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setup.py
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import logging
import time
import requests
import argparse
import json
from azure.mgmt.web import WebSiteManagementClient
from azure.identity import ManagedIdentityCredential, AzureCliCredential, ChainedTokenCredential
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError
# Set up logging configuration globally
logging.getLogger('azure').setLevel(logging.WARNING)
def call_search_api(search_service, search_api_version, resource_type, resource_name, method, credential, body=None):
"""
Calls the Azure Search API with the specified parameters.
"""
token = credential.get_token("https://search.azure.com/.default").token
headers = {
"Authorization": f"Bearer {token}",
'Content-Type': 'application/json'
}
search_endpoint = f"https://{search_service}.search.windows.net/{resource_type}/{resource_name}?api-version={search_api_version}"
response = None
try:
if method not in ["get", "put", "delete"]:
logging.warning(f"[call_search_api] Invalid method {method} ")
if method == "get":
response = requests.get(search_endpoint, headers=headers)
elif method == "put":
response = requests.put(search_endpoint, headers=headers, json=body)
if method == "delete":
response = requests.delete(search_endpoint, headers=headers)
status_code = response.status_code
logging.info(f"[call_search_api] Successfully called search API {method} {resource_type} {resource_name}. Code: {status_code}.")
if response is not None:
status_code = response.status_code
if status_code >= 400:
logging.warning(f"[call_search_api] {status_code} code when calling search API {method} {resource_type} {resource_name}. Reason: {response.reason}.")
try:
response_text_dict = json.loads(response.text)
logging.warning(f"[call_search_api] {status_code} code when calling search API {method} {resource_type} {resource_name}. Message: {response_text_dict['error']['message']}")
except json.JSONDecodeError:
logging.warning(f"[call_search_api] {status_code} Response is not valid JSON. Raw response:\n{response.text}")
else:
logging.info(f"[call_search_api] Successfully called search API {method} {resource_type} {resource_name}. Code: {status_code}.")
except Exception as e:
error_message = str(e)
logging.error(f"Error when calling search API {method} {resource_type} {resource_name}. Error: {error_message}")
def get_function_key(subscription_id, resource_group, function_app_name, credential):
"""
Returns an API key for the given function.
"""
logging.info(f"Obtaining function key after creating or updating its value.")
accessToken = f"Bearer {credential.get_token('https://management.azure.com/.default').token}"
requestUrl = f"https://management.azure.com/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.Web/sites/{function_app_name}/functions/document_chunking/keys/mykey?api-version=2022-03-01"
requestHeaders = {
"Authorization": accessToken,
"Content-Type": "application/json"
}
data = {
"properties": {
"name": "mykey"
}
}
response = requests.put(requestUrl, headers=requestHeaders, json=data)
response_json = json.loads(response.content.decode('utf-8'))
try:
function_key = response_json['properties']['value']
except Exception as e:
function_key = None
logging.error(f"Error when getting function key. Details: {str(e)}.")
return function_key
def approve_private_link_connections(access_token, subscription_id, resource_group, service_name, service_type, api_version):
"""
Approves private link service connections for a given service.
"""
logging.info(f"[approve_private_link_connections] Access token: {access_token[:10]}...")
logging.info(f"[approve_private_link_connections] Subscription ID: {subscription_id}")
logging.info(f"[approve_private_link_connections] Resource group: {resource_group}")
logging.info(f"[approve_private_link_connections] Service name: {service_name}")
logging.info(f"[approve_private_link_connections] Service type: {service_type}")
logging.info(f"[approve_private_link_connections] API version: {api_version}")
list_url = (
f"https://management.azure.com/subscriptions/{subscription_id}"
f"/resourceGroups/{resource_group}/providers/{service_type}/{service_name}"
f"/privateEndpointConnections?api-version={api_version}"
)
logging.debug(f"[approve_private_link_connections] Request URL: {list_url}")
request_headers = {
"Authorization": access_token,
"Content-Type": "application/json"
}
try:
response = requests.get(list_url, headers=request_headers)
response.raise_for_status()
response_json = response.json()
if 'value' not in response_json:
logging.error(
f"Unexpected response structure when fetching private link connections. "
f"Response content: {response.content}"
)
return
for connection in response_json["value"]:
connection_id = connection["id"]
connection_name = connection["name"]
status = connection["properties"]["privateLinkServiceConnectionState"]["status"]
logging.info(f"[approve_private_link_connections] Checking connection '{connection_name}'. Status: {status}.")
