg_edges']}")
|
|
print()
|
|
print(f"NOTE: API cost only. Local Mistral runtime is not monetized.")
|
|
print(f"Results: {OUTPUT_FILE}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|