Function run_Code2Video

src/agent.py:760–807

(
    knowledge_points: List[str], folder_path: Path, parallel=True, batch_size=3, max_workers=8, cfg: RunConfig = RunConfig()
)

def run_Code2Video(
    knowledge_points: List[str], folder_path: Path, parallel=True, batch_size=3, max_workers=8, cfg: RunConfig = RunConfig()
):
    all_results = []

    if parallel:
        batches = []
        for i in range(0, len(knowledge_points), batch_size):
            batch = [(i + j, kp) for j, kp in enumerate(knowledge_points[i : i + batch_size])]
            batches.append((i // batch_size, batch, folder_path))

        print(
            f"🔄 Parallel batch processing mode: {len(batches)} batches, each with {batch_size} knowledge points, {max_workers} concurrent batches"
        )
        with ProcessPoolExecutor(max_workers=max_workers) as executor:
            futures = {executor.submit(process_batch, batch, cfg): batch for batch in batches}
            for future in as_completed(futures):
                try:
                    batch_idx, batch_results = future.result()
                    all_results.extend(batch_results)
                    print(f"✅ Batch {batch_idx + 1} completed")
                except Exception as e:
                    print(f"❌ Batch {batch_idx + 1} processing failed: {e}")
    else:
        print("🔄 Serial processing mode")
        for idx, kp in enumerate(knowledge_points):
            try:
                all_results.append(process_knowledge_point(idx, kp, folder_path, cfg))
            except Exception as e:
                print(f"❌ Serial processing {kp} failed: {e}")
                all_results.append((kp, None, 0, 0))

    successful_runs = [r for r in all_results if r[1] is not None]
    total_runs = len(all_results)
    if not successful_runs:
        print("\nAll knowledge points failed, cannot calculate average.")
        return

    total_duration = sum(r[2] for r in successful_runs)
    total_tokens_consumed = sum(r[3] for r in successful_runs)
    num_successful = len(successful_runs)

    print("\n" + "=" * 50)
    print(f"   Total knowledge points: {total_runs}")
    print(f"   Successfully processed: {num_successful} ({num_successful/total_runs*100:.1f}%)")
    print(f"   Average duration [min]: {total_duration/num_successful:.2f} minutes/knowledge point")
    print(f"   Average token consumption: {total_tokens_consumed/num_successful:,.0f} tokens/knowledge point")
    print("=" * 50)


def get_api_and_output(API_name):