chore: Fix pylint error (#1915)

.mypy.ini

@@ -122,3 +122,6 @@ ignore_missing_imports = True
 
 [mypy-pypdf.*]
 ignore_missing_imports = True
+
+[mypy-qianfan.*]
+ignore_missing_imports = True

dbgpt/model/adapter/embeddings_loader.py

@@ -60,6 +60,7 @@ class EmbeddingLoader:
                 return TongYiEmbeddings(**tongyi_param)
             elif model_name in ["proxy_qianfan"]:
                 from dbgpt.rag.embedding import QianFanEmbeddings
+
                 proxy_param = cast(ProxyEmbeddingParameters, param)
                 qianfan_param = {"api_key": proxy_param.proxy_api_key}
                 if proxy_param.proxy_backend:

dbgpt/model/parameter.py

@@ -563,7 +563,7 @@ class ProxyEmbeddingParameters(BaseEmbeddingModelParameters):
         metadata={
             "tags": "privacy",
             "help": "The api secret of the current embedding model(OPENAI_API_SECRET)",
-        }
+        },
     )
     proxy_api_version: Optional[str] = field(
         default=None,

dbgpt/rag/embedding/__init__.py

@@ -14,8 +14,8 @@ from .embeddings import (  # noqa: F401
     JinaEmbeddings,
     OllamaEmbeddings,
     OpenAPIEmbeddings,
-    TongYiEmbeddings,
     QianFanEmbeddings,
+    TongYiEmbeddings,
 )
 from .rerank import CrossEncoderRerankEmbeddings, OpenAPIRerankEmbeddings  # noqa: F401
 
@@ -34,5 +34,5 @@ __ALL__ = [
     "TongYiEmbeddings",
     "CrossEncoderRerankEmbeddings",
     "OpenAPIRerankEmbeddings",
-    "QianFanEmbeddings"
+    "QianFanEmbeddings",
 ]

dbgpt/rag/embedding/embeddings.py

@@ -926,16 +926,18 @@ class TongYiEmbeddings(BaseModel, Embeddings):
 
 class QianFanEmbeddings(BaseModel, Embeddings):
     """Baidu Qianfan Embeddings embedding models.
-        Embed:
-           .. code-block:: python
 
-               # embed the documents
-               vectors = embeddings.embed_documents([text1, text2, ...])
+    Embed:
+       .. code-block:: python
 
-               # embed the query
-               vectors = embeddings.embed_query(text)
+           # embed the documents
+           vectors = embeddings.embed_documents([text1, text2, ...])
+
+           # embed the query
+           vectors = embeddings.embed_query(text)
+
+    """  # noqa: E501
 
-       """  # noqa: E501
     client: Any
     chunk_size: int = 16
     endpoint: str = ""
@@ -950,7 +952,7 @@ class QianFanEmbeddings(BaseModel, Embeddings):
     """Model name
     you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
 
-    for now, we support Embedding-V1 and 
+    for now, we support Embedding-V1 and
     - Embedding-V1 （默认模型）
     - bge-large-en
     - bge-large-zh
@@ -962,7 +964,7 @@ class QianFanEmbeddings(BaseModel, Embeddings):
         default="text-embedding-v1", description="The name of the model to use."
     )
     init_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    """init kwargs for qianfan client init, such as `query_per_second` which is 
+    """init kwargs for qianfan client init, such as `query_per_second` which is
         associated with qianfan resource object to limit QPS"""
 
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
@@ -983,7 +985,10 @@ class QianFanEmbeddings(BaseModel, Embeddings):
         model_name = kwargs.get("model_name")
 
         if not qianfan_ak or not qianfan_sk or not model_name:
-            raise ValueError("API key, API secret, and model name are required to initialize QianFanEmbeddings.")
+            raise ValueError(
+                "API key, API secret, and model name are required to initialize "
+                "QianFanEmbeddings."
+            )
 
         params = {
             "model": model_name,
@@ -996,6 +1001,7 @@ class QianFanEmbeddings(BaseModel, Embeddings):
         super().__init__(**kwargs)
 
     def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a QianFan embedding model."""
         resp = self.embed_documents([text])
         return resp[0]
 
@@ -1011,7 +1017,7 @@ class QianFanEmbeddings(BaseModel, Embeddings):
                             Each embedding is represented as a list of float values.
         """
         text_in_chunks = [
-            texts[i: i + self.chunk_size]
+            texts[i : i + self.chunk_size]
             for i in range(0, len(texts), self.chunk_size)
         ]
         lst = []

setup.py

@@ -687,7 +687,7 @@ def default_requires():
         "chardet",
         "sentencepiece",
         "ollama",
-        "qianfan"
+        "qianfan",
     ]
     setup_spec.extras["default"] += setup_spec.extras["framework"]
     setup_spec.extras["default"] += setup_spec.extras["rag"]