if status.lower()== "pending":
single_connection_url = f"https://management.azure.com{connection_id}?api-version={api_version}"
logging.debug(f"[approve_private_link_connections] GET single connection URL: {single_connection_url}")
try:
single_conn_response = requests.get(single_connection_url, headers=request_headers)
single_conn_response.raise_for_status()
full_conn_resource = single_conn_response.json()
except requests.HTTPError as http_err:
logging.warning(
f"Failed to GET full connection resource for '{connection_name}': {http_err}. "
f"Response: {single_conn_response.text if 'single_conn_response' in locals() else ''}"
)
continue
full_conn_resource["properties"]["privateLinkServiceConnectionState"]["status"] = "Approved"
full_conn_resource["properties"]["privateLinkServiceConnectionState"]["description"] = "Approved by setup script"
logging.debug(f"[approve_private_link_connections] PUT single connection URL: {single_connection_url}")
approve_response = requests.put(single_connection_url, headers=request_headers, json=full_conn_resource)
if approve_response.status_code in [200, 202]:
logging.info(
f"Approved private endpoint connection '{connection_name}' for service '{service_name}'."
)
else:
logging.warning(
f"Warning: Failed to approve private endpoint connection '{connection_name}' "
f"for service '{service_name}'. Status Code: {approve_response.status_code}, "
f"Response: {approve_response.text}"
)
elif status.lower() == "approved":
logging.info(f"[approve_private_link_connections] Connection '{connection_name}' is already Approved. Skipping re-approval.")
continue
except requests.HTTPError as http_err:
logging.warning(
f"HTTP error occurred when listing/approving private link connections: {http_err}. "
f"Response: {response.text}"
)
except Exception as e:
logging.warning(f"Error occurred when approving private link connections: {e}")
def approve_search_shared_private_access(subscription_id, resource_group, storage_resource_group, aoai_resource_group, function_app_name, storage_account_name, openai_service_name, credential):
"""
Approves Shared Private Access requests for private endpoints.
"""
try:
logging.info("Approving search shared private links.")
try:
token_response = credential.get_token("https://management.azure.com/.default")
access_token = f"Bearer {token_response.token}"
logging.info("Obtained access token successfully.")
except ClientAuthenticationError as e:
logging.error(f"Authentication failed when obtaining access token: {e}")
raise
except Exception as e:
logging.error(f"Unexpected error when obtaining access token: {e}")
raise
try:
approve_private_link_connections(
access_token,
subscription_id,
storage_resource_group,
storage_account_name,
'Microsoft.Storage/storageAccounts',
'2023-01-01'
)
logging.info(f"[approve_private_link_connections] Approved private link connections for Storage Account: {storage_account_name}.")
except Exception as e:
logging.error(f"Failed to approve private link connections for Storage Account '{storage_account_name}': {e}")
raise
try:
approve_private_link_connections(
access_token,
subscription_id,
resource_group,
function_app_name,
'Microsoft.Web/sites',
'2022-09-01'
)
logging.info(f"[approve_private_link_connections] Approved private link connections for Function App: {function_app_name}.")
except Exception as e:
logging.error(f"Failed to approve private link connections for Function App '{function_app_name}': {e}")
raise
try:
approve_private_link_connections(
access_token,
subscription_id,
aoai_resource_group,
openai_service_name,
'Microsoft.CognitiveServices/accounts',
'2022-10-01'
)
logging.info(f"Approved private link connections for Azure OpenAI Service: {openai_service_name}.")
