# Implementation Roadmap - Project Chronicle

**Document Version**: 2.2 (Phase 7 Complete)
**Last Updated**: October 22, 2025
**Status**: Phases 1-7 & 2B Complete ✅ | Phases 8-10 Ready ⏳

---

## Overview

This roadmap provides step-by-step instructions for implementing **Project Chronicle**, a dual-mode customer success story repository built on Microsoft 365.

**Architecture**: 5-component system with verified technology stack
**Implementation Time**: 7-8 hours total
**Current Progress**: Phases 1-7 & 2B complete (6 hours, 70%) | Phases 8-10 ready (~2 hours remaining)

---

## Technology Stack (Verified Available)

| Component | Technology | Status | Evidence |
|-----------|-----------|--------|----------|
| **Interface** | Microsoft Teams | ✅ Available | Standard M365 |
| **AI Agent** | Copilot Studio | ✅ Available | User has access |
| **Automation - Flow 1** | Power Automate (SharePoint trigger) | ✅ Available | Standard connector |
| **Automation - Flow 2** | Azure AI Foundry Inference | ✅ Available | Verified in search |
| **AI Model** | GPT-5-mini | ✅ Deployed | Tested at 95% confidence |
| **Storage** | SharePoint Online | ✅ Available | Standard M365 |
| **Analytics** | Power BI Service | ✅ Available | Standard M365 |

**Azure OpenAI Configuration**:
- **Resource**: openai-stories-capstone
- **Region**: East US
- **Endpoint**: https://openai-stories-capstone.openai.azure.com/
- **Deployment**: gpt-5-mini
- **Model Version**: GPT-5 (released August 7, 2025)

---

## Implementation Phases

### ✅ Phase 1: SharePoint Library Setup (45 minutes) - COMPLETE

**Status**: Already completed in previous sessions

**What Was Built**:
- Document library: "Success Stories"
- 15 metadata columns configured
- Content types defined
- Permissions set

---

### ✅ Phase 2: Copilot Studio Agent (1 hour) - COMPLETE

**Status**: Already completed in previous sessions

**What Was Built**:
- Bot: "Story Finder"
- Manual submission topic with 7 questions
- Knowledge source connected to SharePoint
- Semantic search configured

---

### ✅ Phase 3: Teams Integration (30 minutes) - COMPLETE

**Status**: Already completed in previous sessions

**What Was Built**:
- Bot published to Teams
- Channel integration tested
- User access verified

---

### ✅ Phase 4: Power BI Dashboard (1 hour) - COMPLETE

**Status**: Already completed in previous sessions

**What Was Built**:
- Dashboard with 4 visualizations
- Summary cards for key metrics
- Coverage gap analysis

---

## ✅ PHASES 5-7: COMPLETE | PHASES 8-10: READY FOR IMPLEMENTATION

---

### ✅ Phase 5: Power Automate Flow 1A - Story ID Generator (45 minutes) - COMPLETE

**Status**: ✅ Complete (October 16, 2025)
**Purpose**: Automatically enrich SharePoint List items with Story_IDs and metadata
**Trigger**: When an item is created in Success Stories **List**

**What Was Built**:
- Flow 1A: "Manual Story Entry - ID Generator"
- SharePoint trigger pointing to Success Stories **List**
- Story_ID generation with format: CS-YYYY-NNN
- Automatic enrichment with Source = "Manual Entry"

**How It Works**:
```
Flow 1B creates List item → Flow 1A detects new item (10-30 sec delay) →
Query last Story_ID → Extract number → Increment →
Generate new ID (CS-2025-007) → Update List item with ID and Source
```

**Critical Configuration** (Applied in tonight's session):
- ✅ Trigger List: "Success Stories List" (NOT "Success Stories" Library)
- ✅ Story_ID extraction formula: `@int(last(split(items('Apply_to_each')?['Story_ID'], '-')))`
- ✅ Condition field: `triggerBody()?['Story_ID']` (with underscore)
- ✅ All SharePoint actions use "Success Stories List"

