integrated opentelemetry into application_insights code (#191)

Co-authored-by: Josh Bradley <[email protected]>

Author: KennyZhang1

backend/poetry.lock

@@ -10,6 +10,7 @@ authors = [
     "Douglas Orbaker <[email protected]>",
     "Chris Sanchez <[email protected]>",
     "Shane Solomon <[email protected]>",
+    "Kenny Zhang <[email protected]>"
 ]
 readme = "README.md"
 license = "MIT"
@@ -51,9 +52,8 @@ httpx = ">=0.25.2"
 kubernetes = ">=29.0.0"
 networkx = ">=3.2.1"
 nltk = "*"
-opencensus = ">=0.11.4"
-opencensus-context = ">=0.1.3"
-opencensus-ext-azure = ">=1.1.13"
+azure-monitor-opentelemetry-exporter = "*"
+opentelemetry-sdk = ">=1.27.0"
 pandas = ">=2.2.1"
 pyaml-env = ">=1.2.1"
 pyarrow = ">=15.0.0"

backend/pyproject.toml

@@ -69,7 +69,7 @@ async def global_search_streaming(request: GraphRequest):
     else:
         # Current investigations show that community level 1 is the most useful for global search. Set this as the default value
         COMMUNITY_LEVEL = 1
-    
+
     for index_name in sanitized_index_names:
         validate_index_file_exist(index_name, COMMUNITY_REPORT_TABLE)
         validate_index_file_exist(index_name, ENTITIES_TABLE)
@@ -249,7 +249,7 @@ async def local_search_streaming(request: GraphRequest):
     NODES_TABLE = "output/create_final_nodes.parquet"
     RELATIONSHIPS_TABLE = "output/create_final_relationships.parquet"
     TEXT_UNITS_TABLE = "output/create_final_text_units.parquet"
-    
+
     if isinstance(request.community_level, int):
         COMMUNITY_LEVEL = request.community_level
     else:

backend/src/api/query_streaming.py

@@ -46,7 +46,7 @@ class GraphRequest(BaseModel):
 class GraphResponse(BaseModel):
     result: Any
     context_data: Any
-    
+
 
 class GraphDataResponse(BaseModel):
     nodes: int

backend/src/models.py

@@ -12,8 +12,17 @@
     Optional,
 )
 
+from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter
 from datashaper.workflow.workflow_callbacks import NoopWorkflowCallbacks
-from opencensus.ext.azure.log_exporter import AzureLogHandler
+from opentelemetry._logs import (
+    get_logger_provider,
+    set_logger_provider,
+)
+from opentelemetry.sdk._logs import (
+    LoggerProvider,
+    LoggingHandler,
+)
+from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
 
 
 class ApplicationInsightsWorkflowCallbacks(NoopWorkflowCallbacks):
@@ -74,15 +83,23 @@ def __init_logger(self, connection_string, max_logger_init_retries: int = 10):
             unique_hash = hashlib.sha256(current_time.encode()).hexdigest()
             self._logger_name = f"{self.__class__.__name__}-{unique_hash}"
             if self._logger_name not in logging.Logger.manager.loggerDict:
+                # attach azure monitor log exporter to logger provider
+                logger_provider = LoggerProvider()
+                set_logger_provider(logger_provider)
+                exporter = AzureMonitorLogExporter(connection_string=connection_string)
+                get_logger_provider().add_log_record_processor(
+                    BatchLogRecordProcessor(
+                        exporter=exporter,
+                        schedule_delay_millis=60000,
+                    )
+                )
                 # instantiate new logger
                 self._logger = logging.getLogger(self._logger_name)
                 self._logger.propagate = False
                 # remove any existing handlers
                 self._logger.handlers.clear()
-                # set up Azure Monitor
-                self._logger.addHandler(
-                    AzureLogHandler(connection_string=connection_string)
-                )
+                # fetch handler from logger provider and attach to class
+                self._logger.addHandler(LoggingHandler())
                 # set logging level
                 self._logger.setLevel(logging.DEBUG)
 
@@ -91,8 +108,7 @@ def __init_logger(self, connection_string, max_logger_init_retries: int = 10):
 
     def _format_details(self, details: Dict[str, Any] | None = None) -> Dict[str, Any]:
         """
-        Format the details dictionary to comply with the Application Insights structured.
-
+        Format the details dictionary to comply with the Application Insights structured
         logging Property column standard.
 
