feat: onboard new sample data analyst agent v2

.config/samples-config-v3.json

@@ -937,6 +937,30 @@
             "thumbnailPath": "images/screen009.jpg",
             "gifPath": "images/screen009.jpg",
             "suggested": true
+        },
+        {
+            "id": "data-analyst-agent-v2",
+            "shortId": "data-analyst-v2",
+            "onboardDate": "2025-12-23",
+            "title": "Data Analyst Agent v2",
+            "shortDescription": "Natural language interface for data exploration and visualization.",
+            "fullDescription": "This sample demonstrates how to build an AI-powered data analyst agent using Microsoft Teams SDK that can be integrated into Microsoft Teams. It helps users explore and visualize data through natural language conversations and Adaptive Cards charts.",
+            "types": [
+                "Custom Engine Agent"
+            ],
+            "tags": [
+                "TS",
+                "Custom Engine Agent",
+                "Data Visualization",
+                "Adaptive Cards",
+                "LLM SQL",
+                "Microsoft Teams SDK"
+            ],
+            "time": "10 mins to run",
+            "configuration": "Manual configurations required",
+            "thumbnailPath": "assets/demo.gif",
+            "gifPath": "assets/demo.gif",
+            "suggested": false
         }
     ]
-}
+}

data-analyst-agent-v2/README.md

@@ -39,7 +39,7 @@ The Data Analyst Agent v2 transforms how teams interact with data by providing a
 2. Configure environment variables:
     - Open the project in Visual Studio Code
     - The Microsoft 365 Agents Toolkit will automatically generate the required environment files
-    - Update the `.localConfigs` file (or `.localConfigs.playground` if debug with playground) with your Azure OpenAI configuration:
+    - Update the `.env.local.user` file (or `.env.playground.user` if debug with playground) with your Azure OpenAI configuration:
       - `SECRET_AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key
       - `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
       - `AZURE_OPENAI_DEPLOYMENT_NAME`: Your Azure OpenAI deployment name (e.g., gpt-4o)
@@ -152,6 +152,23 @@ Both evaluations will provide detailed feedback including:
 | Date         | Author     | Comments                               |
 | ------------ | ---------- | -------------------------------------- |
 | Oct 31, 2025  | qinzhouxu   | Onboard sample with Microsoft Teams SDK |
+| Dec 16, 2025  | quke      | fix issue and onboard sample           |
+
+## Known Issues
+
+### Service Principal Creation Failure During Local Debug (Admin Accounts Only)
+
+**Impact**: Local debug only. This does not affect debugging in Microsoft 365 Agents Playground or remote deployment to Azure.
+
+**Symptom**: When running local debug with an M365 admin account, you may encounter the following error:
+
+```
+The client application <client-id> is missing service principal in the tenant <tenant-id>.
+```
+
+**Root Cause**: Teams Developer Portal has a known issue where it cannot automatically create a service principal for M365 admin accounts.
+
+**Solution**: Use a non-admin M365 account for local debugging. You can create a regular user account in your M365 tenant and use it for the local debug workflow.
 
 ## Feedback
 We really appreciate your feedback! If you encounter any issue or error, please report issues to us following the [Supporting Guide](https://github.com/OfficeDev/TeamsFx-Samples/blob/dev/SUPPORT.md). Meanwhile you can make [recording](https://aka.ms/teamsfx-record) of your journey with our product, they really make the product better. Thank you!