except Exception as e:
logging.error(f"Failed to approve private link connections for Azure OpenAI Service '{openai_service_name}': {e}")
raise
except Exception as e:
error_message = str(e)
logging.error(f"Error when approving private link service connection. Please do it manually. Error: {error_message}")
raise
def execute_setup(subscription_id, resource_group, function_app_name, search_principal_id, azure_search_use_mis, enable_managed_identities, enable_env_credentials):
"""
This function performs the necessary steps to set up the ingestion sub components.
"""
logging.info(f"Getting function app {function_app_name} properties.")
credential = ChainedTokenCredential(
ManagedIdentityCredential(),
AzureCliCredential()
)
web_mgmt_client = WebSiteManagementClient(credential, subscription_id)
function_app_settings = web_mgmt_client.web_apps.list_application_settings(resource_group, function_app_name)
function_endpoint = f"https://{function_app_name}.azurewebsites.net"
azure_openai_service_name = function_app_settings.properties["AZURE_OPENAI_SERVICE_NAME"]
search_service = function_app_settings.properties["AZURE_SEARCH_SERVICE"]
search_analyzer_name = function_app_settings.properties["SEARCH_ANALYZER_NAME"]
search_api_version = function_app_settings.properties.get("SEARCH_API_VERSION", "2024-07-01")
search_index_interval = function_app_settings.properties["SEARCH_INDEX_INTERVAL"]
search_index_name = function_app_settings.properties["SEARCH_INDEX_NAME"]
storage_container = function_app_settings.properties["STORAGE_CONTAINER"]
storage_account_name = function_app_settings.properties["STORAGE_ACCOUNT_NAME"]
network_isolation = True if function_app_settings.properties["NETWORK_ISOLATION"].lower() == "true" else False
storage_container = function_app_settings.properties["STORAGE_CONTAINER"]
storage_account_name = function_app_settings.properties["STORAGE_ACCOUNT_NAME"]
azure_openai_embedding_deployment = function_app_settings.properties.get("AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding")
azure_openai_embedding_model = function_app_settings.properties.get("AZURE_OPENAI_EMBEDDING_MODEL", "text-embedding-3-large")
azure_embeddings_vector_size = function_app_settings.properties.get("AZURE_EMBEDDINGS_VECTOR_SIZE", "3072")
azure_storage_resource_group = function_app_settings.properties["AZURE_STORAGE_ACCOUNT_RG"]
azure_aoai_resource_group = function_app_settings.properties["AZURE_AOAI_RG"]
logging.info(f"[execute_setup] Function endpoint: {function_endpoint}")
logging.info(f"[execute_setup] Search service: {search_service}")
logging.info(f"[execute_setup] Search analyzer name: {search_analyzer_name}")
logging.info(f"[execute_setup] Search API version: {search_api_version}")
logging.info(f"[execute_setup] Search index interval: {search_index_interval}")
logging.info(f"[execute_setup] Search index name: {search_index_name}")
logging.info(f"[execute_setup] Storage container: {storage_container}")
logging.info(f"[execute_setup] Storage account name: {storage_account_name}")
logging.info(f"[execute_setup] Embedding deployment name: {azure_openai_embedding_deployment}")
logging.info(f"[execute_setup] Embedding model: {azure_openai_embedding_model}")
logging.info(f"[execute_setup] Embedding vector size: {azure_embeddings_vector_size}")
logging.info(f"[execute_setup] Resource group: {resource_group}")
logging.info(f"[execute_setup] Storage resource group: {azure_storage_resource_group}")
logging.info(f"[execute_setup] Azure OpenAI resource group: {azure_aoai_resource_group}")
###########################################################################
# NL2SQL Elements
###########################################################################
storage_container_nl2sql = "nl2sql"
search_index_name_nl2sql_queries = "nl2sql-queries"
search_index_name_nl2sql_tables = "nl2sql-tables"
search_index_name_nl2sql_measures = "nl2sql-measures" # New measures index
logging.info(f"[execute_setup] NL2SQL Storage container: {storage_container_nl2sql}")
logging.info(f"[execute_setup] NL2SQL Search index name (queries): {search_index_name_nl2sql_queries}")
logging.info(f"[execute_setup] NL2SQL Search index name (tables): {search_index_name_nl2sql_tables}")
logging.info(f"[execute_setup] NL2SQL Search index name (measures): {search_index_name_nl2sql_measures}")
###########################################################################
# Get function key to be used later when creating the skillset
###########################################################################
function_key = get_function_key(subscription_id, resource_group, function_app_name, credential)
if function_key is None:
logging.error(f"Could not get function key. Please make sure the function {function_app_name}/document_chunking is deployed before running this script.")
