The get_text_embeddings_fixed function is incorrectly called—it always only invokes the OpenAI model

[zcxrub]

if llm_config is not None:
from litellm import aembedding

    # Get embedding model from config or use default
    embedding_model = llm_config.get('provider', 'text-embedding-3-small')
    api_base = llm_config.get('base_url', llm_config.get('api_base'))
    
    # Prepare kwargs
    kwargs = {
        'model': embedding_model,
        'input': texts,
        'api_key': llm_config.get('api_token', llm_config.get('api_key'))
    }
    
    if api_base:
        kwargs['api_base'] = api_base
        
    # Handle OpenAI-compatible endpoints
    if api_base and 'openai/' not in embedding_model:
        kwargs['model'] = f"openai/{embedding_model}"
    
    # Get embeddings
    response = await aembedding(**kwargs)
    
    # Extract embeddings from response
    embeddings = []
    for item in response.data:
        embeddings.append(item['embedding'])
        
    return np.array(embeddings)

# Default: use sentence-transformers
else:
    # Lazy load to avoid importing heavy libraries unless needed
    try:
        from sentence_transformers import SentenceTransformer
    except ImportError:
        raise ImportError(
            "sentence-transformers is required for local embeddings. "
            "Install it with: pip install 'crawl4ai[transformer]' or pip install sentence-transformers"
        )
    
    # Cache the model in function attribute to avoid reloading
    if not hasattr(get_text_embeddings, '_models'):
        get_text_embeddings._models = {}
    
    if model_name not in get_text_embeddings._models:
        get_text_embeddings._models[model_name] = SentenceTransformer(model_name)
    
    encoder = get_text_embeddings._models[model_name]
    
    # Batch encode for efficiency
    embeddings = encoder.encode(
        texts,
        batch_size=batch_size,
        show_progress_bar=False,
        convert_to_numpy=True
    )
    
    return embeddings