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Python: [Feature Branch] Merge from main to Azure AI branch (#2111)
* Do not build DevUI assets during .NET project build (#2010) * .NET: Add unit tests for declarative executor SetMultipleVariables (#2016) * Add unit tests for create conversation executor * Update indentation and comment typo. * Added unit tests for declarative executor SetMultipleVariablesExecutor * Updated comments and syntactic sugar * Python: DevUI: Use metadata.entity_id instead of model field (#1984) * DevUI: Use metadata.entity_id for agent/workflow name instead of model field * OpenAI Responses: add explicit request validation * Review feedback * .NET: DevUI - Do not automatically add/map OpenAI services/endpoints (#2014) * Don't add OpenAIResponses as part of Dev UI You should be able to add and remove Dev UI without impacting your other production endpoints. * Remove `AddDevUI()` and do not map OpenAI endpoints from `MapDevUI()` * Fix comment wording * Revise documentation --------- Co-authored-by: Daniel Roth <daroth@microsoft.com> * Python: DevUI: Add OpenAI Responses API proxy support + HIL for Workflows (#1737) * DevUI: Add OpenAI Responses API proxy support with enhanced UI features This commit adds support for proxying requests to OpenAI's Responses API, allowing DevUI to route conversations to OpenAI models when configured to enable testing. Backend changes: - Add OpenAI proxy executor with conversation routing logic - Enhance event mapper to support OpenAI Responses API format - Extend server endpoints to handle OpenAI proxy mode - Update models with OpenAI-specific response types - Remove emojis from logging and CLI output for cleaner text Frontend changes: - Add settings modal with OpenAI proxy configuration UI - Enhance agent and workflow views with improved state management - Add new UI components (separator, switch) for settings - Update debug panel with better event filtering - Improve message renderers for OpenAI content types - Update types and API client for OpenAI integration * update ui, settings modal and workflow input form, add register cleanup hooks. * add workflow HIL support, user mode, other fixes * feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas Implement HIL workflow support allowing workflows to pause for user input with dynamically generated JSON schemas based on response handler type hints. Key Features: - Automatic response schema extraction from @response_handler decorators - Dynamic form generation in UI based on Pydantic/dataclass response types - Checkpoint-based conversation storage for HIL requests/responses - Resume workflow execution after user provides HIL response Backend Changes: - Add extract_response_type_from_executor() to introspect response handlers - Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema() - Map RequestInfoEvent to response.input.requested OpenAI event format - Store HIL responses in conversation history and restore checkpoints Frontend Changes: - Add HILInputModal component with SchemaFormRenderer for dynamic forms - Support Pydantic BaseModel and dataclass response types - Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects - Display original request context alongside response form Testing: - Add tests for checkpoint storage (test_checkpoints.py) - Add schema generation tests for all input types (test_schema_generation.py) - Validate end-to-end HIL flow with spam workflow sample This enables workflows to seamlessly pause execution and request structured user input with type-safe, validated forms generated automatically from response type annotations. * improve HIL support, improve workflow execution view * ui updates * ui updates * improve HIL for workflows, add auth and view modes * update workflow * security improvements , ui fixes * fix mypy error * update loading spinner in ui --------- Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> * .NET: Remove launchSettings.json from .gitignore in dotnet/samples (#2006) * Remove launchSettings.json from .gitignore in dotnet/samples * Update dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Properties/launchSettings.json Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/samples/AGUIClientServer/AGUIServer/Properties/launchSettings.json Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021) Co-authored-by: Victor Dibia <chuvidi2003@gmail.com> * Add Microsoft Agent Framework logo to assets (#2007) * Updated package versions (#2027) * DevUI: Prevent line breaks within words in the agent view (#2024) Co-authored-by: Victor Dibia <chuvidi2003@gmail.com> * .NET [AG-UI]: Adds support for shared state. (#1996) * Product changes * Tests * Dojo project * Cleanups * Python: Fix underlying tool choice bug and all for return to previous Handoff subagent (#2037) * Fix tool_choice override bug and add enable_return_to_previous support * Add unit test for handoff checkpointing * Handle tools when we have them * added missing chatAgent params (#2044) * .NET: fix ChatCompletions Tools serialization (#2043) * fix serialization in chat completions on tools * nit * .NET: assign AgentCard's URL to mapped-endpoint if not defined explicitly (#2047) * fix serialization in chat completions on tools * nit * write e2e test for agent card resolve + adjust behavior * nit * Version 1.0.0-preview.251110.1 (#2048) * .NET: Remove moved OpenAPI sample and point to SK one. (#1997) * Remove moved OpenAPI sample and point to SK one. * Update dotnet/samples/GettingStarted/Agents/README.