100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
"""手动运行真实模型；按配置并发提取并生成最终会议纪要。
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运行：
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D:/miniconda3/envs/wavdownlode/python.exe tests/run_real_model_stream.py
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"""
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from __future__ import annotations
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import os
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import sys
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from time import perf_counter
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(PROJECT_ROOT / "src"))
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from meeting_summary_lab.cli import load_local_env
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from meeting_summary_lab.llm import OpenAICompatibleLLM
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from meeting_summary_lab.pipeline import SummarizationPipeline, chunk_text, rough_token_count
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load_local_env(PROJECT_ROOT / ".env")
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# ===== 可直接修改的测试配置 =====
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INPUT_PATH = PROJECT_ROOT / "examples" / "文件会议 07-13 11_30-Transcript.md"
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OUTPUT_PATH = INPUT_PATH.with_name(f"{INPUT_PATH.stem}-summary.md")
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INTERMEDIATE_DIRECTORY = INPUT_PATH.with_name(f"{INPUT_PATH.stem}-chunks")
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MAX_CONCURRENT_CHUNKS = int(os.environ.get("MEETING_SUMMARY_MAX_CONCURRENT_CHUNKS", "1"))
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CONTEXT_TOKENS = int(os.environ.get("MEETING_SUMMARY_CONTEXT_TOKENS", "10240"))
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MAX_TOKENS = int(os.environ.get("MEETING_SUMMARY_MAX_TOKENS", "1024"))
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TEMPERATURE = float(os.environ.get("MEETING_SUMMARY_TEMPERATURE", "1.0"))
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REQUEST_TIMEOUT_SECONDS = 300
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ENDPOINT = os.environ.get("MEETING_SUMMARY_ENDPOINT", "")
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MODEL = os.environ.get("MEETING_SUMMARY_MODEL", "")
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API_KEY = os.environ.get("MEETING_SUMMARY_API_KEY", os.environ.get("OPENAI_API_KEY", ""))
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# ================================
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def main() -> None:
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started_at = perf_counter()
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try:
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if not INPUT_PATH.is_file():
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raise FileNotFoundError(f"Input transcript not found: {INPUT_PATH}")
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if not ENDPOINT or not MODEL:
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raise ValueError("Set MEETING_SUMMARY_ENDPOINT and MEETING_SUMMARY_MODEL in .env")
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transcript = INPUT_PATH.read_text(encoding="utf-8")
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print(f"Input: {INPUT_PATH}")
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print(f"Endpoint: {ENDPOINT}; model: {MODEL}")
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estimated_tokens = rough_token_count(transcript)
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chunk_threshold = max(1, CONTEXT_TOKENS - 300)
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chunk_count = 1 if estimated_tokens < chunk_threshold else len(
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chunk_text(transcript, max(1, chunk_threshold - 300), 100)
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)
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print(f"Characters: {len(transcript)}; estimated tokens: {estimated_tokens}")
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print(f"Chunks: {chunk_count}; concurrent chunk requests: {MAX_CONCURRENT_CHUNKS}; temperature: {TEMPERATURE}")
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print("Starting concurrent chunk extraction...")
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llm = OpenAICompatibleLLM(
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endpoint=ENDPOINT,
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model=MODEL,
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api_key=API_KEY,
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timeout_seconds=REQUEST_TIMEOUT_SECONDS,
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max_tokens=MAX_TOKENS,
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temperature=TEMPERATURE,
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stream=True,
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)
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def report_progress(message: str) -> None:
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elapsed = perf_counter() - started_at
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print(f"[{elapsed:7.1f}s] {message}", flush=True)
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pipeline = SummarizationPipeline(
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llm=llm,
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context_tokens=CONTEXT_TOKENS,
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max_concurrent_chunks=MAX_CONCURRENT_CHUNKS,
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intermediate_directory=INTERMEDIATE_DIRECTORY,
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final_markdown_path=OUTPUT_PATH,
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progress_callback=report_progress,
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)
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result = pipeline.summarize(transcript)
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print("\n--- completed ---")
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print(f"Chunks: {result.chunk_count}; multilevel: {result.used_multilevel_strategy}")
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print(f"Saved: {OUTPUT_PATH}")
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finally:
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elapsed = perf_counter() - started_at
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print(f"Runtime: {elapsed:.2f}s")
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if __name__ == "__main__":
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main()
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