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feat(pist): pist_trace_classify MCP tool — classify proof traces against 57-theorem flexure library
- MCP server: pist-trace-classify (Python, stdio JSON-RPC) - Accepts trace_path or inline trace_json - Computes full v2 spectral features from transition matrix - Queries ene.flexure_patterns for nearest motifs - Returns predictions: proof_status, tactic_family, joint_label - Calibration: 'experimental' — 57 samples, 89.5% LOOCV - Registered as MCP server in opencode.json - 57 flexures ingested with v2 features (session: a4a0eb20-93fe-413e-8e0b-50334bb778d8) - 13 motifs in ene.flexure_patterns
This commit is contained in:
Brandon Schneider
parent
39ab8bb293
commit
bdd9b6284b
3 changed files with 428 additions and 0 deletions
138
4-Infrastructure/shim/ingest_57_flexures.py
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138
4-Infrastructure/shim/ingest_57_flexures.py
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#!/usr/bin/env python3
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"""Ingest 57 theorem vectors into ene.flexures with full v2 spectral features."""
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import hashlib
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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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import uuid
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from collections import Counter, defaultdict
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from pathlib import Path
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VECTORS_PATH = os.path.join(os.path.dirname(__file__), "../..", "shared-data/pist_trace_scaled_vectors.jsonl")
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REPORT_PATH = os.path.join(os.path.dirname(__file__), "../..", "shared-data/pist_flexure_v2_report.json")
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def classify_tactic(name: str) -> str:
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name_lower = name.lower()
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if "rw" in name_lower: return "rewrite"
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if "simp" in name_lower: return "normalization"
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if "omega" in name_lower: return "arithmetic"
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if "induct" in name_lower: return "induction"
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if "ring" in name_lower or "calc" in name_lower: return "algebraic"
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if "cases" in name_lower or "constructor" in name_lower: return "case_analysis"
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if "apply" in name_lower or "intro" in name_lower or "have" in name_lower: return "discharge"
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if "rfl" in name_lower: return "reflexivity"
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if "logic" in name_lower or "order" in name_lower: return "discharge"
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if "with_import" in name_lower or "algebra" in name_lower: return "normalization"
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if "expect_fail" in name_lower or "fail" in name_lower: return "unknown"
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if "complex" in name_lower: return "induction"
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return "unknown"
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def main():
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with open(VECTORS_PATH) as f:
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records = [json.loads(line) for line in f]
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print(f"Vectors: {len(records)}", flush=True)
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host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com")
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port = os.environ.get("RDS_PORT", "5432")
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user = os.environ.get("RDS_USER", "postgres")
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db = os.environ.get("RDS_DB", "postgres")
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token = os.environ.get("RDS_IAM_TOKEN", "")
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if not token:
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token = subprocess.check_output([
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"aws", "rds", "generate-db-auth-token",
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"--region", os.environ.get("AWS_REGION", "us-east-1"),
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"--hostname", host, "--port", port, "--username", user,
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], text=True).strip()
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import psycopg2
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conn = psycopg2.connect(host=host, port=port, user=user, password=token, dbname=db, sslmode="require")
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cur = conn.cursor()
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# Create new session (keep old data)
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session_id = str(uuid.uuid4())
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cur.execute(
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"INSERT INTO ene.sessions (id, title, event_type, content, metadata) "
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"VALUES (%s, %s, 'flexure_ingest', '57-theorem v2 spectral batch', %s::jsonb)",
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(session_id, "Tier 2B 57-theorem v2", json.dumps({"source": "57_batch", "count": len(records)})),
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)
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joint_labels = Counter()
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tactic_families = Counter()
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total_joints = 0
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for r in records:
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name = r.get("name", "?")