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.2 to 4.0.4.6 (#2031) --- updated-dependencies: - dependency-name: AWSSDK.Extensions.Bedrock.MEAI dependency-version: 4.0.4.6 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * .NET: Separate all memory and rag samples into their own folders (#2000) * Separate all memory and rag samples into their own folders * Fix broken link. * Python: .Net: Dotnet devui compatibility fixes (#2026) * DevUI: Add OpenAI Responses API proxy support with enhanced UI features This commit adds support for proxying requests to OpenAI's Responses API, allowing DevUI to route conversations to OpenAI models when configured to enable testing. Backend changes: - Add OpenAI proxy executor with conversation routing logic - Enhance event mapper to support OpenAI Responses API format - Extend server endpoints to handle OpenAI proxy mode - Update models with OpenAI-specific response types - Remove emojis from logging and CLI output for cleaner text Frontend changes: - Add settings modal with OpenAI proxy configuration UI - Enhance agent and workflow views with improved state management - Add new UI components (separator, switch) for settings - Update debug panel with better event filtering - Improve message renderers for OpenAI content types - Update types and API client for OpenAI integration * update ui, settings modal and workflow input form, add register cleanup hooks. * add workflow HIL support, user mode, other fixes * feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas Implement HIL workflow support allowing workflows to pause for user input with dynamically generated JSON schemas based on response handler type hints. Key Features: - Automatic response schema extraction from @response_handler decorators - Dynamic form generation in UI based on Pydantic/dataclass response types - Checkpoint-based conversation storage for HIL requests/responses - Resume workflow execution after user provides HIL response Backend Changes: - Add extract_response_type_from_executor() to introspect response handlers - Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema() - Map RequestInfoEvent to response.input.requested OpenAI event format - Store HIL responses in conversation history and restore checkpoints Frontend Changes: - Add HILInputModal component with SchemaFormRenderer for dynamic forms - Support Pydantic BaseModel and dataclass response types - Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects - Display original request context alongside response form Testing: - Add tests for checkpoint storage (test_checkpoints.py) - Add schema generation tests for all input types (test_schema_generation.py) - Validate end-to-end HIL flow with spam workflow sample This enables workflows to seamlessly pause execution and request structured user input with type-safe, validated forms generated automatically from response type annotations. * improve HIL support, improve workflow execution view * ui updates * ui updates * improve HIL for workflows, add auth and view modes * update workflow * security improvements , ui fixes * fix mypy error * update loading spinner in ui * DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format * Phase 1: Add /meta endpoint and fix workflow event naming for .NET DevUI compatibility * additional fixes for .NET DevUI workflow visualization item ID tracking **Problem:** .NET DevUI was generating different item IDs for ExecutorInvokedEvent and ExecutorCompletedEvent, causing only the first executor to highlight in the workflow graph. Long executor names and error messages also broke UI layout. **Changes:** - Add ExecutorActionItemResource to match Python DevUI implementation - Track item IDs per executor using dictionary in AgentRunResponseUpdateExtensions - Reuse same item ID across invoked/completed/failed events for proper pairing - Add truncateText() utility to workflow-utils.ts - Truncate executor names to 35 chars in execution timeline - Truncate error messages to 150 chars in workflow graph nodes ** Details:** - ExecutorActionItemResource registered with JSON source generation context - Dictionary cleaned up after executor completion/failure to prevent memory leaks - Frontend item tracking by unique item.id supports multiple executor runs - All changes follow existing codebase patterns and conventions Tested with review-workflow showing correct executor highlighting and state transitions for sequential and concurrent executors. * format fixes, remove cors tests * remove unecessary attributes --------- Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Reuben Bond <reuben.bond@gmail.com> * DevUI: support having both an agent and a workflow with the same id in discovery (#2023) * Python: Fix Model ID attribute not showing up in `invoke_agent` span (#2061) * Best effort to surface the model id to invoke agent span * Fix tests * Fix tests * Version 1.0.0-preview.251107.2 (#2065) * Version 1.0.0-preview.251110.2 (#2067) * Update README.md to change Grafana links to Azure portal links for dashboard access (#1983) * .NET - Enable build & test on branch `feature-foundry-agents` (#2068) * Tests good, mkay * Update .github/workflows/dotnet-build-and-test.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Enable feature build pipelines --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> * Python: Add concrete AGUIChatClient (#2072) * Add concrete AGUIChatClient * Update logging docstrings and conventions * PR feedback * Updates to support client-side tool calls * .NET: Move catalog samples to the HostedAgents folder (#2090) * move catalog samples to the HostedAgents folder * move the catalog samples' projects to the HostedAgents folder * Bump OpenTelemetry.Instrumentation.Runtime from 1.12.0 to 1.13.0 (#1856) --- updated-dependencies: - dependency-name: OpenTelemetry.Instrumentation.Runtime dependency-version: 1.13.