updated annotations of PES's and fixed tests and addressed comments

microsoft · Nov 27, 2024 · 00df0b3 · 00df0b3
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diff --git a/.github/workflows/python-integration-tests.yml b/.github/workflows/python-integration-tests.yml
@@ -20,7 +20,6 @@ permissions:
 env:
   # Configure a constant location for the uv cache
   UV_CACHE_DIR: /tmp/.uv-cache
-  HNSWLIB_NO_NATIVE: 1
   Python_Integration_Tests: Python_Integration_Tests
   AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }} # azure-text-embedding-ada-002
   AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
@@ -92,8 +91,8 @@ jobs:
         if: steps.filter.outputs.python != 'true'
         run: echo "NOT python file"
 
-  python-merge-gate-completions:
-    name: Python Pre-Merge Integration Tests - Completions & Embeddings
+  python-merge-gate-ai-services:
+    name: Python Pre-Merge Integration Tests - AI Services (incl samples using those)
     needs: paths-filter
     if: github.event_name != 'pull_request' && github.event_name != 'schedule' && needs.paths-filter.outputs.pythonChanges == 'true'
     strategy:
@@ -156,8 +155,8 @@ jobs:
           client-id: ${{ secrets.AZURE_CLIENT_ID }}
           tenant-id: ${{ secrets.AZURE_TENANT_ID }}
           subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Run Integration Tests - Completions
-        id: run_tests_completions
+      - name: Run Integration Tests
+        id: run_tests_ai_services
         timeout-minutes: 25
         shell: bash
         run: |
@@ -175,7 +174,7 @@ jobs:
         run: uv cache prune --ci
 
   python-merge-gate-memory:
-    name: Python Pre-Merge Integration Tests - Memory
+    name: Python Pre-Merge Integration Tests - Memory (incl samples using those)
     needs: paths-filter
     if: github.event_name != 'pull_request' && github.event_name != 'schedule' && needs.paths-filter.outputs.pythonChanges == 'true'
     strategy:
@@ -232,7 +231,7 @@ jobs:
           client-id: ${{ secrets.AZURE_CLIENT_ID }}
           tenant-id: ${{ secrets.AZURE_TENANT_ID }}
           subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-      - name: Run Integration Tests - Memory
+      - name: Run Integration Tests
         id: run_tests_memory
         timeout-minutes: 10
         shell: bash
@@ -251,6 +250,7 @@ jobs:
         run: uv cache prune --ci
 
   python-integration-tests:
+    name: Python Integration Tests - Scheduled run
     needs: paths-filter
     if: (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && needs.paths-filter.outputs.pythonChanges == 'true'
     strategy:
@@ -265,8 +265,6 @@ jobs:
     runs-on: ${{ matrix.os }}
     environment: "integration"
     env:
-      HNSWLIB_NO_NATIVE: 1
-      Python_Integration_Tests: Python_Integration_Tests
       UV_PYTHON: ${{ matrix.python-version }}
       MEMORY_CONCEPT_SAMPLE: "true"
       COMPLETIONS_CONCEPT_SAMPLE: "true"
@@ -278,13 +276,7 @@ jobs:
           version: "0.5.x"
           enable-cache: true
           cache-suffix: ${{ runner.os }}-${{ matrix.python-version }}
-      - name: Install dependencies with hnswlib native disabled
-        if: matrix.os == 'macos-latest' && matrix.python-version == '3.11'
-        run: |
-          export HNSWLIB_NO_NATIVE=1
-          uv sync --all-extras --dev
-      - name: Install dependencies with hnswlib native enabled
-        if: matrix.os != 'macos-latest' || matrix.python-version != '3.11'
+      - name: Install dependencies
         run: |
           uv sync --all-extras --dev
       - name: Install Ollama
@@ -389,7 +381,7 @@ jobs:
     strategy:
       max-parallel: 1
       fail-fast: false
-    needs: [python-merge-gate-completions, python-merge-gate-memory, python-integration-tests]
+    needs: [python-merge-gate-ai-services, python-merge-gate-memory, python-integration-tests]
     steps:
       - name: Get Date
         shell: bash

diff --git a/python/samples/concepts/chat_completion/simple_chatbot.py b/python/samples/concepts/chat_completion/simple_chatbot.py
@@ -6,7 +6,7 @@
     Services,
     get_chat_completion_service_and_request_settings,
 )
-from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents import ChatHistory
 
