Merge pull request #86 from Azure-Samples/ai-serivice-upgrade

upgrade sk,others to latest version

ai-service.yaml

@@ -36,7 +36,7 @@ spec:
             memory: 50Mi
           limits:
             cpu: 30m
-            memory: 65Mi
+            memory: 85Mi
         startupProbe:
           httpGet:
             path: /health

charts/aks-store-demo/templates/ai-service.yaml

@@ -70,7 +70,7 @@ spec:
             memory: 50Mi
           limits:
             cpu: 30m
-            memory: 65Mi
+            memory: 85Mi
         startupProbe:
           httpGet:
             path: /health

src/ai-service/requirements.txt

@@ -1,9 +1,9 @@
-fastapi==0.95.2
+fastapi==0.105.0
 uvicorn==0.22.0
-pydantic==1.10.8
+pydantic==2.5.0
 pytest==7.3.1
 httpx
 pyyaml
-semantic-kernel==0.3.1.dev0
+semantic-kernel==0.4.2.dev0
 azure.identity==1.14.0
 requests==2.31.0

src/ai-service/routers/LLM.py

@@ -0,0 +1,70 @@
+from azure.identity import DefaultAzureCredential
+import semantic_kernel as sk
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatCompletion
+from dotenv import load_dotenv
+import os
+
+
+def get_llm():
+    """ Function to initialize the LLM so that it can be used in the app """
+    # Set the useLocalLLM and useAzureOpenAI variables based on environment variables
+    useLocalLLM: bool = False
+    useAzureOpenAI: bool = False
+    kernel = False
+
+    if os.environ.get("USE_LOCAL_LLM"):
+        useLocalLLM = os.environ.get("USE_LOCAL_LLM").lower() == "true"
+
+    if os.environ.get("USE_AZURE_OPENAI"):
+        useAzureOpenAI = os.environ.get("USE_AZURE_OPENAI").lower() == "true"
+
+    # if useLocalLLM and useAzureOpenAI are both set to true, raise an exception
+    if useLocalLLM and useAzureOpenAI:
+        raise Exception("USE_LOCAL_LLM and USE_AZURE_OPENAI environment variables cannot both be set to true")
+
+    # if useLocalLLM or useAzureOpenAI are set to true, get the endpoint from the environment variables
+    if useLocalLLM or useAzureOpenAI:
+        endpoint: str = os.environ.get("AI_ENDPOINT") or os.environ.get("AZURE_OPENAI_ENDPOINT")
+
+        if isinstance(endpoint, str) == False or endpoint == "":
+            raise Exception("AI_ENDPOINT or AZURE_OPENAI_ENDPOINT environment variable must be set when USE_LOCAL_LLM or USE_AZURE_OPENAI is set to true")
+
+    # if not using local LLM, set up the semantic kernel
+    if useLocalLLM:
+        print("Using Local LLM")
+    else:
+        print("Using OpenAI and setting up Semantic Kernel")
+        # Load environment variables from .env file
+        load_dotenv()
+
+        # Initialize the semantic kernel
+        kernel: sk.Kernel = sk.Kernel()
+
+        kernel = sk.Kernel()
+
+        # Get the Azure OpenAI deployment name, API key, and endpoint or OpenAI org id from environment variables
+        api_key: str = os.environ.get("OPENAI_API_KEY")
+        useAzureAD: str = os.environ.get("USE_AZURE_AD")
+
+        if (isinstance(api_key, str) == False or api_key == "") and (isinstance(useAzureAD, str) == False or useAzureAD == ""):
+            raise Exception("OPENAI_API_KEY environment variable must be set")
+
+        if not useAzureOpenAI:
+            org_id = os.environ.get("OPENAI_ORG_ID")
+            if isinstance(org_id, str) == False or org_id == "":
+                raise Exception("OPENAI_ORG_ID environment variable must be set when USE_AZURE_OPENAI is set to False")
+            # Add the OpenAI text completion service to the kernel
+            kernel.add_chat_service("dv", OpenAIChatCompletion("gpt-3.5-turbo", api_key, org_id))
+
+        else:
+            deployment: str = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")
+            # Add the Azure OpenAI text completion service to the kernel
+            if isinstance(useAzureAD, str) == True and useAzureAD.lower() == "true":
+                print("Authenticating to Azure OpenAI with Azure AD Workload Identity")
+                credential = DefaultAzureCredential()
+                access_token = credential.get_token("https://cognitiveservices.azure.com/.default")
+                kernel.add_chat_service("dv", AzureChatCompletion(deployment_name=deployment, endpoint=endpoint, ad_token=access_token.token))
+            else:
+                print("Authenticating to Azure OpenAI with OpenAI API key")
+                kernel.add_chat_service("dv", AzureChatCompletion(deployment_name=deployment, endpoint=endpoint, api_key=api_key))
+    return kernel, useLocalLLM, endpoint