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * .NET: Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 (#1962) * Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 --- updated-dependencies: - dependency-name: Microsoft.SemanticKernel.Agents.Abstractions dependency-version: 1.67.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> * .NET: Bump all Microsoft.SemanticKernel packages from 1.66.* to 1.67.* (#1969) * Initial plan * Update all Microsoft.SemanticKernel packages to 1.67.* Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Remove unrelated changes to package-lock.json and yarn.lock Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: markwallace-microsoft <127216156+markwallace-microsoft@users.noreply.github.com> * .NET: fix: WorkflowAsAgent Sample (#1787) * fix: WorkflowAsAgent Sample * Also makes ChatForwardingExecutor public * feat: Expand ChatForwardingExecutor handled types Make ChatForwardingExecutor match the input types of ChatProtocolExecutor. * fix: Update for the new AgentRunResponseUpdate merge logic AIAgent always sends out List<ChatMessage> now. * Updated (#2076) * Bump vite in /python/samples/demos/chatkit-integration/frontend (#1918) Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 7.1.9 to 7.1.12. - [Release notes](https://github.com/vitejs/vite/releases) - [Changelog](https://github.com/vitejs/vite/blob/v7.1.12/packages/vite/CHANGELOG.md) - [Commits](https://github.com/vitejs/vite/commits/v7.1.12/packages/vite) --- updated-dependencies: - dependency-name: vite dependency-version: 7.1.12 dependency-type: direct:development ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857) --- updated-dependencies: - dependency-name: Roslynator.Analyzers dependency-version: 4.14.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump MishaKav/pytest-coverage-comment from 1.1.57 to 1.1.59 (#2034) Bumps [MishaKav/pytest-coverage-comment](https://github.com/mishakav/pytest-coverage-comment) from 1.1.57 to 1.1.59. - [Release notes](https://github.com/mishakav/pytest-coverage-comment/releases) - [Changelog](https://github.com/MishaKav/pytest-coverage-comment/blob/main/CHANGELOG.md) - [Commits](https://github.com/mishakav/pytest-coverage-comment/compare/v1.1.57...v1.1.59) --- updated-dependencies: - dependency-name: MishaKav/pytest-coverage-comment dependency-version: 1.1.59 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Python: Handle agent user input request in AgentExecutor (#2022) * Handle agent user input request in AgentExecutor * fix test * Address comments * Fix tests * Fix tests * Address comments * Address comments * Python: OpenAI Responses Image Generation Stream Support, Sample and Unit Tests (#1853) * support for image gen streaming * small fixes * fixes * added comment * Python: Fix MCP Tool Parameter Descriptions Not Propagated to LLMs (#1978) * mcp tool description fix * small fix * .NET: Allow extending agent run options via additional properties (#1872) * Allow extending agent run options via additional properties This mirrors the M.E.AI model in ChatOptions.AdditionalProperties which is very useful when building functionality pipelines. Fixes https://github.com/microsoft/agent-framework/issues/1815 * Expand XML documentation Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Add AdditionalProperties tests to AgentRunOptions Co-authored-by: kzu <169707+kzu@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: kzu <169707+kzu@users.noreply.github.com> * Python: Use the last entry in the task history to avoid empty responses (#2101) * Use the last entry in the task history to avoid empty responses * History only contains Messages * Updated package versions (#2104) --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Reuben Bond <203839+ReubenBond@users.noreply.github.com> Co-authored-by: Peter Ibekwe <109177538+peibekwe@users.noreply.github.com> Co-authored-by: Jeff Handley <jeffhandley@users.noreply.github.com> Co-authored-by: Daniel Roth <daroth@microsoft.com> Co-authored-by: Victor Dibia <chuvidi2003@gmail.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Shawn Henry <sphenry@gmail.com> Co-authored-by: Javier Calvarro Nelson <jacalvar@microsoft.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Korolev Dmitry <deagle.gross@gmail.com> Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Reuben Bond <reuben.bond@gmail.com> Co-authored-by: Tao Chen <taochen@microsoft.com> Co-authored-by: wuweng <wuweng@microsoft.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jacob Alber <jaalber@microsoft.com> Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com> Co-authored-by: Daniel Cazzulino <daniel@cazzulino.com> Co-authored-by: kzu <169707+kzu@users.noreply.github.com>
This commit is contained in:
Dmytro Struk
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|
||||
"tag-items": {
|
||||
"value": [
|
||||
{
|
||||
"name": "v0.1",
|
||||
"commit": {
|
||||
"sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
|
||||
"url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
|
||||
},
|
||||
"zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
|
||||
"tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
|
||||
"node_id": "MDQ6VXNlcjE="
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"parameters": {
|
||||
"owner": {
|
||||
"name": "owner",
|
||||
"description": "The account owner of the repository. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"repo": {
|
||||
"name": "repo",
|
||||
"description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
|
||||
"in": "path",
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"per-page": {
|
||||
"name": "per_page",
|
||||
"description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 30
|
||||
}
|
||||
},
|
||||
"page": {
|
||||
"name": "page",
|
||||
