add gpt4o batch analysis

analysis/gpt4o/analysis_batch.py

@@ -0,0 +1,420 @@
+#!/usr/bin/env python3
+"""
+analysis/gpt4o/analysis-batch.py — OpenAI Batch API pipeline
+
+Commands (run manually in order):
+    submit   <input_jsonl> [--model gpt-4o]   — build request file, upload, create batch
+    status   <run_id>                          — check batch status, update manifest
+    download <run_id>                          — download + normalize output, update manifest
+
+File layout (all under analysis/gpt4o/):
+    requests/<run_id>.jsonl     — batch input sent to OpenAI
+    raw/<run_id>.jsonl          — raw batch output from OpenAI
+    runs/<run_id>.json          — run manifest
+    <run_id>_<model>.jsonl      — normalized output (same schema as realtime)
+"""
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+try:
+    import openai
+except ImportError:
+    sys.exit("openai package not installed. Run: pip install openai")
+
+# ---------------------------------------------------------------------------
+# Prompt
+
+_DEFAULT_PROMPT_FILE = Path(__file__).parent.parent / "prompt-1.txt"
+SYSTEM_PROMPT = _DEFAULT_PROMPT_FILE.read_text(encoding="utf-8").strip()
+PROMPT_VERSION = hashlib.sha256(SYSTEM_PROMPT.encode("utf-8")).hexdigest()[:7]
+
+
+def _load_prompt(path: Path) -> None:
+    """Re-read a prompt file, updating module-level SYSTEM_PROMPT and PROMPT_VERSION."""
+    global SYSTEM_PROMPT, PROMPT_VERSION
+    SYSTEM_PROMPT = path.read_text(encoding="utf-8").strip()
+    PROMPT_VERSION = hashlib.sha256(SYSTEM_PROMPT.encode("utf-8")).hexdigest()[:7]
+
+USER_TEMPLATE = """\
+## Proposed Regulation
+Title: {reg_title}
+Description: {reg_desc}
+
+---
+
+## Public Comment
+Comment ID: {comment_id}
+Title: {comment_title}
+Body:
+{comment_text}
+
+---
+Classify this comment per the instructions. Return only JSON.\
+"""
+
+MAX_COMMENT_CHARS = 6000
+
+# ---------------------------------------------------------------------------
+# Directories
+
+_SCRIPT_DIR  = Path(__file__).parent
+REQUESTS_DIR = _SCRIPT_DIR / "requests"
+RAW_DIR      = _SCRIPT_DIR / "raw"
+RUNS_DIR     = _SCRIPT_DIR / "runs"
+
+# ---------------------------------------------------------------------------
+# Core functions (importable for tests)
+
+
+def load_items(path: Path) -> tuple[dict | None, list[dict]]:
+    """Read a scraped JSONL file. Returns (forum_item_or_None, [comment_items])."""
+    forum = None
+    comments = []
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            item = json.loads(line)
+            if "comment_id" in item:
+                comments.append(item)
+            elif "reg_title" in item:
+                forum = item
+    return forum, comments
+
+
+def custom_id_from(comment_id: str) -> str:
+    return f"comment_{comment_id}"
+
+
+def parse_custom_id(custom_id: str) -> str:
+    """Return comment_id from a custom_id string."""
+    return custom_id.removeprefix("comment_")
+
+
+def build_messages(comment: dict, forum: dict | None) -> tuple[list, bool]:
+    """Build OpenAI messages for one comment. Returns (messages, truncated)."""
+    reg_title = (forum or {}).get("reg_title", "[unknown]")
+    reg_desc  = (forum or {}).get("reg_desc",  "[unknown]")
+
+    body = (comment.get("text") or "").strip()
+    truncated = False
+    if not body:
+        body = "[No body text provided]"
+    elif len(body) > MAX_COMMENT_CHARS:
+        body = body[:MAX_COMMENT_CHARS] + "... [truncated]"
+        truncated = True
+
+    user_text = USER_TEMPLATE.format(
+        reg_title=reg_title,
+        reg_desc=reg_desc,
+        comment_id=comment.get("comment_id", ""),
+        comment_title=comment.get("title", ""),
+        comment_text=body,
+    )
+
+    return [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user",   "content": user_text},
+    ], truncated
+
+
+def build_batch_request_line(comment: dict, forum: dict | None, model: str) -> dict:
+    """Build one line of the batch input JSONL."""
