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3047ad3066
* Restructure harness console so that reactive app is the entry point * Further refactoring to split tool formatters, improve UX, make console configurable and fix bugs * Address PR comments. * UX tweak * Fix streaming text bug * Address PR comments.
89 lines
3.8 KiB
C#
89 lines
3.8 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
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// This sample demonstrates how to use a HarnessAgent with the FileAccessProvider
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// to give an agent access to a folder of CSV data files. The agent can read, analyze,
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// and extract information from the data, then write results back as new files.
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//
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// The sample includes a pre-populated `data/` folder with sales transaction data.
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// Ask the agent to analyze the data, produce summaries, or create new output files.
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//
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// Special commands:
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// /exit — End the session.
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#pragma warning disable OPENAI001 // Suppress experimental API warnings for Responses API usage.
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#pragma warning disable MAAI001 // Suppress experimental API warnings for Agents AI experiments.
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using System.ClientModel.Primitives;
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using Azure.Identity;
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using Harness.Shared.Console;
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using Microsoft.Agents.AI;
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using Microsoft.Extensions.AI;
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using OpenAI;
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using OpenAI.Responses;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4";
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const int MaxContextWindowTokens = 1_050_000;
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const int MaxOutputTokens = 128_000;
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// Point the file store at the data/ folder that ships with the sample.
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var dataFolder = Path.Combine(AppContext.BaseDirectory, "data");
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var fileStore = new FileSystemAgentFileStore(dataFolder);
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var instructions =
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"""
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You are a data analyst assistant. You have access to a folder of data files via the FileAccess_* tools.
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## Getting started
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- Start by listing available files with FileAccess_ListFiles to see what data is available.
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- Read the files to understand their structure and contents.
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## Working with data
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- When asked to analyze data, read the relevant files first, then perform the analysis.
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- Show your analysis clearly with tables, summaries, and key insights.
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- When calculations are needed, work through them step by step and show your reasoning.
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## Writing output
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- When asked to produce output files (e.g., reports, summaries, filtered data), use FileAccess_SaveFile to write them.
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- Use appropriate file formats: CSV for tabular data, Markdown for reports.
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- Confirm what you wrote and where.
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## Important
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- Never modify or delete the original input data files unless explicitly asked to do so.
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- If asked about data you haven't read yet, read it first before answering.
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- Always explain your reasoning and thought process as you work through tasks.
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- Always explain what you learned and what you are going to do next between tool calls, so the user can follow along with your thought process.
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""";
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// Create the chat client from the OpenAI provider.
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AIAgent agent =
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new OpenAIClient(
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new BearerTokenPolicy(new DefaultAzureCredential(), "https://ai.azure.com/.default"),
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new OpenAIClientOptions()
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{
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Endpoint = new Uri(endpoint),
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RetryPolicy = new ClientRetryPolicy(3)
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})
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.GetResponsesClient()
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.AsIChatClientWithStoredOutputDisabled(deploymentName)
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.AsHarnessAgent(MaxContextWindowTokens, MaxOutputTokens, new HarnessAgentOptions
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{
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Name = "DataAnalyst",
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Description = "A data analyst assistant that reads, analyzes, and processes data files.",
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AIContextProviders =
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[
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new FileAccessProvider(fileStore),
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],
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ChatOptions = new ChatOptions
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{
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Instructions = instructions,
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MaxOutputTokens = MaxOutputTokens,
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},
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});
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// Run the interactive console session.
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await HarnessConsole.RunAgentAsync(
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agent,
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userPrompt: "Ask me to analyze the data files, produce summaries, or create output files.");
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