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[AI Agents] v1.1.0b4 features (Azure#35788)
* [AI Agents] v1.1.0b4 features * add madeOptional decorator to toolOutputs member * [AI Agents] MCP tool typespec (Azure#35504) * [AI Agents] MCP tool typespec * update typespec * add mcp tool type * add submit_tool_approval required action * tsp compile output * add submitToolApprovals route * corrections from npx tsv * add version annotation to mcp agentNamedToolChoice * add mcp run step * add "always" string to requireApproval, update npx tsv * add tool_resources to CreateRunOptions * add submitToolApprovalDetails * update submit_tool_outputs route to handle approvals * Update models.tsp * npx tsv . * make createRun internal for .NET * add line break to models * Update tspconfig.yaml * Update tspconfig.yaml * review feedback * add example for upload file * add create thread json * update create thread * add json for run operations * npx tsv output * update jsons * correct runs swagger json * swagger updates * swagger updates 2 * swagger updates * swagger updates 3 * swagger updates 4 * swagger updates 5 * swagger changes 6 * swagger updates 7 * swagger updates 8 * swagger prettier * swagger prettier 2 * swagger prettier 3
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‎specification/ai/Azure.AI.Agents/client.tsp

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@@ -73,6 +73,8 @@ using Azure.AI.Agents;
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);
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// nested Runs interface
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@@access(Azure.AI.Agents.Runs.createRun, Access.internal, "csharp");
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@@clientName(Azure.AI.Agents.Runs.createRun, "InternalCreateRun", "csharp");
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@@clientName(Azure.AI.Agents.Runs.createRun, "create", "python");
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@@clientName(Azure.AI.Agents.Runs.listRuns, "list", "python");
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@@clientName(Azure.AI.Agents.Runs.getRun, "get", "python");

‎specification/ai/Azure.AI.Agents/examples/2025-05-15-preview/CreateThreadAndRun_MaximumSet_Gen.json

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{
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"title": "CreateThreadAndRun_MaximumSet",
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"operationId": "CreateThreadAndRun",
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"parameters": {
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"api-version": "2025-05-15-preview",
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"body": {
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"assistant_id": "asst_abc123",
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"thread": {
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"messages": [
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{
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"role": "user",
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"content": {
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"text": "I need help with data analysis",
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"blocks": [
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{
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"type": "text",
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"text": "Please analyze this dataset and provide insights"
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},
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{
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"type": "image_file",
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"image_file": {
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"file_id": "file_abc123",
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"detail": "high"
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}
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}
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]
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},
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"attachments": [
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{
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"file_id": "file_abc123",
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"data_source": {
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"uri": "https://example.com/data.xlsx",
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"type": "uri_asset"
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},
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"tools": [
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{
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"type": "code_interpreter"
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},
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{
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"type": "file_search"
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}
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]
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}
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]
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}
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],
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"tool_resources": {
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"code_interpreter": {
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"file_ids": [
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"file_abc123",
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"file_def456"
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],
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"data_sources": [
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{
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"uri": "https://example.com/data1.csv",
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"type": "uri_asset"
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},
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{
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"uri": "https://example.com/data2.csv",
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"type": "uri_asset"
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}
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]
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},
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"file_search": {
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"vector_store_ids": [
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"vs_abc123"
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],
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"vector_stores": [
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{
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"name": "research_docs",
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"configuration": {
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"data_sources": [
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{
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"uri": "https://example.com/research.pdf",
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"type": "uri_asset"
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}
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]
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}
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}
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]
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},
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"azure_ai_search": {
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"indexes": [
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{
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"index_connection_id": "conn_abc123",
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"index_name": "product_index",
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"query_type": "simple",
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"top_k": 10,
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"filter": "category eq 'electronics'",
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"index_asset_id": "idx_abc123"
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}
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]
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}
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},
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"metadata": {
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"project": "data_analysis",
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"priority": "high"
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}
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},
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"model": "gpt-4",
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"instructions": "You are a data analysis expert. Provide detailed insights and recommendations.",
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"tools": [
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{
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"type": "code_interpreter"
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},
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{
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"type": "file_search"
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},
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{
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"type": "function",
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"function": {
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"name": "analyze_trend",
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"description": "Analyze data trends",
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"parameters": {
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"type": "object",
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"properties": {
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"data": {
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"type": "array"
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},
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"timeframe": {
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"type": "string"
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}
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}
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}
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}
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}
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],
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"tool_resources": {
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"code_interpreter": {
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"file_ids": [
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"file_abc123",
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"file_def456"
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],
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"data_sources": [
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{
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"uri": "https://example.com/global_data.csv",
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"type": "uri_asset"
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}
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]
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},
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"file_search": {
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"vector_store_ids": [
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"vs_abc123"
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]
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}
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},
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"stream": false,
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"truncation_strategy": {
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"type": "auto"
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},
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"tool_choice": "auto",
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"response_format": {
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"type": "text"
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},
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"parallel_tool_calls": true,
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"metadata": {
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"experiment_id": "exp_123",
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"session": "analysis_session_1"
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}
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}
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},
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"responses": {
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"200": {
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"body": {
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"id": "run_abc123",
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"object": "thread.run",
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"thread_id": "thread_abc123",
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"assistant_id": "asst_abc123",
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"status": "in_progress",
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"required_action": {
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"type": "submit_tool_outputs",
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"submit_tool_outputs": {
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"tool_calls": [
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{
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"id": "call_abc123",
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"type": "function",
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"function": {
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"name": "calculate_metrics",
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"arguments": "{\"values\": [1, 2, 3, 4, 5], \"metric_type\": \"mean\"}"
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}
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}
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]
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}
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},
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"last_error": {
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"code": "server_error",
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"message": "jadlgjrkhbhukfc"
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},
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"model": "gpt-4",
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"instructions": "You are a data analysis expert. Provide detailed insights and recommendations.",
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"tools": [
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{
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"type": "code_interpreter"
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},
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{
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"type": "file_search"
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},
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{
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"type": "function",
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"function": {
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"name": "analyze_trend",
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"description": "Analyze data trends",
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"parameters": {
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"type": "object",
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"properties": {
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"data": {
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"type": "array"
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},
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"timeframe": {
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"type": "string"
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}
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}
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}
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}
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}
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],
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"created_at": 1736869200,
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"expires_at": 1736872800,
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"started_at": 1736869201,
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"completed_at": 12,
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"cancelled_at": 18,
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"failed_at": 15,
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"incomplete_details": {
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"reason": "content_filter"
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},
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"usage": {
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"completion_tokens": 25,
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"prompt_tokens": 2,
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"total_tokens": 10
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},
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"temperature": 0.7,
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"top_p": 0.9,
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"max_prompt_tokens": 4096,
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"max_completion_tokens": 2048,
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"truncation_strategy": {
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"type": "auto"
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},
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"tool_choice": "auto",
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"response_format": {
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"type": "text"
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},
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"tool_resources": {
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"code_interpreter": {
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"file_ids": [
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"file_abc123",
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"file_def456"
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],
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"data_sources": [
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{
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"uri": "https://example.com/global_data.csv",
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"type": "uri_asset"
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}
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]
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},
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"file_search": {
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"vector_store_ids": [
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"vs_abc123"
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]
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}
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},
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"parallel_tool_calls": true,
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"metadata": {
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"experiment_id": "exp_123",
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"session": "analysis_session_1"
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}
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}
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}
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}
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}

