scripts/: separate production from experimental and deprecated
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)
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#!/usr/bin/env python3
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"""E1 corrected re-run — cascade orientation passed via custom_extraction_instructions."""
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import json
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import os
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import requests
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import time
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from pathlib import Path
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import psycopg2
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from dotenv import load_dotenv
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load_dotenv(Path.home() / "aaronai" / ".env")
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EXPERIMENTS = Path.home() / "aaronai" / "experiments"
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SAMPLE_FILE = EXPERIMENTS / "cascade_reextract_sample.json"
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RESULTS_FILE = EXPERIMENTS / "cascade_reextract_results.json"
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PG_DSN = os.environ["PG_DSN"]
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SIDECAR_URL = "http://localhost:8001"
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TEST_GROUP_ID = "aaron_cascade_test"
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MAX_DOC_CHARS = 12000
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METADATA_PROMPT = """You are a metadata extraction system. Given a document, produce structural and content metadata in strict JSON format.
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Do not summarize the content beyond the one-sentence summary field. Do not extract entities or relationships. Do not interpret meaning. Produce only the metadata schema below.
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Output JSON only. No prose, no explanation, no markdown code fences.
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Schema:
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{
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"language": "<ISO 639-1 code>",
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"char_length": <integer>,
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"primary_format": "<prose|slides|code|structured|mixed>",
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"structural_signals": {
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"has_headings": <boolean>,
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"has_bullet_lists": <boolean>,
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"has_numbered_lists": <boolean>,
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"has_tables": <boolean>,
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"has_code_blocks": <boolean>,
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"has_dates": <boolean>
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},
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"content_signals": {
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"has_named_people": <boolean>,
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"has_institutional_language": <boolean>,
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"has_technical_terminology": <boolean>,
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"has_first_person": <boolean>,
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"has_quotations": <boolean>
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},
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"domain_class": "<technical|administrative|educational|personal|conversational>",
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"one_sentence_summary": "<one sentence describing what the document is about>"
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}
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Document:
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"""
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def get_pg():
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return psycopg2.connect(PG_DSN)
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def fetch_source_text(source):
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conn = get_pg()
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cur = conn.cursor()
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cur.execute("""
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SELECT STRING_AGG(document, E'\n\n' ORDER BY id) AS full_doc
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FROM embeddings WHERE source = %s
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""", (source,))
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row = cur.fetchone()
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conn.close()
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if row is None or row[0] is None:
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return None
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return row[0]
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def run_mistral_metadata(text):
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truncated = text[:MAX_DOC_CHARS]
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prompt = METADATA_PROMPT + truncated
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response = requests.post(
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"http://localhost:11434/api/generate",
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json={"model": "mistral:latest", "prompt": prompt, "stream": False, "format": "json"},
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timeout=180,
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)
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response.raise_for_status()
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raw = response.json()["response"]
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try:
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metadata = json.loads(raw)
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metadata["char_length"] = len(truncated)
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return metadata
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except json.JSONDecodeError:
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return {"error": "JSON parse failed", "raw": raw[:500]}
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def format_metadata_as_orientation(metadata):
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"""Format metadata as orient-not-bound extraction instructions."""
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if "error" in metadata:
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return None
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summary = metadata.get("one_sentence_summary", "")
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domain = metadata.get("domain_class", "unknown")
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fmt = metadata.get("primary_format", "unknown")
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return (
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f"This is a {domain} document in {fmt} format. "
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f"Summary: {summary} "
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f"This metadata is provided to orient your extraction, not to constrain it. "
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f"Extract entities and relationships freely from the document text itself; "
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f"the metadata is descriptive context, not a checklist."
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)
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def submit_episode_singular(name, content, custom_instructions):
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"""Submit episode to Graphiti's singular /episodes endpoint with cascade orientation."""
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payload = {
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"name": name,
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"content": content[:MAX_DOC_CHARS],
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"source_description": "e1_corrected_run", # neutral label, not the cascade text
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"timestamp": "2026-04-28T00:00:00",
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"group_id": TEST_GROUP_ID,
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"custom_extraction_instructions": custom_instructions,
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}
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response = requests.post(f"{SIDECAR_URL}/episodes", json=payload, timeout=300)
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response.raise_for_status()
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return response.json()
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def main():
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with open(SAMPLE_FILE) as f:
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sample = json.load(f)
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selected = sample["selected"]
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print(f"E1 CORRECTED re-run — {len(selected)} episodes via /episodes (singular)")
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print(f"Cascade orientation passed in custom_extraction_instructions.\n")
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results = []
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for i, ep in enumerate(selected, 1):
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name = ep["name"]
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bucket = ep["bucket"]
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print(f"[{i}/{len(selected)}] [{bucket}] {name}")
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record = {"name": name, "bucket": bucket, "tier1_entities": ep["entities"]}
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print(f" Fetching source text...", end=" ", flush=True)
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text = fetch_source_text(name)
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if text is None:
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print("FAILED — no chunks in pgvector")
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record["error"] = "no source text"
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results.append(record)
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continue
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record["doc_chars"] = len(text)
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print(f"{len(text)} chars")
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print(f" Generating Mistral metadata...", end=" ", flush=True)
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t0 = time.time()
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metadata = run_mistral_metadata(text)
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elapsed = time.time() - t0
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record["metadata"] = metadata
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record["metadata_elapsed_s"] = round(elapsed, 1)
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if "error" in metadata:
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print(f"FAILED in {elapsed:.1f}s")
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else:
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print(f"{elapsed:.1f}s — domain={metadata.get('domain_class')}, format={metadata.get('primary_format')}")
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custom_instructions = format_metadata_as_orientation(metadata)
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record["custom_extraction_instructions"] = custom_instructions
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print(f" Submitting via /episodes (singular) with custom_extraction_instructions...", end=" ", flush=True)
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t0 = time.time()
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try:
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result = submit_episode_singular(name, text, custom_instructions)
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elapsed = time.time() - t0
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print(f"{elapsed:.1f}s — OK")
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record["submit_elapsed_s"] = round(elapsed, 1)
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record["submit_result"] = result
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except Exception as e:
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elapsed = time.time() - t0
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print(f"{elapsed:.1f}s — FAILED: {e}")
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record["submit_error"] = str(e)
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results.append(record)
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with open(RESULTS_FILE, "w") as f:
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json.dump({"results": results}, f, indent=2, default=str)
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print()
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print(f"\nDone. Results saved to {RESULTS_FILE}")
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if __name__ == "__main__":
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main()
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