Cleanup

.devcontainer/devcontainer.json

@@ -4,7 +4,7 @@
         "dockerfile": "Dockerfile",
         "args": {
             "VARIANT": "bullseye",
-            "VERSION": "3.11"
+            "VERSION": "3.12"
         }
     },
     "features": {

README.md

@@ -77,6 +77,27 @@ curl -X 'POST' \
   "correlationToken": "1234567890"
 }'
 
+curl -X 'POST' \
+  "$URL/ask" \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "question": "Does The Lost City have any sequels planned? True or False",
+  "type": "true_or_false",
+  "correlationToken": "1234567890"
+}'
+
+curl -X 'POST' \
+  "$URL/ask" \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "question": "Which country does The Lost Planet take place in?",
+  "type": "multiple_choice",
+  "correlationToken": "1234567890"
+}'
+
+
 ```
 
 ## Deploy resources for Phase 1
@@ -86,7 +107,7 @@ Run the following script
 ```
 azd env get-values | grep AZURE_ENV_NAME
 source <(azd env get-values | grep AZURE_ENV_NAME)
-bash ./azd-hooks/deploy.sh phase1 $AZURE_ENV_NAME
+bash ./azd-hooks/deploy.sh phase3 $AZURE_ENV_NAME
 ```
 
 All the other phases work the same.

infra/app/phase1.bicep

@@ -76,6 +76,14 @@ module app '../core/host/container-app-upsert.bicep' = {
         name: 'OPENAI_API_TYPE'
         value: 'azure'
       }
+      {
+        name: 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME'
+        value: 'text-embedding-ada-002'
+      }
+      {
+        name: 'AZURE_OPENAI_EMBEDDING_MODEL'
+        value: 'text-embedding-ada-002'
+      }
     ]
     targetPort: 80
   }

infra/app/phaseX.bicep

@@ -70,6 +70,14 @@ module app '../core/host/container-app-upsert.bicep' = {
         name: 'OPENAI_API_TYPE'
         value: 'azure'
       }
+      {
+        name: 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME'
+        value: 'text-embedding-ada-002'
+      }
+      {
+        name: 'AZURE_OPENAI_EMBEDDING_MODEL'
+        value: 'text-embedding-ada-002'
+      }
     ]
     targetPort: 8080
   }

requirements.txt

@@ -1,13 +1,7 @@
-langchain==0.2.0
-langchain-community==0.2.1
-langchain-core==0.2.3
-langchain-openai==0.1.8
 openai==1.30.5
 pydantic==2.6.4
 python-dotenv==1.0.1
 tiktoken==0.7.0
-markdown==3.6
-unstructured==0.14.3
 azure-search-documents==11.4.0
 azure-identity==1.15.0
 uvicorn==0.23.2

src-agents/phase1/notebook_p1.ipynb

@@ -182,7 +182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.-1"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

src-agents/phase1/requirements.txt

@@ -1,15 +1,8 @@
-langchain==0.2.0
-langchain-community==0.2.1
-langchain-core==0.2.3
-langchain-openai==0.1.8
 openai==1.30.5
 pydantic==2.6.4
 python-dotenv==1.0.1
 tiktoken==0.7.0
-markdown==3.6
-unstructured==0.14.3
 azure-search-documents==11.4.0
 azure-identity==1.15.0
 uvicorn==0.23.2
-fastapi==0.110.0
-starlette==0.37.2
+fastapi==0.110.0

src-agents/phase2/main.py

@@ -10,7 +10,6 @@
 from azure.search.documents.models import (
     VectorizedQuery
 )
-from langchain_openai import AzureOpenAIEmbeddings
 
 app = FastAPI()
 
@@ -44,14 +43,19 @@ class Answer(BaseModel):
 else:
     token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
     client = AzureOpenAI(
-        azure_ad_token_provider=token_provider,
+        azure_ad_token_provider = token_provider,
         api_version = os.getenv("AZURE_OPENAI_VERSION"),
         azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),
     )
 
 deployment_name = os.getenv("AZURE_OPENAI_COMPLETION_DEPLOYMENT_NAME")
 index_name = "movies-semantic-index"
 service_endpoint = os.getenv("AZURE_AI_SEARCH_ENDPOINT")
+embedding_model = os.getenv("AZURE_OPENAI_EMBEDDING_MODEL")
+
+# use an embeddingsmodel to create embeddings
+def get_embedding(text, model=embedding_model):
+    return client.embeddings.create(input = [text], model=model).data[0].embedding
 
