Method call

api/dashscope_client.py:748–825

Batch call to DashScope embedder Args: input: List of input texts model_kwargs: Model parameters force_recreate: Whether to force recreation Returns: Batch embedding output

(
        self, input: BatchEmbedderInputType, model_kwargs: Optional[Dict] = {}, force_recreate: bool = False
    )

        self.cache_path = f'./embedding_cache/{embedding_cache_file_name}_{self.embedder.__class__.__name__}_dashscope_embeddings.pkl'

    def call(
        self, input: BatchEmbedderInputType, model_kwargs: Optional[Dict] = {}, force_recreate: bool = False
    ) -> BatchEmbedderOutputType:
        """
        Batch call to DashScope embedder
        
        Args:
            input: List of input texts
            model_kwargs: Model parameters
            force_recreate: Whether to force recreation
            
        Returns:
            Batch embedding output
        """
        # Check cache first
        
        if not force_recreate and os.path.exists(self.cache_path):
            try:
                with open(self.cache_path, 'rb') as f:
                    embeddings = pickle.load(f)
                    log.info(f"Loaded cached DashScope embeddings from: {self.cache_path}")
                return embeddings
            except Exception as e:
                log.warning(f"Failed to load cache file {self.cache_path}: {e}, proceeding with fresh embedding")
        
        if isinstance(input, str):
            input = [input]
        
        n = len(input)
        embeddings: List[EmbedderOutput] = []
        
        log.info(f"Starting DashScope batch embedding processing, total {n} texts, batch size: {self.batch_size}")
        
        for i in tqdm(
            range(0, n, self.batch_size),
            desc="DashScope batch embedding",
            disable=False,
        ):
            batch_input = input[i : min(i + self.batch_size, n)]
            
            try:
                # Use correct calling method: directly call embedder instance
                batch_output = self.embedder(
                    input=batch_input, model_kwargs=model_kwargs
                )
                embeddings.append(batch_output)
                
                # Validate batch output
                if batch_output.error:
                    log.error(f"Batch {i//self.batch_size + 1} embedding failed: {batch_output.error}")
                elif batch_output.data:
                    log.debug(f"Batch {i//self.batch_size + 1} successfully generated {len(batch_output.data)} embedding vectors")
                else:
                    log.warning(f"Batch {i//self.batch_size + 1} returned no embedding data")
                    
            except Exception as e:
                log.error(f"Batch {i//self.batch_size + 1} processing exception: {e}")
                # Create error embedding output