feat: Modularity and Infra standardization code changes

.github/agents/speckit.contentpack.agent.md

@@ -0,0 +1,273 @@
+---
+description: Create a new content pack (agent team + data + knowledge base) using the reference template and step-by-step guide.
+---
+
+# Content Packs
+
+Optional, drop-in extensions to the Multi-Agent Custom Automation Engine. A pack
+ships everything needed to add a domain-specific agent team **without touching
+core code**.
+
+The core solution works fine when the `content_packs/` folder is empty or absent.
+
+---
+
+## Folder Structure
+
+```
+content_packs/
+└── <pack_name>/
+    ├── pack.json               # optional — declares search indexes + blob uploads
+    ├── agent_teams/
+    │   └── *.json              # required — one or more team config files (any name)
+    ├── datasets/               # optional — source data for grounding
+    │   ├── data/*.csv
+    │   └── docs/*.pdf
+    └── scripts/                # optional — pack-local utilities
+```
+
+- `<pack_name>` should be lowercase snake_case (e.g. `pet_food`, `legal_review`).
+- JSON files inside `agent_teams/` can be named anything — the upload script
+  globs all `*.json` files in that directory.
+
+---
+
+## Designing Your Agent Team
+
+Every pack needs at least one team config JSON in `agent_teams/`. The agents you
+create should be tailored to your domain — there is no fixed pattern. Consider:
+
+**What does the user need?** Design agents around the tasks your users will
+perform, not around a template. Ask yourself:
+
+| Question | Design decision |
+|----------|----------------|
+| Does the team need to look up data? | Add a **ResearchAgent** with `use_knowledge_base: true` |
+| Does it need to generate images? | Add an agent with `use_toolbox: true` and the appropriate `toolbox_filter` |
+| Should it ask the user clarifying questions? | Set `user_responses: true` on the relevant agent |
+| Are there multiple distinct tasks? | Add specialist agents and a **TriageAgent** to route between them |
+| Is it a simple Q&A over data? | A single agent with KB access may be enough — no triage needed |
+
+**Examples from this repo:**
+
+| Pack | Agents | Why |
+|------|--------|-----|
+| `content_gen` | Triage → Planning → Research → TextContent → ImageContent → Compliance | Complex creative workflow with multiple output types |
+| `contract_compliance` | Triage → Research → Analysis | Document review with KB lookup |
+| `hr_onboarding` | Single team (no KB) | Workflow-only, no data grounding needed |
+
+**Key rules:**
+- Every agent needs a unique `input_key` (used for routing between agents).
+- The `team_id` must be a valid UUID using only hex characters (0-9, a-f).
+  Use the pattern `00000000-0000-0000-0000-00000000NNNN` where NNNN is unique.
+- At least one `starting_tasks` entry is required (the example prompt shown in the UI).
+
+### Required Fields (validation will reject uploads without these)
+
+**Team-level:**
+```jsonc
+{
+  "id": "1",
+  "team_id": "00000000-0000-0000-0000-000000000008",
+  "name": "Your Team Name",
+  "status": "visible",           // REQUIRED — team won't appear in UI without this
+  "deployment_name": "gpt-4.1-mini",
+  ...
+}
+```
+
+**Each agent must include `type`:**
+```jsonc
+{
+  "input_key": "my_agent",
+  "type": "",                    // REQUIRED — empty string is fine, but field must exist
+  "name": "MyAgent",
+  ...
+}
+```
+
+**Each starting task must include `created`, `creator`, `logo`:**
+```jsonc
+"starting_tasks": [
+  {
+    "id": "task-1",
+    "name": "Example Task",
+    "prompt": "A sample prompt users can click to start",
+    "created": "",              // REQUIRED — empty string is fine
+    "creator": "",              // REQUIRED — empty string is fine
+    "logo": ""                  // REQUIRED — empty string is fine
+  }
+]
+```
+
+> Missing any of these fields results in a 400 error during upload.
+> Use an existing pack (e.g. `hr_onboarding/agent_teams/hr.json`) as a
+> reference for the full required schema.
+
+---
+
+## Data & Knowledge Bases
+
+If your agents need to search domain-specific data, you need three things wired together:
+
+```
+CSV/PDF ──► AI Search Index ──► Knowledge Base (MCP) ──► Agent
+            (pack.json)         (seed_knowledge_bases.py)  (agent_teams/*.json)
+```
+
+### Step 1 — Add your data
+
+Put source files in `datasets/data/`. Supported formats:
+
+| Format | How it's indexed |
+|--------|-----------------|
+| CSV | One document per row. Columns become searchable fields. |
+| PDF/DOCX | Uploaded to blob; use `blob_indexes` in `pack.json` for document-crack indexing. |
+
+### Step 2 — Create `pack.json`
+
+```jsonc
+{
+  "name": "your_pack",
+  "description": "What this pack does",
+  "search_indexes": [
+    {
+      "index_name": "your-pack-data-index",
+      "csv_path": "datasets/data/your_data.csv",
+      "key_field": "id",
+      "title_field": "product_name"
+    }
+  ],
+  "blob_uploads": [