"description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
|
||||
"in": "query",
|
||||
"schema": {
|
||||
"type": "integer",
|
||||
"default": 1
|
||||
}
|
||||
}
|
||||
},
|
||||
"responses": {
|
||||
"not_found": {
|
||||
"description": "Resource not found",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/basic-error"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"link": {
|
||||
"example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
|
||||
"schema": {
|
||||
"type": "string"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"paths": {
|
||||
"/repos/{owner}/{repo}/tags": {
|
||||
"get": {
|
||||
"summary": "List repository tags",
|
||||
"description": "",
|
||||
"tags": [
|
||||
"repos"
|
||||
],
|
||||
"operationId": "repos/list-tags",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/tag"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/tag-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "repos",
|
||||
"subcategory": "repos"
|
||||
}
|
||||
}
|
||||
},
|
||||
"/repos/{owner}/{repo}/labels": {
|
||||
"get": {
|
||||
"summary": "List labels for a repository",
|
||||
"description": "Lists all labels for a repository.",
|
||||
"tags": [
|
||||
"issues"
|
||||
],
|
||||
"operationId": "issues/list-labels-for-repo",
|
||||
"externalDocs": {
|
||||
"description": "API method documentation",
|
||||
"url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
|
||||
},
|
||||
"parameters": [
|
||||
{
|
||||
"$ref": "#/components/parameters/owner"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/repo"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/per-page"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/parameters/page"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"200": {
|
||||
"description": "Response",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/label"
|
||||
}
|
||||
},
|
||||
"examples": {
|
||||
"default": {
|
||||
"$ref": "#/components/examples/label-items"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"headers": {
|
||||
"Link": {
|
||||
"$ref": "#/components/headers/link"
|
||||
}
|
||||
}
|
||||
},
|
||||
"404": {
|
||||
"$ref": "#/components/responses/not_found"
|
||||
}
|
||||
},
|
||||
"x-github": {
|
||||
"githubCloudOnly": false,
|
||||
"enabledForGitHubApps": true,
|
||||
"category": "issues",
|
||||
"subcategory": "labels"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
-33
@@ -1,33 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample demonstrates how to use a ChatClientAgent with function tools provided via an OpenAPI spec.
|
||||
// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.SemanticKernel;
|
||||
using Microsoft.SemanticKernel.Plugins.OpenApi;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
// Load the OpenAPI Spec from a file.
|
||||
KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
|
||||
|
||||
// Convert the Semantic Kernel plugin to Agent Framework function tools.
|
||||
// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
|
||||
Kernel kernel = new();
|
||||
List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
|
||||
|
||||
// Create the chat client and agent, and provide the OpenAPI function tools to the agent.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(instructions: "You are a helpful assistant", tools: tools);
|
||||
|
||||
// Run the agent with the OpenAPI function tools.
|
||||
Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github."));
|
||||
@@ -1,21 +0,0 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -1,158 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to add a basic custom memory component to an agent.
|
||||
// The memory component subscribes to all messages added to the conversation and
|
||||
// extracts the user's name and age if provided.
|
||||
// The component adds a prompt to ask for this information if it is not already known
|
||||
// and provides it to the model before each invocation if known.
|
||||
|
||||
using System.Text;
|
||||
using System.Text.Json;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
using OpenAI.Chat;
|
||||
using SampleApp;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
ChatClient chatClient = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName);
|
||||
|
||||
// Create the agent and provide a factory to add our custom memory component to
|
||||
// all threads created by the agent. Here each new memory component will have its own
|
||||
// user info object, so each thread will have its own memory.
|
||||
// In real world applications/services, where the user info would be persisted in a database,
|
||||
// and preferably shared between multiple threads used by the same user, ensure that the
|
||||
// factory reads the user id from the current context and scopes the memory component
|
||||
// and its storage to that user id.
|
||||
AIAgent agent = chatClient.CreateAIAgent(new ChatClientAgentOptions()
|
||||
{
|
||||
Instructions = "You are a friendly assistant. Always address the user by their name.",
|
||||
AIContextProviderFactory = ctx => new UserInfoMemory(chatClient.AsIChatClient(), ctx.SerializedState, ctx.JsonSerializerOptions)
|
||||
});
|
||||
|
||||
// Create a new thread for the conversation.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
Console.WriteLine(">> Use thread with blank memory\n");
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
Console.WriteLine(await agent.RunAsync("Hello, what is the square root of 9?", thread));
|
||||
Console.WriteLine(await agent.RunAsync("My name is Ruaidhrí", thread));
|
||||
Console.WriteLine(await agent.RunAsync("I am 20 years old", thread));
|
||||
|
||||
// We can serialize the thread. The serialized state will include the state of the memory component.