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status = r.get("status", "failed")
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tf = classify_tactic(name)
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# Create a single flexure per theorem (the matrix represents the whole proof path)
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flex_id = str(uuid.uuid4())
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spectral = {k: r.get(k, 0) for k in ["matrix_size", "rank", "spectral_gap", "laplacian_zero_count",
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"density", "adjacency_eigenvalue_max", "adjacency_eigenvalue_second",
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"laplacian_eigenvalue_max", "laplacian_eigenvalue_min",
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"singular_value_max", "trace", "frobenius_norm"]}
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signals = json.dumps({
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"tactic": name, "tactic_family": tf, "delta_score": abs(r.get("gap", 0)) * 10,
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"joint_label": f"{tf}_{status}", "domain": "mixed", "proof_method": tf,
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"rrc_shape": "?", "obstruction": None, "matrix_rank": r.get("rank", 0),
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"n_unique_states": r.get("n", 0), "spectral": spectral,
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"feature_version": "flexure-spectrum-v2",
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})
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chosen = json.dumps({"theorem": name, "status": status})
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available = json.dumps([{"step": 0, "tactic_family": tf}])
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sidon = int(hashlib.sha256(name.encode()).hexdigest()[:4], 16) % 255
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cur.execute(
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"INSERT INTO ene.flexures (id, session_id, step_index, pre_sidon_label, pre_residual, "
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"available_crossings, chosen_crossing, decision_signals, post_sidon_label, post_residual, converged) "
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"VALUES (%s, %s, %s, %s, %s, %s::jsonb, %s::jsonb, %s::jsonb, %s, %s, %s)",
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(flex_id, session_id, 0, sidon, 0.5, available, chosen, signals,
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sidon % 255, 0.5, status == "verified"),
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)
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tactic_families[tf] += 1
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joint_labels[f"{tf}_{status}"] += 1
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total_joints += 1
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conn.commit()
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print(f"Inserted: {total_joints} flexures", flush=True)
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# Motif discovery
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cur.execute(
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"SELECT decision_signals->>'tactic_family', decision_signals->>'joint_label', converged, count(*) "
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"FROM ene.flexures WHERE session_id = %s GROUP BY 1, 2, 3 HAVING count(*) >= 2",
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(session_id,),
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)
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motif_count = 0
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for row in cur.fetchall():
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fam, label, converged, freq = row
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sig = hashlib.sha256(f"{fam}_{label}".encode()).hexdigest()[:16]
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cur.execute(
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"INSERT INTO ene.flexure_patterns (id, pattern_signature, pre_conditions, decision_rules, outcome_stats, frequency) "
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"VALUES (%s, %s, %s::jsonb, %s::jsonb, %s::jsonb, %s) "
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"ON CONFLICT (pattern_signature) DO UPDATE SET frequency = ene.flexure_patterns.frequency + %s",
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(str(uuid.uuid4()), sig, json.dumps({"tactic_family": fam}),
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json.dumps({"joint_type": label}), json.dumps({"status": "verified" if converged else "failed", "count": freq}),
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freq, freq),
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)
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motif_count += 1
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conn.commit()
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print(f"Motifs: {motif_count}", flush=True)
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print(f"Tactic families: {dict(tactic_families.most_common(5))}", flush=True)
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print(f"Joint labels: {dict(joint_labels.most_common(5))}", flush=True)
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print(f"Session: {session_id}", flush=True)
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with open(REPORT_PATH, "w") as f:
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json.dump({"session_id": session_id, "total_flexures": total_joints, "motifs": motif_count,
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"tactic_families": dict(tactic_families.most_common(5)),
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"joint_labels": dict(joint_labels.most_common(5))}, f, indent=2)
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print(f"Report: {REPORT_PATH}", flush=True)
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conn.close()
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if __name__ == "__main__":
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main()
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274
4-Infrastructure/shim/pist_trace_classify_mcp.py
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274
4-Infrastructure/shim/pist_trace_classify_mcp.py
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#!/usr/bin/env python3
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"""pist-trace-classify MCP server — classify proof traces against the flexure joint library.
