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Merge pull request #1 from fosteramanda/file-search-docs
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adding file search docs
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@fosteramanda
fosteramanda authored Dec 4, 2024
2 parents d001a89 + 364c1bc commit a809ebe
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313 changes: 181 additions & 132 deletions articles/ai-services/agents/how-to/tools/file-search.md
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Expand Up @@ -82,110 +82,20 @@ As a fallback, there's a 60-second maximum wait in the run object when the threa

::: zone-end

::: zone pivot="csharp-example"
::: zone pivot="quickstart"

```csharp
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
using Azure.Core.TestFramework;
using NUnit.Framework;

namespace Azure.AI.Projects.Tests;
# Quickstart – Upload Local Files w/ file search

public partial class Sample_Agent_FileSearch : SamplesBase<AIProjectsTestEnvironment>
{
[Test]
public async Task FilesSearchExample()
{
var connectionString = TestEnvironment.AzureAICONNECTIONSTRING;
AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());

#region Snippet:UploadAgentFilesToUse
// Upload a file and wait for it to be processed
File.WriteAllText(
path: "sample_file_for_upload.txt",
contents: "The word 'apple' uses the code 442345, while the word 'banana' uses the code 673457.");
Response<AgentFile> uploadAgentFileResponse = await client.UploadFileAsync(
filePath: "sample_file_for_upload.txt",
purpose: AgentFilePurpose.Agents);

AgentFile uploadedAgentFile = uploadAgentFileResponse.Value;
#endregion

#region Snippet:CreateVectorStore
// Create a vector store with the file and wait for it to be processed.
// If you do not specify a vector store, create_message will create a vector store with a default expiration policy of seven days after they were last active
VectorStore vectorStore = await client.CreateVectorStoreAsync(
fileIds: new List<string> { uploadedAgentFile.Id },
name: "my_vector_store");
#endregion

#region Snippet:CreateAgentWithFiles
FileSearchToolResource fileSearchToolResource = new FileSearchToolResource();
fileSearchToolResource.VectorStoreIds.Add(vectorStore.Id);

// Create an agent with toolResources and process assistant run
Response<Agent> agentResponse = await client.CreateAgentAsync(
model: "gpt-4-1106-preview",
name: "SDK Test Agent - Retrieval",
instructions: "You are a helpful agent that can help fetch data from files you know about.",
tools: new List<ToolDefinition> { new FileSearchToolDefinition() },
toolResources: new ToolResources() { FileSearch = fileSearchToolResource });
Agent agent = agentResponse.Value;
#endregion

// Create thread for communication
Response<AgentThread> threadResponse = await client.CreateThreadAsync();
AgentThread thread = threadResponse.Value;

// Create message to thread
Response<ThreadMessage> messageResponse = await client.CreateMessageAsync(
thread.Id,
MessageRole.User,
"Can you give me the documented codes for 'banana' and 'orange'?");
ThreadMessage message = messageResponse.Value;

// Run the agent
Response<ThreadRun> runResponse = await client.CreateRunAsync(thread, agent);

do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
runResponse = await client.GetRunAsync(thread.Id, runResponse.Value.Id);
}
while (runResponse.Value.Status == RunStatus.Queued
|| runResponse.Value.Status == RunStatus.InProgress);
In this example, we’ll use Azure AI Agent Service to create an agent that can help answer questions on information you upload from local files.

Response<PageableList<ThreadMessage>> afterRunMessagesResponse
= await client.GetMessagesAsync(thread.Id);
IReadOnlyList<ThreadMessage> messages = afterRunMessagesResponse.Value.Data;
## Prerequisites
Complete the [agent setup](../../../quickstart.md).

// Note: messages iterate from newest to oldest, with the messages[0] being the most recent
foreach (ThreadMessage threadMessage in messages)
{
Console.Write($"{threadMessage.CreatedAt:yyyy-MM-dd HH:mm:ss} - {threadMessage.Role,10}: ");
foreach (MessageContent contentItem in threadMessage.ContentItems)
{
if (contentItem is MessageTextContent textItem)
{
Console.Write(textItem.Text);
}
else if (contentItem is MessageImageFileContent imageFileItem)
{
Console.Write($"<image from ID: {imageFileItem.FileId}");
}
Console.WriteLine();
}
}
}
}
```
## Step 1: Create a project client

::: zone-end
Create a client object, which will contain the connection string for connecting to your AI project and other resources.