|
||||
var threadElement = thread.Serialize();
|
||||
|
||||
Console.WriteLine("\n>> Use deserialized thread with previously created memories\n");
|
||||
|
||||
// Later we can deserialize the thread and continue the conversation with the previous memory component state.
|
||||
var deserializedThread = agent.DeserializeThread(threadElement);
|
||||
Console.WriteLine(await agent.RunAsync("What is my name and age?", deserializedThread));
|
||||
|
||||
Console.WriteLine("\n>> Read memories from memory component\n");
|
||||
|
||||
// It's possible to access the memory component via the thread's GetService method.
|
||||
var userInfo = deserializedThread.GetService<UserInfoMemory>()?.UserInfo;
|
||||
|
||||
// Output the user info that was captured by the memory component.
|
||||
Console.WriteLine($"MEMORY - User Name: {userInfo?.UserName}");
|
||||
Console.WriteLine($"MEMORY - User Age: {userInfo?.UserAge}");
|
||||
|
||||
Console.WriteLine("\n>> Use new thread with previously created memories\n");
|
||||
|
||||
// It is also possible to set the memories in a memory component on an individual thread.
|
||||
// This is useful if we want to start a new thread, but have it share the same memories as a previous thread.
|
||||
var newThread = agent.GetNewThread();
|
||||
if (userInfo is not null && newThread.GetService<UserInfoMemory>() is UserInfoMemory newThreadMemory)
|
||||
{
|
||||
newThreadMemory.UserInfo = userInfo;
|
||||
}
|
||||
|
||||
// Invoke the agent and output the text result.
|
||||
// This time the agent should remember the user's name and use it in the response.
|
||||
Console.WriteLine(await agent.RunAsync("What is my name and age?", newThread));
|
||||
|
||||
namespace SampleApp
|
||||
{
|
||||
/// <summary>
|
||||
/// Sample memory component that can remember a user's name and age.
|
||||
/// </summary>
|
||||
internal sealed class UserInfoMemory : AIContextProvider
|
||||
{
|
||||
private readonly IChatClient _chatClient;
|
||||
|
||||
public UserInfoMemory(IChatClient chatClient, UserInfo? userInfo = null)
|
||||
{
|
||||
this._chatClient = chatClient;
|
||||
this.UserInfo = userInfo ?? new UserInfo();
|
||||
}
|
||||
|
||||
public UserInfoMemory(IChatClient chatClient, JsonElement serializedState, JsonSerializerOptions? jsonSerializerOptions = null)
|
||||
{
|
||||
this._chatClient = chatClient;
|
||||
|
||||
this.UserInfo = serializedState.ValueKind == JsonValueKind.Object ?
|
||||
serializedState.Deserialize<UserInfo>(jsonSerializerOptions)! :
|
||||
new UserInfo();
|
||||
}
|
||||
|
||||
public UserInfo UserInfo { get; set; }
|
||||
|
||||
public override async ValueTask InvokedAsync(InvokedContext context, CancellationToken cancellationToken = default)
|
||||
{
|
||||
// Try and extract the user name and age from the message if we don't have it already and it's a user message.
|
||||
if ((this.UserInfo.UserName is null || this.UserInfo.UserAge is null) && context.RequestMessages.Any(x => x.Role == ChatRole.User))
|
||||
{
|
||||
var result = await this._chatClient.GetResponseAsync<UserInfo>(
|
||||
context.RequestMessages,
|
||||
new ChatOptions()
|
||||
{
|
||||
Instructions = "Extract the user's name and age from the message if present. If not present return nulls."
|
||||
},
|
||||
cancellationToken: cancellationToken);
|
||||
|
||||
this.UserInfo.UserName ??= result.Result.UserName;
|
||||
this.UserInfo.UserAge ??= result.Result.UserAge;
|
||||
}
|
||||
}
|
||||
|
||||
public override ValueTask<AIContext> InvokingAsync(InvokingContext context, CancellationToken cancellationToken = default)
|
||||
{
|
||||
StringBuilder instructions = new();
|
||||
|
||||
// If we don't already know the user's name and age, add instructions to ask for them, otherwise just provide what we have to the context.
|
||||
instructions
|
||||
.AppendLine(
|
||||
this.UserInfo.UserName is null ?
|
||||
"Ask the user for their name and politely decline to answer any questions until they provide it." :
|
||||
$"The user's name is {this.UserInfo.UserName}.")
|
||||
.AppendLine(
|
||||
this.UserInfo.UserAge is null ?
