refactor/batch-openai prep
This commit is contained in:
@@ -1,11 +1,14 @@
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#!/usr/bin/env python3
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"""
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analysis/gpt4o/analysis-batch.py — OpenAI Batch API pipeline
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analysis_batch.py — OpenAI Batch API pipeline
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Commands (run manually in order):
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submit <input_jsonl> [--model gpt-4o] — build request file, upload, create batch
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status <run_id> — check batch status, update manifest
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download <run_id> — download + normalize output, update manifest
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submit <input_jsonl> [--model gpt-4o] [--limit N]
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— build request file, upload, create batch
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status [run_id] — check batch status, update manifest
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download [run_id] — download + normalize output, update manifest
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run_id defaults to the most recent run in runs/ when omitted.
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File layout (all under analysis/gpt4o/):
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requests/<run_id>.jsonl — batch input sent to OpenAI
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@@ -29,6 +32,79 @@ try:
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except ImportError:
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sys.exit("openai package not installed. Run: pip install openai")
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# ---------------------------------------------------------------------------
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# Model limits and token estimation
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# Max enqueued tokens across ALL concurrent batches for this model
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# (docs/openai.md pricing table, updated 2026-05-05).
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# NOTE: your org tier may be lower — if a submit fails, use --limit to reduce chunk size.
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MODEL_LIMITS: dict[str, int] = {
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"gpt-5.5": 900_000,
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"gpt-5.4": 900_000,
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"gpt-5.4-mini": 2_000_000,
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"gpt-5.4-nano": 200_000,
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"gpt-4o": 900_000,
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"gpt-4o-mini": 2_000_000,
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"gpt-o4-mini": 2_000_000,
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}
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_DEFAULT_TOKEN_LIMIT = 900_000
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# tiktoken encoding per model family; unknown models fall back to o200k_base
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_MODEL_ENCODING: dict[str, str] = {
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"gpt-5.5": "o200k_base",
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"gpt-5.4": "o200k_base",
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"gpt-5.4-mini": "o200k_base",
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"gpt-5.4-nano": "o200k_base",
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"gpt-4o": "o200k_base",
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"gpt-4o-mini": "o200k_base",
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"gpt-o4-mini": "o200k_base",
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}
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# Leave 10% headroom below the published limit
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_LIMIT_BUFFER = 0.90
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def estimate_tokens(messages: list[dict], model: str) -> int:
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"""Estimate token count for a messages list.
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Uses tiktoken when available (exact for OpenAI models); falls back to
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chars/3 + 4-token overhead per message for unknown/Anthropic models.
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"""
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try:
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import tiktoken
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enc = tiktoken.get_encoding(_MODEL_ENCODING.get(model, "o200k_base"))
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return sum(4 + len(enc.encode(m["content"])) for m in messages)
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except ImportError:
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return sum(4 + len(m["content"]) // 3 for m in messages)
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def chunk_comments_by_tokens(
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comments: list[dict], forum: dict | None, model: str
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) -> list[list[dict]]:
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"""Split comments into chunks where each chunk fits under the model token limit."""
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raw_limit = MODEL_LIMITS.get(model, _DEFAULT_TOKEN_LIMIT)
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token_limit = int(raw_limit * _LIMIT_BUFFER)
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chunks: list[list[dict]] = []
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current: list[dict] = []
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current_tokens = 0
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for comment in comments:
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messages, _ = build_messages(comment, forum)
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tokens = estimate_tokens(messages, model)
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if current and current_tokens + tokens > token_limit:
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chunks.append(current)
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current = [comment]
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current_tokens = tokens
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else:
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current.append(comment)
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current_tokens += tokens
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if current:
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chunks.append(current)
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return chunks
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# ---------------------------------------------------------------------------
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# Prompt
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@@ -221,6 +297,15 @@ def make_manifest(
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}
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def _latest_run_id() -> str:
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"""Return the run_id of the most recently saved manifest, or exit if none found."""
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runs = list(RUNS_DIR.glob("*.json")) if RUNS_DIR.exists() else []
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if not runs:
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sys.exit(f"No runs found in {RUNS_DIR}. Submit a batch first.")
