Vanna 配置自定义向量化模型embedding，问题解决

使用阿里百炼的向量模型，向量数据库使用milivus；
 

#配置embedding参数，部署在本地就使用本地base_url Au:weide
CostomEmbedding=OpenAIEmbeddingFunction(model_name='text-embedding-v3',  api_key='sk-xxxxxxxxxx', base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",dimensions=768)

#配置milvus  http://x.x.x.x:19530,把自定义的embedding写入
Milvus_VectorStore.__init__(self, config={"embedding_function": CostomEmbedding,"milvus_client": MilvusClient(uri="http://x.x.x.x:19530")})

创建vn的完整代码：

class MyVanna(Milvus_VectorStore,QianWenAI_Chat):
    def __init__(self, config=None):

        #配置embedding参数，部署在本地就使用本地base_url Au:weide
        CostomEmbedding=OpenAIEmbeddingFunction(model_name='text-embedding-v3',  api_key='sk-xxxxxx', base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",dimensions=768)

        #配置milvus  http://x.x.x.x:19530
        Milvus_VectorStore.__init__(self, config={"embedding_function": CostomEmbedding,"milvus_client": MilvusClient(uri="http://x.x.x.x:19530")})


        # 创建 OpenAI 客户端
        self.client = OpenAI(
            api_key='sk-xxxxxxxxx',
            base_url='https://dashscope.aliyuncs.com/compatible-mode/v1',
            timeout=30
        )
        QianWenAI_Chat.__init__(self, config={'client': self.client, 'model': 'qwen-plus'})
        logging.info("ai Initializing successfully...")

vn=MyVanna({'model':'qwen-plus'})