         Args:

backend/src/reporting/application_insights_workflow_callbacks.py

@@ -343,23 +343,28 @@ deployAzureResources () {
         --parameters "enablePrivateEndpoints=$ENABLE_PRIVATE_ENDPOINTS" \
         --parameters "acrName=$CONTAINER_REGISTRY_NAME" \
         --output json)
+    # errors in deployment may not be caught by exitIfCommandFailed function so we also check the output for errors
     exitIfCommandFailed $? "Error deploying Azure resources..."
+    exitIfValueEmpty "$AZURE_DEPLOY_RESULTS" "Error deploying Azure resources..."
     AZURE_OUTPUTS=$(jq -r .properties.outputs <<< $AZURE_DEPLOY_RESULTS)
-    exitIfCommandFailed $? "Error parsing outputs from Azure resource deployment..."
+    exitIfCommandFailed $? "Error parsing outputs from Azure deployment..."
+    exitIfValueEmpty "$AZURE_OUTPUTS" "Error parsing outputs from Azure deployment..."
     assignAOAIRoleToManagedIdentity
 }
 
 validateSKUs() {
     # Run SKU validation functions unless skip flag is set
-    if [ $2 = true ]; then
-        checkSKUAvailability $1
-        checkSKUQuotas $1
+    local location=$1
+    local validate_skus=$2
+    if [ $validate_skus = true ]; then
+        checkSKUAvailability $location
+        checkSKUQuotas $location
     fi
 }
 
 checkSKUAvailability() {
     # Function to validate that the required SKUs are not restricted for the given region
-    printf "Checking Location for SKU Availability... "
+    printf "Checking cloud region for VM sku availability... "
     local location=$1
     local sku_checklist=("standard_d4s_v5" "standard_d8s_v5" "standard_e8s_v5")
     for sku in ${sku_checklist[@]}; do
@@ -682,6 +687,9 @@ startBanner
 checkRequiredTools
 populateParams $PARAMS_FILE
 
+# Check SKU availability and quotas
+validateSKUs $LOCATION $VALIDATE_SKUS_FLAG
+
 # Create resource group
 createResourceGroupIfNotExists $LOCATION $RESOURCE_GROUP
 
@@ -690,7 +698,6 @@ createSshkeyIfNotExists $RESOURCE_GROUP
 
 # Deploy Azure resources
 checkForApimSoftDelete
-validateSKUs $LOCATION $VALIDATE_SKUS_FLAG
 deployAzureResources
 
 # Deploy the graphrag backend docker image to ACR

infra/deploy.sh

@@ -30,6 +30,9 @@ ingress:
     service.beta.kubernetes.io/azure-load-balancer-internal: "true"
 
 graphragConfig:
+  AI_SEARCH_AUDIENCE: ""
+  AI_SEARCH_URL: ""
+  APP_INSIGHTS_CONNECTION_STRING: ""
   COSMOS_URI_ENDPOINT: ""
   GRAPHRAG_API_BASE: ""
   GRAPHRAG_API_VERSION: ""
@@ -40,7 +43,6 @@ graphragConfig:
   GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME: ""
   REPORTERS: "blob,console,app_insights"
   STORAGE_ACCOUNT_BLOB_URL: ""
-  AI_SEARCH_URL: ""
 
 master:
   name: "master"

infra/helm/graphrag/values.yaml

@@ -327,20 +327,28 @@
     "%%time\n",
     "\n",
     "\n",
-    "def global_search(index_name: str | list[str], query: str, community_level: int) -> requests.Response:\n",
+    "def global_search(\n",
+    "    index_name: str | list[str], query: str, community_level: int\n",
+    ") -> requests.Response:\n",
     "    \"\"\"Run a global query over the knowledge graph(s) associated with one or more indexes\"\"\"\n",
     "    url = endpoint + \"/query/global\"\n",
     "    # optional parameter: community level to query the graph at (default for global query = 1)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    return requests.post(url, json=request, headers=headers)\n",
     "\n",
     "\n",
     "# perform a global query\n",
     "global_response = global_search(\n",
-    "    index_name=index_name, query=\"Summarize the main topics found in this data\", community_level=1\n",
+    "    index_name=index_name,\n",
+    "    query=\"Summarize the main topics found in this data\",\n",
+    "    community_level=1,\n",
     ")\n",
     "global_response_data = parse_query_response(global_response, return_context_data=True)\n",
-    "global_response_data\n"
+    "global_response_data"
    ]
   },
   {
@@ -361,17 +369,25 @@
     "%%time\n",
     "\n",
     "\n",
-    "def local_search(index_name: str | list[str], query: str, community_level: int) -> requests.Response:\n",
+    "def local_search(\n",
+    "    index_name: str | list[str], query: str, community_level: int\n",
+    ") -> requests.Response:\n",
     "    \"\"\"Run a local query over the knowledge graph(s) associated with one or more indexes\"\"\"\n",
     "    url = endpoint + \"/query/local\"\n",
     "    # optional parameter: community level to query the graph at (default for local query = 2)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    return requests.post(url, json=request, headers=headers)\n",
     "\n",
     "\n",
     "# perform a local query\n",
     "local_response = local_search(\n",
-    "    index_name=index_name, query=\"Summarize the main topics found in this data\", community_level=2\n",
+    "    index_name=index_name,\n",
+    "    query=\"Summarize the main topics found in this data\",\n",
+    "    community_level=2,\n",
     ")\n",
     "local_response_data = parse_query_response(local_response, return_context_data=True)\n",
     "local_response_data"
@@ -380,7 +396,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "graphrag-venv",
    "language": "python",
    "name": "python3"
   },

