Feat: support azure openai for temporary (#101)

api/core/llm/llm_builder.py

@@ -4,9 +4,14 @@ from langchain.callbacks import CallbackManager
 from langchain.llms.fake import FakeListLLM
 
 from core.constant import llm_constant
+from core.llm.error import ProviderTokenNotInitError
+from core.llm.provider.base import BaseProvider
 from core.llm.provider.llm_provider_service import LLMProviderService
+from core.llm.streamable_azure_chat_open_ai import StreamableAzureChatOpenAI
+from core.llm.streamable_azure_open_ai import StreamableAzureOpenAI
 from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
 from core.llm.streamable_open_ai import StreamableOpenAI
+from models.provider import ProviderType
 
 
 class LLMBuilder:
@@ -31,16 +36,23 @@ class LLMBuilder:
         if model_name == 'fake':
             return FakeListLLM(responses=[])
 
+        provider = cls.get_default_provider(tenant_id)
+
         mode = cls.get_mode_by_model(model_name)
         if mode == 'chat':
-            # llm_cls = StreamableAzureChatOpenAI
-            llm_cls = StreamableChatOpenAI
+            if provider == 'openai':
+                llm_cls = StreamableChatOpenAI
+            else:
+                llm_cls = StreamableAzureChatOpenAI
         elif mode == 'completion':
-            llm_cls = StreamableOpenAI
+            if provider == 'openai':
+                llm_cls = StreamableOpenAI
+            else:
+                llm_cls = StreamableAzureOpenAI
         else:
             raise ValueError(f"model name {model_name} is not supported.")
 
-        model_credentials = cls.get_model_credentials(tenant_id, model_name)
+        model_credentials = cls.get_model_credentials(tenant_id, provider, model_name)
 
         return llm_cls(
             model_name=model_name,
@@ -86,18 +98,31 @@ class LLMBuilder:
             raise ValueError(f"model name {model_name} is not supported.")
 
     @classmethod
-    def get_model_credentials(cls, tenant_id: str, model_name: str) -> dict:
+    def get_model_credentials(cls, tenant_id: str, model_provider: str, model_name: str) -> dict:
         """
         Returns the API credentials for the given tenant_id and model_name, based on the model's provider.
         Raises an exception if the model_name is not found or if the provider is not found.
         """
         if not model_name:
             raise Exception('model name not found')
+        #
+        # if model_name not in llm_constant.models:
+        #     raise Exception('model {} not found'.format(model_name))
 
-        if model_name not in llm_constant.models:
-            raise Exception('model {} not found'.format(model_name))
-
-        model_provider = llm_constant.models[model_name]
+        # model_provider = llm_constant.models[model_name]
 
         provider_service = LLMProviderService(tenant_id=tenant_id, provider_name=model_provider)
         return provider_service.get_credentials(model_name)
+
+    @classmethod
+    def get_default_provider(cls, tenant_id: str) -> str:
+        provider = BaseProvider.get_valid_provider(tenant_id)
+        if not provider:
+            raise ProviderTokenNotInitError()
+
+        if provider.provider_type == ProviderType.SYSTEM.value:
+            provider_name = 'openai'
+        else:
+            provider_name = provider.provider_name
+
+        return provider_name

api/core/llm/provider/azure_provider.py

@@ -36,10 +36,9 @@ class AzureProvider(BaseProvider):
         """
         Returns the API credentials for Azure OpenAI as a dictionary.
         """
-        encrypted_config = self.get_provider_api_key(model_id=model_id)
-        config = json.loads(encrypted_config)
+        config = self.get_provider_api_key(model_id=model_id)
         config['openai_api_type'] = 'azure'
-        config['deployment_name'] = model_id
+        config['deployment_name'] = model_id.replace('.', '')
         return config
 
     def get_provider_name(self):
@@ -51,12 +50,11 @@ class AzureProvider(BaseProvider):
         """
         try:
             config = self.get_provider_api_key()
-            config = json.loads(config)
         except:
             config = {
                 'openai_api_type': 'azure',
                 'openai_api_version': '2023-03-15-preview',
-                'openai_api_base': 'https://foo.microsoft.com/bar',
+                'openai_api_base': 'https://<your-domain-prefix>.openai.azure.com/',
                 'openai_api_key': ''
             }
 
