支持deepseek客户端

model-providers/model_providers/core/model_runtime/model_providers/deepseek/_common.py

@@ -0,0 +1,59 @@
+from typing import Dict, List, Type
+
+import openai
+from httpx import Timeout
+
+from model_providers.core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+
+
+class _CommonDeepseek:
+    def _to_credential_kwargs(self, credentials: dict) -> dict:
+        """
+        Transform credentials to kwargs for model instance
+
+        :param credentials:
+        :return:
+        """
+        credentials_kwargs = {
+            "api_key": credentials["api_key"],
+            "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
+            "max_retries": 1,
+        }
+
+        if "base_url" in credentials and credentials["base_url"]:
+            credentials_kwargs["base_url"] = credentials["base_url"]
+
+        return credentials_kwargs
+
+    @property
+    def _invoke_error_mapping(self) -> Dict[Type[InvokeError], List[Type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
+            InvokeServerUnavailableError: [openai.InternalServerError],
+            InvokeRateLimitError: [openai.RateLimitError],
+            InvokeAuthorizationError: [
+                openai.AuthenticationError,
+                openai.PermissionDeniedError,
+            ],
+            InvokeBadRequestError: [
+                openai.BadRequestError,
+                openai.NotFoundError,
+                openai.UnprocessableEntityError,
+                openai.APIError,
+            ],
+        }

model-providers/model_providers/core/model_runtime/model_providers/deepseek/deepseek.py

@@ -0,0 +1,18 @@
+import logging
+
+from model_providers.core.model_runtime.model_providers.__base.model_provider import (
+    ModelProvider,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class DeepseekProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        pass

model-providers/model_providers/core/model_runtime/model_providers/deepseek/deepseek.yaml

@@ -0,0 +1,44 @@
+provider: deepseek
+label:
+  en_US: Deepseek
+icon_large:
+  en_US: icon_l_en.svg
+icon_small:
+  en_US: icon_s_en.svg
+background: "#F9FAFB"
+help:
+  title:
+    en_US: How to integrate with Deepseek
+    zh_Hans: 如何集成 Deepseek
+  url:
+    en_US: "deepseek"
+supported_model_types:
+  - llm
+configurate_methods:
+  - customizable-model
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+    placeholder:
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: base_url
+      label:
+        zh_Hans: API Base
+        en_US: API Base
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 API Base
+        en_US: Enter your API Base

model-providers/model_providers/core/model_runtime/model_providers/ollama/_common.py

@@ -27,9 +27,8 @@ class _CommonOllama:
             "max_retries": 1,
         }
 
-        if "openai_api_base" in credentials and credentials["openai_api_base"]:
+        if "base_url" in credentials and credentials["base_url"]:
-            credentials["openai_api_base"] = credentials["openai_api_base"].rstrip("/")
+            credentials_kwargs["base_url"] = credentials["base_url"]
-            credentials_kwargs["base_url"] = credentials["openai_api_base"] + "/v1"
 
         return credentials_kwargs
 

model-providers/model_providers/core/model_runtime/model_providers/ollama/llm/llm.py

@@ -1,6 +1,7 @@
 import logging
 from collections.abc import Generator
 from typing import List, Optional, Union, cast
+from decimal import Decimal
 
 import tiktoken
 from openai import OpenAI, Stream
@@ -39,7 +40,7 @@ from model_providers.core.model_runtime.entities.model_entities import (
     FetchFrom,
     I18nObject,
     ModelType,
-    PriceConfig,
+    PriceConfig, ModelFeature, ModelPropertyKey, DefaultParameterName, ParameterRule, ParameterType,
 )
 from model_providers.core.model_runtime.errors.validate import (
     CredentialsValidateFailedError,
@@ -1116,47 +1117,223 @@ class OllamaLargeLanguageModel(_CommonOllama, LargeLanguageModel):
         return num_tokens
 
     def get_customizable_model_schema(
-        self, model: str, credentials: dict
+            self, model: str, credentials: dict
     ) -> AIModelEntity:
         """
-        OpenAI supports fine-tuning of their models. This method returns the schema of the base model
+        Get customizable model schema.
-        but renamed to the fine-tuned model name.
 
         :param model: model name
         :param credentials: credentials
 
         :return: model schema
         """
-        if not model.startswith("ft:"):
+        extras = {}
-            base_model = model
-        else:
-            # get base_model
-            base_model = model.split(":")[1]
 