exit(1)
###########################################################################
# Approve Search Shared Private Links (if needed)
###########################################################################
logging.info("Approving search shared private links.")
approve_search_shared_private_access(subscription_id, resource_group, azure_storage_resource_group, azure_aoai_resource_group, function_app_name, storage_account_name, azure_openai_service_name, credential)
###########################################################################
# Creating blob containers
###########################################################################
# Note: this step was removed since the storage account and container are already created by azd provision
###############################################################################
# Creating AI Search datasource
###############################################################################
def create_datasource(search_service, search_api_version, datasource_name, storage_connection_string, container_name, credential, subfolder=None):
body = {
"description": f"Datastore for {datasource_name}",
"type": "azureblob",
"dataDeletionDetectionPolicy": {
"@odata.type": "#Microsoft.Azure.Search.NativeBlobSoftDeleteDeletionDetectionPolicy"
},
"credentials": {
"connectionString": storage_connection_string
},
"container": {
"name": container_name,
"query": f"{subfolder}/" if subfolder else ""
}
}
call_search_api(search_service, search_api_version, "datasources", f"{datasource_name}-datasource", "put", credential, body)
logging.info("Creating datasources step.")
start_time = time.time()
storage_connection_string = f"ResourceId=/subscriptions/{subscription_id}/resourceGroups/{azure_storage_resource_group}/providers/Microsoft.Storage/storageAccounts/{storage_account_name}/;"
create_datasource(search_service, search_api_version, f"{search_index_name}", storage_connection_string, storage_container, credential)
nl2sql_subfolders = {
"queries": search_index_name_nl2sql_queries,
"tables": search_index_name_nl2sql_tables,
"measures": search_index_name_nl2sql_measures # New datasource for measures
}
for subfolder, index_name in nl2sql_subfolders.items():
create_datasource(search_service, search_api_version, index_name, storage_connection_string, "nl2sql", credential, subfolder=subfolder)
response_time = time.time() - start_time
logging.info(f"Create datastores step. {round(response_time, 2)} seconds")
###############################################################################
# Creating indexes
###############################################################################
def create_index_body(index_name, fields, content_fields_name, keyword_field_name, vector_profile_name="myHnswProfile", vector_algorithm_name="myHnswConfig"):
body = {
"name": index_name,
"fields": fields,
"corsOptions": {
"allowedOrigins": ["*"],
"maxAgeInSeconds": 60
},
"vectorSearch": {
"profiles": [
{
"name": vector_profile_name,
"algorithm": vector_algorithm_name
}
],
"algorithms": [
{
"name": vector_algorithm_name,
"kind": "hnsw",
"hnswParameters": {
"m": 4,
"efConstruction": 400,
"efSearch": 500,
"metric": "cosine"
}
}
]
},
"semantic": {
"configurations": [
{
"name": "my-semantic-config",
"prioritizedFields": {
"prioritizedContentFields": [
{
"fieldName": field_name
}
for field_name in content_fields_name
]
}
}
]
}
}
if keyword_field_name is not None:
body["semantic"]["configurations"][0]["prioritizedFields"]["prioritizedKeywordsFields"] = [
{
"fieldName": keyword_field_name
}
]
return body
logging.info("Creating indexes.")