data-analyst-agent-v2/evals/judge/ac.ts

@@ -48,9 +48,9 @@
     const prompt = new ChatPrompt({
         instructions: systemMessage,
         model: new OpenAIChatModel({
-            model: process.env.AOAI_MODEL!,
-            apiKey: process.env.AOAI_API_KEY!,
-            endpoint: process.env.AOAI_ENDPOINT!,
+            model: process.env.AZURE_OPENAI_DEPLOYMENT_NAME!,
+            apiKey: process.env.AZURE_OPENAI_API_KEY!,
+            endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
             apiVersion: '2025-04-01-preview',
         }),
     }).function(
@@ -90,7 +90,26 @@
 ************
 [END DATA]`;
             const res = await prompt.send(userPrompt, { autoFunctionCalling: false });
-            const functionCallArgs = res.function_calls?.[0].arguments;
+
+            // Parse function call from content (model returns JSON in content when autoFunctionCalling is false)
+            let functionCallArgs: { result?: boolean; reasoning?: string } | undefined;
+
+            if (res.function_calls?.[0]?.arguments) {
+                functionCallArgs = res.function_calls[0].arguments;
+            } else if (res.content) {
+                // Try to parse JSON from content
+                try {
+                    const jsonMatch = res.content.match(/\{[\s\S]*"result"\s*:\s*(true|false)[\s\S]*"reasoning"\s*:\s*"([^"]+(?:\\.[^"]*)*)"[\s\S]*\}/);
+                    if (jsonMatch) {
+                        functionCallArgs = {
+                            result: jsonMatch[1] === 'true',
+                            reasoning: jsonMatch[2].replace(/\\"/g, '"').replace(/\\n/g, '\n')
+                        };
+                    }
+                } catch (e) {
+                    // Parsing failed, will use fallback
+                }
+            }
 
             return {
                 result: functionCallArgs?.result || false,

data-analyst-agent-v2/evals/judge/sql.ts

@@ -32,9 +32,9 @@
     const prompt = new ChatPrompt({
         instructions: systemMessage,
         model: new OpenAIChatModel({
-            model: process.env.AOAI_MODEL!,
-            apiKey: process.env.AOAI_API_KEY!,
-            endpoint: process.env.AOAI_ENDPOINT!,
+            model: process.env.AZURE_OPENAI_DEPLOYMENT_NAME!,
+            apiKey: process.env.AZURE_OPENAI_API_KEY!,
+            endpoint: process.env.AZURE_OPENAI_ENDPOINT!,
             apiVersion: '2025-04-01-preview',
         }),
     }).function(
@@ -73,7 +73,26 @@
 ************
 [END DATA]`;
             const res = await prompt.send(userPrompt, { autoFunctionCalling: false });
-            const functionCallArgs = res.function_calls?.[0]?.arguments;
+
+            // Parse function call from content (model returns JSON in content when autoFunctionCalling is false)
+            let functionCallArgs: { result?: boolean; reasoning?: string } | undefined;
+
+            if (res.function_calls?.[0]?.arguments) {
+                functionCallArgs = res.function_calls[0].arguments;
+            } else if (res.content) {
+                // Try to parse JSON from content
+                try {
+                    const jsonMatch = res.content.match(/\{[\s\S]*"result"\s*:\s*(true|false)[\s\S]*"reasoning"\s*:\s*"([^"]+(?:\\.[^"]*)*)"[\s\S]*\}/);
+                    if (jsonMatch) {
+                        functionCallArgs = {
+                            result: jsonMatch[1] === 'true',
+                            reasoning: jsonMatch[2].replace(/\\"/g, '"').replace(/\\n/g, '\n')
+                        };
+                    }
+                } catch (e) {
+                    // Parsing failed, will use fallback
+                }
+            }
 
             return {
                 result: functionCallArgs?.result || false,

data-analyst-agent-v2/evals/skills/adaptive-card-generation.ts

@@ -1,5 +1,6 @@
 import { ACJudge } from '../judge/ac';
 import { AgentEvaluator } from './base-evaluator';
+import { generateChartCard } from '../../src/cards';
 
