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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to add files to agent during the vector store creation.
USAGE:
python sample_agents_enterprise_file_search.py
Before running the sample:
pip install azure-ai-projects azure-ai-agents azure-identity azure-ai-ml
Set these environment variables with your own values:
1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Azure AI Foundry portal.
2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Azure AI Foundry project.
3) AZURE_BLOB_URI - The URI of the blob storage where the file is uploaded. In the format:
azureml://subscriptions/{subscription-id}/resourcegroups/{resource-group-name}/workspaces/{workspace-name}/datastores/{datastore-name}/paths/{path-to-file}
"""
import os
from azure.ai.projects import AIProjectClient
from azure.ai.agents.models import (
FileSearchTool,
ListSortOrder,
VectorStoreDataSource,
VectorStoreDataSourceAssetType,
RunStatus,
)
from azure.identity import DefaultAzureCredential
project_client = AIProjectClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
with project_client:
agents_client = project_client.agents
# [START upload_file_and_create_agent_with_file_search]
# If provided, we will upload the local file to Azure and will use it for vector store creation.
# Otherwise, we'll use a previously created dataset reference
if "AZURE_BLOB_URI" in os.environ:
asset_uri = os.environ["AZURE_BLOB_URI"]
else:
dataset_name = os.environ["AZURE_DATASET_NAME"]
dataset_version = os.environ["AZURE_DATASET_VERSION"]
dataset = project_client.datasets.get(name=dataset_name, version=dataset_version)
asset_uri = dataset.id
# Create a vector store and wait for it to be processed
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
vector_store = agents_client.vector_stores.create_and_poll(data_sources=[ds], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")
vector_store_files = {}
for fle in agents_client.vector_store_files.list(vector_store.id):
uploaded_file = agents_client.files.get(fle.id)
vector_store_files[fle.id] = uploaded_file.filename
# Create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
# Notices that FileSearchTool as tool and tool_resources must be added or the agent unable to search the file
agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are helpful agent",
tools=file_search_tool.definitions,
tool_resources=file_search_tool.resources,
)
# [END upload_file_and_create_agent_with_file_search]
print(f"Created agent, agent ID: {agent.id}")
thread = agents_client.threads.create()
print(f"Created thread, thread ID: {thread.id}")
message = agents_client.messages.create(
thread_id=thread.id, role="user", content="What is the content of the files you have access to?"
)
print(f"Created message, message ID: {message.id}")
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
print(f"Created run, run ID: {run.id}")
if run.status == RunStatus.FAILED:
print(f"Run failed with: |{run.last_error}|")
agents_client.vector_stores.delete(vector_store.id)
print("Deleted vector store")
agents_client.delete_agent(agent.id)
print("Deleted agent")
messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for msg in messages:
if msg.text_messages:
last_text = msg.text_messages[-1].text.value
for annotation in msg.text_messages[-1].text.annotations:
citation = vector_store_files.get(annotation.file_citation.file_id, annotation.file_citation.file_id)
last_text = last_text.replace(annotation.text, f" [{citation}]")
print(f"{msg.role}: {last_text}")
|