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latest = max(runs, key=lambda p: p.stat().st_mtime)
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return latest.stem
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def load_manifest(run_id: str) -> dict:
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path = RUNS_DIR / f"{run_id}.json"
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return json.loads(path.read_text(encoding="utf-8"))
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@@ -235,6 +320,55 @@ def save_manifest(manifest: dict) -> None:
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# ---------------------------------------------------------------------------
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# Subcommand: submit
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def _submit_chunk(
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chunk: list[dict],
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forum: dict | None,
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input_path: Path,
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input_sha256: str,
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model: str,
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client,
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chunk_index: int,
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total_chunks: int,
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) -> str:
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"""Upload and submit one chunk of comments. Returns the run_id."""
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import uuid
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run_id = str(uuid.uuid4())
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label = f"chunk {chunk_index + 1}/{total_chunks}" if total_chunks > 1 else "single batch"
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REQUESTS_DIR.mkdir(parents=True, exist_ok=True)
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request_path = REQUESTS_DIR / f"{run_id}.jsonl"
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with open(request_path, "w", encoding="utf-8") as f:
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for comment in chunk:
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line = build_batch_request_line(comment, forum, model)
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f.write(json.dumps(line, ensure_ascii=False) + "\n")
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print(f"[{label}] Wrote {len(chunk)} requests → {request_path}", file=sys.stderr)
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with open(request_path, "rb") as f:
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uploaded = client.files.create(file=f, purpose="batch")
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print(f"[{label}] Uploaded: {uploaded.id}", file=sys.stderr)
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batch = client.batches.create(
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input_file_id=uploaded.id,
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endpoint="/v1/chat/completions",
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completion_window="24h",
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metadata={"run_id": run_id, "input_filename": str(input_path)},
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)
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print(f"[{label}] Batch created: {batch.id} status={batch.status}", file=sys.stderr)
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manifest = make_manifest(
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run_id=run_id,
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input_filename=str(input_path),
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input_sha256=input_sha256,
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model=model,
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batch_id=batch.id,
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records_submitted=len(chunk),
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request_filename=str(request_path),
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)
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save_manifest(manifest)
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return run_id
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def cmd_submit(args, client) -> None:
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_load_prompt(Path(args.prompt))
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print(f"Prompt: {args.prompt} (version {PROMPT_VERSION})", file=sys.stderr)
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@@ -250,49 +384,39 @@ def cmd_submit(args, client) -> None:
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if forum is None:
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print("Warning: no ForumItem found — regulation context will be [unknown].", file=sys.stderr)
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import uuid
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run_id = str(uuid.uuid4())
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if args.limit:
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comments = comments[:args.limit]
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print(f"Limiting to {len(comments)} comments (--limit {args.limit}).", file=sys.stderr)
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token_limit = int(MODEL_LIMITS.get(args.model, _DEFAULT_TOKEN_LIMIT) * _LIMIT_BUFFER)
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chunks = chunk_comments_by_tokens(comments, forum, args.model)
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total = len(chunks)
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print(
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f"Model: {args.model} token limit: {token_limit:,} "
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f"→ {len(comments)} comments split into {total} chunk(s).",
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file=sys.stderr,
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)
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input_sha256 = hashlib.sha256(input_path.read_bytes()).hexdigest()
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# Build batch request file
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REQUESTS_DIR.mkdir(parents=True, exist_ok=True)
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request_path = REQUESTS_DIR / f"{run_id}.jsonl"
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with open(request_path, "w", encoding="utf-8") as f:
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for comment in comments:
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line = build_batch_request_line(comment, forum, args.model)
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f.write(json.dumps(line, ensure_ascii=False) + "\n")
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# Submit only the first chunk — the enqueued token limit is a TOTAL across all
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# concurrent batches, so stacking multiple submissions will exceed the quota.
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# Wait for each batch to complete before submitting the next.