---

#### Key Corrections Applied (October 16, 2025)

**Fix 1: Trigger Location**
```yaml
BEFORE (Broken):
  Trigger: When an item is created
  List Name: Success Stories  # ❌ Document Library

AFTER (Working):
  Trigger: When an item is created
  List Name: Success Stories List  # ✅ SharePoint List
```

**Fix 2: Story_ID Extraction Formula**
```javascript
BEFORE (Broken):
  @variables('lastNumber') = items('Apply_to_each')?['Story_ID']
  // Returns "CS-2025-001" (string) → Type error

AFTER (Working):
  @variables('lastNumber') = @int(last(split(items('Apply_to_each')?['Story_ID'], '-')))
  // Extracts 1 from "CS-2025-001" → Integer ✅
```

**Fix 3: Condition Field Name**
```yaml
BEFORE (Broken):
  Condition: empty(triggerBody()?['StoryID'])  # ❌ No underscore

AFTER (Working):
  Condition: empty(triggerBody()?['Story_ID'])  # ✅ With underscore
```

**Fix 4: "Get items" Action**
```yaml
Issue: Action had empty name "" causing template errors

Solution: Deleted and recreated
  Action: SharePoint → Get items
  List Name: Success Stories List  # ✅ Not "Success Stories"
  Order By: Created desc
  Top Count: 1
  Filter: Story_ID ne null
```

---

#### Complete Flow 1A Structure (As Built)

```yaml
Flow Name: Manual Story Entry - ID Generator

Trigger:
  Action: When an item is created
  Site: https://insightonline.sharepoint.com/sites/di_dataai-AIXtrain-Data-Fall2025
  List Name: Success Stories List  # ✅ Critical: Must be List, not Library

Condition (Outer):
  Expression: empty(triggerBody()?['Story_ID'])
  # ✅ Only process items without Story_ID

If yes (Story_ID is empty):

  Step 1: Get items (Find last Story_ID)
    Site: [Same site]
    List Name: Success Stories List
    Order By: Created desc
    Top Count: 1
    Filter Query: Story_ID ne null

  Step 2: Initialize variable lastNumber
    Name: lastNumber
    Type: Integer
    Value: 0

  Step 3: Apply to each (body('Get_items')?['value'])
    Set variable: lastNumber
      Value: @int(last(split(items('Apply_to_each')?['Story_ID'], '-')))
      # ✅ Extracts number from "CS-2025-001" → 1

  Step 4: Initialize variable newNumber
    Name: newNumber
    Type: Integer
    Value: @{add(variables('lastNumber'), 1)}
    # ✅ Increment: 1 + 1 = 2

  Step 5: Compose storyIDYear
    Input: @{formatDateTime(utcNow(), 'yyyy')}
    # ✅ Extracts year: "2025"

  Step 6: Compose storyIDNumber
    Input: @{if(less(variables('newNumber'), 10), concat('00', string(variables('newNumber'))), if(less(variables('newNumber'), 100), concat('0', string(variables('newNumber'))), string(variables('newNumber'))))}
    # ✅ Formats number: 2 → "002"

  Step 7: Compose storyID
    Input: CS-@{outputs('Compose_storyIDYear')}-@{outputs('Compose_storyIDNumber')}
    # ✅ Combines: "CS-2025-002"

  Step 8: Update item (Success Stories List)
    Item ID: @{triggerBody()?['ID']}
    Story_ID: @{outputs('Compose_storyID')}
    Source: Manual Entry
    # ✅ Updates the List item with new Story_ID
```

---

### ✅ Phase 2B: Bot Automation with Power Automate Flow 1B (1 hour) - COMPLETE

**Status**: ✅ Complete (October 15, 2025)
**Purpose**: Automate Success Stories List population from bot conversations
**Trigger**: Manual trigger (instant cloud flow)

**What Was Built**:
- Flow 1B: "Bot to SharePoint - Story Submission"
- Called by Copilot Studio after 7-question interview
- Creates Success Stories List items programmatically
- Passes all metadata from bot conversation

**How It Works**:
```
User answers 7 questions in Teams →
Copilot Studio calls Flow 1B with answers →
Flow 1B creates List item →
Flow 1A enriches with Story_ID (reuses Phase 5!)
```

