code/subclaim_support_extraction/inference_extract_subclaims_vllm.py

import os
import json
import tqdm
import argparse
from openai import OpenAI

# -----------------------------
#  API CONFIGURATION
# -----------------------------
LOCAL_API_URL = "http://172.16.34.29:8004/v1"
LOCAL_MODEL_NAME = "/home/mshahidul/readctrl_model/full_model/qwen3-32B_subclaims-extraction-8b_ctx_fp16"

client = OpenAI(
    base_url=LOCAL_API_URL,
    api_key="EMPTY"
)

# -----------------------------
#  SUBCLAIM EXTRACTION PROMPT
# -----------------------------
def extraction_prompt(medical_text: str) -> str:
    return f"""
You are an expert medical annotator.

Your task is to extract granular, factual subclaims from the provided medical text.
A subclaim is the smallest standalone factual unit that can be independently verified.

Instructions:
1. Read the medical text carefully.
2. Extract factual statements explicitly stated in the text.
3. Each subclaim must:
   - Contain exactly ONE factual assertion
   - Come directly from the text (no inference or interpretation)
   - Preserve original wording as much as possible
   - Include any negation, uncertainty, or qualifier (e.g., "may", "not", "suggests")
4. Do NOT:
   - Combine multiple facts into one subclaim
   - Add new information
   - Rephrase or normalize terminology
   - Include opinions or recommendations
5. Return ONLY a valid JSON array of strings.
6. Use double quotes and valid JSON formatting only (no markdown, no commentary).

Medical Text:
{medical_text}

Return format:
[
  "subclaim 1",
  "subclaim 2"
]
""".strip()


# -----------------------------
#  INFERENCE FUNCTION (vLLM API)
# -----------------------------
def infer_subclaims_api(medical_text: str, temperature: float = 0.2, max_tokens: int = 2048, retries: int = 1) -> list:
    if not medical_text or not medical_text.strip():
        return []

    prompt = extraction_prompt(medical_text)
    
    try:
        response = client.chat.completions.create(
            model=LOCAL_MODEL_NAME,
            messages=[{"role": "user", "content": prompt}],
            temperature=temperature,
            max_tokens=max_tokens,
        )

        output_text = response.choices[0].message.content.strip()
        
        if "</think>" in output_text:
            output_text = output_text.split("</think>")[-1].strip()

        start_idx = output_text.find('[')
        end_idx = output_text.rfind(']') + 1
        
        if start_idx != -1 and end_idx > start_idx:
            content = output_text[start_idx:end_idx]
            parsed = json.loads(content)
            if isinstance(parsed, list):
                return parsed
        
        raise ValueError("Incomplete JSON list")

    except (json.JSONDecodeError, ValueError, Exception) as e:
        if retries > 0:
            new_max = max_tokens + 2048 
            print(f"\n[Warning] API error/truncation: {e}. Retrying with {new_max} tokens...")
            return infer_subclaims_api(medical_text, temperature, max_tokens=new_max, retries=retries-1)
        
        return [output_text] if 'output_text' in locals() else []

# -----------------------------
#  MAIN EXECUTION
# -----------------------------
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_file", type=str, required=True)
    parser.add_argument("--start", type=int, default=0, help="Start index in the dataset")
    parser.add_argument("--end", type=int, default=None, help="End index (exclusive) in the dataset")
    args = parser.parse_args()
    
    INPUT_FILE = args.input_file
    file_name = os.path.basename(INPUT_FILE).split(".json")[0]
    SAVE_FOLDER = "/home/mshahidul/readctrl/data/extracting_subclaim"
    os.makedirs(SAVE_FOLDER, exist_ok=True)
    
    # Range-specific output naming helps if you want to run parallel jobs
    range_suffix = f"_{args.start}_{args.end if args.end is not None else 'end'}"
    OUTPUT_FILE = os.path.join(SAVE_FOLDER, f"extracted_subclaims_{file_name}{range_suffix}.json")

    with open(INPUT_FILE, "r") as f:
        full_data = json.load(f)
    
    if args.end is None:
        args.end = len(full_data)

    # Slice the data based on user input
    data_subset = full_data[args.start:args.end]
    print(f"Processing range [{args.start} : {args.end if args.end else len(full_data)}]. Total: {len(data_subset)} items.")

    # Load existing progress if available
    processed_data = {}
    if os.path.exists(OUTPUT_FILE):
        with open(OUTPUT_FILE, "r") as f:
            existing_list = json.load(f)
            processed_data = {str(item.get("id")): item for item in existing_list}

    for item in tqdm.tqdm(data_subset):
        item_id = str(item.get("id"))
        
        # Check if this item in the subset was already processed
        if item_id in processed_data:
            continue
        
        # 1. Process Fulltext
        f_sub = infer_subclaims_api(item.get("fulltext", ""), max_tokens=3072, retries=2)

        # 2. Process Summary
        s_sub = infer_subclaims_api(item.get("summary", ""), max_tokens=2048, retries=1)

        # 3. Save Entry
        processed_data[item_id] = {
            "id": item_id,
            "fulltext": item.get("fulltext", ""),
            "fulltext_subclaims": f_sub,
            "summary": item.get("summary", ""),
            "summary_subclaims": s_sub
        }

        # Periodic checkpoint
        if len(processed_data) % 20 == 0:
            with open(OUTPUT_FILE, "w") as f:
                json.dump(list(processed_data.values()), f, indent=4, ensure_ascii=False)

    # Final Save
    with open(OUTPUT_FILE, "w") as f:
        json.dump(list(processed_data.values()), f, indent=4, ensure_ascii=False)

    print(f"Range extraction completed. File saved at: {OUTPUT_FILE}")