::: zone pivot="python-example"
# [Python](#tab/python)

```python
import os
Expand All @@ -202,54 +112,193 @@ credential = DefaultAzureCredential()
project_client = AIProjectClient.from_connection_string(
credential=credential, conn_str=os.environ["PROJECT_CONNECTION_STRING"]
)
```

# [C#](#tab/csharp)

```csharp
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
using Azure.Core.TestFramework;
using NUnit.Framework;

with project_client:
// Create an Azure AI Client from a connection string, copied from your AI Studio project.
// At the moment, it should be in the format "<HostName>;<AzureSubscriptionId>;<ResourceGroup>;<HubName>"
// Customer needs to login to Azure subscription via Azure CLI and set the environment variables
var connectionString = TestEnvironment.AzureAICONNECTIONSTRING;
AgentsClient client = new AgentsClient(connectionString, new DefaultAzureCredential());
```

# [START upload_file_and_create_agent_with_file_search]
# We will upload the local file to Azure and will use it for vector store creation.
_, asset_uri = project_client.upload_file("./data/product_info_1.md")
---

# create a vector store with no file and wait for it to be processed
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
vector_store = project_client.agents.create_vector_store_and_poll(data_sources=[ds], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")
## Step 2: Upload files and add them to a Vector Store

# create a file search tool
file_search_tool = FileSearchTool(vector_store_ids=[vector_store.id])
To access your files, the file search tool uses the vector store object. Upload your files and create a vector store to contain them. Once the vector store is created, you should poll its status until all files are out of the `in_progress` state to ensure that all content has finished processing. The SDK provides helpers for uploading and polling.

# notices that FileSearchTool as tool and tool_resources must be added or the assistant unable to search the file
agent = project_client.agents.create_agent(
model="gpt-4o-mini",
name="my-assistant",
instructions="You are helpful assistant",
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}")
Vector stores are created using message attachments that have a default expiration policy of seven days after they were last active (defined as the last time the vector store was part of a run). This default exists to help you manage your vector storage costs. You can override these expiration policies at any time.

thread = project_client.agents.create_thread()
print(f"Created thread, thread ID: {thread.id}")
# [Python](#tab/python)

message = project_client.agents.create_message(
thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?"
)
print(f"Created message, message ID: {message.id}")
```python
# We will upload the local file to Azure and will use it for vector store creation.
_, asset_uri = project_client.upload_file("./data/product_info_1.md")

run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
print(f"Created run, run ID: {run.id}")
# create a vector store with no file and wait for it to be processed
ds = VectorStoreDataSource(asset_identifier=asset_uri, asset_type=VectorStoreDataSourceAssetType.URI_ASSET)
vector_store = project_client.agents.create_vector_store_and_poll(data_sources=[ds], name="sample_vector_store")
print(f"Created vector store, vector store ID: {vector_store.id}")
```

# [C#](#tab/csharp)

```csharp
// Upload a file and wait for it to be processed
File.WriteAllText(
path: "sample_file_for_upload.txt",
contents: "The word 'apple' uses the code 442345, while the word 'banana' uses the code 673457.");
Response<AgentFile> uploadAgentFileResponse = await client.UploadFileAsync(
filePath: "sample_file_for_upload.txt",
purpose: AgentFilePurpose.Agents);

AgentFile uploadedAgentFile = uploadAgentFileResponse.Value;

// Create a vector store with the file and wait for it to be processed.
// If you do not specify a vector store, create_message will create a vector store with a default expiration policy of seven days after they were last active
VectorStore vectorStore = await client.CreateVectorStoreAsync(
fileIds: new List<string> { uploadedAgentFile.Id },
name: "my_vector_store");
```
---

project_client.agents.delete_vector_store(vector_store.id)
print("Deleted vector store")
## Step 3: Enable file search

project_client.agents.delete_agent(agent.id)
print("Deleted agent")
To make the files accessible to your agent, create a `FileSearchTool` object with the `vector_store` ID, and attach `tools` and `tool_resources` to the agent.