**Critical Configuration**:
- ✅ Flow Type: Instant cloud flow (manual trigger)
- ✅ Input Parameters: 14 parameters (all bot question answers)
- ✅ SharePoint Action: Create item in "Success Stories List"
- ✅ All fields mapped from Flow 1B inputs

**Integration Points**:
- ✅ Copilot Studio → Flow 1B: Bot calls flow with conversation data
- ✅ Flow 1B → SharePoint: Creates List item
- ✅ SharePoint → Flow 1A: Triggers Story_ID generation (Phase 5)
- ✅ Result: Fully automated story submission (100% hands-free!)

---

### ✅ Phase 6: Add Knowledge Sources to Copilot Studio (30 minutes) - COMPLETE

**Status**: ✅ Complete (October 17, 2025)
**Purpose**: Enable multi-source search across Success Stories and Knowledge Library

**What Was Built**:
- Knowledge Source 1: Success Stories List (structured metadata)
- Knowledge Source 2: Success Stories Library (uploaded documents)
- Knowledge Source 3: Data & AI Knowledge Library (project documents) ← NEW!

**Knowledge Source 3 Configuration**:
```yaml
Name: Knowledge Library
Site URL: https://insightonline.sharepoint.com/sites/di_dataai
Document Library: Shared Documents
Folder Path: Knowledge Library/Project Documents
Include Subfolders: Yes
Status: ✅ Active and indexed
```

**Multi-Source Search Testing** (October 17, 2025):
- ✅ Test 1: "Show me project documents" → Multiple sources returned
- ✅ Test 2: "Find Azure projects" → Success Stories + Knowledge Library
- ✅ Test 3: "Show me win wires" → Win wire documents from Knowledge Library
- ✅ Test 4: Client-specific queries → Documents organized by client folders

**Tyler Sprau's Knowledge Library Initiative**:
- **Owner**: Tyler Sprau (Services Manager - Data & AI)
- **Goal**: Consolidate project documents from local devices/OneDrive/Teams → centralized Knowledge Library
- **Target Date**: 10/31/2025
- **Structure**: Client Name/Project Name/
- **Contents**: Deliverables, architecture diagrams, user guides, win wires, marketing materials
- **Value**: Rich source of success stories for Phase 8 bulk ingestion

---

### ✅ Phase 7: Ingestion Configuration Setup (30 minutes) - COMPLETE

**Status**: ✅ Complete (October 22, 2025)
**Purpose**: Create configuration list for flexible multi-location support

---

#### Ingestion Config List (As Built)

**SharePoint List URL**:
```
https://insightonline.sharepoint.com/sites/di_dataai-AIXtrain-Data-Fall2025/Lists/Ingestion%20Config/AllItems.aspx
```

**Columns Created** (6 total):
1. **Title** (default) - Display name for the location
2. **Location_Name** (Text, Required) - Unique identifier
3. **SharePoint_URL** (Hyperlink, Required) - Full site URL
4. **Folder_Path** (Text, Required) - Path to monitor (e.g., /Project Documents)
5. **Structure_Type** (Choice, Required) - Client/Project | Year/Client/Project | Custom
6. **Enabled** (Yes/No, Default: Yes) - Toggle location on/off
7. **Priority** (Number, Default: 1) - Processing order (1 = highest)

**Configuration Entry 1** (Created October 22, 2025):
```yaml
Title: Data & AI Knowledge Library
Location_Name: Data_AI_KB
SharePoint_URL: https://insightonline.sharepoint.com/sites/di_dataai
Folder_Path: /Project Documents
Structure_Type: Client/Project
Enabled: Yes
Priority: 1
```

**Item URL**:
```
https://insightonline.sharepoint.com/sites/di_dataai-AIXtrain-Data-Fall2025/Lists/Ingestion%20Config/DispForm.aspx?ID=1
```