+    {
+      "container": "your-pack-dataset",
+      "source": "datasets/data",
+      "pattern": "*.csv"
+    }
+  ]
+}
+```
+
+The `index_name` must be globally unique on your search service and will be
+referenced in the KB registration.
+
+### Step 3 — Register the Knowledge Base
+
+Add an entry to `infra/scripts/post-provision/seed_knowledge_bases.py` in the `KNOWLEDGE_BASES`
+dict. Place it before the `# ── Example Pack ──` comment block:
+
+```python
+    # ── Your Pack ──
+    "your-pack-data-kb": {
+        "description": "What this KB provides",
+        "model": {
+            "kind": "azureOpenAI",
+            "azureOpenAIParameters": {
+                "resourceUri": AI_SERVICES_ENDPOINT,
+                "deploymentId": "gpt-4.1-mini",
+                "modelName": "gpt-4.1-mini",
+            },
+        },
+        "sources": [
+            {
+                "name": "your-pack-data-ks",
+                "description": "What the source data contains",
+                "index_name": "your-pack-data-index",  # Must match pack.json
+                "searchable_fields": ["content", "title"],
+            },
+        ],
+    },
+```
+
+**Naming convention (must be consistent across files):**
+
+| Item | Pattern | Example |
+|------|---------|---------|
+| KB name (dict key) | `<pack>-<purpose>-kb` | `pet-food-catalog-kb` |
+| Knowledge Source name | `<pack>-<purpose>-ks` | `pet-food-catalog-ks` |
+| Search index name | `<pack>-<purpose>-index` | `pet-food-catalog-index` |
+
+### Step 4 — Connect agents to the KB
+
+In your team config JSON, set these fields on the agent that needs search:
+
+```jsonc
+"use_knowledge_base": true,
+"knowledge_base_name": "your-pack-data-kb"  // Must match the key in seed_knowledge_bases.py
+```
+
+The agent's `system_message` should instruct it to **always use the search tool**
+and **never hallucinate data**.
+
+---
+
+## Registering in the Deployment Script
+
+Edit `infra/scripts/post-provision/Selecting-Team-Config-And-Data.ps1`. Search for `NEW CONTENT PACK`
+— each insertion point has a comment template. There are **4 things** to do:
+
+| # | What | Where |
+|---|------|-------|
+| 1 | Add `Write-Host "N. Your Pack Name"` | Menu display section |
+| 2 | Add `elseif ($useCaseSelection -eq "N") { ... }` | Selection handler |
+| 3 | Add deployment block (team config upload + `Deploy-ContentPack`) | After Content Gen block |
+| 4 | Add `-or $useCaseSelection -eq "N"` to network/KB/success conditions | Only if pack has data |
+
+After adding your entry, update `$allOption` to `N + 1` so "All" is always last.
+
+---
+
+## Agent Tool Options Reference
+
+| Field | Type | Purpose |
+|-------|------|---------|
+| `use_knowledge_base` | bool | Connects a Foundry IQ KB as an MCP search tool |
+| `knowledge_base_name` | string | Name of the KB (must exist in `seed_knowledge_bases.py`) |
+| `use_file_search` | bool | Attaches an Azure AI vector store for file-level RAG |
+| `vector_store_name` | string | Name of the vector store in Foundry |
+| `use_toolbox` | bool | Connects MCP toolbox tools (e.g., `generate_marketing_image`) |
+| `toolbox_filter` | string | Tag filter for which toolbox tools are available |
+| `coding_tools` | bool | Enables code interpreter sandbox |
+| `user_responses` | bool | Allows the agent to pause and ask the user a question |
+
+> **`user_responses` guidance:** Default to `false` unless the user explicitly
+> asks for the agent to collect human feedback or ask clarifying questions.
+> When `false`, the agent runs autonomously without pausing for input. Only set
+> to `true` on agents whose design requires them to ask the user a question
+> mid-workflow (e.g., an intake agent gathering requirements).
+
+---
+
+## Deploying
+
+```bash
+# 1. Deploy infrastructure
+azd up
+
+# 2. Provision pack resources — select your pack or "All"
+./infra/scripts/post-provision/Selecting-Team-Config-And-Data.ps1 -ResourceGroup <rg>
+```
+
+---
+
+## Checklist
+
+- [ ] `content_packs/<pack>/agent_teams/` has at least one valid JSON team config
+- [ ] `team_id` is a valid hex UUID (0-9, a-f only)
+- [ ] `starting_tasks` has at least one example prompt
+- [ ] If using a KB: `pack.json` exists with matching `index_name`
+- [ ] If using a KB: `datasets/data/` contains the source files
+- [ ] If using a KB: entry added to `seed_knowledge_bases.py` with matching names
+- [ ] If using a KB: agent has `use_knowledge_base: true` + correct `knowledge_base_name`
+- [ ] Agent `system_message` tells it to search (not hallucinate)
+- [ ] Pack registered in `Selecting-Team-Config-And-Data.ps1` (all 4 locations)
+- [ ] `$allOption` updated if you added a new menu number
+
+---
+
+## Removing a Pack
+
+Delete the pack folder. Previously uploaded team configs remain in Cosmos until
+deleted via `DELETE /api/v4/team_configs/{team_id}`. Search indexes and blob
+containers are also left in place — clean up with `az search` / `az storage`.
+

