ollama 代码升级，使用openai协议

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

@@ -0,0 +1,60 @@
+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 _CommonOllama:
+    def _to_credential_kwargs(self, credentials: dict) -> dict:
+        """
+        Transform credentials to kwargs for model instance
+
+        :param credentials:
+        :return:
+        """
+        credentials_kwargs = {
+            "openai_api_key": "Empty",
+            "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
+            "max_retries": 1,
+        }
+
+        if "openai_api_base" in credentials and credentials["openai_api_base"]:
+            credentials["openai_api_base"] = credentials["openai_api_base"].rstrip("/")
+            credentials_kwargs["base_url"] = credentials["openai_api_base"] + "/v1"
+
+        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/ollama/ollama.py

@@ -7,7 +7,7 @@ from model_providers.core.model_runtime.model_providers.__base.model_provider im
 logger = logging.getLogger(__name__)
 
 
-class OpenAIProvider(ModelProvider):
+class OllamaProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials

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

@@ -11,7 +11,7 @@ help:
     en_US: How to integrate with Ollama
     zh_Hans: 如何集成 Ollama
   url:
-    en_US: https://docs.dify.ai/tutorials/model-configuration/ollama
+    en_US: "ollama"
 supported_model_types:
   - llm
   - text-embedding
@@ -26,73 +26,13 @@ model_credential_schema:
       en_US: Enter your model name
       zh_Hans: 输入模型名称
   credential_form_schemas:
-    - variable: base_url
+
+    - variable: openai_api_base
       label:
-        zh_Hans: 基础 URL
+        zh_Hans: API Base
-        en_US: Base URL
+        en_US: API Base
       type: text-input
-      required: true
-      placeholder:
-        zh_Hans: Ollama server 的基础 URL，例如 http://192.168.1.100:11434
-        en_US: Base url of Ollama server, e.g. http://192.168.1.100:11434
-    - variable: mode
-      show_on:
-        - variable: __model_type
-          value: llm
-      label:
-        zh_Hans: 模型类型
-        en_US: Completion mode
-      type: select
-      required: true
-      default: chat
-      placeholder:
-        zh_Hans: 选择对话类型
-        en_US: Select completion mode
-      options:
-        - value: completion
-          label:
-            en_US: Completion
-            zh_Hans: 补全
-        - value: chat
-          label:
-            en_US: Chat
-            zh_Hans: 对话
-    - variable: context_size
-      label:
-        zh_Hans: 模型上下文长度
-        en_US: Model context size
-      required: true
-      type: text-input
-      default: '4096'
-      placeholder:
-        zh_Hans: 在此输入您的模型上下文长度
-        en_US: Enter your Model context size
-    - variable: max_tokens
-      label:
-        zh_Hans: 最大 token 上限
-        en_US: Upper bound for max tokens
-      show_on:
-        - variable: __model_type
-          value: llm
-      default: '4096'
-      type: text-input
-      required: true
-    - variable: vision_support
-      label:
-        zh_Hans: 是否支持 Vision
-        en_US: Vision support
-      show_on:
-        - variable: __model_type
-          value: llm
-      default: 'false'
-      type: radio
       required: false
-      options:
+      placeholder:
-        - value: 'true'
+        zh_Hans: 在此输入您的 API Base
-          label:
+        en_US: Enter your API Base
-            en_US: "Yes"
-            zh_Hans: 是
-        - value: 'false'
-          label:
-            en_US: "No"
-            zh_Hans: 否

model-providers/tests/conftest.py

@@ -136,7 +136,7 @@ def init_server(logging_conf: dict, providers_file: str) -> None:
             yield f"http://127.0.0.1:20000"
         finally:
             print("")
-            boot.destroy()
+            # boot.destroy()
 
     except SystemExit:
 

model-providers/tests/ollama_providers_test/model_providers.yaml

@@ -0,0 +1,10 @@
+
+ollama:
+  model_credential:
+    - model: 'llama3'
+      model_type: 'llm'
+      model_credentials:
+        openai_api_base: 'http://172.21.80.1:11434'
+
+
+

model-providers/tests/ollama_providers_test/test_ollama_service.py

@@ -0,0 +1,34 @@
+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="llama3", openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/ollama/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")
+
+
+@pytest.mark.requires("openai")
+def test_embedding(init_server: str):
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-large",
+                                  openai_api_key="YOUR_API_KEY",
+                                  openai_api_base=f"{init_server}/zhipuai/v1")
+
+    text = "你好"
+
+    query_result = embeddings.embed_query(text)
+
+    logger.info("\033[1;32m" + f"embeddings: {query_result}" + "\033[0m")

model-providers/tests/openai_providers_test/model_providers.yaml

@@ -1,5 +1,10 @@
-openai:
+
-  provider_credential:
+ollama:
-    openai_api_key: 'sk-'
+  model_credential:
-    openai_organization: ''
+    - model: 'llama3'
-    openai_api_base: ''
+      model_type: 'llm'
+      model_credentials:
+        openai_api_base: 'http://172.21.80.1:11434'
+
+
+

model-providers/tests/openai_providers_test/test_ollama_service.py

@@ -6,6 +6,7 @@ import logging
 
 logger = logging.getLogger(__name__)
 
+
 @pytest.mark.requires("openai")
 def test_llm(init_server: str):
     llm = ChatOpenAI(openai_api_key="YOUR_API_KEY", openai_api_base=f"{init_server}/openai/v1")

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

@@ -0,0 +1,10 @@
+
+ollama:
+  model_credential:
+    - model: 'llama3'
+      model_type: 'llm'
+      model_credentials:
+        openai_api_base: 'http://172.21.80.1:11434'
+
+
+

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

@@ -0,0 +1,42 @@
+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="ollama", model_type=ModelType.LLM
+    )
+    provider_model_bundle_emb = provider_manager.get_provider_model_bundle(
+        provider="ollama", model_type=ModelType.TEXT_EMBEDDING
+    )
+    predefined_models = (
+        provider_model_bundle_llm.model_type_instance.predefined_models()
+    )
+
+    logger.info(f"predefined_models: {predefined_models}")