**Why This Matters**:
- ✅ **No hardcoded paths** - Flow 2 reads config from this list
- ✅ **Easy expansion** - Add new locations by adding rows (no Flow edits)
- ✅ **Flexible structures** - Supports different folder organization patterns
- ✅ **Toggle control** - Enable/disable locations without code changes
- ✅ **Future-ready** - Supports Tyler's initiative as more documents are added

**How Flow 2 Will Use This** (Phase 8):
```
Flow 2 triggered by new file →
  Read Ingestion Config List →
  Find config for current SharePoint site →
  Extract Structure_Type (Client/Project) →
  Parse folder path accordingly →
  Extract Client = Acme Healthcare, Project = Cloud Migration →
  Send to Azure OpenAI with parsed metadata →
  Create Success Stories List item
```

**✅ Phase 7 Complete**: Configuration-driven ingestion ready for Phase 8

---

### ⏳ Phase 8: Power Automate Flow 2 - Bulk Document Ingestion (80% Complete)

**Purpose**: Automated extraction of success stories from project documents

**Technology**: Azure AI Foundry Inference connector with GPT-5-mini (verified available) + AI Builder for document text extraction

**Progress Summary**:
- ✅ Steps 8.1-8.3: COMPLETE (Trigger, config, path parsing) - 45 min done
- ⏳ Step 8.4: IN PROGRESS - AI Builder extraction needed - 45 min remaining
- ✅ Step 8.5: COMPLETE - Minor body update needed - 5 min
- ✅ Steps 8.6-8.7: COMPLETE (Parse JSON, create list item) - 35 min done
- ⏳ Steps 8.8-8.9: OPTIONAL (Teams notification, error handling)
- ⏳ Step 8.10: PENDING (Testing after 8.4-8.5 complete)

**Total Completion**: 80 minutes done out of 100 minutes core work = **80% complete**
**Remaining Work**: ~50 minutes (45 min Step 8.4 + 5 min Step 8.5 update)

---

#### ✅ Step 8.1: Create Flow 2 (10 minutes) - COMPLETE

1. **Navigate to Power Automate**:
   - Go to https://make.powerautomate.com
   - Click **+ Create** → **Automated cloud flow**

2. **Configure Trigger**:
   - Flow name: `Bulk Document Ingestion - AI Extraction`
   - Search: `SharePoint`
   - Select: **"When a file is created"**
   - Click **Create**

3. **Set Trigger Parameters**:
   - **Site Address**: [Data & AI Knowledge Library URL]
   - **Folder Id**: Browse to `/Project Documents` (or root if monitoring all folders)
   - **Include Nested Folders**: Yes
   - Click **+ New step**

---

#### ✅ Step 8.2: Get Configuration (15 minutes) - COMPLETE

**Purpose**: Retrieve ingestion config to determine folder structure parsing

1. **Add Action: Get Items**:
   - **Action**: SharePoint → Get items
   - **Site Address**: [Success Stories site URL]
   - **List Name**: `Ingestion Config`
   - **Filter Query**:
     ```
     Enabled eq true and SharePoint_URL eq '[current site URL]'
     ```
   - **Top Count**: 1

2. **Add Variable: Initialize configFound**:
   - **Name**: configFound
   - **Type**: Boolean
   - **Value**:
     ```
     greater(length(body('Get_items')?['value']), 0)
     ```

3. **Add Condition: Check Config**:
   - **If no config**: Terminate with error
   - **If config found**: Continue

4. **Add Variables for Config**:
   - **structureType**:
     ```
     body('Get_items')?['value'][0]?['Structure_Type']?['Value']
     ```
   - **locationName**:
     ```
     body('Get_items')?['value'][0]?['Location_Name']
     ```

---

#### ✅ Step 8.3: Parse Folder Path (20 minutes) - COMPLETE

**Purpose**: Extract Client and Project names from file path

1. **Add Action: Compose - Full Path**:
   - **Action**: Data Operations → Compose
   - **Inputs**: `triggerOutputs()?['body/{Path}']`
   - **Example Output**: `/Project Documents/Acme Healthcare/Cloud Migration 2024/Win Wire.docx`