.github/prompts/speckit.contentpack.prompt.md

@@ -0,0 +1,3 @@
+---
+agent: speckit.contentpack
+---

.github/requirements.txt

@@ -0,0 +1,30 @@
+fastapi==0.137.1
+uvicorn==0.49.0
+autogen-agentchat==0.7.5
+azure-cosmos==4.16.1
+azure-monitor-opentelemetry==1.8.8
+azure-monitor-events-extension==0.1.0
+azure-identity==1.25.3
+python-dotenv==1.2.2
+python-multipart==0.0.32
+opentelemetry-api==1.40.0
+opentelemetry-sdk==1.40.0
+opentelemetry-exporter-otlp-proto-grpc==1.40.0
+opentelemetry-instrumentation-fastapi==0.61b0
+opentelemetry-instrumentation-openai==0.61.0
+opentelemetry-exporter-otlp-proto-http==1.40.0
+
+semantic-kernel[azure]==1.32.2
+azure-ai-projects==1.0.0b11
+openai==1.84.0
+azure-ai-inference==1.0.0b9
+azure-search-documents==12.1.0b1
+azure-ai-evaluation==1.11.0
+
+# Date and internationalization
+babel==2.18.0
+
+# Testing tools
+pytest==8.4.2 # Compatible version for pytest-asyncio
+pytest-asyncio==0.24.0
+pytest-cov==5.0.0

.github/workflows/deploy-v2.yml

@@ -11,7 +11,7 @@ on:
       - completed
     branches:
       - main
-      - dev-v4
+      - dev
       - hotfix
   workflow_dispatch:
     inputs:

.github/workflows/deploy-waf.yml

@@ -45,10 +45,10 @@ jobs:
           GPT41_MINI_MIN_CAPACITY: ${{ env.GPT41_MINI_MIN_CAPACITY }}
           AZURE_REGIONS: ${{ vars.AZURE_REGIONS }}
         run: |
-          chmod +x infra/scripts/checkquota.sh
-          if ! infra/scripts/checkquota.sh; then
+          chmod +x infra/scripts/pre-provision/checkquota.sh
+          if ! infra/scripts/pre-provision/checkquota.sh; then
             # If quota check fails due to insufficient quota, set the flag
-            if grep -q "No region with sufficient quota found" infra/scripts/checkquota.sh; then
+            if grep -q "No region with sufficient quota found" infra/scripts/pre-provision/checkquota.sh; then
               echo "QUOTA_FAILED=true" >> $GITHUB_ENV
             fi
             exit 1  # Fail the pipeline if any other failure occurs