 credential = None
 if "AZURE_AI_SEARCH_KEY" in os.environ:
@@ -65,13 +69,6 @@ class Answer(BaseModel):
     credential
 )
 
-# use an embeddingsmodel to create embeddings
-embeddings_model = AzureOpenAIEmbeddings(    
-    azure_deployment = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME"),
-    openai_api_version = os.getenv("OPENAI_EMBEDDING_API_VERSION"),
-    model= os.getenv("AZURE_OPENAI_EMBEDDING_MODEL")
-)
-
 @app.get("/")
 async def root():
     return {"message": "Hello Smorgs"}
@@ -81,7 +78,6 @@ async def get_products(query: str = None):
     """
     Returns a status of the app
     """
-
 @app.post("/ask", summary="Ask a question", operation_id="ask") 
 async def ask_question(ask: Ask):
     """
@@ -114,4 +110,4 @@ async def ask_question(ask: Ask):
     answer.promptTokensUsed = response.usage.prompt_tokens
     answer.completionTokensUsed = response.usage.completion_tokens
 
-    return answer
+    return answer

src-agents/phase2/notebook_p2.ipynb

@@ -159,7 +159,7 @@
    "metadata": {},
    "source": [
     "## Load custom data\n",
-    "Here's how you load custom data into an embedding model and how you use langchain to query it.\n"
+    "Here's how you load custom vector data by means of an embedding model and then query it.\n"
    ]
   },
   {
@@ -170,15 +170,18 @@
    "source": [
     "import os\n",
     "import json\n",
-    "from langchain_openai import AzureOpenAIEmbeddings\n",
+    "from openai import AzureOpenAI\n",
     "from azure.search.documents import SearchClient\n",
     "\n",
+    "client = AzureOpenAI(\n",
+    "        api_key = os.getenv(\"AZURE_OPENAI_API_KEY\"),  \n",
+    "        api_version = os.getenv(\"AZURE_OPENAI_VERSION\"),\n",
+    "        azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\")\n",
+    "    )\n",
+    "\n",
     "# use an embeddingsmodel to create embeddings\n",
-    "embeddings_model = AzureOpenAIEmbeddings(    \n",
-    "    azure_deployment = os.getenv(\"AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME\"),\n",
-    "    openai_api_version = os.getenv(\"OPENAI_EMBEDDING_API_VERSION\"),\n",
-    "    model= os.getenv(\"AZURE_OPENAI_EMBEDDING_MODEL\")\n",
-    ")\n",
+    "def get_embedding(text, model=os.getenv(\"AZURE_OPENAI_EMBEDDING_MODEL\")):\n",
+    "    return client.embeddings.create(input = [text], model=model).data[0].embedding\n",
     "\n",
     "# 1. define function to parse csv row and create embedding for overview text\n",
     "def parseMovie(movie):\n",
@@ -190,7 +193,7 @@
     "        (\"year\", str(movie[\"movie_year\"])),\n",
     "        (\"rating\", str(movie[\"movie_rating\"])),\n",
     "        (\"plot\", movie[\"movie_plot\"]),\n",
-    "        (\"vector\", embeddings_model.embed_query(movie[\"movie_plot\"]))\n",
+    "        (\"vector\", get_embedding(movie[\"movie_plot\"]))\n",
     "    ])\n",
     "\n",
     "# 2. load movies from csv\n",
@@ -254,7 +257,7 @@
     "question = \"Tell me about the latest Ant Man movie. When was it released?\"\n",
     "\n",
     "# create a vectorized query based on the question\n",
-    "vector = VectorizedQuery(vector=embeddings_model.embed_query(question), k_nearest_neighbors=5, fields=\"vector\")\n",
+    "vector = VectorizedQuery(vector=get_embedding(question), k_nearest_neighbors=5, fields=\"vector\")\n",
     "\n",
     "\n",
     "# create search client to retrieve movies from the vector store\n",
@@ -318,11 +321,15 @@
     "    \"\"\"\n",
     "    Ask a question\n",
     "    \"\"\"\n",
-    "    #####\n",
-    "    # implement rag flow here\n",
-    "    ######\n",
     "    \n",
-    "    print(answer)\n",
+    "    answer = \"I dont know\"\n",
+    "\n",
+    "    #####\\n\",\n",
+    "    # implement rag flow here\\n\",\n",
+    "    ######\\n\",\n",
+    "   \n",
+    "    \n",