2. **Add Variable: Initialize pathSegments**:
   - **Name**: pathSegments
   - **Type**: Array
   - **Value**:
     ```
     split(outputs('Compose_-_Full_Path'), '/')
     ```

3. **Add Switch: Structure Type**:
   - **On**: `@{variables('structureType')}`

   **Case 1: "Client/Project"**:
   - Set clientName: `@{variables('pathSegments')[2]}`
   - Set projectName: `@{variables('pathSegments')[3]}`

   **Case 2: "Year/Client/Project"**:
   - Set clientName: `@{variables('pathSegments')[3]}`
   - Set projectName: `@{variables('pathSegments')[4]}`

   **Default**:
   - Set clientName: `Unknown`
   - Set projectName: `@{triggerOutputs()?['body/{Name}']}`

---

#### ⏳ Step 8.4: Extract Text from Documents (AI Builder) (45 minutes) - IN PROGRESS

**What You Already Built**:
- ✅ "Get file content" action (retrieves binary file from SharePoint)

**What Needs to Be Added** (45 minutes remaining):
Add multi-file-type support using AI Builder for Office document text extraction.

1. **Add Variable: fileExtension** (2 minutes)
   - **Action**: Initialize variable
   - **Name**: `fileExtension`
   - **Type**: String
   - **Value**:
     ```
     @{toLower(substring(triggerBody()?['{FilenameWithExtension}'], add(lastIndexOf(triggerBody()?['{FilenameWithExtension}'], '.'), 1)))}
     ```

2. **Add Switch Control** (5 minutes)
   - **Action**: Switch
   - **On**: Select `fileExtension` variable
   - This will route different file types to different extraction methods

3. **Configure 4 Cases for File Types** (30 minutes total):

   **CASE 1: txt files (direct text)** (5 minutes)
   - **Equals**: `txt`
   - **Add Action**: Compose
     - **Inputs**: Select **File Content** from "Get file content"
     - **Rename**: `Compose - extractedText - txt`

   **CASE 2: docx files (AI Builder extraction)** (8 minutes)
   - **Equals**: `docx`
   - **Add Action**: Extract information from documents (AI Builder)
     - **Document source**: Directly from file content
     - **Document content**: Select **File Content** from "Get file content"
     - **Document type**: General documents
   - **Add Action**: Compose
     - **Inputs**: Select **Result text** from "Extract information from documents"
     - **Rename**: `Compose - extractedText - docx`

   **CASE 3: pptx files (AI Builder extraction)** (8 minutes)
   - **Equals**: `pptx`
   - **Add Action**: Extract information from documents (AI Builder)
     - **Document source**: Directly from file content
     - **Document content**: Select **File Content** from "Get file content"
     - **Document type**: General documents
   - **Add Action**: Compose
     - **Inputs**: Select **Result text** from "Extract information from documents"
     - **Rename**: `Compose - extractedText - pptx`

   **CASE 4: pdf files (AI Builder extraction)** (8 minutes)
   - **Equals**: `pdf`
   - **Add Action**: Extract information from documents (AI Builder)
     - **Document source**: Directly from file content
     - **Document content**: Select **File Content** from "Get file content"
     - **Document type**: General documents
   - **Add Action**: Compose
     - **Inputs**: Select **Result text** from "Extract information from documents"
     - **Rename**: `Compose - extractedText - pdf`

   **DEFAULT CASE: Unsupported file types** (1 minute)
   - **Add Action**: Terminate
     - **Status**: Failed
     - **Message**: `Unsupported file type: @{variables('fileExtension')}`

4. **Why AI Builder?**
   - Azure OpenAI cannot parse binary Office file formats (.docx, .pptx, .pdf)
   - AI Builder "Extract information from documents" converts Office files to plain text
   - Plain text can then be sent to Azure OpenAI for metadata extraction

---

#### ✅ Step 8.5: Call Azure OpenAI (30 minutes) - COMPLETE (Body Update Needed - 5 min)

**What You Already Built**:
- ✅ HTTP action configured with Azure AI Foundry Inference
- ✅ System message configured
- ✅ User message template configured
- ✅ Temperature and max tokens set

**What Needs to Be Updated** (5 minutes):
Update the "Document Content" line in the User Message to use `coalesce()` function to pick the extracted text from whichever Switch case executed.