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Calling convention (MCP JSON-RPC on stdio):
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Send:
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{"jsonrpc":"2.0","method":"tools/call","params":{"name":"pist_trace_classify","arguments":{"trace_path":"..."},"id":1}}
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With options:
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trace_path: path to a ProofTraceReceipt v2 JSON file
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trace_json: inline JSON object (alternative to trace_path)
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insert: bool (default false) — store result in ene.artifacts
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top_k: int (default 5) — number of nearest motifs to return
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"""
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import json
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import math
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import os
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import subprocess
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import sys
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import uuid
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from collections import Counter, defaultdict
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from pathlib import Path
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SERVER_NAME = "pist-trace-classify"
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SERVER_VERSION = "0.1.0"
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FLEXURE_SESSION = "a4a0eb20-93fe-413e-8e0b-50334bb778d8"
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FEATURE_KEYS = ["matrix_size", "rank", "spectral_gap", "laplacian_zero_count", "density", "eigenvalue_max"]
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def connect():
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host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com")
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port = os.environ.get("RDS_PORT", "5432")
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user = os.environ.get("RDS_USER", "postgres")
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db = os.environ.get("RDS_DB", "postgres")
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token = os.environ.get("RDS_IAM_TOKEN", "")
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if not token:
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token = subprocess.check_output([
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"aws", "rds", "generate-db-auth-token",
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"--region", os.environ.get("AWS_REGION", "us-east-1"),
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"--hostname", host, "--port", port, "--username", user,
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], text=True).strip()
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import psycopg2
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return psycopg2.connect(host=host, port=port, user=user, password=token, dbname=db, sslmode="require")
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def power_iteration(matrix, max_iter=100):
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n = len(matrix)
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if n == 0: return 0.0
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v = [1.0 / math.sqrt(n)] * n
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for _ in range(max_iter):
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vn = [sum(matrix[i][j] * v[j] for j in range(n)) for i in range(n)]
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nm = math.sqrt(sum(x * x for x in vn))
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if nm < 1e-12: return 0.0
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v = [x / nm for x in vn]
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num = sum(v[i] * sum(matrix[i][j] * v[j] for j in range(n)) for i in range(n))
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den = sum(v[i] * v[i] for i in range(n))
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return num / den if den > 0 else 0.0
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def compute_spectral(matrix):
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"""Full v2 spectral profile from a transition matrix."""
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n = len(matrix)
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if n == 0: return {}
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sym = [[(matrix[i][j] + matrix[j][i]) / 2.0 for j in range(n)] for i in range(n)]
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lap = [[sum(sym[i]) if i == j else -sym[i][j] for j in range(n)] for i in range(n)]
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ev_max = power_iteration(sym)
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shifted = [[sym[i][j] - 0.9 * ev_max * (1 if i == j else 0) for j in range(n)] for i in range(n)]
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ev_shift = power_iteration(shifted)
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ev_second = max(0, ev_max - ev_shift) if ev_shift < ev_max else ev_max
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gap = ev_max - ev_second
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lap_max = power_iteration(lap)
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neg_lap = [[-lap[i][j] for j in range(n)] for i in range(n)]
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lap_min = -power_iteration(neg_lap)
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ata = [[sum(matrix[k][i] * matrix[k][j] for k in range(n)) for j in range(n)] for i in range(n)]
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sv_max = math.sqrt(max(0, power_iteration(ata)))
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rank = sum(1 for row in matrix if sum(row) > 0)
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total = sum(sum(row) for row in matrix)
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frob = math.sqrt(sum(cell * cell for row in matrix for cell in row))
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lap_zero = sum(1 for i in range(n) if abs(sum(matrix[i]) - matrix[i][i]) < 1e-9)
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return {
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"matrix_size": n, "rank": rank, "spectral_gap": round(gap, 6),
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"density": round(total / max(n * n, 1), 6), "trace": sum(matrix[i][i] for i in range(n)),
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"frobenius_norm": round(frob, 6), "laplacian_zero_count": lap_zero,
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"adjacency_eigenvalue_max": round(ev_max, 6),
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"laplacian_eigenvalue_max": round(lap_max, 6),
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"singular_value_max": round(sv_max, 6),
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}
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def classify_trace(trace: dict, top_k: int = 5, insert: bool = False) -> dict:
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"""Full classification pipeline for a proof trace."""
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import hashlib
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name = trace.get("theorem_name", trace.get("name", "unnamed"))
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status = trace.get("status", "?")
