Direct llm call

[puja-trivedi]

No description provided.

[gemini-code-assist]

Code Review

This pull request introduces scripts and prompts for performing neuroscience-wide named-entity recognition (NER) using OpenAI and OpenRouter models, including support for local Grobid PDF parsing. The feedback highlights several critical improvements: resolving a configuration conflict in pyproject.toml where static dependencies are defined despite being marked as dynamic, safely handling potential null values in the LLM's JSON output to prevent TypeError crashes, and ensuring uploaded files are deleted from OpenAI's servers using a try...finally block to avoid exceeding storage quotas.

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[gemini-code-assist]

Since dependencies is declared as a dynamic field under [project] (i.e., dynamic = ["version", "dependencies"]), any static dependencies defined in [project.dependencies] will be ignored or cause build/locking issues with Poetry. Instead, the requests dependency should be added to [tool.poetry.dependencies].

[gemini-code-assist]

If the LLM returns null for "entities" or "key_terms" (which is common when no entities are found or if the generation is incomplete), payload.get("entities", []) will return None. Calling len(None) will then raise a TypeError and crash the script. It is safer to check the type of the returned value before calling len().

Suggested change

	n_entities = len(payload.get("entities", [])) if isinstance(payload, dict) else 0
	n_terms = len(payload.get("key_terms", [])) if isinstance(payload, dict) else 0
	entities_list = payload.get("entities") if isinstance(payload, dict) else None
	n_entities = len(entities_list) if isinstance(entities_list, list) else 0
	terms_list = payload.get("key_terms") if isinstance(payload, dict) else None
	n_terms = len(terms_list) if isinstance(terms_list, list) else 0

[gemini-code-assist]

If the LLM returns null for "entities" or "key_terms", payload.get("entities", []) will return None. Calling len(None) will then raise a TypeError and crash the script. It is safer to check the type of the returned value before calling len().

Suggested change

	n_entities = len(payload.get("entities", [])) if isinstance(payload, dict) else 0
	n_terms = len(payload.get("key_terms", [])) if isinstance(payload, dict) else 0
	entities_list = payload.get("entities") if isinstance(payload, dict) else None
	n_entities = len(entities_list) if isinstance(entities_list, list) else 0
	terms_list = payload.get("key_terms") if isinstance(payload, dict) else None
	n_terms = len(terms_list) if isinstance(terms_list, list) else 0

[gemini-code-assist]

The uploaded file is persisted indefinitely on OpenAI's servers, which can eventually exhaust the user's file storage quota or accumulate unnecessary files. It is highly recommended to wrap the file upload and streaming process in a try...finally block to ensure the uploaded file is deleted after the request completes.

    uploaded = None
    try:
        vprint(f"Uploading file: {args.file} ...")
        with open(args.file, "rb") as fh:
            uploaded = client.files.create(file=fh, purpose="user_data")
        vprint(f"  uploaded (file_id={uploaded.id})")

        vprint(f"Sending request to model '{args.model}' (streaming; this may take a while)...")
        request_input = [
            {
                "role": "user",
                "content": [
                    {"type": "input_file", "file_id": uploaded.id},
                    {
                        "type": "input_text",
                        "text": USER_PROMPT.format(metadata_json=json.dumps(metadata)),
                    },
                ],
            }
        ]

        # Only send sampling controls when explicitly requested; some reasoning
        # models reject temperature/seed outright.
        request_kwargs = {}
        if args.temperature is not None:
            request_kwargs["temperature"] = args.temperature
        if args.seed is not None:
            request_kwargs["seed"] = args.seed
        if request_kwargs:
            vprint(f"  sampling controls: {request_kwargs}")

        chunks = []
        chars = 0
        next_report = 2000  # print a progress line every ~2000 chars
        with client.responses.stream(
            model=args.model,
            instructions=system_prompt,
            input=request_input,
            **request_kwargs,
        ) as stream:
            for event in stream:
                if event.type == "response.output_text.delta":
                    chunks.append(event.delta)
                    chars += len(event.delta)
                    if chars >= next_report:
                        vprint(f"  ...streaming, {chars} chars received so far")
                        next_report += 2000
                elif event.type == "error":
                    print(f"  stream error: {event.error}")
            final_response = stream.get_final_response()  # surfaces any terminal API error
    finally:
        if uploaded is not None:
            vprint(f"Deleting uploaded file {uploaded.id} ...")
            try:
                client.files.delete(uploaded.id)
            except Exception as exc:
                print(f"WARNING: Failed to delete uploaded file {uploaded.id}: {exc}")

[gemini-code-assist]

Similarly, if "entities" is None or not a list, payload.get("entities", []) or [] can still result in None or an invalid type. Explicitly validating that entities is a list prevents potential runtime errors during iteration.

Suggested change

	entities = payload.get("entities", []) or []
	entities = payload.get("entities")
	if not isinstance(entities, list):
	entities = []

[gemini-code-assist]

Similarly, if "entities" is None or not a list, payload.get("entities", []) or [] can still result in None or an invalid type. Explicitly validating that entities is a list prevents potential runtime errors during iteration.

        entities = payload.get("entities")
        if not isinstance(entities, list):
            entities = []