**Current User Message** (lines 1-8 are correct, only line 9 needs update):
```
Extract success story metadata from this document:

Client: @{variables('clientName')}
Project: @{variables('projectName')}
Filename: @{triggerOutputs()?['body/{Name}']}

Document Content:
@{body('Get_file_content')}  ← OLD - only works for .txt
```

**Updated User Message** (change line 9 only):
```
Extract success story metadata from this document:

Client: @{variables('clientName')}
Project: @{variables('projectName')}
Filename: @{triggerOutputs()?['body/{Name}']}

Document Content:
@{coalesce(body('Compose_-_extractedText_-_txt'), body('Compose_-_extractedText_-_docx'), body('Compose_-_extractedText_-_pptx'), body('Compose_-_extractedText_-_pdf'))}  ← NEW - picks whichever case executed
```

**Why `coalesce()`?**
- Only ONE Switch case executes based on file extension
- `coalesce()` returns the first non-null value
- If .docx file: only `Compose_-_extractedText_-_docx` has a value, others are null
- This automatically selects the correct extracted text for any file type

**All Other Settings Remain the Same**:
- System message: No changes needed
- Temperature: 0.3 (already configured)
- Max tokens: 500 (already configured)

---

#### ✅ Step 8.6: Parse JSON Response (15 minutes) - COMPLETE

1. **Add Action: Parse JSON**:
   - **Content**: `body('Generate_a_completion')?['choices'][0]?['message']?['content']`
   - **Schema**: Click "Use sample payload" and paste:
     ```json
     {
       "title": "Example Title",
       "business_challenge": "Example challenge",
       "solution": "Example solution",
       "outcomes": "Example outcomes",
       "products_used": ["Product 1"],
       "industry": "Technology",
       "customer_size": "Enterprise",
       "deployment_type": "Cloud"
     }
     ```

2. **Add Error Handling**:
   - Configure action: **Run after** → Include "has failed"
   - If parsing fails: Log error and use default values

---

#### ✅ Step 8.7: Create Success Story List Item (20 minutes) - COMPLETE

1. **Add Action: Create item**:
   - **Site Address**: [Success Stories site URL]
   - **List Name**: `Success Stories List`

2. **Map Fields from AI Extraction**:
   ```yaml
   Title: @{body('Parse_JSON')?['title']}
   Client_Name: @{variables('clientName')}
   Project_Name: @{variables('projectName')}
   Industry: @{body('Parse_JSON')?['industry']}
   Business_Challenge: @{body('Parse_JSON')?['business_challenge']}
   Solution_Delivered: @{body('Parse_JSON')?['solution']}
   Outcomes_Achieved: @{body('Parse_JSON')?['outcomes']}
   Customer_Size: @{body('Parse_JSON')?['customer_size']}
   Deployment_Type: @{body('Parse_JSON')?['deployment_type']}
   Source: Bulk Document Ingestion
   Document_URL: @{triggerOutputs()?['body/{Link}']}
   Products_Used: @{join(body('Parse_JSON')?['products_used'], ', ')}
   ```

3. **Note**: Story_ID will be auto-generated by Flow 1A!

---

#### ⏳ Step 8.8: Send Teams Notification (15 minutes) - OPTIONAL

**Purpose**: Notify team when new story is created

1. **Add Action: Post adaptive card**:
   - **Connector**: Microsoft Teams
   - **Team**: [Your team]
   - **Channel**: [Your channel, e.g., "Success Stories"]