start_time = time.time()
vector_profile_name = "myHnswProfile"
vector_algorithm_name = "myHnswConfig"
indices = [
{
"index_name": search_index_name, # RAG index
"fields": [
{
"name": "id",
"type": "Edm.String",
"key": True,
"analyzer": "keyword",
"searchable": True,
"retrievable": True
},
{
"name": "parent_id",
"type": "Edm.String",
"searchable": False,
"retrievable": True
},
{
"name": "metadata_storage_path",
"type": "Edm.String",
"searchable": False,
"sortable": False,
"filterable": False,
"facetable": False
},
{
"name": "metadata_storage_name",
"type": "Edm.String",
"searchable": False,
"sortable": False,
"filterable": False,
"facetable": False
},
{
"name": "metadata_storage_last_modified",
"type": "Edm.DateTimeOffset",
"searchable": False,
"sortable": True,
"retrievable": True,
"filterable": True
},
{
"name": "metadata_security_id",
"type": "Collection(Edm.String)",
"searchable": False,
"retrievable": True,
"filterable": True
},
{
"name": "chunk_id",
"type": "Edm.Int32",
"searchable": False,
"retrievable": True
},
{
"name": "content",
"type": "Edm.String",
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "imageCaptions",
"type": "Edm.String",
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "page",
"type": "Edm.Int32",
"searchable": False,
"retrievable": True
},
{
"name": "offset",
"type": "Edm.Int64",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "length",
"type": "Edm.Int32",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "title",
"type": "Edm.String",
"filterable": True,
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "category",
"type": "Edm.String",
"filterable": True,
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "filepath",
"type": "Edm.String",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "url",
"type": "Edm.String",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "summary",
"type": "Edm.String",
"filterable": False,
"searchable": True,
"retrievable": True
},
{
"name": "relatedImages",
"type": "Collection(Edm.String)",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "relatedFiles",
"type": "Collection(Edm.String)",
"filterable": False,
"searchable": False,
"retrievable": True
},
{
"name": "source",
"type": "Edm.String",
"searchable": False,
"retrievable": True,
"filterable": True
},
{
"name": "contentVector",
"type": "Collection(Edm.Single)",
"searchable": True,
"retrievable": True,
"dimensions": azure_embeddings_vector_size,
"vectorSearchProfile": vector_profile_name
},
{
"name": "captionVector",
"type": "Collection(Edm.Single)",
"searchable": True,
"retrievable": True,
"dimensions": azure_embeddings_vector_size,
"vectorSearchProfile": vector_profile_name
}
],
"content_fields_name": ["content", "imageCaptions"],
"keyword_field_name": "category"
},
{
"index_name": search_index_name_nl2sql_queries,
"fields": [
{
"name": "id",
"type": "Edm.String",
"key": True,
"searchable": False,
"filterable": False,
"sortable": False,
"facetable": False
},
{
"name": "datasource",
"type": "Edm.String",
"searchable": True,
"filterable": True,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "question",
"type": "Edm.String",
"searchable": True,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False,
"analyzer": search_analyzer_name
},
{
"name": "query",
"type": "Edm.String",
"searchable": False,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "reasoning",
"type": "Edm.String",
"searchable": True,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "contentVector",
"type": "Collection(Edm.Single)",
"searchable": True,
"retrievable": True,