 # This sample shows how to create a chatbot. This sample uses the following two main components:
 # - a ChatCompletionService: This component is responsible for generating responses to user messages.
@@ -26,7 +26,7 @@
 # - Services.ONNX
 # - Services.VERTEX_AI
 # Please make sure you have configured your environment correctly for the selected chat completion service.
-chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
+chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
 
 # This is the system message that gives the chatbot its personality.
 system_message = """
@@ -64,10 +64,11 @@ async def chat() -> bool:
         chat_history=chat_history,
         settings=request_settings,
     )
-    print(f"Mosscap:> {response}")
+    if response:
+        print(f"Mosscap:> {response}")
 
-    # Add the chat message to the chat history to keep track of the conversation.
-    chat_history.add_assistant_message(str(response))
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(response)
 
     return True
 

diff --git a/python/samples/concepts/chat_completion/simple_chatbot_kernel_function.py b/python/samples/concepts/chat_completion/simple_chatbot_kernel_function.py
@@ -6,9 +6,9 @@
     Services,
     get_chat_completion_service_and_request_settings,
 )
-from semantic_kernel.contents.chat_history import ChatHistory
-from semantic_kernel.functions.kernel_arguments import KernelArguments
-from semantic_kernel.kernel import Kernel
+from semantic_kernel import Kernel
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.functions import KernelArguments
 
 # This sample shows how to create a chatbot using a kernel function.
 # This sample uses the following two main components:
@@ -97,15 +97,15 @@ async def chat() -> bool:
         user_input=user_input,
     )
 
-    answer = await kernel.invoke(chat_function, kernel_arguments)
+    answer = await kernel.invoke(plugin_name="ChatBot", function_name="Chat", arguments=kernel_arguments)
     # Alternatively, you can invoke the function directly with the kernel as an argument:
     # answer = await chat_function.invoke(kernel, kernel_arguments)
-
-    print(f"Mosscap:> {answer}")
-
-    # Add the chat message to the chat history to keep track of the conversation.
-    chat_history.add_user_message(user_input)
-    chat_history.add_assistant_message(str(answer))
+    if answer:
+        print(f"Mosscap:> {answer}")
+        # Since the user_input is rendered by the template, it is not yet part of the chat history, so we add it here.
+        chat_history.add_user_message(user_input)
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(answer.value)
 
     return True
 

diff --git a/python/samples/concepts/chat_completion/simple_chatbot_logit_bias.py b/python/samples/concepts/chat_completion/simple_chatbot_logit_bias.py
@@ -6,7 +6,7 @@
     Services,
     get_chat_completion_service_and_request_settings,
 )
-from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents import ChatHistory
 
 # This sample shows how to create a chatbot that whose output can be biased using logit bias.
 # This sample uses the following three main components:
@@ -33,7 +33,6 @@
 
 # Create a chat history object with the system message.
 chat_history = ChatHistory(system_message=system_message)
-
 # Create a list of tokens whose bias value will be reduced.
 # The token ids of these words can be obtained using the GPT Tokenizer: https://platform.openai.com/tokenizer
 # the targeted model series is GPT-4o & GPT-4o mini
@@ -61,7 +60,7 @@
 ]
 # Configure the logit bias settings to minimize the likelihood of the
 # tokens in the banned_tokens list appearing in the output.
-request_settings.logit_bias = {k: -100 for k in banned_tokens}
+request_settings.logit_bias = {k: -100 for k in banned_tokens}  # type: ignore
 
 
 async def chat() -> bool:
@@ -86,10 +85,11 @@ async def chat() -> bool:
         chat_history=chat_history,
         settings=request_settings,
     )
-    print(f"Mosscap:> {response}")
+    if response:
+        print(f"Mosscap:> {response}")
 