2. **Adaptive Card JSON**:
   ```json
   {
     "type": "AdaptiveCard",
     "body": [
       {
         "type": "TextBlock",
         "size": "Medium",
         "weight": "Bolder",
         "text": "New Success Story Created"
       },
       {
         "type": "FactSet",
         "facts": [
           {
             "title": "Title:",
             "value": "@{body('Parse_JSON')?['title']}"
           },
           {
             "title": "Client:",
             "value": "@{variables('clientName')}"
           },
           {
             "title": "Project:",
             "value": "@{variables('projectName')}"
           },
           {
             "title": "Industry:",
             "value": "@{body('Parse_JSON')?['industry']}"
           },
           {
             "title": "Source:",
             "value": "Bulk Document Ingestion"
           }
         ]
       }
     ],
     "actions": [
       {
         "type": "Action.OpenUrl",
         "title": "View Document",
         "url": "@{triggerOutputs()?['body/{Link}']}"
       }
     ],
     "$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
     "version": "1.4"
   }
   ```

---

#### ⏳ Step 8.9: Error Handling & Logging (10 minutes) - OPTIONAL

1. **Add Scope Actions**:
   - Wrap Steps 8.5-8.7 in a **Scope** action
   - Name: "AI Extraction and List Creation"

2. **Configure Scope**:
   - **Run after**: This action runs after all previous steps

3. **Add Parallel Branch - Error Handling**:
   - **Run after**: Scope "has failed"
   - **Action**: Compose → Error Log
   - **Content**:
     ```json
     {
       "error_time": "@{utcNow()}",
       "file_path": "@{triggerOutputs()?['body/{Path}']}",
       "file_name": "@{triggerOutputs()?['body/{Name}']}",
       "error_message": "@{result('Scope')[0]['error']['message']}"
     }
     ```

4. **Add Action: Send email** (optional):
   - To: Your email
   - Subject: `Flow 2 Error: @{triggerOutputs()?['body/{Name}']}`
   - Body: `@{outputs('Compose_-_Error_Log')}`

---

#### ⏳ Step 8.10: Testing Flow 2 (30 minutes) - PENDING (After Steps 8.4-8.5 Complete)

**Test Strategy**:

1. **Test 1: Upload Test Document**
   - Upload a sample project document to Knowledge Library
   - Verify Flow 2 triggers
   - Check AI extraction results
   - Confirm List item created
   - Verify Story_ID assigned by Flow 1A

2. **Test 2: Verify Metadata Accuracy**
   - Compare AI-extracted fields to document content
   - Verify client/project names parsed correctly
   - Check products_used array formatting

3. **Test 3: Error Handling**
   - Upload a non-text file (image, etc.)
   - Verify error handling works
   - Check error log output

4. **Test 4: Teams Notification**
   - Verify notification appears in Teams channel
   - Check all fields display correctly
   - Test "View Document" link

5. **Test 5: Power BI Integration**
   - Confirm new story appears in Power BI dashboard
   - Verify all visualizations update
   - Check filtering works

**Expected Flow 2 Run Time**: 30-60 seconds per document

**✅ Phase 8 Complete**: Bulk ingestion operational with AI metadata extraction

---

### ⏳ Phase 9: End-to-End Testing (1 hour)

**Purpose**: Comprehensive testing of all integrated components

---

#### Test Scenario 1: Manual Story Submission (15 minutes)

**Steps**:
1. Open Teams
2. Start conversation with Story Finder bot
3. Answer all 7 questions
4. Verify:
   - ✅ Success Stories List item created
   - ✅ Story_ID assigned (CS-2025-XXX)
   - ✅ Source = "Manual Entry"
   - ✅ Power BI dashboard updated

---

#### Test Scenario 2: Bulk Document Ingestion (20 minutes)

**Steps**:
1. Upload new project document to Knowledge Library
2. Wait 2-3 minutes for Flow 2
3. Verify:
   - ✅ AI extracted metadata accurately
   - ✅ Client/Project names parsed correctly
   - ✅ Success Stories List item created
   - ✅ Story_ID assigned
   - ✅ Source = "Bulk Document Ingestion"
   - ✅ Teams notification sent
   - ✅ Power BI dashboard updated

---

#### Test Scenario 3: Multi-Source Search (15 minutes)

**Steps**:
1. Open Teams → Story Finder bot
2. Test queries:
   - "Find stories about [client name]"
   - "Show me Azure projects"
   - "What industries do we serve?"
   - "Show me win wires"
3. Verify:
   - ✅ Results from all 3 knowledge sources
   - ✅ Snippets and links provided
   - ✅ Source attribution correct