+    messages, _ = build_messages(comment, forum)
+    return {
+        "custom_id": custom_id_from(comment["comment_id"]),
+        "method": "POST",
+        "url": "/v1/chat/completions",
+        "body": {
+            "model": model,
+            "messages": messages,
+            "response_format": {"type": "json_object"},
+            "temperature": 0.0,
+        },
+    }
+
+
+def normalize_output_line(
+    raw_line: dict,
+    comment_lookup: dict,
+    run_id: str,
+    analyzed_at: str,
+    model: str,
+    prompt_version: str,
+) -> dict:
+    """Convert one raw batch output line into a normalized analysis record.
+
+    comment_lookup: {comment_id: CommentItem dict}
+    prompt_version: taken from the run manifest so it reflects what was submitted.
+    """
+    comment_id = parse_custom_id(raw_line.get("custom_id", ""))
+    comment = comment_lookup.get(comment_id, {})
+
+    base = {
+        "run_id":         run_id,
+        "forum_id":       comment.get("forum_id", ""),
+        "comment_id":     comment_id,
+        "analyzed_at":    analyzed_at,
+        "model":          model,
+        "prompt_version": prompt_version,
+        "input_title":    comment.get("title", ""),
+        "truncated":      len(comment.get("text") or "") > MAX_COMMENT_CHARS,
+    }
+
+    # Check for outer-level batch error (e.g. batch_expired)
+    if raw_line.get("error"):
+        err = raw_line["error"]
+        err_msg = err.get("message", str(err)) if isinstance(err, dict) else str(err)
+        return {**base, "stance": None, "stance_confidence": None,
+                "stance_rationale": None, "tone": None, "tags": None, "error": err_msg}
+
+    response = raw_line.get("response") or {}
+    if response.get("status_code") != 200:
+        return {**base, "stance": None, "stance_confidence": None,
+                "stance_rationale": None, "tone": None, "tags": None,
+                "error": f"status {response.get('status_code')}"}
+
+    try:
+        content = response["body"]["choices"][0]["message"]["content"]
+        data = json.loads(content)
+        keys = ("stance", "stance_confidence", "stance_rationale", "tone", "tags")
+        parsed = {k: data.get(k) for k in keys}
+        return {**base, **parsed, "error": None}
+    except Exception as exc:
+        return {**base, "stance": None, "stance_confidence": None,
+                "stance_rationale": None, "tone": None, "tags": None, "error": str(exc)}
+
+
+def make_manifest(
+    run_id: str,
+    input_filename: str,
+    input_sha256: str,
+    model: str,
+    batch_id: str,
+    records_submitted: int,
+    request_filename: str,
+) -> dict:
+    return {
+        "run_id":                 run_id,
+        "input_filename":         input_filename,
+        "input_sha256":           input_sha256,
+        "prompt_hash":            PROMPT_VERSION,
+        "model":                  model,
+        "batch_id":               batch_id,
+        "records_submitted":      records_submitted,
+        "records_completed":      None,
+        "records_failed":         None,
+        "request_filename":       request_filename,
+        "raw_output_filename":    None,
+        "normalized_output_filename": None,
+        "created_at":             datetime.now(timezone.utc).isoformat(),
+        "completed_at":           None,
+    }
+
+
+def load_manifest(run_id: str) -> dict:
+    path = RUNS_DIR / f"{run_id}.json"
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def save_manifest(manifest: dict) -> None:
+    RUNS_DIR.mkdir(parents=True, exist_ok=True)
+    path = RUNS_DIR / f"{manifest['run_id']}.json"
+    path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False), encoding="utf-8")
+
+
+# ---------------------------------------------------------------------------
+# Subcommand: submit
+
+def cmd_submit(args, client) -> None:
+    _load_prompt(Path(args.prompt))
+    print(f"Prompt: {args.prompt}  (version {PROMPT_VERSION})", file=sys.stderr)
+
+    input_path = Path(args.input)
+    if not input_path.exists():
+        sys.exit(f"File not found: {input_path}")
+
+    print(f"Reading {input_path} ...", file=sys.stderr)
+    forum, comments = load_items(input_path)
+    if not comments:
+        sys.exit("No comment items found in input file.")