"dimensions": azure_embeddings_vector_size,
"vectorSearchProfile": vector_profile_name
}
],
"content_fields_name": ["question"],
"keyword_field_name": None
},
{
"index_name": search_index_name_nl2sql_tables,
"fields": [
{
"name": "id",
"type": "Edm.String",
"key": True,
"searchable": False,
"filterable": False,
"sortable": False,
"facetable": False
},
{
"name": "table",
"type": "Edm.String",
"searchable": True,
"retrievable": True
},
{
"name": "description",
"type": "Edm.String",
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "datasource",
"type": "Edm.String",
"searchable": True,
"retrievable": True
},
{
"name": "columns",
"type": "Collection(Edm.ComplexType)",
"fields": [
{
"name": "name",
"type": "Edm.String",
"searchable": True,
"retrievable": True
},
{
"name": "description",
"type": "Edm.String",
"searchable": True,
"retrievable": True,
"analyzer": search_analyzer_name
},
{
"name": "type",
"type": "Edm.String",
"searchable": False,
"retrievable": True
},
{
"name": "examples",
"type": "Collection(Edm.String)",
"searchable": False,
"retrievable": True
}
]
},
{
"name": "contentVector",
"type": "Collection(Edm.Single)",
"searchable": True,
"retrievable": True,
"dimensions": azure_embeddings_vector_size,
"vectorSearchProfile": vector_profile_name
}
],
"content_fields_name": ["description"],
"keyword_field_name": "table"
},
{
"index_name": search_index_name_nl2sql_measures,
"fields": [
{
"name": "id",
"type": "Edm.String",
"key": True,
"searchable": False,
"filterable": False,
"sortable": False,
"facetable": False
},
{
"name": "datasource",
"type": "Edm.String",
"searchable": True,
"filterable": True,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "name",
"type": "Edm.String",
"searchable": True,
"filterable": True,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "description",
"type": "Edm.String",
"searchable": True,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False,
"analyzer": search_analyzer_name
},
{
"name": "type",
"type": "Edm.String",
"searchable": True,
"filterable": True,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "source_table",
"type": "Edm.String",
"searchable": True,
"filterable": True,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "data_type",
"type": "Edm.String",
"searchable": True,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "source_model",
"type": "Edm.String",
"searchable": True,
"filterable": False,
"retrievable": True,
"sortable": False,
"facetable": False
},
{
"name": "contentVector",
"type": "Collection(Edm.Single)",
"searchable": True,
"retrievable": True,
"dimensions": azure_embeddings_vector_size,
"vectorSearchProfile": vector_profile_name
}
],
"content_fields_name": ["description"],
"keyword_field_name": "description"
}
]
for index in indices:
body = create_index_body(
index_name=index["index_name"],
fields=index["fields"],
content_fields_name=index["content_fields_name"],
keyword_field_name=index["keyword_field_name"],
vector_profile_name=vector_profile_name,
vector_algorithm_name=vector_algorithm_name
)
call_search_api(search_service, search_api_version, "indexes", index["index_name"], "delete", credential)
call_search_api(search_service, search_api_version, "indexes", index["index_name"], "put", credential, body)
response_time = time.time() - start_time
logging.info(f"Indexes created in {round(response_time, 2)} seconds")
###########################################################################
# 04 Creating AI Search skillsets
###########################################################################
logging.info("04 Creating skillsets step.")