---

#### Test Scenario 4: Configuration Changes (10 minutes)

**Steps**:
1. Open Ingestion Config list
2. Add new configuration entry (test location)
3. Upload document to new location
4. Verify:
   - ✅ Flow 2 reads new config
   - ✅ Folder structure parsed correctly
   - ✅ Story created successfully

---

**✅ Phase 9 Complete**: End-to-end system tested and validated

---

### ⏳ Phase 10: Documentation & Handoff (30 minutes)

**Purpose**: Create user guides and admin documentation

---

#### Deliverable 1: User Guide (15 minutes)

**Content**:
1. How to submit stories via Teams bot
2. How to search for stories
3. How to view Power BI dashboard
4. How to upload documents for bulk ingestion

**Format**: SharePoint page or PDF

---

#### Deliverable 2: Admin Guide (15 minutes)

**Content**:
1. Flow 1A/1B maintenance
2. Flow 2 troubleshooting
3. Copilot Studio updates
4. Power BI dashboard refresh
5. Adding new ingestion locations
6. Azure OpenAI cost monitoring

**Format**: SharePoint page or PDF

---

**✅ Phase 10 Complete**: Documentation delivered, system ready for production

---

## Next Steps After Phase 10

1. **Monitor Usage** (Week 1-2):
   - Track bot conversations
   - Monitor Flow 2 success rate
   - Review AI extraction accuracy
   - Collect user feedback

2. **Optimization** (Week 3-4):
   - Tune AI prompts based on results
   - Adjust Power BI visualizations
   - Add new ingestion locations
   - Expand bot capabilities

3. **Scale** (Month 2+):
   - Add more knowledge sources
   - Integrate with other systems
   - Implement advanced analytics
   - Automate reporting

---

## Project Status Summary

**Completed Phases** (7 of 10):
- ✅ Phase 1: SharePoint Library Setup (45 min)
- ✅ Phase 2: Copilot Studio Agent (1 hour)
- ✅ Phase 3: Teams Integration (30 min)
- ✅ Phase 4: Power BI Dashboard (1 hour)
- ✅ Phase 5: Flow 1A - Story ID Generator (45 min)
- ✅ Phase 2B: Flow 1B - Bot Automation (1 hour)
- ✅ Phase 6: Knowledge Sources (30 min)
- ✅ **Phase 7: Ingestion Config** (30 min) ← **COMPLETE!**

**Remaining Phases** (3 of 10):
- ⏳ Phase 8: Flow 2 - Bulk Ingestion (2-3 hours)
- ⏳ Phase 9: End-to-End Testing (1 hour)
- ⏳ Phase 10: Documentation (30 min)

**Progress**: 70% complete (6 hours invested, ~2 hours remaining)

---

## Quick Reference: Connector Names (Verified Available)

**For Power Automate Flow Creation**:

| Component | Connector Name | Action Name |
|-----------|---------------|-------------|
| **Flow 1 Trigger** | SharePoint | "When an item is created" |
| **Flow 2 Trigger** | SharePoint | "When a file is created" |
| **AI Processing** | Azure AI Foundry Inference | "Generate a completion for a conversation" |
| **Alternative AI** | Azure OpenAI | "Create a completion" |
| **Notifications** | Microsoft Teams | "Post adaptive card in a chat or channel" |

---

## Azure OpenAI Configuration Reference

**Endpoint**: https://openai-stories-capstone.openai.azure.com/
**Region**: East US
**Deployment**: gpt-5-mini
**API Version**: 2024-08-01-preview
**Authentication**: API Key

**Key 1**: %R5*$PxE@F1j$tZh ⚠️ REGENERATE AFTER PROJECT
**Key 2**: 3lzUmyOhZktAWIPI... ⚠️ REGENERATE AFTER PROJECT

---

**Document Status**: ✅ Phase 7 Complete - Ready for Phase 8 Implementation
**Confidence Level**: 95% (all technology verified, config infrastructure ready)
**Last Updated**: October 22, 2025