+    if forum is None:
+        print("Warning: no ForumItem found — regulation context will be [unknown].", file=sys.stderr)
+
+    import uuid
+    run_id = str(uuid.uuid4())
+    input_sha256 = hashlib.sha256(input_path.read_bytes()).hexdigest()
+
+    # Build batch request file
+    REQUESTS_DIR.mkdir(parents=True, exist_ok=True)
+    request_path = REQUESTS_DIR / f"{run_id}.jsonl"
+    with open(request_path, "w", encoding="utf-8") as f:
+        for comment in comments:
+            line = build_batch_request_line(comment, forum, args.model)
+            f.write(json.dumps(line, ensure_ascii=False) + "\n")
+
+    print(f"Wrote {len(comments)} requests → {request_path}", file=sys.stderr)
+
+    # Upload to OpenAI
+    print("Uploading request file ...", file=sys.stderr)
+    with open(request_path, "rb") as f:
+        uploaded = client.files.create(file=f, purpose="batch")
+    print(f"Uploaded: {uploaded.id}", file=sys.stderr)
+
+    # Create batch
+    batch = client.batches.create(
+        input_file_id=uploaded.id,
+        endpoint="/v1/chat/completions",
+        completion_window="24h",
+        metadata={"run_id": run_id, "input_filename": str(input_path)},
+    )
+    print(f"Batch created: {batch.id}  status={batch.status}", file=sys.stderr)
+
+    # Save manifest
+    manifest = make_manifest(
+        run_id=run_id,
+        input_filename=str(input_path),
+        input_sha256=input_sha256,
+        model=args.model,
+        batch_id=batch.id,
+        records_submitted=len(comments),
+        request_filename=str(request_path),
+    )
+    save_manifest(manifest)
+
+    print(f"\nrun_id: {run_id}", file=sys.stderr)
+    print(f"Check status:   python analysis/gpt4o/analysis-batch.py status {run_id}", file=sys.stderr)
+    print(run_id)  # stdout for scripting
+
+
+# ---------------------------------------------------------------------------
+# Subcommand: status
+
+def cmd_status(args, client) -> None:
+    manifest = load_manifest(args.run_id)
+    batch = client.batches.retrieve(manifest["batch_id"])
+
+    counts = batch.request_counts
+    print(f"status:     {batch.status}")
+    print(f"completed:  {counts.completed}/{counts.total}")
+    print(f"failed:     {counts.failed}")
+
+    manifest["records_completed"] = counts.completed
+    manifest["records_failed"]    = counts.failed
+    save_manifest(manifest)
+
+    if batch.status == "completed":
+        print(f"\nReady to download. Run:")
+        print(f"  python analysis/gpt4o/analysis-batch.py download {args.run_id}")
+
+
+# ---------------------------------------------------------------------------
+# Subcommand: download
+
+def cmd_download(args, client) -> None:
+    manifest = load_manifest(args.run_id)
+    batch = client.batches.retrieve(manifest["batch_id"])
+
+    if batch.status != "completed":
+        sys.exit(f"Batch not complete yet (status={batch.status}). Run 'status' to check.")