src/ai-service/routers/description_generator.py

@@ -1,74 +1,13 @@
-from azure.identity import DefaultAzureCredential
+from typing import Any, List, Dict
 from fastapi import APIRouter, Request, status
 from fastapi.responses import Response, JSONResponse
-import semantic_kernel as sk
-from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatCompletion
-from dotenv import load_dotenv
-from typing import Any, List, Dict
-import os
 import requests
 import json
+from routers.LLM import get_llm 
 
-# Set the useLocalLLM and useAzureOpenAI variables based on environment variables
-useLocalLLM: bool = False
-useAzureOpenAI: bool = False
-
-if os.environ.get("USE_LOCAL_LLM"):
-    useLocalLLM = os.environ.get("USE_LOCAL_LLM").lower() == "true"
-
-if os.environ.get("USE_AZURE_OPENAI"):
-    useAzureOpenAI = os.environ.get("USE_AZURE_OPENAI").lower() == "true"
-
-# if useLocalLLM and useAzureOpenAI are both set to true, raise an exception
-if useLocalLLM and useAzureOpenAI:
-    raise Exception("USE_LOCAL_LLM and USE_AZURE_OPENAI environment variables cannot both be set to true")
-
-# if useLocalLLM or useAzureOpenAI are set to true, get the endpoint from the environment variables
-if useLocalLLM or useAzureOpenAI:
-    endpoint: str = os.environ.get("AI_ENDPOINT") or os.environ.get("AZURE_OPENAI_ENDPOINT")
-
-    if isinstance(endpoint, str) == False or endpoint == "":
-        raise Exception("AI_ENDPOINT or AZURE_OPENAI_ENDPOINT environment variable must be set when USE_LOCAL_LLM or USE_AZURE_OPENAI is set to true")
-
-# if not using local LLM, set up the semantic kernel
-if useLocalLLM:
-    print("Using Local LLM")
-else:
-    print("Using OpenAI and setting up Semantic Kernel")
-    # Load environment variables from .env file
-    load_dotenv()
-
-    # Initialize the semantic kernel
-    kernel: sk.Kernel = sk.Kernel()
-
-    kernel = sk.Kernel()
-
-    # Get the Azure OpenAI deployment name, API key, and endpoint or OpenAI org id from environment variables
-    api_key: str = os.environ.get("OPENAI_API_KEY")
-    useAzureAD: str = os.environ.get("USE_AZURE_AD")
-
-    if (isinstance(api_key, str) == False or api_key == "") and (isinstance(useAzureAD, str) == False or useAzureAD == ""):
-        raise Exception("OPENAI_API_KEY environment variable must be set")
-
-    if not useAzureOpenAI:
-        org_id = os.environ.get("OPENAI_ORG_ID")
-        if isinstance(org_id, str) == False or org_id == "":
-            raise Exception("OPENAI_ORG_ID environment variable must be set when USE_AZURE_OPENAI is set to False")
-        # Add the OpenAI text completion service to the kernel
-        kernel.add_chat_service("dv", OpenAIChatCompletion("gpt-3.5-turbo", api_key, org_id))
-
-    else:
-        deployment: str = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME")
-        # Add the Azure OpenAI text completion service to the kernel
-        if isinstance(useAzureAD, str) == True and useAzureAD.lower() == "true":
-            print("Authenticating to Azure OpenAI with Azure AD Workload Identity")
-            credential = DefaultAzureCredential()
-            access_token = credential.get_token("https://cognitiveservices.azure.com/.default")
-            kernel.add_chat_service("dv", AzureChatCompletion(deployment_name=deployment, endpoint=endpoint, api_key=access_token.token, ad_auth=True))
-        else:
-            print("Authenticating to Azure OpenAI with OpenAI API key")
-            kernel.add_chat_service("dv", AzureChatCompletion(deployment, endpoint, api_key))
-
+# initialize the model that would be used for the app
+kernel, useLocalLLM, endpoint = get_llm()
+if not useLocalLLM:
     # Import semantic skills from the "skills" directory
     skills_directory: str = "skills"
     productFunctions: dict = kernel.import_semantic_skill_from_directory(skills_directory, "ProductSkill")