Add Azure AI Studio as provider (#7549)

Co-authored-by: Hélio Lúcio <canais.hlucio@voegol.com.br>

api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.py

@@ -0,0 +1,17 @@
+import logging
+
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class AzureAIStudioProvider(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

api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.yaml

@@ -0,0 +1,65 @@
+provider: azure_ai_studio
+label:
+  zh_Hans: Azure AI Studio
+  en_US: Azure AI Studio
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+description:
+  en_US: Azure AI Studio
+  zh_Hans: Azure AI Studio
+background: "#93c5fd"
+help:
+  title:
+    en_US: How to deploy customized model on Azure AI Studio
+    zh_Hans: 如何在Azure AI Studio上的私有化部署的模型
+  url:
+    en_US: https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models
+    zh_Hans: https://learn.microsoft.com/zh-cn/azure/ai-studio/how-to/deploy-models
+supported_model_types:
+  - llm
+  - rerank
+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: endpoint
+      label:
+        en_US: Azure AI Studio Endpoint
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 请输入你的Azure AI Studio推理端点
+        en_US: 'Enter your API Endpoint, eg: https://example.com'
+    - variable: api_key
+      required: true
+      label:
+        en_US: API Key
+        zh_Hans: API Key
+      type: secret-input
+      placeholder:
+        en_US: Enter your Azure AI Studio API Key
+        zh_Hans: 在此输入您的 Azure AI Studio API Key
+      show_on:
+        - variable: __model_type
+          value: llm
+    - variable: jwt_token
+      required: true
+      label:
+        en_US: JWT Token
+        zh_Hans: JWT令牌
+      type: secret-input
+      placeholder:
+        en_US: Enter your Azure AI Studio JWT Token
+        zh_Hans: 在此输入您的 Azure AI Studio 推理 API Key
+      show_on:
+        - variable: __model_type
+          value: rerank

api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py

@@ -0,0 +1,334 @@
+import logging
+from collections.abc import Generator
+from typing import Any, Optional, Union
+
+from azure.ai.inference import ChatCompletionsClient
+from azure.ai.inference.models import StreamingChatCompletionsUpdate
+from azure.core.credentials import AzureKeyCredential
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    DecodeError,
+    DeserializationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceModifiedError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    SerializationError,
+    ServiceRequestError,
+    ServiceResponseError,
+)
+
+from core.model_runtime.callbacks.base_callback import Callback
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    I18nObject,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+)
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+
+logger = logging.getLogger(__name__)
+
+
+class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
+    """
+    Model class for Azure AI Studio large language model.
+    """
+
+    client: Any = None
+
+    from azure.ai.inference.models import StreamingChatCompletionsUpdate
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        """
+        Invoke large language model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+
+        if not self.client:
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("api_key")
+            self.client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
+
+        messages = [{"role": msg.role.value, "content": msg.content} for msg in prompt_messages]
+
+        payload = {
+            "messages": messages,
+            "max_tokens": model_parameters.get("max_tokens", 4096),
+            "temperature": model_parameters.get("temperature", 0),
+            "top_p": model_parameters.get("top_p", 1),
+            "stream": stream,
+        }
+
+        if stop:
+            payload["stop"] = stop
+
+        if tools:
+            payload["tools"] = [tool.model_dump() for tool in tools]
+
+        try:
+            response = self.client.complete(**payload)
+
+            if stream:
+                return self._handle_stream_response(response, model, prompt_messages)
+            else:
+                return self._handle_non_stream_response(response, model, prompt_messages, credentials)
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+    def _handle_stream_response(self, response, model: str, prompt_messages: list[PromptMessage]) -> Generator:
+        for chunk in response:
+            if isinstance(chunk, StreamingChatCompletionsUpdate):
+                if chunk.choices:
+                    delta = chunk.choices[0].delta
+                    if delta.content:
+                        yield LLMResultChunk(
+                            model=model,
+                            prompt_messages=prompt_messages,
+                            delta=LLMResultChunkDelta(
+                                index=0,
+                                message=AssistantPromptMessage(content=delta.content, tool_calls=[]),
+                            ),
+                        )
+
+    def _handle_non_stream_response(
+        self, response, model: str, prompt_messages: list[PromptMessage], credentials: dict
+    ) -> LLMResult:
+        assistant_text = response.choices[0].message.content
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text)
+        usage = self._calc_response_usage(
+            model, credentials, response.usage.prompt_tokens, response.usage.completion_tokens
+        )
+        result = LLMResult(model=model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage)
+
+        if hasattr(response, "system_fingerprint"):
+            result.system_fingerprint = response.system_fingerprint
+
+        return result
+
+    def _invoke_result_generator(
+        self,
+        model: str,
+        result: Generator,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Generator:
+        """
+        Invoke result generator
+
+        :param result: result generator
+        :return: result generator
+        """
+        callbacks = callbacks or []
+        prompt_message = AssistantPromptMessage(content="")
+        usage = None
+        system_fingerprint = None
+        real_model = model
+
+        try:
+            for chunk in result:
+                if isinstance(chunk, dict):
+                    content = chunk["choices"][0]["message"]["content"]
+                    usage = chunk["usage"]
+                    chunk = LLMResultChunk(
+                        model=model,
+                        prompt_messages=prompt_messages,
+                        delta=LLMResultChunkDelta(
+                            index=0,
+                            message=AssistantPromptMessage(content=content, tool_calls=[]),
+                        ),
+                        system_fingerprint=chunk.get("system_fingerprint"),
+                    )
+
+                yield chunk
+
+                self._trigger_new_chunk_callbacks(
+                    chunk=chunk,
+                    model=model,
+                    credentials=credentials,
+                    prompt_messages=prompt_messages,
+                    model_parameters=model_parameters,
+                    tools=tools,
+                    stop=stop,
+                    stream=stream,
+                    user=user,
+                    callbacks=callbacks,
+                )
+
+                prompt_message.content += chunk.delta.message.content
+                real_model = chunk.model
+                if hasattr(chunk.delta, "usage"):
+                    usage = chunk.delta.usage
+
+                if chunk.system_fingerprint:
+                    system_fingerprint = chunk.system_fingerprint
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+        self._trigger_after_invoke_callbacks(
+            model=model,
+            result=LLMResult(
+                model=real_model,
+                prompt_messages=prompt_messages,
+                message=prompt_message,
+                usage=usage if usage else LLMUsage.empty_usage(),
+                system_fingerprint=system_fingerprint,
+            ),
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
+            callbacks=callbacks,
+        )
+
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:
+        """
+        # Implement token counting logic here
+        # Might need to use a tokenizer specific to the Azure AI Studio model
+        return 0
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("api_key")
+            client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
+            client.get_model_info()
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @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: [
+                ServiceRequestError,
+            ],
+            InvokeServerUnavailableError: [
+                ServiceResponseError,
+            ],
+            InvokeAuthorizationError: [
+                ClientAuthenticationError,
+            ],
+            InvokeBadRequestError: [
+                HttpResponseError,
+                DecodeError,
+                ResourceExistsError,
+                ResourceNotFoundError,
+                ResourceModifiedError,
+                ResourceNotModifiedError,
+                SerializationError,
+                DeserializationError,
+            ],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        Used to define customizable model schema
+        """
+        rules = [
+            ParameterRule(
+                name="temperature",
+                type=ParameterType.FLOAT,
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
+            ),
+            ParameterRule(
+                name="top_p",
+                type=ParameterType.FLOAT,
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
+            ),
+            ParameterRule(
+                name="max_tokens",
+                type=ParameterType.INT,
+                use_template="max_tokens",
+                min=1,
+                default=512,
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
+        ]
+
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.LLM,
+            features=[],
+            model_properties={},
+            parameter_rules=rules,
+        )
+
+        return entity

api/core/model_runtime/model_providers/azure_ai_studio/rerank/rerank.py

@@ -0,0 +1,164 @@
+import json
+import logging
+import os
+import ssl
+import urllib.request
+from typing import Optional
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+logger = logging.getLogger(__name__)
+
+
+class AzureRerankModel(RerankModel):
+    """
+    Model class for Azure AI Studio rerank model.
+    """
+
+    def _allow_self_signed_https(self, allowed):
+        # bypass the server certificate verification on client side
+        if allowed and not os.environ.get("PYTHONHTTPSVERIFY", "") and getattr(ssl, "_create_unverified_context", None):
+            ssl._create_default_https_context = ssl._create_unverified_context
+
+    def _azure_rerank(self, query_input: str, docs: list[str], endpoint: str, api_key: str):
+        #   self._allow_self_signed_https(True)  # Enable if using self-signed certificate
+
+        data = {"inputs": query_input, "docs": docs}
+
+        body = json.dumps(data).encode("utf-8")
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+
+        req = urllib.request.Request(endpoint, body, headers)
+
+        try:
+            with urllib.request.urlopen(req) as response:
+                result = response.read()
+                return json.loads(result)
+        except urllib.error.HTTPError as error:
+            logger.error(f"The request failed with status code: {error.code}")
+            logger.error(error.info())
+            logger.error(error.read().decode("utf8", "ignore"))
+            raise
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n
+        :param user: unique user id
+        :return: rerank result
+        """
+        try:
+            if len(docs) == 0:
+                return RerankResult(model=model, docs=[])
+
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("jwt_token")
+
+            if not endpoint or not api_key:
+                raise ValueError("Azure endpoint and API key must be provided in credentials")
+
+            result = self._azure_rerank(query, docs, endpoint, api_key)
+            logger.info(f"Azure rerank result: {result}")
+
+            rerank_documents = []
+            for idx, (doc, score_dict) in enumerate(zip(docs, result)):
+                score = score_dict["score"]
+                rerank_document = RerankDocument(index=idx, text=doc, score=score)
+
+                if score_threshold is None or score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            rerank_documents.sort(key=lambda x: x.score, reverse=True)
+
+            if top_n:
+                rerank_documents = rerank_documents[:top_n]
+
+            return RerankResult(model=model, docs=rerank_documents)
+
+        except Exception as e:
+            logger.exception(f"Exception in Azure rerank: {e}")
+            raise
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @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: [urllib.error.URLError],
+            InvokeServerUnavailableError: [urllib.error.HTTPError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError, json.JSONDecodeError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.RERANK,
+            model_properties={},
+            parameter_rules=[],
+        )
+
+        return entity

api/poetry.lock

@@ -551,6 +551,69 @@ files = [
 [package.dependencies]
 cryptography = "*"
 
+[[package]]
+name = "azure-ai-inference"
+version = "1.0.0b3"
+description = "Microsoft Azure Ai Inference Client Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "azure-ai-inference-1.0.0b3.tar.gz", hash = "sha256:1e99dc74c3b335a457500311bbbadb348f54dc4c12252a93cb8ab78d6d217ff0"},
+    {file = "azure_ai_inference-1.0.0b3-py3-none-any.whl", hash = "sha256:6734ca7334c809a170beb767f1f1455724ab3f006cb60045e42a833c0e764403"},
+]
+
+[package.dependencies]
+azure-core = ">=1.30.0"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.6.0"
+
+[[package]]
+name = "azure-ai-ml"
+version = "1.19.0"
+description = "Microsoft Azure Machine Learning Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "azure-ai-ml-1.19.0.tar.gz", hash = "sha256:94bb1afbb0497e539ae75455fc4a51b6942b5b68b3a275727ecce6ceb250eff9"},
+    {file = "azure_ai_ml-1.19.0-py3-none-any.whl", hash = "sha256:f0385af06efbeae1f83113613e45343508d1288fd2f05857619e7c7d4d4f5302"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1"
+azure-core = ">=1.23.0"
+azure-mgmt-core = ">=1.3.0"
+azure-storage-blob = ">=12.10.0"
+azure-storage-file-datalake = ">=12.2.0"
+azure-storage-file-share = "*"
+colorama = "*"
+isodate = "*"
+jsonschema = ">=4.0.0"
+marshmallow = ">=3.5"
+msrest = ">=0.6.18"
+opencensus-ext-azure = "*"
+opencensus-ext-logging = "*"
+pydash = ">=6.0.0"
+pyjwt = "*"
+pyyaml = ">=5.1.0"
+strictyaml = "*"
+tqdm = "*"
+typing-extensions = "*"
+
+[package.extras]
+designer = ["mldesigner"]
+mount = ["azureml-dataprep-rslex (>=2.22.0)"]
+
+[[package]]
+name = "azure-common"
+version = "1.1.28"
+description = "Microsoft Azure Client Library for Python (Common)"
+optional = false
+python-versions = "*"
+files = [
+    {file = "azure-common-1.1.28.zip", hash = "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3"},
+    {file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
+]
+
 [[package]]
 name = "azure-core"
 version = "1.30.2"
@@ -587,6 +650,20 @@ cryptography = ">=2.5"
 msal = ">=1.24.0"
 msal-extensions = ">=0.3.0"
 
+[[package]]
+name = "azure-mgmt-core"
+version = "1.4.0"
+description = "Microsoft Azure Management Core Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "azure-mgmt-core-1.4.0.zip", hash = "sha256:d195208340094f98e5a6661b781cde6f6a051e79ce317caabd8ff97030a9b3ae"},
+    {file = "azure_mgmt_core-1.4.0-py3-none-any.whl", hash = "sha256:81071675f186a585555ef01816f2774d49c1c9024cb76e5720c3c0f6b337bb7d"},
+]
+
+[package.dependencies]
+azure-core = ">=1.26.2,<2.0.0"
+
 [[package]]
 name = "azure-storage-blob"
 version = "12.13.0"
@@ -603,6 +680,42 @@ azure-core = ">=1.23.1,<2.0.0"
 cryptography = ">=2.1.4"
 msrest = ">=0.6.21"
 
+[[package]]
+name = "azure-storage-file-datalake"
+version = "12.8.0"
+description = "Microsoft Azure File DataLake Storage Client Library for Python"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "azure-storage-file-datalake-12.8.0.zip", hash = "sha256:12e6306e5efb5ca28e0ccd9fa79a2c61acd589866d6109fe5601b18509da92f4"},
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+
+[package.dependencies]
+azure-core = ">=1.23.1,<2.0.0"
+azure-storage-blob = ">=12.13.0,<13.0.0"
+msrest = ">=0.6.21"
+
+[[package]]
+name = "azure-storage-file-share"
+version = "12.17.0"
+description = "Microsoft Azure Azure File Share Storage Client Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+cryptography = ">=2.1.4"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aio = ["azure-core[aio] (>=1.28.0)"]
+
 [[package]]
 name = "backoff"
 version = "2.2.1"
@@ -3952,6 +4065,41 @@ files = [
 [package.dependencies]
 ply = "*"
 
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
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+
 [[package]]
 name = "kaleido"
 version = "0.2.1"
@@ -5277,6 +5425,65 @@ typing-extensions = ">=4.7,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
+[[package]]
+name = "opencensus"
+version = "0.11.4"
+description = "A stats collection and distributed tracing framework"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+google-api-core = {version = ">=1.0.0,<3.0.0", markers = "python_version >= \"3.6\""}
+opencensus-context = ">=0.1.3"
+six = ">=1.16,<2.0"
+
+[[package]]
+name = "opencensus-context"
+version = "0.1.3"
+description = "OpenCensus Runtime Context"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[[package]]
+name = "opencensus-ext-azure"
+version = "1.1.13"
+description = "OpenCensus Azure Monitor Exporter"
+optional = false
+python-versions = "*"
+files = [
+    {file = "opencensus-ext-azure-1.1.13.tar.gz", hash = "sha256:aec30472177005379ba56a702a097d618c5f57558e1bb6676ec75f948130692a"},
+    {file = "opencensus_ext_azure-1.1.13-py2.py3-none-any.whl", hash = "sha256:06001fac6f8588ba00726a3a7c6c7f2fc88bc8ad12a65afdca657923085393dd"},
+]
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+[package.dependencies]
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+azure-identity = ">=1.5.0,<2.0.0"
+opencensus = ">=0.11.4,<1.0.0"
+psutil = ">=5.6.3"
+requests = ">=2.19.0"
+
+[[package]]
+name = "opencensus-ext-logging"
+version = "0.1.1"
+description = "OpenCensus logging Integration"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "opencensus_ext_logging-0.1.1-py2.py3-none-any.whl", hash = "sha256:cfdaf5da5d8b195ff3d1af87a4066a6621a28046173f6be4b0b6caec4a3ca89f"},
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+
+[package.dependencies]
+opencensus = ">=0.8.0,<1.0.0"
+
 [[package]]
 name = "openpyxl"
 version = "3.1.5"
@@ -6021,6 +6228,35 @@ files = [
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 ]
 
+[[package]]
+name = "psutil"
+version = "6.0.0"
+description = "Cross-platform lib for process and system monitoring in Python."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+files = [
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+]
+
+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
 [[package]]
 name = "psycopg2-binary"
 version = "2.9.9"
@@ -6403,6 +6639,23 @@ azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
+[[package]]
+name = "pydash"
+version = "8.0.3"
+description = "The kitchen sink of Python utility libraries for doing \"stuff\" in a functional way. Based on the Lo-Dash Javascript library."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+typing-extensions = ">3.10,<4.6.0 || >4.6.0"
+
+[package.extras]
+dev = ["build", "coverage", "furo", "invoke", "mypy", "pytest", "pytest-cov", "pytest-mypy-testing", "ruff", "sphinx", "sphinx-autodoc-typehints", "tox", "twine", "wheel"]
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -7170,6 +7423,21 @@ hiredis = {version = ">1.0.0", optional = true, markers = "extra == \"hiredis\""
 hiredis = ["hiredis (>1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
 
+[[package]]
+name = "referencing"
+version = "0.35.1"
+description = "JSON Referencing + Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+rpds-py = ">=0.7.0"
+
 [[package]]
 name = "regex"
 version = "2024.7.24"
@@ -7377,6 +7645,118 @@ pygments = ">=2.13.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
+[[package]]
+name = "rpds-py"
+version = "0.20.0"
+description = "Python bindings to Rust's persistent data structures (rpds)"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "rpds_py-0.20.0.tar.gz", hash = "sha256:d72a210824facfdaf8768cf2d7ca25a042c30320b3020de2fa04640920d4e121"},
+]
+
 [[package]]
 name = "rsa"
 version = "4.9"
@@ -7987,6 +8367,20 @@ anyio = ">=3.4.0,<5"
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
+[[package]]
+name = "strictyaml"
+version = "1.7.3"
+description = "Strict, typed YAML parser"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "strictyaml-1.7.3-py3-none-any.whl", hash = "sha256:fb5c8a4edb43bebb765959e420f9b3978d7f1af88c80606c03fb420888f5d1c7"},
+    {file = "strictyaml-1.7.3.tar.gz", hash = "sha256:22f854a5fcab42b5ddba8030a0e4be51ca89af0267961c8d6cfa86395586c407"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.6.0"
+
 [[package]]
 name = "sympy"
 version = "1.13.2"
@@ -9669,4 +10063,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "04f970820de691f40fc9fb30f5ff0618b0f1a04d3315b14467fb88e475fa1243"
+content-hash = "0b912a7d500c4ff3c7f0c51877e55de70cec5317a990f9e882600e32a30a610e"

api/pyproject.toml

@@ -188,6 +188,8 @@ zhipuai = "1.0.7"
 # Related transparent dependencies with pinned verion
 # required by main implementations
 ############################################################
+azure-ai-ml = "^1.19.0"
+azure-ai-inference = "^1.0.0b3"
 volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"}
 [tool.poetry.group.indriect.dependencies]
 kaleido = "0.2.1"

api/tests/integration_tests/model_runtime/azure_ai_studio/test_llm.py

@@ -0,0 +1,113 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessageTool,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.llm.llm import AzureAIStudioLargeLanguageModel
+from tests.integration_tests.model_runtime.__mock.azure_ai_studio import setup_azure_ai_studio_mock
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_validate_credentials(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="gpt-35-turbo",
+            credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
+        )
+
+    model.validate_credentials(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+    )
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_invoke_model(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    result = model.invoke(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        stream=False,
+        user="abc-123",
+    )
+
+    assert isinstance(result, LLMResult)
+    assert len(result.message.content) > 0
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_invoke_stream_model(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    result = model.invoke(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        stream=True,
+        user="abc-123",
+    )
+
+    assert isinstance(result, Generator)
+
+    for chunk in result:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+        if chunk.delta.finish_reason is not None:
+            assert chunk.delta.usage is not None
+            assert chunk.delta.usage.completion_tokens > 0
+
+
+def test_get_num_tokens():
+    model = AzureAIStudioLargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+    )
+
+    assert num_tokens == 21

api/tests/integration_tests/model_runtime/azure_ai_studio/test_provider.py

@@ -0,0 +1,17 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.azure_ai_studio import AzureAIStudioProvider
+
+
+def test_validate_provider_credentials():
+    provider = AzureAIStudioProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(credentials={})
+
+    provider.validate_provider_credentials(
+        credentials={"api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"), "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")}
+    )

api/tests/integration_tests/model_runtime/azure_ai_studio/test_rerank.py

@@ -0,0 +1,50 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.rerank_entities import RerankResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureAIStudioRerankModel
+
+
+def test_validate_credentials():
+    model = AzureAIStudioRerankModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="azure-ai-studio-rerank-v1",
+            credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
+            query="What is the capital of the United States?",
+            docs=[
+                "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                "Census, Carson City had a population of 55,274.",
+                "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                "are a political division controlled by the United States. Its capital is Saipan.",
+            ],
+            score_threshold=0.8,
+        )
+
+
+def test_invoke_model():
+    model = AzureAIStudioRerankModel()
+
+    result = model.invoke(
+        model="azure-ai-studio-rerank-v1",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_JWT_TOKEN"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        query="What is the capital of the United States?",
+        docs=[
+            "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+            "Census, Carson City had a population of 55,274.",
+            "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+            "are a political division controlled by the United States. Its capital is Saipan.",
+        ],
+        score_threshold=0.8,
+    )
+
+    assert isinstance(result, RerankResult)
+    assert len(result.docs) == 1
+    assert result.docs[0].index == 1
+    assert result.docs[0].score >= 0.8