+
+    run_id    = manifest["run_id"]
+    model     = manifest["model"]
+    model_slug = model.replace("/", "-")
+
+    # Download raw output
+    RAW_DIR.mkdir(parents=True, exist_ok=True)
+    raw_path = RAW_DIR / f"{run_id}.jsonl"
+    raw_text = client.files.content(batch.output_file_id).text
+    raw_path.write_text(raw_text, encoding="utf-8")
+    print(f"Raw output → {raw_path}", file=sys.stderr)
+
+    # Build comment lookup from original input for reconciliation
+    input_path = Path(manifest["input_filename"])
+    _, comments = load_items(input_path)
+    comment_lookup = {c["comment_id"]: c for c in comments}
+
+    # Normalize
+    completed_at = datetime.now(timezone.utc).isoformat()
+    if batch.completed_at:
+        completed_at = datetime.fromtimestamp(batch.completed_at, tz=timezone.utc).isoformat()
+
+    normalized_path = _SCRIPT_DIR / f"{run_id}_{model_slug}.jsonl"
+    n_ok = n_err = 0
+    with open(normalized_path, "w", encoding="utf-8") as out:
+        for line in raw_text.splitlines():
+            if not line.strip():
+                continue
+            raw_line = json.loads(line)
+            record = normalize_output_line(raw_line, comment_lookup, run_id, completed_at, model, manifest["prompt_hash"])
+            out.write(json.dumps(record, ensure_ascii=False) + "\n")
+            if record["error"]:
+                n_err += 1
+            else:
+                n_ok += 1
+
+    print(f"Normalized → {normalized_path}  ({n_ok} ok, {n_err} errors)", file=sys.stderr)
+
+    manifest["records_completed"]         = n_ok
+    manifest["records_failed"]            = n_err
+    manifest["raw_output_filename"]       = str(raw_path)
+    manifest["normalized_output_filename"] = str(normalized_path)
+    manifest["completed_at"]              = completed_at
+    save_manifest(manifest)
+    print(f"Manifest updated → {RUNS_DIR / run_id}.json", file=sys.stderr)
+
+
+# ---------------------------------------------------------------------------
+# CLI
+
+def main() -> None:
+    load_dotenv()
+
+    api_key = os.environ.get("OPENAI_API_KEY")
+    if not api_key:
+        sys.exit("OPENAI_API_KEY not set. Create a .env file or export the variable.")
+
+    parser = argparse.ArgumentParser(
+        description="Public comment batch analysis pipeline.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    p_submit = sub.add_parser("submit", help="Build and submit a batch job")
+    p_submit.add_argument("input", help="Path to scraped JSONL file")
+    p_submit.add_argument("--model", default="gpt-4o", help="OpenAI model (default: gpt-4o)")
+    p_submit.add_argument(
+        "--prompt",
+        default=str(_DEFAULT_PROMPT_FILE),
+        help="Path to system prompt file (default: analysis/prompt-1.txt)",
+    )
+
+    p_status = sub.add_parser("status", help="Check batch status")
+    p_status.add_argument("run_id", help="run_id from submit output")
+
+    p_download = sub.add_parser("download", help="Download and normalize completed batch")
+    p_download.add_argument("run_id", help="run_id from submit output")
+
+    args = parser.parse_args()
+    client = openai.OpenAI(api_key=api_key)
+
+    if args.command == "submit":
+        cmd_submit(args, client)
+    elif args.command == "status":
+        cmd_status(args, client)
+    elif args.command == "download":
+        cmd_download(args, client)
+
+
+if __name__ == "__main__":
+    main()

analysis/gpt4o/runs/5b8714a7-0666-40a2-9d69-2d9ce9074406.json

@@ -0,0 +1,16 @@
+{
+  "run_id": "5b8714a7-0666-40a2-9d69-2d9ce9074406",
+  "input_filename": "output\\f452.jsonl",
+  "input_sha256": "59dcc8b13cc2a386977a8b934c498c7e639b7e684a94ca1bfd10a14878670018",
+  "prompt_hash": "cb41250",
+  "model": "gpt-4o",
+  "batch_id": "batch_69fa579c7cd081909c049715838df6c6",
+  "records_submitted": 9083,
+  "records_completed": 0,
+  "records_failed": 0,
+  "request_filename": "C:\\Users\\moses\\projects\\vath\\analysis\\gpt4o\\requests\\5b8714a7-0666-40a2-9d69-2d9ce9074406.jsonl",
+  "raw_output_filename": null,
+  "normalized_output_filename": null,
+  "created_at": "2026-05-05T20:48:28.268022+00:00",
+  "completed_at": null
+}