-        # get model schema
+        if "vision_support" in credentials and credentials["vision_support"] == "true":
-        models = self.predefined_models()
+            extras["features"] = [ModelFeature.VISION]
-        model_map = {model.model: model for model in models}
-        if base_model not in model_map:
-            raise ValueError(f"Base model {base_model} not found")
-
-        base_model_schema = model_map[base_model]
-
-        base_model_schema_features = base_model_schema.features or []
-        base_model_schema_model_properties = base_model_schema.model_properties or {}
-        base_model_schema_parameters_rules = base_model_schema.parameter_rules or []
 
         entity = AIModelEntity(
             model=model,
             label=I18nObject(zh_Hans=model, en_US=model),
             model_type=ModelType.LLM,
-            features=[feature for feature in base_model_schema_features],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                key: property
+                ModelPropertyKey.MODE: credentials.get("mode"),
-                for key, property in base_model_schema_model_properties.items()
+                ModelPropertyKey.CONTEXT_SIZE: int(
+                    credentials.get("context_size", 4096)
+                ),
             },
-            parameter_rules=[rule for rule in base_model_schema_parameters_rules],
+            parameter_rules=[
-            pricing=base_model_schema.pricing,
+                ParameterRule(
+                    name=DefaultParameterName.TEMPERATURE.value,
+                    use_template=DefaultParameterName.TEMPERATURE.value,
+                    label=I18nObject(en_US="Temperature"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="The temperature of the model. "
+                              "Increasing the temperature will make the model answer "
+                              "more creatively. (Default: 0.8)"
+                    ),
+                    default=0.8,
+                    min=0,
+                    max=2,
+                ),
+                ParameterRule(
+                    name=DefaultParameterName.TOP_P.value,
+                    use_template=DefaultParameterName.TOP_P.value,
+                    label=I18nObject(en_US="Top P"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="Works together with top-k. A higher value (e.g., 0.95) will lead to "
+                              "more diverse text, while a lower value (e.g., 0.5) will generate more "
+                              "focused and conservative text. (Default: 0.9)"
+                    ),
+                    default=0.9,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name="top_k",
+                    label=I18nObject(en_US="Top K"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Reduces the probability of generating nonsense. "
+                              "A higher value (e.g. 100) will give more diverse answers, "
+                              "while a lower value (e.g. 10) will be more conservative. (Default: 40)"
+                    ),
+                    default=40,
+                    min=1,
+                    max=100,
+                ),
+                ParameterRule(
+                    name="repeat_penalty",
+                    label=I18nObject(en_US="Repeat Penalty"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="Sets how strongly to penalize repetitions. "
+                              "A higher value (e.g., 1.5) will penalize repetitions more strongly, "
+                              "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"
+                    ),
+                    default=1.1,
+                    min=-2,
+                    max=2,
+                ),
+                ParameterRule(
+                    name="num_predict",
+                    use_template="max_tokens",
+                    label=I18nObject(en_US="Num Predict"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Maximum number of tokens to predict when generating text. "
+                              "(Default: 128, -1 = infinite generation, -2 = fill context)"
+                    ),
+                    default=128,
+                    min=-2,
+                    max=int(credentials.get("max_tokens", 4096)),
+                ),
+                ParameterRule(
+                    name="mirostat",
+                    label=I18nObject(en_US="Mirostat sampling"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Enable Mirostat sampling for controlling perplexity. "
+                              "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"
+                    ),
+                    default=0,
+                    min=0,
+                    max=2,
+                ),
+                ParameterRule(
+                    name="mirostat_eta",
+                    label=I18nObject(en_US="Mirostat Eta"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="Influences how quickly the algorithm responds to feedback from "
+                              "the generated text. A lower learning rate will result in slower adjustments, "
+                              "while a higher learning rate will make the algorithm more responsive. "
+                              "(Default: 0.1)"
+                    ),
+                    default=0.1,
+                    precision=1,
+                ),
+                ParameterRule(
+                    name="mirostat_tau",
+                    label=I18nObject(en_US="Mirostat Tau"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="Controls the balance between coherence and diversity of the output. "
+                              "A lower value will result in more focused and coherent text. (Default: 5.0)"
+                    ),
+                    default=5.0,
+                    precision=1,
+                ),
+                ParameterRule(
+                    name="num_ctx",
+                    label=I18nObject(en_US="Size of context window"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Sets the size of the context window used to generate the next token. "
+                              "(Default: 2048)"
+                    ),
+                    default=2048,
+                    min=1,
+                ),
+                ParameterRule(
+                    name="num_gpu",
+                    label=I18nObject(en_US="Num GPU"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="The number of layers to send to the GPU(s). "
+                              "On macOS it defaults to 1 to enable metal support, 0 to disable."
+                    ),
+                    default=1,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name="num_thread",
+                    label=I18nObject(en_US="Num Thread"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Sets the number of threads to use during computation. "
+                              "By default, Ollama will detect this for optimal performance. "
+                              "It is recommended to set this value to the number of physical CPU cores "
+                              "your system has (as opposed to the logical number of cores)."
+                    ),
+                    min=1,
+                ),
+                ParameterRule(
+                    name="repeat_last_n",
+                    label=I18nObject(en_US="Repeat last N"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Sets how far back for the model to look back to prevent repetition. "
+                              "(Default: 64, 0 = disabled, -1 = num_ctx)"
+                    ),
+                    default=64,
+                    min=-1,
+                ),
+                ParameterRule(
+                    name="tfs_z",
+                    label=I18nObject(en_US="TFS Z"),
+                    type=ParameterType.FLOAT,
+                    help=I18nObject(
+                        en_US="Tail free sampling is used to reduce the impact of less probable tokens "
+                              "from the output. A higher value (e.g., 2.0) will reduce the impact more, "
+                              "while a value of 1.0 disables this setting. (default: 1)"
+                    ),
+                    default=1,
+                    precision=1,
+                ),
+                ParameterRule(
+                    name="seed",
+                    label=I18nObject(en_US="Seed"),
+                    type=ParameterType.INT,
+                    help=I18nObject(
+                        en_US="Sets the random number seed to use for generation. Setting this to "
+                              "a specific number will make the model generate the same text for "
+                              "the same prompt. (Default: 0)"
+                    ),
+                    default=0,
+                ),
+                ParameterRule(
+                    name="format",
+                    label=I18nObject(en_US="Format"),
+                    type=ParameterType.STRING,
+                    help=I18nObject(
+                        en_US="the format to return a response in."
+                              " Currently the only accepted value is json."
+                    ),
+                    options=["json"],
+                ),
+            ],
+            pricing=PriceConfig(
+                input=Decimal(credentials.get("input_price", 0)),
+                output=Decimal(credentials.get("output_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
+            **extras,
         )
 