notebooks/1-Quickstart.ipynb

@@ -324,11 +324,17 @@
     "    return requests.get(url, headers=headers)\n",
     "\n",
     "\n",
-    "def global_search(index_name: str | list[str], query: str, community_level: int) -> requests.Response:\n",
+    "def global_search(\n",
+    "    index_name: str | list[str], query: str, community_level: int\n",
+    ") -> requests.Response:\n",
     "    \"\"\"Run a global query over the knowledge graph(s) associated with one or more indexes\"\"\"\n",
     "    url = endpoint + \"/query/global\"\n",
     "    # optional parameter: community level to query the graph at (default for global query = 1)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    return requests.post(url, json=request, headers=headers)\n",
     "\n",
     "\n",
@@ -338,7 +344,11 @@
     "    \"\"\"Run a global query across one or more indexes and stream back the response\"\"\"\n",
     "    url = endpoint + \"/query/streaming/global\"\n",
     "    # optional parameter: community level to query the graph at (default for global query = 1)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    context_list = []\n",
     "    with requests.post(url, json=request, headers=headers, stream=True) as r:\n",
     "        r.raise_for_status()\n",
@@ -358,11 +368,17 @@
     "    display(pd.DataFrame.from_dict(context_list[0][\"reports\"]).head(10))\n",
     "\n",
     "\n",
-    "def local_search(index_name: str | list[str], query: str, community_level: int) -> requests.Response:\n",
+    "def local_search(\n",
+    "    index_name: str | list[str], query: str, community_level: int\n",
+    ") -> requests.Response:\n",
     "    \"\"\"Run a local query over the knowledge graph(s) associated with one or more indexes\"\"\"\n",
     "    url = endpoint + \"/query/local\"\n",
     "    # optional parameter: community level to query the graph at (default for local query = 2)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    return requests.post(url, json=request, headers=headers)\n",
     "\n",
     "\n",
@@ -372,7 +388,11 @@
     "    \"\"\"Run a global query across one or more indexes and stream back the response\"\"\"\n",
     "    url = endpoint + \"/query/streaming/local\"\n",
     "    # optional parameter: community level to query the graph at (default for local query = 2)\n",
-    "    request = {\"index_name\": index_name, \"query\": query, \"community_level\": community_level}\n",
+    "    request = {\n",
+    "        \"index_name\": index_name,\n",
+    "        \"query\": query,\n",
+    "        \"community_level\": community_level,\n",
+    "    }\n",
     "    context_list = []\n",
     "    with requests.post(url, json=request, headers=headers, stream=True) as r:\n",
     "        r.raise_for_status()\n",
@@ -746,7 +766,9 @@
     "%%time\n",
     "# pass in a single index name as a string or to query across multiple indexes, set index_name=[myindex1, myindex2]\n",
     "global_response = global_search(\n",
-    "    index_name=index_name, query=\"Summarize the main topics found in this data\", community_level=1\n",
+    "    index_name=index_name,\n",
+    "    query=\"Summarize the main topics found in this data\",\n",
+    "    community_level=1,\n",
     ")\n",
     "# print the result and save context data in a variable\n",
     "global_response_data = parse_query_response(global_response, return_context_data=True)\n",
@@ -769,7 +791,9 @@
    "outputs": [],
    "source": [
     "global_search_streaming(\n",
-    "    index_name=index_name, query=\"Summarize the main topics found in this data\", community_level=1\n",
+    "    index_name=index_name,\n",
+    "    query=\"Summarize the main topics found in this data\",\n",
+    "    community_level=1,\n",
     ")"
    ]
   },
@@ -797,7 +821,7 @@
     "local_response = local_search(\n",
     "    index_name=index_name,\n",
     "    query=\"Who are the primary actors in these communities?\",\n",
-    "    community_level=2\n",
+    "    community_level=2,\n",
     ")\n",
     "# print the result and save context data in a variable\n",
     "local_response_data = parse_query_response(local_response, return_context_data=True)\n",
@@ -822,7 +846,7 @@
     "local_search_streaming(\n",
     "    index_name=index_name,\n",
     "    query=\"Who are the primary actors in these communities?\",\n",
-    "    community_level=2\n",
+    "    community_level=2,\n",
     ")"
    ]
   },