feat(model): Support deploy rerank model (#1522)

dbgpt/model/parameter.py

@@ -255,6 +255,10 @@ class BaseEmbeddingModelParameters(BaseModelParameters):
     def build_kwargs(self, **kwargs) -> Dict:
         pass
 
+    def is_rerank_model(self) -> bool:
+        """Check if the model is a rerank model"""
+        return False
+
 
 @dataclass
 class EmbeddingModelParameters(BaseEmbeddingModelParameters):
@@ -272,6 +276,19 @@ class EmbeddingModelParameters(BaseEmbeddingModelParameters):
         },
     )
 
+    rerank: Optional[bool] = field(
+        default=False, metadata={"help": "Whether the model is a rerank model"}
+    )
+
+    max_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Max length for input sequences. Longer sequences will be "
+            "truncated. If None, max length of the model will be used, just for rerank"
+            " model now."
+        },
+    )
+
     def build_kwargs(self, **kwargs) -> Dict:
         model_kwargs, encode_kwargs = None, None
         if self.device:
@@ -280,10 +297,16 @@ class EmbeddingModelParameters(BaseEmbeddingModelParameters):
             encode_kwargs = {"normalize_embeddings": self.normalize_embeddings}
         if model_kwargs:
             kwargs["model_kwargs"] = model_kwargs
+        if self.is_rerank_model():
+            kwargs["max_length"] = self.max_length
         if encode_kwargs:
             kwargs["encode_kwargs"] = encode_kwargs
         return kwargs
 
+    def is_rerank_model(self) -> bool:
+        """Check if the model is a rerank model"""
+        return self.rerank
+
 
 @dataclass
 class ModelParameters(BaseModelParameters):
@@ -537,26 +560,35 @@ class ProxyEmbeddingParameters(BaseEmbeddingModelParameters):
         metadata={"help": "Tto support Azure OpenAI Service custom deployment names"},
     )
 
+    rerank: Optional[bool] = field(
+        default=False, metadata={"help": "Whether the model is a rerank model"}
+    )
+
     def build_kwargs(self, **kwargs) -> Dict:
         params = {
             "openai_api_base": self.proxy_server_url,
             "openai_api_key": self.proxy_api_key,
             "openai_api_type": self.proxy_api_type if self.proxy_api_type else None,
-            "openai_api_version": self.proxy_api_version
-            if self.proxy_api_version
-            else None,
+            "openai_api_version": (
+                self.proxy_api_version if self.proxy_api_version else None
+            ),
             "model": self.proxy_backend,
-            "deployment": self.proxy_deployment
-            if self.proxy_deployment
-            else self.proxy_backend,
+            "deployment": (
+                self.proxy_deployment if self.proxy_deployment else self.proxy_backend
+            ),
         }
         for k, v in kwargs:
             params[k] = v
         return params
 
+    def is_rerank_model(self) -> bool:
+        """Check if the model is a rerank model"""
+        return self.rerank
+
 
 _EMBEDDING_PARAMETER_CLASS_TO_NAME_CONFIG = {
-    ProxyEmbeddingParameters: "proxy_openai,proxy_azure,proxy_http_openapi,proxy_ollama",
+    ProxyEmbeddingParameters: "proxy_openai,proxy_azure,proxy_http_openapi,"
+    "proxy_ollama,rerank_proxy_http_openapi",
 }
 
 EMBEDDING_NAME_TO_PARAMETER_CLASS_CONFIG = {}