NgTech-LTD/gns3-server
2
0
Fork 0
mirror of https://github.com/GNS3/gns3-server.git synced 2026-09-07 18:45:26 +03:00
Code Issues Packages Projects Releases Wiki Activity
gns3-server/docs/gns3-copilot/template-based-configuration-roadmap.md
YueGuobin 382b2aefa9
docs: add link creation templates section to roadmap 
Add comprehensive link creation templates documentation covering:
- Batch link creation workflow with HITL confirmations
- Link template schema with pattern-based connectivity
- Common topology patterns (Spine-Leaf, Ring, Mesh, Star, Three-tier)
- Intelligent port allocation strategies (round-robin, optimized)
- Performance benchmarks showing 99.9% token savings for large topologies
- Complete workflow example combining all three template systems
2026-03-20 09:44:03 +08:00

2417 lines
87 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

# Template-Based System with HITL - Future Roadmap
**Status:** 💡 Proposed
**Target Version:** Next Release
**Last Updated:** 2026-03-20
## Overview
This document outlines the plan for implementing **template-based systems with Human-in-the-Loop (HITL) confirmations** for both **device configuration** and **node creation** in GNS3 AI Copilot.
### Motivation
#### Current Configuration Challenges
The current implementation requires AI to generate complete configuration commands for every device, which:
- **Consumes excessive tokens:** Each device configuration is generated independently (~150 tokens/device × 10 devices = 1500 tokens)
- **Lacks user control:** Configurations are executed immediately without human review
- **No reusability:** Similar configurations must be regenerated from scratch
- **Higher error risk:** Direct execution without preview or confirmation
#### Current Node Creation Challenges
Similarly, creating multiple nodes has significant inefficiencies:
- **Token waste:** Each node creation requires ~50 tokens for tool calls (100 nodes = 5000 tokens)
- **Slow execution:** Nodes are created serially or with limited parallelism
- **No batch operations:** Cannot create groups of related nodes efficiently
- **Manual positioning:** Each node must be positioned individually
### Proposed Solution
Implement a **unified template-based HITL workflow** for both configuration and node creation:
1. **AI generates template** → Human reviews and confirms
2. **AI generates parameters (optional)** → Human reviews and confirms
3. **Local execution** → Results displayed
**Expected Benefits:**
- **98-99% token savings** for large-scale operations (1000+ devices/nodes)
- **90%+ time savings** through parallel execution and batch operations
- **Full user control** with preview and confirmation at every step
- **Template reusability** across similar operations
---
## Architecture Design
### Workflow Diagram
```
User Request: "Configure OSPF on all routers"
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Jinja2 Template │
│ │
│ Output: │
│ { │
│ "template_content": "router ospf {{ pid }}\n...", │
│ "description": "OSPF basic configuration", │
│ "params_schema": { │
│ "process_id": "int - OSPF process ID", │
│ "networks": "List[Dict] - network list", │
│ "area": "str - area ID" │
│ } │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 1: Template Review │
│ │
│ User sees: │
│ - Template content (Jinja2 syntax) │
│ - Parameter schema │
│ - Example rendered output │
│ │
│ Options: [✓ Confirm] [✏️ Modify] [❌ Cancel] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: AI Generates Parameters │
│ │
│ Output: │
│ { │
│ "project_id": "uuid-xxx", │
│ "device_params": [ │
│ { │
│ "device_name": "R1", │
│ "process_id": 1, │
│ "networks": [{"ip": "192.168.1.0", "mask": "0.0.0.255"}], │
│ "area": "0" │
│ }, │
│ ... // More devices │
│ ] │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 2: Parameter Review │
│ │
│ User sees: │
│ - Parameter preview per device │
│ - Rendered configuration commands │
│ - Summary of changes │
│ │
│ Options: [✓ Execute] [✏️ Modify] [👁️ Preview] [❌ Cancel] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Local Rendering & Execution │
│ │
│ Process: │
│ 1. Render template with parameters (0 tokens) │
│ 2. Call existing ExecuteMultipleDeviceConfigCommands │
│ 3. Return execution results │
└─────────────────────────────────────────────────────────────┘
```
### Token Consumption Comparison
#### Scenario: Configure OSPF on 10 Cisco Routers
| Approach | Token Usage | Breakdown |
|----------|-------------|-----------|
| **Current Method** | **~1500 tokens** | 150 tokens/device × 10 devices |
| **Template Method** | **~400 tokens** | Template: 150 + Parameters: 250 |
| **Savings** | **73%** | 1100 tokens saved |
#### Scenario: Configure VLANs on 20 Switches
| Approach | Token Usage | Breakdown |
|----------|-------------|-----------|
| **Current Method** | **~1600 tokens** | 80 tokens/switch × 20 switches |
| **Template Method** | **~400 tokens** | Template: 100 + Parameters: 300 |
| **Savings** | **75%** | 1200 tokens saved |
#### 🔥 Scenario: Large-Scale Topology - 500+ Routers
**This is where the template-based approach truly shines for rapid environment provisioning.**
| Approach | Token Usage | Execution Time | Breakdown |
|----------|-------------|----------------|-----------|
| **Current Method (AI)** | **~75,000 tokens** | ~25 minutes | 150 tokens/device × 500 devices, serial execution |
| **Template + AI** | **~5,000 tokens** | ~10 minutes | Template once + AI generates params, but slow |
| **Template + Rules (Direct)** | **~400 tokens** | **~3 minutes** | Template once + rule engine (0 tokens) + parallel execution |
| **Savings** | **99.5%** | **88%** | **Game-changing for large deployments** |
**Key Insight:** For environments with **hundreds or thousands of nodes**, the direct execution mode (skipping AI) becomes critical for rapid topology preparation.