|
||||
"Ask the user for their age and politely decline to answer any questions until they provide it." :
|
||||
$"The user's age is {this.UserInfo.UserAge}.");
|
||||
|
||||
return new ValueTask<AIContext>(new AIContext
|
||||
{
|
||||
Instructions = instructions.ToString()
|
||||
});
|
||||
}
|
||||
|
||||
public override JsonElement Serialize(JsonSerializerOptions? jsonSerializerOptions = null)
|
||||
{
|
||||
return JsonSerializer.SerializeToElement(this.UserInfo, jsonSerializerOptions);
|
||||
}
|
||||
}
|
||||
|
||||
internal sealed class UserInfo
|
||||
{
|
||||
public string? UserName { get; set; }
|
||||
public int? UserAge { get; set; }
|
||||
}
|
||||
}
|
||||
-21
@@ -1,21 +0,0 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -1,84 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use TextSearchProvider to add retrieval augmented generation (RAG)
|
||||
// capabilities to an AI agent. The provider runs a search against an external knowledge base
|
||||
// before each model invocation and injects the results into the model context.
|
||||
|
||||
// Also see the AgentWithRAG folder for more advanced RAG scenarios.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Data;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
TextSearchProviderOptions textSearchOptions = new()
|
||||
{
|
||||
// Run the search prior to every model invocation and keep a short rolling window of conversation context.
|
||||
SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
|
||||
RecentMessageMemoryLimit = 6,
|
||||
};
|
||||
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(new ChatClientAgentOptions
|
||||
{
|
||||
Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
|
||||
AIContextProviderFactory = ctx => new TextSearchProvider(MockSearchAsync, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions)
|
||||
});
|
||||
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
Console.WriteLine(">> Asking about returns\n");
|
||||
Console.WriteLine(await agent.RunAsync("Hi! I need help understanding the return policy.", thread));
|
||||
|
||||
Console.WriteLine("\n>> Asking about shipping\n");
|
||||
Console.WriteLine(await agent.RunAsync("How long does standard shipping usually take?", thread));
|
||||
|
||||
Console.WriteLine("\n>> Asking about product care\n");
|
||||
Console.WriteLine(await agent.RunAsync("What is the best way to maintain the TrailRunner tent fabric?", thread));
|
||||
|
||||
static Task<IEnumerable<TextSearchProvider.TextSearchResult>> MockSearchAsync(string query, CancellationToken cancellationToken)
|
||||
{
|
||||
// The mock search inspects the user's question and returns pre-defined snippets
|
||||
// that resemble documents stored in an external knowledge source.
|
||||
List<TextSearchProvider.TextSearchResult> results = new();
|
||||
|
||||
if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
results.Add(new()
|
||||
{
|
||||
SourceName = "Contoso Outdoors Return Policy",
|
||||
SourceLink = "https://contoso.com/policies/returns",
|
||||
Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
|
||||
});
|
||||
}
|
||||
|
||||
if (query.Contains("shipping", StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
results.Add(new()
|
||||
{
|
||||
SourceName = "Contoso Outdoors Shipping Guide",
|
||||
SourceLink = "https://contoso.com/help/shipping",
|
||||
Text = "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days within the continental United States. Expedited options are available at checkout."
|
||||
});
|
||||
}
|
||||
|
||||
if (query.Contains("tent", StringComparison.OrdinalIgnoreCase) || query.Contains("fabric", StringComparison.OrdinalIgnoreCase))
|
||||
{
|
||||
results.Add(new()
|
||||
{
|
||||
SourceName = "TrailRunner Tent Care Instructions",
|
||||
SourceLink = "https://contoso.com/manuals/trailrunner-tent",
|
||||
Text = "Clean the tent fabric with lukewarm water and a non-detergent soap. Allow it to air dry completely before storage and avoid prolonged UV exposure to extend the lifespan of the waterproof coating."
|
||||
});
|
||||
}
|
||||
|
||||
return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
|
||||
}
|
||||
-22
@@ -1,22 +0,0 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Mem0\Microsoft.Agents.AI.Mem0.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
@@ -1,64 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to use the Mem0Provider to persist and recall memories for an agent.
|
||||
// The sample stores conversation messages in a Mem0 service and retrieves relevant memories
|
||||
// for subsequent invocations, even across new threads.
|
||||
|
||||
using System.Net.Http.Headers;
|
||||
using System.Text.Json;
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Agents.AI.Mem0;
|
||||
using Microsoft.Extensions.AI;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
|
||||
var mem0ServiceUri = Environment.GetEnvironmentVariable("MEM0_ENDPOINT") ?? throw new InvalidOperationException("MEM0_ENDPOINT is not set.");
|
||||
var mem0ApiKey = Environment.GetEnvironmentVariable("MEM0_APIKEY") ?? throw new InvalidOperationException("MEM0_APIKEY is not set.");
|
||||
|
||||
// Create an HttpClient for Mem0 with the required base address and authentication.