         return entity

model-providers/model_providers/core/model_runtime/model_providers/ollama/ollama.yaml

@@ -27,7 +27,7 @@ model_credential_schema:
       zh_Hans: 输入模型名称
   credential_form_schemas:
 
-    - variable: openai_api_base
+    - variable: base_url
       label:
         zh_Hans: API Base
         en_US: API Base

model-providers/tests/deepseek_providers_test/model_providers.yaml

@@ -0,0 +1,11 @@
+
+deepseek:
+  model_credential:
+    - model: 'deepseek-chat'
+      model_type: 'llm'
+      model_credentials:
+        base_url: 'https://api.deepseek.com'
+        api_key: 'sk-dcb625fcbc1e497d80b7b9493b51d758'
+
+
+

model-providers/tests/deepseek_providers_test/test_deepseek_service.py

@@ -0,0 +1,21 @@
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+import pytest
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.requires("openai")
+def test_llm(init_server: str):
+    llm = ChatOpenAI(model_name="deepseek-chat", openai_api_key="sk-dcb625fcbc1e497d80b7b9493b51d758", openai_api_base=f"{init_server}/deepseek/v1")
+    template = """Question: {question}
+    
+    Answer: Let's think step by step."""
+
+    prompt = PromptTemplate.from_template(template)
+
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    responses = llm_chain.run("你好")
+    logger.info("\033[1;32m" + f"llm_chain: {responses}" + "\033[0m")

model-providers/tests/ollama_providers_test/model_providers.yaml

@@ -4,7 +4,7 @@ ollama:
     - model: 'llama3'
       model_type: 'llm'
       model_credentials:
-        openai_api_base: 'http://172.21.80.1:11434'
+        base_url: 'http://172.21.80.1:11434/v1'
 
 
 

model-providers/tests/unit_tests/deepseek/model_providers.yaml

@@ -0,0 +1,11 @@
+
+deepseek:
+  model_credential:
+    - model: 'deepseek-chat'
+      model_type: 'llm'
+      model_credentials:
+        base_url: 'https://api.deepseek.com'
+        api_key: 'sk-dcb625fcbc1e497d80b7b9493b51d758'
+
+
+

model-providers/tests/unit_tests/deepseek/test_provider_manager_models.py

@@ -0,0 +1,39 @@
+import asyncio
+import logging
+
+import pytest
+from omegaconf import OmegaConf
+
+from model_providers import BootstrapWebBuilder, _to_custom_provide_configuration
+from model_providers.core.model_manager import ModelManager
+from model_providers.core.model_runtime.entities.model_entities import ModelType
+from model_providers.core.provider_manager import ProviderManager
+
+logger = logging.getLogger(__name__)
+
+
+def test_ollama_provider_manager_models(logging_conf: dict, providers_file: str) -> None:
+    logging.config.dictConfig(logging_conf)  # type: ignore
+    # 读取配置文件
+    cfg = OmegaConf.load(
+        providers_file
+    )
+    # 转换配置文件
+    (
+        provider_name_to_provider_records_dict,
+        provider_name_to_provider_model_records_dict,
+    ) = _to_custom_provide_configuration(cfg)
+    # 创建模型管理器
+    provider_manager = ProviderManager(
+        provider_name_to_provider_records_dict=provider_name_to_provider_records_dict,
+        provider_name_to_provider_model_records_dict=provider_name_to_provider_model_records_dict,
+    )
+
+    provider_model_bundle_llm = provider_manager.get_provider_model_bundle(
+        provider="deepseek", model_type=ModelType.LLM
+    )
+    predefined_models = (
+        provider_model_bundle_llm.model_type_instance.predefined_models()
+    )
+
+    logger.info(f"predefined_models: {predefined_models}")

model-providers/tests/unit_tests/ollama/model_providers.yaml

@@ -4,7 +4,7 @@ ollama:
     - model: 'llama3'
       model_type: 'llm'
       model_credentials:
-        openai_api_base: 'http://172.21.80.1:11434'
+        base_url: 'http://172.21.80.1:11434/v1'