+    "    print('Answer:', answer)\n",
     "    return answer"
    ]
   },
@@ -340,9 +347,9 @@
    "outputs": [],
    "source": [
     "\n",
-    "ask = Ask(question=\"Tell me about the latest Ant Man movie. When was it released? \", type=QuestionType.estimation)\n",
+    "ask = Ask(question=\"How many actors were featured in The Smonger Games?\", type=QuestionType.estimation)\n",
     "answer = await ask_question(ask)\n",
-    "print('Answer:', answer)\n"
+    "print('Answer:', answer)"
    ]
   },
   {
@@ -372,7 +379,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.-1"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,

src-agents/phase2/requirements.txt

@@ -1,13 +1,7 @@
-langchain==0.2.0
-langchain-community==0.2.1
-langchain-core==0.2.3
-langchain-openai==0.1.8
 openai==1.30.5
 pydantic==2.6.4
 python-dotenv==1.0.1
 tiktoken==0.7.0
-markdown==3.6
-unstructured==0.14.3
 azure-search-documents==11.4.0
 azure-identity==1.15.0
 uvicorn==0.23.2

src-agents/phase3/notebook_p3.ipynb

@@ -92,14 +92,14 @@
    "source": [
     "import requests\n",
     "\n",
-    "smoorghApi = \"http://localhost:5393\"\n",
+    "smoorghApi = \"https://.northeurope.azurecontainerapps.io/\"\n",
     "\n",
     "def get_movie_rating(title):\n",
     "    try:\n",
     "        response = requests.get(f\"{smoorghApi}/rating/{title}\")\n",
     "        print('The api response for rating is:', response.text)\n",
     "        return response.text\n",
-    "\n",
+    "    \n",
     "    except:\n",
     "        return \"Sorry, I couldn't find a rating for that movie.\"\n",
     "\n",
@@ -119,7 +119,7 @@
     "        return response.text\n",
     "\n",
     "    except:\n",
-    "        return \"Sorry, I couldn't find a actor for that movie.\"\n",
+    "        return \"Sorry, I couldn't find an actor for that movie.\"\n",
     "    \n",
     "def get_movie_location(title):\n",
     "    try:\n",
@@ -128,7 +128,7 @@
     "        return response.text\n",
     "\n",
     "    except:\n",
-    "        return \"Sorry, I couldn't find a actor for that movie.\"\n",
+    "        return \"Sorry, I couldn't find a location for that movie.\"\n",
     "\n",
     "def get_movie_genre(title):\n",
     "    try:\n",
@@ -137,7 +137,7 @@
     "        return response.text\n",
     "\n",
     "    except:\n",
-    "        return \"Sorry, I couldn't find a actor for that movie.\"\n",
+    "        return \"Sorry, I couldn't find a genre for that movie.\"\n",
     "\n",
     "print(get_movie_rating(\"The Lost Planet\"))\n",
     "\n",
@@ -228,7 +228,7 @@
     "    \n",
     "    question = ask.question\n",
     "    messages= [{\"role\" : \"assistant\", \"content\" : question}\n",
-    "               , { \"role\" : \"system\", \"content\" : \"Answer the question of the user and use the tools available to you\"}\n",
+    "               , { \"role\" : \"system\", \"content\" : \"Answer the question of the user and use the tools available to you.\"}\n",
     "               ]\n",
     "    first_response = client.chat.completions.create(\n",
     "        model = deployment_name,\n",
@@ -299,7 +299,7 @@
    "outputs": [],
    "source": [
     "\n",
-    "ask = Ask(question=\"Which country does 'The Crystal of Power' take place in??\", type=QuestionType.estimation)\n",
+    "ask = Ask(question=\"Which country does 'The Lost Planet' take place in?\", type=QuestionType.estimation)\n",
     "answer = await ask_question(ask)\n",
     "print('Answer:', answer )\n"
    ]

src-agents/phase3/requirements.txt

@@ -1,13 +1,7 @@
-langchain==0.2.0
-langchain-community==0.2.1
-langchain-core==0.2.3
-langchain-openai==0.1.8
 openai==1.30.5
 pydantic==2.6.4
 python-dotenv==1.0.1
 tiktoken==0.7.0
-markdown==3.6
-unstructured==0.14.3
 azure-search-documents==11.4.0
 azure-identity==1.15.0
 uvicorn==0.23.2