|
||||
using HttpClient mem0HttpClient = new();
|
||||
mem0HttpClient.BaseAddress = new Uri(mem0ServiceUri);
|
||||
mem0HttpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Token", mem0ApiKey);
|
||||
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(new ChatClientAgentOptions()
|
||||
{
|
||||
Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details.",
|
||||
AIContextProviderFactory = ctx => ctx.SerializedState.ValueKind is not JsonValueKind.Null or JsonValueKind.Undefined
|
||||
// If each thread should have its own Mem0 scope, you can create a new id per thread here:
|
||||
// ? new Mem0Provider(mem0HttpClient, new Mem0ProviderScope() { ThreadId = Guid.NewGuid().ToString() })
|
||||
// In this case we are storing memories scoped by application and user instead so that memories are retained across threads.
|
||||
? new Mem0Provider(mem0HttpClient, new Mem0ProviderScope() { ApplicationId = "getting-started-agents", UserId = "sample-user" })
|
||||
// For cases where we are restoring from serialized state:
|
||||
: new Mem0Provider(mem0HttpClient, ctx.SerializedState, ctx.JsonSerializerOptions)
|
||||
});
|
||||
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
// Clear any existing memories for this scope to demonstrate fresh behavior.
|
||||
Mem0Provider mem0Provider = thread.GetService<Mem0Provider>()!;
|
||||
await mem0Provider.ClearStoredMemoriesAsync();
|
||||
|
||||
Console.WriteLine(await agent.RunAsync("Hi there! My name is Taylor and I'm planning a hiking trip to Patagonia in November.", thread));
|
||||
Console.WriteLine(await agent.RunAsync("I'm travelling with my sister and we love finding scenic viewpoints.", thread));
|
||||
|
||||
Console.WriteLine("\nWaiting briefly for Mem0 to index the new memories...\n");
|
||||
await Task.Delay(TimeSpan.FromSeconds(2));
|
||||
|
||||
Console.WriteLine(await agent.RunAsync("What do you already know about my upcoming trip?", thread));
|
||||
|
||||
Console.WriteLine("\n>> Serialize and deserialize the thread to demonstrate persisted state\n");
|
||||
JsonElement serializedThread = thread.Serialize();
|
||||
AgentThread restoredThread = agent.DeserializeThread(serializedThread);
|
||||
Console.WriteLine(await agent.RunAsync("Can you recap the personal details you remember?", restoredThread));
|
||||
|
||||
Console.WriteLine("\n>> Start a new thread that shares the same Mem0 scope\n");
|
||||
AgentThread newThread = agent.GetNewThread();
|
||||
Console.WriteLine(await agent.RunAsync("Summarize what you already know about me.", newThread));
|
||||
-23
@@ -1,23 +0,0 @@
|
||||
<Project Sdk="Microsoft.NET.Sdk">
|
||||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>net9.0</TargetFramework>
|
||||
|
||||
<Nullable>enable</Nullable>
|
||||
<ImplicitUsings>enable</ImplicitUsings>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<PackageReference Include="Azure.AI.OpenAI" />
|
||||
<PackageReference Include="Azure.Identity" />
|
||||
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
|
||||
<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
|
||||
<PackageReference Include="System.Linq.Async" />
|
||||
</ItemGroup>
|
||||
|
||||
<ItemGroup>
|
||||
<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
|
||||
</ItemGroup>
|
||||
|
||||
</Project>
|
||||
-60
@@ -1,60 +0,0 @@
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
// This sample shows how to create and use a simple AI agent that stores chat messages in a vector store using the ChatHistoryMemoryProvider.
|
||||
// It can then use the chat history from prior conversations to inform responses in new conversations.
|
||||
|
||||
using Azure.AI.OpenAI;
|
||||
using Azure.Identity;
|
||||
using Microsoft.Agents.AI;
|
||||
using Microsoft.Extensions.AI;
|
||||
using Microsoft.Extensions.VectorData;
|
||||
using Microsoft.SemanticKernel.Connectors.InMemory;
|
||||
using OpenAI;
|
||||
|
||||
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
|
||||
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
|
||||
var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
|
||||
|
||||
// Create a vector store to store the chat messages in.
|
||||
// For demonstration purposes, we are using an in-memory vector store.
|
||||
// Replace this with a vector store implementation of your choice that can persist the chat history long term.
|
||||
VectorStore vectorStore = new InMemoryVectorStore(new InMemoryVectorStoreOptions()
|
||||
{
|
||||
EmbeddingGenerator = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
|
||||
.GetEmbeddingClient(embeddingDeploymentName)
|
||||
.AsIEmbeddingGenerator()
|
||||
});
|
||||
|
||||
// Create the agent and add the ChatHistoryMemoryProvider to store chat messages in the vector store.