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run_id = _submit_chunk(chunks[0], forum, input_path, input_sha256, args.model, client, 0, total)
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print(f"Wrote {len(comments)} requests → {request_path}", file=sys.stderr)
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print(f"\nBatch 1/{total} submitted.", file=sys.stderr)
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print(f" status: python analysis/gpt4o/analysis_batch.py status {run_id}", file=sys.stderr)
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print(f" download: python analysis/gpt4o/analysis_batch.py download {run_id}", file=sys.stderr)
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# Upload to OpenAI
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print("Uploading request file ...", file=sys.stderr)
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with open(request_path, "rb") as f:
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uploaded = client.files.create(file=f, purpose="batch")
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print(f"Uploaded: {uploaded.id}", file=sys.stderr)
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if total > 1:
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remaining = sum(len(c) for c in chunks[1:])
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print(f"\n{total - 1} more chunk(s) remaining ({remaining} comments).", file=sys.stderr)
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print("After this batch completes and is downloaded, rerun submit with --limit to get the next chunk:", file=sys.stderr)
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offset = len(chunks[0])
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for idx, chunk in enumerate(chunks[1:], start=2):
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print(f" chunk {idx}/{total}: comments {offset}–{offset + len(chunk) - 1}", file=sys.stderr)
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offset += len(chunk)
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# Create batch
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batch = client.batches.create(
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input_file_id=uploaded.id,
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endpoint="/v1/chat/completions",
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completion_window="24h",
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metadata={"run_id": run_id, "input_filename": str(input_path)},
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)
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print(f"Batch created: {batch.id} status={batch.status}", file=sys.stderr)
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# Save manifest
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manifest = make_manifest(
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run_id=run_id,
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input_filename=str(input_path),
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input_sha256=input_sha256,
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model=args.model,
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batch_id=batch.id,
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records_submitted=len(comments),
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request_filename=str(request_path),
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)
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save_manifest(manifest)
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print(f"\nrun_id: {run_id}", file=sys.stderr)
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print(f"Check status: python analysis/gpt4o/analysis-batch.py status {run_id}", file=sys.stderr)
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print(run_id) # stdout for scripting
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@@ -300,7 +424,10 @@ def cmd_submit(args, client) -> None:
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# Subcommand: status
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def cmd_status(args, client) -> None:
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manifest = load_manifest(args.run_id)
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run_id = args.run_id or _latest_run_id()
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if not args.run_id:
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print(f"(using latest run: {run_id})", file=sys.stderr)
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manifest = load_manifest(run_id)
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batch = client.batches.retrieve(manifest["batch_id"])
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counts = batch.request_counts
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@@ -314,14 +441,17 @@ def cmd_status(args, client) -> None:
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if batch.status == "completed":
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print(f"\nReady to download. Run:")
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print(f" python analysis/gpt4o/analysis-batch.py download {args.run_id}")
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print(f" python analysis/gpt4o/analysis_batch.py download {run_id}")
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# ---------------------------------------------------------------------------
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# Subcommand: download
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def cmd_download(args, client) -> None:
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manifest = load_manifest(args.run_id)
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run_id = args.run_id or _latest_run_id()
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if not args.run_id:
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print(f"(using latest run: {run_id})", file=sys.stderr)
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manifest = load_manifest(run_id)
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batch = client.batches.retrieve(manifest["batch_id"])
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if batch.status != "completed":
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@@ -398,12 +528,18 @@ def main() -> None:
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default=str(_DEFAULT_PROMPT_FILE),
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help="Path to system prompt file (default: analysis/prompt-1.txt)",
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)
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p_submit.add_argument(
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"--limit", type=int, default=None, metavar="N",
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help="Submit only the first N comments (useful for staying under token quota)",
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)
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p_status = sub.add_parser("status", help="Check batch status")
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p_status.add_argument("run_id", help="run_id from submit output")
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p_status.add_argument("run_id", nargs="?", default=None,
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help="run_id from submit (default: most recent run)")
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p_download = sub.add_parser("download", help="Download and normalize completed batch")
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p_download.add_argument("run_id", help="run_id from submit output")
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p_download.add_argument("run_id", nargs="?", default=None,
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help="run_id from submit (default: most recent run)")
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args = parser.parse_args()
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client = openai.OpenAI(api_key=api_key)
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@@ -1,16 +0,0 @@
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{
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"run_id": "5b8714a7-0666-40a2-9d69-2d9ce9074406",
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"input_filename": "output\\f452.jsonl",
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"input_sha256": "59dcc8b13cc2a386977a8b934c498c7e639b7e684a94ca1bfd10a14878670018",
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"prompt_hash": "cb41250",
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"model": "gpt-4o",
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"batch_id": "batch_69fa579c7cd081909c049715838df6c6",
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"records_submitted": 9083,
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"records_completed": 0,
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"records_failed": 0,
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"request_filename": "C:\\Users\\moses\\projects\\vath\\analysis\\gpt4o\\requests\\5b8714a7-0666-40a2-9d69-2d9ce9074406.jsonl",
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"raw_output_filename": null,
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"normalized_output_filename": null,
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"created_at": "2026-05-05T20:48:28.268022+00:00",
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"completed_at": null
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}
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