@@ -65,7 +63,7 @@ class AzureProvider(BaseProvider):
                 config = {
                     'openai_api_type': 'azure',
                     'openai_api_version': '2023-03-15-preview',
-                    'openai_api_base': 'https://foo.microsoft.com/bar',
+                    'openai_api_base': 'https://<your-domain-prefix>.openai.azure.com/',
                     'openai_api_key': ''
                 }
 

api/core/llm/provider/base.py

@@ -14,7 +14,7 @@ class BaseProvider(ABC):
     def __init__(self, tenant_id: str):
         self.tenant_id = tenant_id
 
-    def get_provider_api_key(self, model_id: Optional[str] = None, prefer_custom: bool = True) -> str:
+    def get_provider_api_key(self, model_id: Optional[str] = None, prefer_custom: bool = True) -> Union[str | dict]:
         """
         Returns the decrypted API key for the given tenant_id and provider_name.
         If the provider is of type SYSTEM and the quota is exceeded, raises a QuotaExceededError.
@@ -43,23 +43,35 @@ class BaseProvider(ABC):
         Returns the Provider instance for the given tenant_id and provider_name.
         If both CUSTOM and System providers exist, the preferred provider will be returned based on the prefer_custom flag.
         """
-        providers = db.session.query(Provider).filter(
-            Provider.tenant_id == self.tenant_id,
-            Provider.provider_name == self.get_provider_name().value
-        ).order_by(Provider.provider_type.desc() if prefer_custom else Provider.provider_type).all()
+        return BaseProvider.get_valid_provider(self.tenant_id, self.get_provider_name().value, prefer_custom)
+
+    @classmethod
+    def get_valid_provider(cls, tenant_id: str, provider_name: str = None, prefer_custom: bool = False) -> Optional[Provider]:
+        """
+        Returns the Provider instance for the given tenant_id and provider_name.
+        If both CUSTOM and System providers exist, the preferred provider will be returned based on the prefer_custom flag.
+        """
+        query = db.session.query(Provider).filter(
+            Provider.tenant_id == tenant_id
+        )
+
+        if provider_name:
+            query = query.filter(Provider.provider_name == provider_name)
+
+        providers = query.order_by(Provider.provider_type.desc() if prefer_custom else Provider.provider_type).all()
 
         custom_provider = None
         system_provider = None
 
         for provider in providers:
-            if provider.provider_type == ProviderType.CUSTOM.value:
+            if provider.provider_type == ProviderType.CUSTOM.value and provider.is_valid and provider.encrypted_config:
                 custom_provider = provider
-            elif provider.provider_type == ProviderType.SYSTEM.value:
+            elif provider.provider_type == ProviderType.SYSTEM.value and provider.is_valid:
                 system_provider = provider
 
-        if custom_provider and custom_provider.is_valid and custom_provider.encrypted_config:
+        if custom_provider:
             return custom_provider
-        elif system_provider and system_provider.is_valid:
+        elif system_provider:
             return system_provider
         else:
             return None
@@ -80,7 +92,7 @@ class BaseProvider(ABC):
         try:
             config = self.get_provider_api_key()
         except:
-            config = 'THIS-IS-A-MOCK-TOKEN'
+            config = ''
 
         if obfuscated:
             return self.obfuscated_token(config)

api/core/llm/streamable_azure_chat_open_ai.py

@@ -1,12 +1,50 @@
-import requests
 from langchain.schema import BaseMessage, ChatResult, LLMResult
 from langchain.chat_models import AzureChatOpenAI
-from typing import Optional, List
+from typing import Optional, List, Dict, Any
+
+from pydantic import root_validator
 
 from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
 
 
 class StreamableAzureChatOpenAI(AzureChatOpenAI):
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        try:
+            import openai
+        except ImportError:
+            raise ValueError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+        try:
+            values["client"] = openai.ChatCompletion
+        except AttributeError:
+            raise ValueError(
+                "`openai` has no `ChatCompletion` attribute, this is likely "
+                "due to an old version of the openai package. Try upgrading it "
+                "with `pip install --upgrade openai`."
+            )
+        if values["n"] < 1:
+            raise ValueError("n must be at least 1.")
+        if values["n"] > 1 and values["streaming"]:
+            raise ValueError("n must be 1 when streaming.")
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling OpenAI API."""
+        return {
+            **super()._default_params,
+            "engine": self.deployment_name,
+            "api_type": self.openai_api_type,
+            "api_base": self.openai_api_base,
+            "api_version": self.openai_api_version,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }
+
     def get_messages_tokens(self, messages: List[BaseMessage]) -> int:
         """Get the number of tokens in a list of messages.
 