start_time = time.time()
body = {
"name": f"{search_index_name}-skillset-chunking",
"description":"SKillset to do document chunking",
"skills":[
{
"@odata.type":"#Microsoft.Skills.Custom.WebApiSkill",
"name":"document-chunking",
"description":"Extract chunks from documents.",
"httpMethod":"POST",
"timeout":"PT230S",
"context":"/document",
"batchSize":1,
"inputs":[
{
"name":"documentUrl",
"source":"/document/metadata_storage_path"
},
{
"name":"documentSasToken",
"source":"/document/metadata_storage_sas_token"
},
{
"name":"documentContentType",
"source":"/document/metadata_content_type"
}
],
"outputs":[
{
"name":"chunks",
"targetName":"chunks"
}
]
}
],
"indexProjections": {
"selectors": [
{
"targetIndexName":f"{search_index_name}",
"parentKeyFieldName": "parent_id",
"sourceContext": "/document/chunks/*",
"mappings": [
{
"name": "chunk_id",
"source": "/document/chunks/*/chunk_id",
"inputs": []
},
{
"name": "offset",
"source": "/document/chunks/*/offset",
"inputs": []
},
{
"name": "length",
"source": "/document/chunks/*/length",
"inputs": []
},
{
"name": "page",
"source": "/document/chunks/*/page",
"inputs": []
},
{
"name": "title",
"source": "/document/chunks/*/title",
"inputs": []
},
{
"name": "category",
"source": "/document/chunks/*/category",
"inputs": []
},
{
"name": "url",
"source": "/document/chunks/*/url",
"inputs": []
},
{
"name": "relatedImages",
"source": "/document/chunks/*/relatedImages",
"inputs": []
},
{
"name": "relatedFiles",
"source": "/document/chunks/*/relatedFiles",
"inputs": []
},
{
"name": "filepath",
"source": "/document/chunks/*/filepath",
"inputs": []
},
{
"name": "content",
"source": "/document/chunks/*/content",
"inputs": []
},
{
"name": "imageCaptions",
"source": "/document/chunks/*/imageCaptions",
"inputs": []
},
{
"name": "summary",
"source": "/document/chunks/*/summary",
"inputs": []
},
{
"name": "source",
"source": "/document/chunks/*/source",
"inputs": []
},
{
"name": "captionVector",
"source": "/document/chunks/*/captionVector",
"inputs": []
},
{
"name": "contentVector",
"source": "/document/chunks/*/contentVector",
"inputs": []
},
{
"name": "metadata_storage_last_modified",
"source": "/document/metadata_storage_last_modified",
"inputs": []
},
{
"name": "metadata_storage_name",
"source": "/document/metadata_storage_name",
"inputs": []
},
{
"name": "metadata_storage_path",
"source": "/document/metadata_storage_path",
"inputs": []
},
{
"name": "metadata_security_id",
"source": "/document/metadata_security_id",
"inputs": []
}
]
}
],
"parameters": {
"projectionMode": "skipIndexingParentDocuments"
}
}
}
if azure_search_use_mis:
body['skills'][0]['uri'] = f"{function_endpoint}/api/document-chunking"
body['skills'][0]['authResourceId'] = f"api://{search_principal_id}"
else:
body['skills'][0]['uri'] = f"{function_endpoint}/api/document-chunking?code={function_key}"
call_search_api(search_service, search_api_version, "skillsets", f"{search_index_name}-skillset-chunking", "delete", credential)
call_search_api(search_service, search_api_version, "skillsets", f"{search_index_name}-skillset-chunking", "put", credential, body)
# creating skillsets for the NL2SQL indexes
def create_embedding_skillset(skillset_name, resource_uri, deployment_id, model_name, input_field, output_field, dimensions):
skill = {
"@odata.type": "#Microsoft.Skills.Text.AzureOpenAIEmbeddingSkill",
"name": f"{skillset_name}-embedding-skill",
"description": f"Generates embeddings for {input_field}.",
"resourceUri": resource_uri,
"deploymentId": deployment_id,
"modelName": model_name,
"dimensions": dimensions,
"context":"/document",
"inputs": [
{
"name": "text",
"source": f"/document/{input_field}"
}
],
"outputs": [
{
"name": "embedding",
"targetName": output_field
}
]
}
skillset_body = {
"name": skillset_name,
"description": f"Skillset for generating embeddings for {skillset_name} index.",
"skills": [skill]
}
return skillset_body
resource_uri = f"https://{azure_openai_service_name}.openai.azure.com/"
deployment_id = azure_openai_embedding_deployment
model_name = azure_openai_embedding_model
skillsets = [
{
"skillset_name": "queries-skillset",
"input_field": "question",
"output_field": "contentVector"
},
{
"skillset_name": "tables-skillset",
"input_field": "description",
"output_field": "contentVector"
},
{
"skillset_name": "measures-skillset", # New measures skillset
"input_field": "description",