|
||||
AIAgent agent = new AzureOpenAIClient(
|
||||
new Uri(endpoint),
|
||||
new AzureCliCredential())
|
||||
.GetChatClient(deploymentName)
|
||||
.CreateAIAgent(new ChatClientAgentOptions
|
||||
{
|
||||
Instructions = "You are good at telling jokes.",
|
||||
Name = "Joker",
|
||||
AIContextProviderFactory = (ctx) => new ChatHistoryMemoryProvider(
|
||||
vectorStore,
|
||||
collectionName: "chathistory",
|
||||
vectorDimensions: 3072,
|
||||
// Configure the scope values under which chat messages will be stored.
|
||||
// In this case, we are using a fixed user ID and a unique thread ID for each new thread.
|
||||
storageScope: new() { UserId = "UID1", ThreadId = new Guid().ToString() },
|
||||
// Configure the scope which would be used to search for relevant prior messages.
|
||||
// In this case, we are searching for any messages for the user across all threads.
|
||||
searchScope: new() { UserId = "UID1" })
|
||||
});
|
||||
|
||||
// Start a new thread for the agent conversation.
|
||||
AgentThread thread = agent.GetNewThread();
|
||||
|
||||
// Run the agent with the thread that stores conversation history in the vector store.
|
||||
Console.WriteLine(await agent.RunAsync("I like jokes about Pirates. Tell me a joke about a pirate.", thread));
|
||||
|
||||
// Start a second thread. Since we configured the search scope to be across all threads for the user,
|
||||
// the agent should remember that the user likes pirate jokes.
|
||||
AgentThread thread2 = agent.GetNewThread();
|
||||
|
||||
// Run the agent with the second thread.
|
||||
Console.WriteLine(await agent.RunAsync("Tell me a joke that I might like.", thread2));
|
||||
@@ -28,8 +28,8 @@ Before you begin, ensure you have the following prerequisites:
|
||||
|---|---|
|
||||
|[Running a simple agent](./Agent_Step01_Running/)|This sample demonstrates how to create and run a basic agent with instructions|
|
||||
|[Multi-turn conversation with a simple agent](./Agent_Step02_MultiturnConversation/)|This sample demonstrates how to implement a multi-turn conversation with a simple agent|
|
||||
|[Using function tools with a simple agent](./Agent_Step03.1_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|
||||
|[Using OpenAPI function tools with a simple agent](./Agent_Step03.2_UsingFunctionTools_FromOpenAPI/)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent|
|
||||
|[Using function tools with a simple agent](./Agent_Step03_UsingFunctionTools/)|This sample demonstrates how to use function tools with a simple agent|
|
||||
|[Using OpenAPI function tools with a simple agent](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/AgentFrameworkMigration/AzureOpenAI/Step04_ToolCall_WithOpenAPI)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)|
|
||||
|[Using function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
|
||||
|[Structured output with a simple agent](./Agent_Step05_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
|
||||
|[Persisted conversations with a simple agent](./Agent_Step06_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
|
||||
@@ -39,14 +39,11 @@ Before you begin, ensure you have the following prerequisites:
|
||||
|[Exposing a simple agent as MCP tool](./Agent_Step10_AsMcpTool/)|This sample demonstrates how to expose an agent as an MCP tool|
|
||||
|[Using images with a simple agent](./Agent_Step11_UsingImages/)|This sample demonstrates how to use image multi-modality with an AI agent|
|
||||
|[Exposing a simple agent as a function tool](./Agent_Step12_AsFunctionTool/)|This sample demonstrates how to expose an agent as a function tool|
|
||||
|[Using memory with an agent](./Agent_Step13_Memory/)|This sample demonstrates how to create a simple memory component and use it with an agent|
|
||||
|[Background responses with tools and persistence](./Agent_Step13_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|
||||
|[Using middleware with an agent](./Agent_Step14_Middleware/)|This sample demonstrates how to use middleware with an agent|
|
||||
|[Using plugins with an agent](./Agent_Step15_Plugins/)|This sample demonstrates how to use plugins with an agent|
|
||||
|[Reducing chat history size](./Agent_Step16_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
|
||||
|[Background responses](./Agent_Step17_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
|
||||
|[Adding RAG with text search](./Agent_Step18_TextSearchRag/)|This sample demonstrates how to enrich agent responses with retrieval augmented generation using the text search provider|
|
||||
|[Using Mem0-backed memory](./Agent_Step19_Mem0Provider/)|This sample demonstrates how to use the Mem0Provider to persist and recall memories across conversations|
|
||||
|[Background responses with tools and persistence](./Agent_Step20_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
|
||||
|
||||
## Running the samples from the console
|
||||
|
||||
|
||||
Reference in New Issue
Block a user