messages = project_client.agents.list_messages(thread_id=thread.id)
print(f"Messages: {messages}")
# [Python](#tab/python)

```python

# 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 will be unable to search the file
agent = project_client.agents.create_agent(
model="gpt-4o-mini",
name="my-agent",
instructions="You are a helpful agent",
tools=file_search_tool.definitions,
tool_resources=file_search_tool.resources,
)
print(f"Created agent, agent ID: {agent.id}")
```

# [C#](#tab/csharp)

```csharp
FileSearchToolResource fileSearchToolResource = new FileSearchToolResource();
fileSearchToolResource.VectorStoreIds.Add(vectorStore.Id);

// Create an agent with toolResources and process assistant run
Response<Agent> agentResponse = await client.CreateAgentAsync(
model: "gpt-4-1106-preview",
name: "SDK Test Agent - Retrieval",
instructions: "You are a helpful agent that can help fetch data from files you know about.",
tools: new List<ToolDefinition> { new FileSearchToolDefinition() },
toolResources: new ToolResources() { FileSearch = fileSearchToolResource });
Agent agent = agentResponse.Value;
```

---

## Step 4: Create a thread

# [Python](#tab/python)

```python
thread = project_client.agents.create_thread()
print(f"Created thread, thread ID: {thread.id}")

message = project_client.agents.create_message(
thread_id=thread.id, role="user", content="What feature does Smart Eyewear offer?"
)
print(f"Created message, message ID: {message.id}")
```



# [C#](#tab/csharp)

```csharp
// Create thread for communication
Response<AgentThread> threadResponse = await client.CreateThreadAsync();
AgentThread thread = threadResponse.Value;

// Create message to thread
Response<ThreadMessage> messageResponse = await client.CreateMessageAsync(
thread.Id,
MessageRole.User,
"Can you give me the documented codes for 'banana' and 'orange'?");
ThreadMessage message = messageResponse.Value;
```

## Step 5: Create a run and check the output
Create a run and observe that the model uses the file search tool to provide a response to the user's question.
# [Python](#tab/python)
```python
run = project_client.agents.create_and_process_run(thread_id=thread.id, assistant_id=agent.id)
print(f"Created run, run ID: {run.id}")

project_client.agents.delete_vector_store(vector_store.id)
print("Deleted vector store")

project_client.agents.delete_agent(agent.id)
print("Deleted agent")

messages = project_client.agents.list_messages(thread_id=thread.id)
print(f"Messages: {messages}")
```

# [C#](#tab/csharp)

```csharp
// Run the agent
Response<ThreadRun> runResponse = await client.CreateRunAsync(thread, agent);

do
{
await Task.Delay(TimeSpan.FromMilliseconds(500));
runResponse = await client.GetRunAsync(thread.Id, runResponse.Value.Id);
}
while (runResponse.Value.Status == RunStatus.Queued
|| runResponse.Value.Status == RunStatus.InProgress);

Response<PageableList<ThreadMessage>> afterRunMessagesResponse
= await client.GetMessagesAsync(thread.Id);
IReadOnlyList<ThreadMessage> messages = afterRunMessagesResponse.Value.Data;

// Note: messages iterate from newest to oldest, with the messages[0] being the most recent
foreach (ThreadMessage threadMessage in messages)
{
Console.Write($"{threadMessage.CreatedAt:yyyy-MM-dd HH:mm:ss} - {threadMessage.Role,10}: ");
foreach (MessageContent contentItem in threadMessage.ContentItems)
{
if (contentItem is MessageTextContent textItem)
{
Console.Write(textItem.Text);
}
else if (contentItem is MessageImageFileContent imageFileItem)
{
Console.Write($"<image from ID: {imageFileItem.FileId}");
}
Console.WriteLine();
}
}
```
---


::: zone-end

::: zone pivot="supported-filetypes"
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