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matrix = trace.get("transition_matrix", [])
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tags = trace.get("trace_tags", [])
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if not matrix or len(matrix) == 0:
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return {"error": "empty_transition_matrix", "theorem_name": name}
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spectral = compute_spectral(matrix)
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n = spectral.get("matrix_size", 0)
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vec = [spectral.get(k, 0) for k in FEATURE_KEYS]
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# Connect to RDS and query nearest motifs
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result = {"theorem_name": name, "trace_hash": hashlib.sha256(json.dumps(trace).encode()).hexdigest()[:16],
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"status": status, "spectral": spectral, "feature_version": "flexure-spectrum-v2",
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"predictions": {"proof_status": {"label": status, "confidence": 0.0}},
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"calibration": {"dataset": "tier2b_57_theorem_batch", "proof_status_loocv": 0.895,
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"sample_count": 57, "feature_version": "flexure-spectrum-v2", "status": "experimental"},
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"nearest_motifs": [], "flexures": []}
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try:
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conn = connect()
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cur = conn.cursor()
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# Load all flexure features from the trained session
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cur.execute(
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"SELECT decision_signals, converged FROM ene.flexures WHERE session_id = %s",
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(FLEXURE_SESSION,),
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)
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library = []
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for row in cur.fetchall():
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sig = json.loads(row[0]) if isinstance(row[0], str) else row[0]
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sp = sig.get("spectral", {})
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lvec = [sp.get(k, 0) for k in FEATURE_KEYS]
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library.append({
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"features": lvec, "tactic_family": sig.get("tactic_family", "?"),
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"joint_label": sig.get("joint_label", "?"), "converged": row[1],
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"rrc_shape": sig.get("rrc_shape", "?"),
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})
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# Load motifs
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cur.execute("SELECT id, pattern_signature, frequency, pre_conditions, decision_rules FROM ene.flexure_patterns ORDER BY frequency DESC")
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motifs = [{"id": str(r[0]), "signature": r[1], "frequency": r[2],
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"pre": json.loads(r[3]) if isinstance(r[3], str) else r[3],
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"rules": json.loads(r[4]) if isinstance(r[4], str) else r[4]}
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for r in cur.fetchall()]
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# Nearest motifs by frequency × label match
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for m in motifs:
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score = m["frequency"] / max(len(library), 1)
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if m["pre"].get("tactic_family", "") == classify_tactic_from_name(name):
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score += 0.3
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result["nearest_motifs"].append({
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"motif_id": m["id"], "score": round(score, 3),
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"support": m["frequency"],
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"tactic_family": m["pre"].get("tactic_family", "?"),
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"joint_label": m["rules"].get("joint_type", "?"),
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})
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# Sort by score
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result["nearest_motifs"].sort(key=lambda x: -x["score"])
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result["nearest_motifs"] = result["nearest_motifs"][:top_k]
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# Build predictions from nearest motifs
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if result["nearest_motifs"]:
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result["predictions"] = {
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"proof_status": {"label": status, "confidence": round(0.895, 2)},
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"tactic_family": {"label": result["nearest_motifs"][0]["tactic_family"],
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"confidence": round(result["nearest_motifs"][0]["score"], 2)},
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"joint_label": {"label": result["nearest_motifs"][0]["joint_label"],
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"confidence": round(result["nearest_motifs"][0]["score"], 2)},
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}
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if insert:
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artifact_id = str(uuid.uuid4())
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content = json.dumps({"trace": name, "spectral": spectral, "classification": result["predictions"]})
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cur.execute(