 new AgentEvaluator({
     evalName: 'ac-eval',
@@ -8,7 +9,65 @@ new AgentEvaluator({
     judge: ACJudge,
     generatePrompt: (tc) =>
         `Create an appropriate visualization for this data: ${JSON.stringify(tc.input_data)}. Please return a single card.\nUse the following type of visualization: ${tc.visualization_type}.`,
-    extractGenerated: (agent) => JSON.stringify(agent.attachments?.[0] ?? {}),
+    extractGenerated: (agent, response) => {
+        // First check if there are actual attachments from function execution
+        if (agent.attachments?.length > 0) {
+            const attachment = agent.attachments[0];
+            return JSON.stringify(attachment.content);
+        }
+
+        // When model returns JSON in content instead of calling functions
+        const content = response.content || '';
+
+        try {
+            // Try format 1: {"name":"functions.generate_card","arguments":{...}}
+            let startIdx = content.indexOf('{"name":"functions.generate_card"');
+            if (startIdx !== -1) {
+                let braceCount = 0;
+                let endIdx = startIdx;
+                for (let i = startIdx; i < content.length; i++) {
+                    if (content[i] === '{') braceCount++;
+                    else if (content[i] === '}') braceCount--;
+                    if (braceCount === 0) {
+                        endIdx = i + 1;
+                        break;
+                    }
+                }
+                const jsonStr = content.substring(startIdx, endIdx);
+                const functionCall = JSON.parse(jsonStr);
+                const { chartType, rows, options } = functionCall.arguments;
+                if (chartType && rows) {
+                    const attachment = generateChartCard(chartType, rows, options);
+                    return JSON.stringify(attachment.content);
+                }
+            }
+
+            // Try format 2: Direct {"chartType":"...","rows":[...],...} at start of content
+            startIdx = content.indexOf('{"chartType"');
+            if (startIdx !== -1) {
+                let braceCount = 0;
+                let endIdx = startIdx;
+                for (let i = startIdx; i < content.length; i++) {
+                    if (content[i] === '{') braceCount++;
+                    else if (content[i] === '}') braceCount--;
+                    if (braceCount === 0) {
+                        endIdx = i + 1;
+                        break;
+                    }
+                }
+                const jsonStr = content.substring(startIdx, endIdx);
+                const { chartType, rows, options } = JSON.parse(jsonStr);
+                if (chartType && rows) {
+                    const attachment = generateChartCard(chartType, rows, options);
+                    return JSON.stringify(attachment.content);
+                }
+            }
+        } catch (e) {
+            // Silent fail
+        }
+
+        return '{}';
+    },
     extractExpected: (tc) => JSON.stringify(tc.expected_card),
     extractInput: (tc) => JSON.stringify(tc.input_data),
 }).run(process.argv.includes('--run-one'));

data-analyst-agent-v2/evals/skills/sql-generation.ts

@@ -6,9 +6,19 @@
     fileName: 'sql-eval.jsonl',
     autoFunctionCalling: false,
     judge: SQLJudge,
-    generatePrompt: (tc) => `Here's the user query: ${tc.user_query}. Let the SQL Prompt generate a SQL query based on the user's input and execute it.`,
-    extractGenerated: (_, response) =>
-        response.function_calls?.[0].arguments.text || 'MISSING_SQL_OUTPUT',
+    generatePrompt: (tc) => `Here's the user query: ${tc.user_query}. Generate a SQL query to answer this question.`,
+    extractGenerated: (_, response) => {
+        // When autoFunctionCalling is false, the model returns function call as JSON in content
+        const content = response.content || '';
+        // Try to extract JSON from content
+        const jsonMatch = content.match(/\{"name":\s*"functions\.execute_sql".*?"query"\s*:\s*"([^"]+(?:\\.[^"]*)*)".*?\}/s);
+        if (jsonMatch) {
+            // Unescape the query string
+            return jsonMatch[1].replace(/\\n/g, '\n').replace(/\\"/g, '"');
+        }
+        // Fallback: try to parse as function_calls array
+        return response.function_calls?.[0]?.arguments?.query || 'MISSING_SQL_OUTPUT';
+    },
     extractExpected: (tc) => tc.sql_query,
     extractInput: (tc) => tc.user_query,
 }).run(process.argv.includes('--run-one'));