-    # Add the chat message to the chat history to keep track of the conversation.
-    chat_history.add_assistant_message(str(response))
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(response)
 
     return True
 

diff --git a/python/samples/concepts/chat_completion/simple_chatbot_streaming.py b/python/samples/concepts/chat_completion/simple_chatbot_streaming.py
@@ -1,13 +1,12 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
-from functools import reduce
 
 from samples.concepts.setup.chat_completion_services import (
     Services,
     get_chat_completion_service_and_request_settings,
 )
-from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents import ChatHistory, StreamingChatMessageContent
 
 # This sample shows how to create a chatbot that streams responses.
 # This sample uses the following two main components:
@@ -70,15 +69,16 @@ async def chat() -> bool:
     )
 
     # Capture the chunks of the response and print them as they come in.
-    chunks = []
+    chunks: list[StreamingChatMessageContent] = []
     print("Mosscap:> ", end="")
     async for chunk in response:
-        chunks.append(chunk)
-        print(chunk, end="")
+        if chunk:
+            chunks.append(chunk)
+            print(chunk, end="")
     print("")
 
     # Combine the chunks into a single message to add to the chat history.
-    full_message = reduce(lambda first, second: first + second, chunks)
+    full_message = sum(chunks[1:], chunks[0])
     # Add the chat message to the chat history to keep track of the conversation.
     chat_history.add_message(full_message)
 

diff --git a/python/samples/concepts/chat_completion/simple_chatbot_with_image.py b/python/samples/concepts/chat_completion/simple_chatbot_with_image.py
@@ -6,10 +6,7 @@
     Services,
     get_chat_completion_service_and_request_settings,
 )
-from semantic_kernel.contents.chat_history import ChatHistory
-from semantic_kernel.contents.chat_message_content import ChatMessageContent
-from semantic_kernel.contents.image_content import ImageContent
-from semantic_kernel.contents.text_content import TextContent
+from semantic_kernel.contents import AuthorRole, ChatHistory, ChatMessageContent, ImageContent, TextContent
 
 # This sample shows how to create a chatbot that responds to user messages with image input.
 # This sample uses the following three main components:
@@ -30,6 +27,11 @@
 # - Services.ONNX
 # - Services.VERTEX_AI
 # Please make sure you have configured your environment correctly for the selected chat completion service.
+
+# [NOTE]
+# Not all models support image input. Make sure to select a model that supports image input.
+# Not all services support image input from an image URI. If your image is saved in a remote location,
+# make sure to use a service that supports image input from a URI.
 chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
 
 IMAGE_URI = "https://upload.wikimedia.org/wikipedia/commons/d/d5/Half-timbered_mansion%2C_Zirkel%2C_East_view.jpg"
@@ -41,12 +43,6 @@
 image_content_local = ImageContent.from_image_file(IMAGE_PATH)
 
 
-# [NOTE]
-# Not all models support image input. Make sure to select a model that supports image input.
-# Not all services support image input from an image URI. If your image is saved in a remote location,
-# make sure to use a service that supports image input from a URI.
-
-
 # This is the system message that gives the chatbot its personality.
 system_message = """
 You are an image reviewing chat bot. Your name is Mosscap and you have one goal critiquing images that are supplied.
@@ -56,7 +52,7 @@
 chat_history = ChatHistory(system_message=system_message)
 chat_history.add_message(
     ChatMessageContent(
-        role="user",
+        role=AuthorRole.USER,
         items=[TextContent(text="What is in this image?"), image_content_local],
     )
 )
@@ -90,10 +86,11 @@ async def chat(skip_user_input: bool = False) -> bool:
         chat_history=chat_history,
         settings=request_settings,
     )
-    print(f"Mosscap:> {response}")
+    if response:
+        print(f"Mosscap:> {response}")
 
-    # Add the chat message to the chat history to keep track of the conversation.
-    chat_history.add_assistant_message(str(response))
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(response)
 
     return True