‎specification/ai/Azure.AI.Agents/examples/2025-05-15-preview/CreateThreadAndRun_MinimumSet_Gen.json

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{
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"title": "CreateThreadAndRun_MinimumSet",
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"operationId": "CreateThreadAndRun",
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"parameters": {
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"api-version": "2025-05-15-preview",
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"body": {
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"assistant_id": "asst_abc123"
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}
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},
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"responses": {
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"200": {
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"body": {
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"id": "run_abc123",
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"object": "thread.run",
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"thread_id": "thread_abc123",
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"assistant_id": "asst_abc123",
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"status": "queued",
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"required_action": {
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"type": "submit_tool_outputs",
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"submit_tool_outputs": {
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"tool_calls": [
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{
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"id": "call_abc123",
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"type": "function",
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"function": {
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"name": "calculate_metrics",
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"arguments": "{\"values\": [1, 2, 3, 4, 5], \"metric_type\": \"mean\"}"
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}
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}
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]
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}
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},
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"last_error": {
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"code": "server_error",
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"message": "jadlgjrkhbhukfc"
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},
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"model": "gpt-4",
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"instructions": "You are a helpful assistant.",
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"tools": [
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null
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],
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"created_at": 1736869200,
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"expires_at": 1736872800,
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"started_at": 5,
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"completed_at": 12,
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"cancelled_at": 18,
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"failed_at": 15,
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"incomplete_details": {
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"reason": "content_filter"
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},
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"usage": {
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"completion_tokens": 25,
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"prompt_tokens": 2,
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"total_tokens": 10
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},
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"temperature": 0.7,
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"top_p": 0.9,
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"max_prompt_tokens": 4096,
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"max_completion_tokens": 2048,
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"truncation_strategy": {
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"type": "auto"
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},
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"tool_choice": {
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"type": "function",
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"function": {
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"name": "calculate_metrics"
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}
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},
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"response_format": {
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"type": "text"
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},
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"tool_resources": {},
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"parallel_tool_calls": true,
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"metadata": {}
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}
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}
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}
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}

‎specification/ai/Azure.AI.Agents/examples/2025-05-15-preview/Files_UploadFile_MaximumSet_Gen.json

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{
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"title": "Files_UploadFile_MaximumSet",
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"operationId": "Files_UploadFile",
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"parameters": {
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"api-version": "2025-05-15-preview",
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"purpose": "fine-tune",
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"file": [
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0,
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1,
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0
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],
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"filename": "q"
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},
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"responses": {
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"200": {
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"body": {
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"object": "file",
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"id": "zhbn",
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"bytes": 7,
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"filename": "q",
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"created_at": 17,
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"purpose": "fine-tune",
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"status": "uploaded",
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"status_details": "kgcllmehsrzwegmcrz"
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}
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}
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}
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}

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