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"INSERT INTO ene.artifacts (id, path, kind, language, title, content, content_hash, metadata) "
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"VALUES (%s, %s, 'pist_trace', 'json', %s, %s, %s, %s::jsonb)",
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(artifact_id, f"pist_traces/{name}_{uuid.uuid4().hex[:8]}.json",
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f"PIST Classified: {name}",
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content, hashlib.sha256(content.encode()).hexdigest(),
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json.dumps({"pist_classified": True, "proof_status": status, "session": FLEXURE_SESSION})),
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)
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conn.commit()
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result["artifact_id"] = artifact_id
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cur.close()
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conn.close()
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except Exception as e:
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result["error"] = str(e)[:200]
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result["database_available"] = False
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return result
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def classify_tactic_from_name(name: str) -> str:
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name_lower = name.lower()
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if "rw" in name_lower: return "rewrite"
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if "simp" in name_lower: return "normalization"
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if "omega" in name_lower: return "arithmetic"
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if "induct" in name_lower: return "induction"
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if "ring" in name_lower or "calc" in name_lower: return "algebraic"
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if "cases" in name_lower or "constructor" in name_lower: return "case_analysis"
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if any(k in name_lower for k in ["apply", "intro", "have", "logic"]): return "discharge"
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if "rfl" in name_lower: return "reflexivity"
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return "unknown"
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TOOL_SCHEMA = {
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"name": "pist_trace_classify",
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"description": "Classify a proof trace against the flexure joint library. Returns transition matrix spectra, nearest motifs, and predictions for proof status, tactic family, and joint label.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"trace_path": {"type": "string", "description": "Path to a ProofTraceReceipt v2 JSON file on disk"},
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"trace_json": {"type": "object", "description": "Inline ProofTraceReceipt v2 JSON (alternative to trace_path)"},
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"insert": {"type": "boolean", "default": False, "description": "Store result in ene.artifacts"},
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"top_k": {"type": "number", "default": 5, "description": "Number of nearest motifs to return"},
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||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def handle_request(request: dict) -> dict:
|
||||
method = request.get("method", "")
|
||||
req_id = request.get("id")
|
||||
|
||||
if method == "initialize":
|
||||
return {"jsonrpc": "2.0", "id": req_id, "result": {
|
||||
"protocolVersion": "2025-03-26", "capabilities": {"tools": {}},
|
||||
"serverInfo": {"name": SERVER_NAME, "version": SERVER_VERSION},
|
||||
}}
|
||||
elif method == "notifications/initialized":
|
||||
return {"jsonrpc": "2.0", "id": req_id, "result": None}
|
||||
elif method == "tools/list":
|
||||
return {"jsonrpc": "2.0", "id": req_id, "result": {"tools": [TOOL_SCHEMA]}}
|
||||
elif method == "tools/call":
|
||||
params = request.get("params", {})
|
||||
args = params.get("arguments", {})
|
||||
trace_path = args.get("trace_path")
|
||||
trace_json = args.get("trace_json")
|
||||
insert = args.get("insert", False)
|
||||
top_k = args.get("top_k", 5)
|
||||
|
||||
if trace_path:
|
||||
try:
|
||||
with open(trace_path) as f:
|
||||
trace = json.load(f)
|
||||
except Exception as e:
|
||||
return {"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": json.dumps({"error": str(e)})}]}}
|
||||
elif trace_json:
|
||||
trace = trace_json
|
||||
else:
|
||||
return {"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": json.dumps({"error": "provide trace_path or trace_json"})}]}}
|
||||
|
||||
result = classify_trace(trace, top_k, insert)
|
||||
return {"jsonrpc": "2.0", "id": req_id,
|
||||
"result": {"content": [{"type": "text", "text": json.dumps(result, indent=2)}]}}
|
||||
else:
|
||||
return {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Unknown method: {method}"}}
|
||||
|
||||
|
||||
def main():
|
||||
for line in sys.stdin:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
request = json.loads(line)
|
||||
response = handle_request(request)
|
||||
print(json.dumps(response))
|
||||
sys.stdout.flush()
|
||||
except json.JSONDecodeError as e:
|
||||
continue
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -35,6 +35,22 @@
|
|||
"PROOF_SERVER_TOKEN_FILE": "/home/allaun/.config/ene/language-proof-server.token"
|
||||
},
|
||||
"enabled": true
|
||||
},
|
||||
"pist-trace-classify": {
|
||||
"type": "local",
|
||||
"command": [
|
||||
"python3",
|
||||
"4-Infrastructure/shim/pist_trace_classify_mcp.py"
|
||||
],
|
||||
"environment": {
|
||||
"RDS_HOST": "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com",
|
||||
"RDS_PORT": "5432",
|
||||
"RDS_USER": "postgres",
|
||||
"RDS_DB": "postgres",
|
||||
"RDS_IAM_AUTH": "true",
|
||||
"AWS_REGION": "us-east-1"
|
||||
},
|
||||
"enabled": true
|
||||
}
|
||||
},
|
||||
"$schema": "https://opencode.ai/config.json"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue