From ce9281af21100ddef9435f8308130121ee3d9742 Mon Sep 17 00:00:00 2001 From: Martyna Marcinkowska <61530975+tectonia@users.noreply.github.com> Date: Fri, 14 Jun 2024 16:59:20 +0100 Subject: [PATCH] feat: Add a new Orchestrator "prompt_flow" (#1026) Co-authored-by: Adam Dougal --- .env.sample | 8 +- .../batch/utilities/helpers/env_helper.py | 8 + .../batch/utilities/helpers/llm_helper.py | 12 + .../orchestrator/orchestration_strategy.py | 1 + .../utilities/orchestrator/prompt_flow.py | 93 + .../utilities/orchestrator/strategies.py | 3 + code/frontend/vite.config.ts | 1 + .../utilities/helpers/test_config_helper.py | 2 +- .../utilities/helpers/test_llm_helper.py | 28 + .../orchestrator/test_prompt_flow.py | 271 ++ docs/LOCAL_DEPLOYMENT.md | 2 +- docs/TEAMS_LOCAL_DEPLOYMENT.md | 2 +- infra/prompt-flow/create-prompt-flow.sh | 18 + package-lock.json | 12 + poetry.lock | 2188 ++++++++--------- pyproject.toml | 3 +- 16 files changed, 1533 insertions(+), 1119 deletions(-) create mode 100644 code/backend/batch/utilities/orchestrator/prompt_flow.py create mode 100644 code/tests/utilities/orchestrator/test_prompt_flow.py create mode 100644 package-lock.json diff --git a/.env.sample b/.env.sample index aaed17d15..1bdf23530 100644 --- a/.env.sample +++ b/.env.sample @@ -46,8 +46,12 @@ AZURE_FORM_RECOGNIZER_KEY= # Azure AI Content Safety for filtering out the inappropriate questions or answers AZURE_CONTENT_SAFETY_ENDPOINT= AZURE_CONTENT_SAFETY_KEY= -# Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel) or LangChain (langchain) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. +# Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel), LangChain (langchain) or Prompt Flow (prompt_flow) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. ORCHESTRATION_STRATEGY=openai_function +# If selected Prompt Flow as orchestration strategy, please provide the following environment variables. Note that Prompt Flow does not support RBAC authentication currently. +AZURE_ML_WORKSPACE_NAME= +PROMPT_FLOW_DEPLOYMENT_NAME= +PROMPT_FLOW_ENDPOINT_NAME= #Speech-to-text feature AZURE_SPEECH_SERVICE_KEY= AZURE_SPEECH_SERVICE_REGION= @@ -58,4 +62,4 @@ AZURE_AUTH_TYPE=keys USE_KEY_VAULT=true AZURE_KEY_VAULT_ENDPOINT= # Chat conversation type to decide between custom or byod (bring your own data) conversation type -CONVERSATION_FLOW= \ No newline at end of file +CONVERSATION_FLOW= diff --git a/code/backend/batch/utilities/helpers/env_helper.py b/code/backend/batch/utilities/helpers/env_helper.py index 5e790b684..ea3b0f429 100644 --- a/code/backend/batch/utilities/helpers/env_helper.py +++ b/code/backend/batch/utilities/helpers/env_helper.py @@ -227,6 +227,14 @@ def __load_config(self, **kwargs) -> None: "LOAD_CONFIG_FROM_BLOB_STORAGE" ) + self.AZURE_ML_WORKSPACE_NAME = os.getenv( + "AZURE_ML_WORKSPACE_NAME", "" + ) + + self.PROMPT_FLOW_ENDPOINT_NAME = os.getenv("PROMPT_FLOW_ENDPOINT_NAME", "") + + self.PROMPT_FLOW_DEPLOYMENT_NAME = os.getenv("PROMPT_FLOW_DEPLOYMENT_NAME", "") + def should_use_data(self) -> bool: if ( self.AZURE_SEARCH_SERVICE diff --git a/code/backend/batch/utilities/helpers/llm_helper.py b/code/backend/batch/utilities/helpers/llm_helper.py index b3f75ab39..7dfc58002 100644 --- a/code/backend/batch/utilities/helpers/llm_helper.py +++ b/code/backend/batch/utilities/helpers/llm_helper.py @@ -6,6 +6,8 @@ from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import ( AzureChatPromptExecutionSettings, ) +from azure.ai.ml import MLClient +from azure.identity import DefaultAzureCredential from .env_helper import EnvHelper @@ -154,3 +156,13 @@ def get_sk_service_settings(self, service: AzureChatCompletion): max_tokens=self.llm_max_tokens, ), ) + + def get_ml_client(self): + if not hasattr(self, "_ml_client"): + self._ml_client = MLClient( + DefaultAzureCredential(), + self.env_helper.AZURE_SUBSCRIPTION_ID, + self.env_helper.AZURE_RESOURCE_GROUP, + self.env_helper.AZURE_ML_WORKSPACE_NAME, + ) + return self._ml_client diff --git a/code/backend/batch/utilities/orchestrator/orchestration_strategy.py b/code/backend/batch/utilities/orchestrator/orchestration_strategy.py index c1b15b8e5..bc212e1c2 100644 --- a/code/backend/batch/utilities/orchestrator/orchestration_strategy.py +++ b/code/backend/batch/utilities/orchestrator/orchestration_strategy.py @@ -5,3 +5,4 @@ class OrchestrationStrategy(Enum): OPENAI_FUNCTION = "openai_function" LANGCHAIN = "langchain" SEMANTIC_KERNEL = "semantic_kernel" + PROMPT_FLOW = "prompt_flow" diff --git a/code/backend/batch/utilities/orchestrator/prompt_flow.py b/code/backend/batch/utilities/orchestrator/prompt_flow.py new file mode 100644 index 000000000..15cf1a518 --- /dev/null +++ b/code/backend/batch/utilities/orchestrator/prompt_flow.py @@ -0,0 +1,93 @@ +import logging +from typing import List +import json +import tempfile + +from .orchestrator_base import OrchestratorBase +from ..common.answer import Answer +from ..helpers.llm_helper import LLMHelper +from ..helpers.env_helper import EnvHelper + +logger = logging.getLogger(__name__) + + +class PromptFlowOrchestrator(OrchestratorBase): + def __init__(self) -> None: + super().__init__() + self.llm_helper = LLMHelper() + self.env_helper = EnvHelper() + + # Get the ML client, endpoint and deployment names + self.ml_client = self.llm_helper.get_ml_client() + self.enpoint_name = self.env_helper.PROMPT_FLOW_ENDPOINT_NAME + self.deployment_name = self.env_helper.PROMPT_FLOW_DEPLOYMENT_NAME + + async def orchestrate( + self, user_message: str, chat_history: List[dict], **kwargs: dict + ) -> list[dict]: + # Call Content Safety tool on question + if self.config.prompts.enable_content_safety: + if response := self.call_content_safety_input(user_message): + return response + + transformed_chat_history = self.transform_chat_history(chat_history) + + file_name = self.transform_data_into_file( + user_message, transformed_chat_history + ) + + # Call the Prompt Flow service + try: + response = self.ml_client.online_endpoints.invoke( + endpoint_name=self.enpoint_name, + request_file=file_name, + deployment_name=self.deployment_name, + ) + result = json.loads(response) + logger.debug(result) + except Exception as error: + logger.error("The request failed: %s", error) + raise RuntimeError(f"The request failed: {error}") from error + + # Transform response into answer for further processing + answer = Answer(question=user_message, answer=result["chat_output"]) + + # Call Content Safety tool on answer + if self.config.prompts.enable_content_safety: + if response := self.call_content_safety_output(user_message, answer.answer): + return response + + # Format the output for the UI + messages = self.output_parser.parse( + question=answer.question, + answer=answer.answer, + source_documents=answer.source_documents, + ) + return messages + + def transform_chat_history(self, chat_history): + transformed_chat_history = [] + for i, message in enumerate(chat_history): + if message["role"] == "user": + user_message = message["content"] + assistant_message = "" + if ( + i + 1 < len(chat_history) + and chat_history[i + 1]["role"] == "assistant" + ): + assistant_message = chat_history[i + 1]["content"] + transformed_chat_history.append( + { + "inputs": {"chat_input": user_message}, + "outputs": {"chat_output": assistant_message}, + } + ) + return transformed_chat_history + + def transform_data_into_file(self, user_message, chat_history): + # Transform data input into a file for the Prompt Flow service + data = {"chat_input": user_message, "chat_history": chat_history} + body = str.encode(json.dumps(data)) + with tempfile.NamedTemporaryFile(delete=False) as file: + file.write(body) + return file.name diff --git a/code/backend/batch/utilities/orchestrator/strategies.py b/code/backend/batch/utilities/orchestrator/strategies.py index 83ab80005..349cd0aa8 100644 --- a/code/backend/batch/utilities/orchestrator/strategies.py +++ b/code/backend/batch/utilities/orchestrator/strategies.py @@ -2,6 +2,7 @@ from .open_ai_functions import OpenAIFunctionsOrchestrator from .lang_chain_agent import LangChainAgent from .semantic_kernel import SemanticKernelOrchestrator +from .prompt_flow import PromptFlowOrchestrator def get_orchestrator(orchestration_strategy: str): @@ -11,5 +12,7 @@ def get_orchestrator(orchestration_strategy: str): return LangChainAgent() elif orchestration_strategy == OrchestrationStrategy.SEMANTIC_KERNEL.value: return SemanticKernelOrchestrator() + elif orchestration_strategy == OrchestrationStrategy.PROMPT_FLOW.value: + return PromptFlowOrchestrator() else: raise Exception(f"Unknown orchestration strategy: {orchestration_strategy}") diff --git a/code/frontend/vite.config.ts b/code/frontend/vite.config.ts index ee97ab42d..5f640e43f 100644 --- a/code/frontend/vite.config.ts +++ b/code/frontend/vite.config.ts @@ -10,6 +10,7 @@ export default defineConfig({ sourcemap: true }, server: { + host: true, proxy: { "/api": { target: "http://127.0.0.1:5050", diff --git a/code/tests/utilities/helpers/test_config_helper.py b/code/tests/utilities/helpers/test_config_helper.py index 4bd4568f7..a95982506 100644 --- a/code/tests/utilities/helpers/test_config_helper.py +++ b/code/tests/utilities/helpers/test_config_helper.py @@ -446,7 +446,7 @@ def test_get_available_orchestration_strategies(config: Config): # then assert sorted(orchestration_strategies) == sorted( - ["openai_function", "langchain", "semantic_kernel"] + ["openai_function", "langchain", "prompt_flow", "semantic_kernel"] ) diff --git a/code/tests/utilities/helpers/test_llm_helper.py b/code/tests/utilities/helpers/test_llm_helper.py index 66131347e..81ac42d3e 100644 --- a/code/tests/utilities/helpers/test_llm_helper.py +++ b/code/tests/utilities/helpers/test_llm_helper.py @@ -13,6 +13,11 @@ AZURE_OPENAI_MODEL = "mock-model" AZURE_OPENAI_MAX_TOKENS = "100" AZURE_OPENAI_EMBEDDING_MODEL = "mock-embedding-model" +AZURE_SUBSCRIPTION_ID = "mock-subscription-id" +AZURE_RESOURCE_GROUP = "mock-resource-group" +AZURE_ML_WORKSPACE_NAME = "mock-ml-workspace" +PROMPT_FLOW_ENDPOINT_NAME = "mock-endpoint-name" +PROMPT_FLOW_DEPLOYMENT_NAME = "mock-deployment-name" @pytest.fixture(autouse=True) @@ -26,6 +31,11 @@ def env_helper_mock(): env_helper.AZURE_OPENAI_MODEL = AZURE_OPENAI_MODEL env_helper.AZURE_OPENAI_MAX_TOKENS = AZURE_OPENAI_MAX_TOKENS env_helper.AZURE_OPENAI_EMBEDDING_MODEL = AZURE_OPENAI_EMBEDDING_MODEL + env_helper.AZURE_SUBSCRIPTION_ID = AZURE_SUBSCRIPTION_ID + env_helper.AZURE_RESOURCE_GROUP = AZURE_RESOURCE_GROUP + env_helper.AZURE_ML_WORKSPACE_NAME = AZURE_ML_WORKSPACE_NAME + env_helper.PROMPT_FLOW_ENDPOINT_NAME = PROMPT_FLOW_ENDPOINT_NAME + env_helper.PROMPT_FLOW_DEPLOYMENT_NAME = PROMPT_FLOW_DEPLOYMENT_NAME yield env_helper @@ -127,3 +137,21 @@ def test_generate_embeddings_returns_embeddings(azure_openai_mock): # then assert actual_embeddings == expected_embeddings + +@patch('backend.batch.utilities.helpers.llm_helper.DefaultAzureCredential') +@patch('backend.batch.utilities.helpers.llm_helper.MLClient') +def test_get_ml_client_initializes_with_expected_parameters( + mock_ml_client, mock_default_credential, env_helper_mock): + # given + llm_helper = LLMHelper() + + # when + llm_helper.get_ml_client() + + # then + mock_ml_client.assert_called_once_with( + mock_default_credential.return_value, + env_helper_mock.AZURE_SUBSCRIPTION_ID, + env_helper_mock.AZURE_RESOURCE_GROUP, + env_helper_mock.AZURE_ML_WORKSPACE_NAME + ) diff --git a/code/tests/utilities/orchestrator/test_prompt_flow.py b/code/tests/utilities/orchestrator/test_prompt_flow.py new file mode 100644 index 000000000..902b59d0b --- /dev/null +++ b/code/tests/utilities/orchestrator/test_prompt_flow.py @@ -0,0 +1,271 @@ +from unittest.mock import ANY, AsyncMock, MagicMock, patch + +import pytest +from backend.batch.utilities.orchestrator.prompt_flow import ( + PromptFlowOrchestrator, +) +from backend.batch.utilities.parser.output_parser_tool import OutputParserTool + + +@pytest.fixture(autouse=True) +def llm_helper_mock(): + with patch("backend.batch.utilities.orchestrator.prompt_flow.LLMHelper") as mock: + llm_helper = mock.return_value + + mock_ml_client = MagicMock() + llm_helper.get_ml_client.return_value = mock_ml_client + + yield llm_helper, mock_ml_client + + +@pytest.fixture(autouse=True) +def env_helper_mock(): + with patch("backend.batch.utilities.orchestrator.prompt_flow.EnvHelper") as mock: + + env_helper = mock.return_value + + env_helper.PROMPT_FLOW_ENDPOINT_NAME = "endpoint_name" + env_helper.PROMPT_FLOW_DEPLOYMENT_NAME = "deployment_name" + + yield env_helper + + +@pytest.fixture() +def orchestrator(): + with patch( + "backend.batch.utilities.orchestrator.prompt_flow.OrchestratorBase.__init__" + ): + orchestrator = PromptFlowOrchestrator() + + orchestrator.config = MagicMock() + orchestrator.config.prompts.enable_content_safety = True + + orchestrator.call_content_safety_input = MagicMock(return_value=None) + orchestrator.call_content_safety_output = MagicMock(return_value=None) + + orchestrator.output_parser = OutputParserTool() + + yield orchestrator + + +def test_prompt_flow_init_initializes_with_expected_attributes( + orchestrator: PromptFlowOrchestrator, llm_helper_mock +): + _, mock_ml_client = llm_helper_mock + assert orchestrator.ml_client is mock_ml_client + assert orchestrator.enpoint_name == "endpoint_name" + assert orchestrator.deployment_name == "deployment_name" + + +@pytest.mark.asyncio +async def test_orchestrate_returns_content_safety_response_for_unsafe_input( + orchestrator: PromptFlowOrchestrator, +): + # given + user_message = "bad question" + content_safety_response = [ + { + "role": "tool", + "content": '{"citations": [], "intent": "bad question"}', + "end_turn": False, + }, + { + "role": "assistant", + "content": "Content safety response input.", + "end_turn": True, + }, + ] + orchestrator.call_content_safety_input.return_value = content_safety_response + + # when + response = await orchestrator.orchestrate(user_message, []) + + # then + orchestrator.call_content_safety_input.assert_called_once_with(user_message) + assert response == content_safety_response + + +@pytest.mark.asyncio +async def test_orchestrate_returns_expected_chat_response( + orchestrator: PromptFlowOrchestrator, +): + # given + user_message = "question" + chat_history = [] + expected_result = [ + { + "role": "tool", + "content": '{"citations": [], "intent": "question"}', + "end_turn": False, + }, + { + "role": "assistant", + "content": "answer", + "end_turn": True, + }, + ] + chat_output = {"chat_output": "answer", "citations": ["", []]} + + orchestrator.transform_chat_history = MagicMock(return_value=[]) + orchestrator.ml_client.online_endpoints.invoke = AsyncMock(return_value=chat_output) + + # when + with patch("json.loads", return_value=chat_output): + response = await orchestrator.orchestrate(user_message, chat_history) + + # then + orchestrator.transform_chat_history.assert_called_once_with(chat_history) + orchestrator.ml_client.online_endpoints.invoke.assert_called_once_with( + endpoint_name="endpoint_name", + request_file=ANY, + deployment_name="deployment_name", + ) + assert response == expected_result + + +@pytest.mark.asyncio +async def test_orchestrate_returns_error_response(orchestrator: PromptFlowOrchestrator): + # given + user_message = "question" + chat_history = [] + error = Exception() + orchestrator.ml_client.online_endpoints.invoke = AsyncMock(side_effect=error) + + # when & then + with pytest.raises(RuntimeError): + await orchestrator.orchestrate(user_message, chat_history) + + +@pytest.mark.asyncio +async def test_orchestrate_returns_content_safety_response_for_unsafe_output( + orchestrator: PromptFlowOrchestrator, +): + # given + user_message = "question" + chat_output = {"chat_output": "bad-response", "citations": ["", []]} + content_safety_response = [ + { + "role": "tool", + "content": "Content safety response output.", + "end_turn": True, + }, + ] + orchestrator.call_content_safety_output.return_value = content_safety_response + + with patch.object( + orchestrator.ml_client, + "invoke", + new_callable=AsyncMock, + return_value=chat_output, + ), patch("json.loads", return_value=chat_output): + # when + response = await orchestrator.orchestrate(user_message, []) + + # then + orchestrator.call_content_safety_output.assert_called_once_with( + user_message, "bad-response" + ) + assert response == content_safety_response + + +def test_transform_chat_history_returns_expected_format( + orchestrator: PromptFlowOrchestrator, +): + # given + chat_history = [ + {"role": "user", "content": "Hi!"}, + { + "content": "Hello! How can I assist you today?", + "end_turn": True, + "role": "assistant", + }, + ] + + # when + result = orchestrator.transform_chat_history(chat_history) + + # then + expected_result = [ + { + "inputs": {"chat_input": "Hi!"}, + "outputs": {"chat_output": "Hello! How can I assist you today?"}, + } + ] + assert result == expected_result + + +def test_transform_chat_history_returns_empty_for_no_chat_history( + orchestrator: PromptFlowOrchestrator, +): + # given + chat_history = [] + + # when + result = orchestrator.transform_chat_history(chat_history) + + # then + assert result == [] + + +def test_transform_chat_history_handles_multiple_messages_correctly( + orchestrator: PromptFlowOrchestrator, +): + # given + chat_history = [ + {"role": "user", "content": "Hi!"}, + { + "content": "Hello! How can I assist you today?", + "end_turn": True, + "role": "assistant", + }, + {"role": "user", "content": "Can you help with employee benefits?"}, + { + "content": "What information are you looking for?", + "end_turn": True, + "role": "assistant", + }, + ] + + # when + result = orchestrator.transform_chat_history(chat_history) + + # then + expected_result = [ + { + "inputs": {"chat_input": "Hi!"}, + "outputs": {"chat_output": "Hello! How can I assist you today?"}, + }, + { + "inputs": {"chat_input": "Can you help with employee benefits?"}, + "outputs": {"chat_output": "What information are you looking for?"}, + }, + ] + assert result == expected_result + + +def test_transform_chat_history_handles_no_assistant_message_correctly( + orchestrator: PromptFlowOrchestrator, +): + # given + chat_history = [ + {"role": "user", "content": "Hi!"}, + {"role": "user", "content": "Hello!"}, + { + "content": "Hello! How can I assist you today?", + "end_turn": True, + "role": "assistant", + }, + ] + + # when + result = orchestrator.transform_chat_history(chat_history) + + # then + expected_result = [ + {"inputs": {"chat_input": "Hi!"}, "outputs": {"chat_output": ""}}, + { + "inputs": {"chat_input": "Hello!"}, + "outputs": {"chat_output": "Hello! How can I assist you today?"}, + }, + ] + assert result == expected_result diff --git a/docs/LOCAL_DEPLOYMENT.md b/docs/LOCAL_DEPLOYMENT.md index dbe14d2f9..8aca81929 100644 --- a/docs/LOCAL_DEPLOYMENT.md +++ b/docs/LOCAL_DEPLOYMENT.md @@ -209,7 +209,7 @@ Execute the above [shell command](#L81) to run the function locally. You may nee |AZURE_FORM_RECOGNIZER_ENDPOINT||The name of the Azure Form Recognizer for extracting the text from the documents| |AZURE_FORM_RECOGNIZER_KEY||The key of the Azure Form Recognizer for extracting the text from the documents| |APPLICATIONINSIGHTS_CONNECTION_STRING||The Application Insights connection string to store the application logs| -|ORCHESTRATION_STRATEGY | openai_function | Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel), LangChain (langchain) or Prompt FLow (prompt_flow) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. | +|ORCHESTRATION_STRATEGY | openai_function | Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel), LangChain (langchain) or Prompt Flow (prompt_flow) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. Prompt Flow option is still in development and does not support RBAC or integrated vectorization as of yet.| |AZURE_CONTENT_SAFETY_ENDPOINT | | The endpoint of the Azure AI Content Safety service | |AZURE_CONTENT_SAFETY_KEY | | The key of the Azure AI Content Safety service| |AZURE_SPEECH_SERVICE_KEY | | The key of the Azure Speech service| diff --git a/docs/TEAMS_LOCAL_DEPLOYMENT.md b/docs/TEAMS_LOCAL_DEPLOYMENT.md index 715186e56..3042e4f3c 100644 --- a/docs/TEAMS_LOCAL_DEPLOYMENT.md +++ b/docs/TEAMS_LOCAL_DEPLOYMENT.md @@ -83,7 +83,7 @@ Or use the [Azure Functions VS Code extension](https://marketplace.visualstudio. |AZURE_FORM_RECOGNIZER_ENDPOINT||The name of the Azure Form Recognizer for extracting the text from the documents| |AZURE_FORM_RECOGNIZER_KEY||The key of the Azure Form Recognizer for extracting the text from the documents| |APPLICATIONINSIGHTS_CONNECTION_STRING||The Application Insights connection string to store the application logs| -|ORCHESTRATION_STRATEGY | openai_function | Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel) or LangChain (langchain) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. | +|ORCHESTRATION_STRATEGY | openai_function | Orchestration strategy. Use Azure OpenAI Functions (openai_function), Semantic Kernel (semantic_kernel), LangChain (langchain) or Prompt Flow (prompt_flow) for messages orchestration. If you are using a new model version 0613 select any strategy, if you are using a 0314 model version select "langchain". Note that both `openai_function` and `semantic_kernel` use OpenAI function calling. Prompt Flow option is still in development and does not support RBAC or integrated vectorization as of yet.| |AZURE_CONTENT_SAFETY_ENDPOINT | | The endpoint of the Azure AI Content Safety service | |AZURE_CONTENT_SAFETY_KEY | | The key of the Azure AI Content Safety service| |AZURE_SPEECH_SERVICE_KEY | | The key of the Azure Speech service| diff --git a/infra/prompt-flow/create-prompt-flow.sh b/infra/prompt-flow/create-prompt-flow.sh index 93a91e2f5..016e0d011 100755 --- a/infra/prompt-flow/create-prompt-flow.sh +++ b/infra/prompt-flow/create-prompt-flow.sh @@ -19,6 +19,8 @@ while IFS='=' read -r key value; do "AZURE_OPENAI_EMBEDDING_MODEL") openai_embedding_model=$value ;; "AZURE_SEARCH_SERVICE") search_service=$value ;; "AZURE_SEARCH_INDEX") search_index=$value ;; + "FRONTEND_WEBSITE_NAME") frontend_website_link=$value ;; + "ADMIN_WEBSITE_NAME") admin_website_link=$value ;; esac done <=1.28.0)"] [[package]] name = "babel" -version = "2.14.0" +version = "2.15.0" description = 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hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"}, + {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"}, ] [[package]] @@ -7241,20 +7203,20 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.18.1" +version = "3.19.2" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, - {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, + {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, + {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "2419c84d6aa5d498f5b362f59b0a15acdf6a92e3679508c606dcf9e98911e045" +content-hash = "9e75694b189434a0830c432703410528ae72550b7f90be8d7f76b10802caad68" diff --git a/pyproject.toml b/pyproject.toml index a5cae19a3..d12410d96 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,7 +37,8 @@ opentelemetry-instrumentation-httpx = "^0.45b0" pillow = "10.3.0" azure-mgmt-cognitiveservices = "^13.5.0" jsonschema = "^4.22.0" -semantic-kernel = {version = "^1.0.4", python = "<3.13"} +semantic-kernel = {version = "1.0.4", python = "<3.13"} +azure-ai-ml = "^1.16.0" [tool.poetry.group.dev.dependencies] pytest = "^8.2.2"