api/core/llm/streamable_azure_open_ai.py

@@ -0,0 +1,64 @@
+import os
+
+from langchain.llms import AzureOpenAI
+from langchain.schema import LLMResult
+from typing import Optional, List, Dict, Mapping, Any
+
+from pydantic import root_validator
+
+from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
+
+
+class StreamableAzureOpenAI(AzureOpenAI):
+    openai_api_type: str = "azure"
+    openai_api_version: str = ""
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        try:
+            import openai
+
+            values["client"] = openai.Completion
+        except ImportError:
+            raise ValueError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+        if values["streaming"] and values["n"] > 1:
+            raise ValueError("Cannot stream results when n > 1.")
+        if values["streaming"] and values["best_of"] > 1:
+            raise ValueError("Cannot stream results when best_of > 1.")
+        return values
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        return {**super()._invocation_params, **{
+            "api_type": self.openai_api_type,
+            "api_base": self.openai_api_base,
+            "api_version": self.openai_api_version,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }}
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {**super()._identifying_params, **{
+            "api_type": self.openai_api_type,
+            "api_base": self.openai_api_base,
+            "api_version": self.openai_api_version,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }}
+
+    @handle_llm_exceptions
+    def generate(
+            self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        return super().generate(prompts, stop)
+
+    @handle_llm_exceptions_async
+    async def agenerate(
+            self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        return await super().agenerate(prompts, stop)

api/core/llm/streamable_chat_open_ai.py

@@ -1,12 +1,52 @@
+import os
+
 from langchain.schema import BaseMessage, ChatResult, LLMResult
 from langchain.chat_models import ChatOpenAI
-from typing import Optional, List
+from typing import Optional, List, Dict, Any
+
+from pydantic import root_validator
 
 from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
 
 
 class StreamableChatOpenAI(ChatOpenAI):
 
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        try:
+            import openai
+        except ImportError:
+            raise ValueError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+        try:
+            values["client"] = openai.ChatCompletion
+        except AttributeError:
+            raise ValueError(
+                "`openai` has no `ChatCompletion` attribute, this is likely "
+                "due to an old version of the openai package. Try upgrading it "
+                "with `pip install --upgrade openai`."
+            )
+        if values["n"] < 1:
+            raise ValueError("n must be at least 1.")
+        if values["n"] > 1 and values["streaming"]:
+            raise ValueError("n must be 1 when streaming.")
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling OpenAI API."""
+        return {
+            **super()._default_params,
+            "api_type": 'openai',
+            "api_base": os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+            "api_version": None,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }
+
     def get_messages_tokens(self, messages: List[BaseMessage]) -> int:
         """Get the number of tokens in a list of messages.
 

api/core/llm/streamable_open_ai.py

@@ -1,12 +1,54 @@
+import os
+
 from langchain.schema import LLMResult
-from typing import Optional, List
+from typing import Optional, List, Dict, Any, Mapping
 from langchain import OpenAI
+from pydantic import root_validator
 
 from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
 
 
 class StreamableOpenAI(OpenAI):
 
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        try:
+            import openai
+
+            values["client"] = openai.Completion
+        except ImportError:
+            raise ValueError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+        if values["streaming"] and values["n"] > 1:
+            raise ValueError("Cannot stream results when n > 1.")
+        if values["streaming"] and values["best_of"] > 1:
+            raise ValueError("Cannot stream results when best_of > 1.")
+        return values
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        return {**super()._invocation_params, **{
+            "api_type": 'openai',
+            "api_base": os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+            "api_version": None,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }}
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {**super()._identifying_params, **{
+            "api_type": 'openai',
+            "api_base": os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+            "api_version": None,
+            "api_key": self.openai_api_key,
+            "organization": self.openai_organization if self.openai_organization else None,
+        }}
+
+
     @handle_llm_exceptions
     def generate(
             self, prompts: List[str], stop: Optional[List[str]] = None