---
## Core Components
### 1. New LangChain Tools
#### Tool 1: `GenerateConfigTemplate`
```python
class GenerateConfigTemplate(BaseTool):
"""
Generates Jinja2 configuration templates for human review.
This tool ONLY generates templates. No configuration is executed.
Input:
{
"project_id": "project-uuid",
"device_type": "cisco_ios | huawei_vrp | ...",
"requirement": "user requirement description"
}
Output:
{
"template_content": "jinja2 template string",
"template_description": "human-readable description",
"params_schema": {
"param_name": "type - description"
},
"rendered_example": "example output with sample data"
}
"""
name = "generate_config_template"
description = "Generate Jinja2 templates for network device configuration"
```
#### Tool 2: `GenerateTemplateParams`
```python
class GenerateTemplateParams(BaseTool):
"""
Generates parameter data for confirmed templates.
Uses the template that was confirmed in the previous step.
Input:
{
"project_id": "project-uuid",
"confirmed_template": { ... }, # From previous step
"topology_context": { ... }
}
Output:
{
"project_id": "project-uuid",
"device_params": [
{
"device_name": "R1",
"param1": "value1",
"param2": "value2"
}
],
"preview": {
"R1": ["config", "commands"],
"R2": ["config", "commands"]
}
}
"""
name = "generate_template_params"
description = "Generate parameters for confirmed configuration templates"
```
#### Tool 3: `ExecuteTemplateBasedConfig`
```python
class ExecuteTemplateBasedConfig(BaseTool):
"""
Executes configuration using confirmed template and parameters.
This tool ONLY executes. No generation happens here.
Input:
{
"project_id": "project-uuid",
"confirmed_template": "jinja2 template",
"confirmed_params": [ ... ]
}
Output:
{
"results": [
{
"device_name": "R1",
"status": "success",
"config_commands": ["command1", "command2"],
"output": "execution output"
}
]
}
"""
name = "execute_template_based_config"
description = "Execute configuration from templates (0 token cost)"
```
### 2. Template Renderer Module
```python
# gns3server/agent/gns3_copilot/config_templates/template_renderer.py
from jinja2 import Environment, BaseLoader
class ConfigTemplateRenderer:
"""
Renders Jinja2 templates for network device configuration.
Key features:
- Preserves configuration indentation
- Supports conditionals and loops
- No token consumption (local execution)
"""
def __init__(self):
self.env = Environment(
loader=BaseLoader(),
trim_l_blocks=True, # Remove left whitespace
trim_r_blocks=True, # Remove right whitespace
lstrip_blocks=True, # Strip leading whitespace
keep_trailing_newline=False,
autoescape=False # Don't escape config commands
)
def render(self, template: str, params: dict) -> list[str]:
"""
Render template and return configuration commands.
Args:
template: Jinja2 template string
params: Template parameters
Returns:
List of configuration commands (one per line)
"""
tmpl = self.env.from_string(template)
rendered = tmpl.render(**params)
# Split into commands and filter empty lines
commands = [
line.strip()
for line in rendered.split('\n')
if line.strip()
]
return commands
```
### 3. Session State Management
```python
# gns3server/agent/gns3_copilot/template_session_manager.py
class TemplateSessionManager:
"""
Manages template state across HITL workflow.
Stores:
- Confirmed templates (awaiting parameter generation)
- Template metadata (description, schema)
- Session history
"""
def __init__(self):
self.sessions = {} # project_id -> session_data
def save_template(self, project_id: str, template_data: dict):
"""Save user-confirmed template to session."""
if project_id not in self.sessions:
self.sessions[project_id] = {}
self.sessions[project_id]['confirmed_template'] = template_data
self.sessions[project_id]['updated_at'] = datetime.now()
def get_template(self, project_id: str) -> dict | None:
"""Retrieve confirmed template for session."""
return self.sessions.get(project_id, {}).get('confirmed_template')
def clear_session(self, project_id: str):
"""Clear session data after execution or cancellation."""
if project_id in self.sessions:
del self.sessions[project_id]
```
### 4. Updated System Prompt
```python
# gns3server/agent/gns3_copilot/prompts/template_workflow_prompt.py
TEMPLATE_WORKFLOW_GUIDE = """
# Configuration Generation with HITL Workflow
When users request device configuration, follow this THREE-STEP process:
## Step 1: Generate Configuration Template
Use `generate_config_template` to create a Jinja2 template.
**IMPORTANT:** Wait for user confirmation before proceeding.
### Template Format Example
```jinja2
router ospf {{ process_id }}
{% for network in networks %}
network {{ network.ip }} {{ network.mask }} area {{ area }}
{% endfor %}
```
### Parameter Schema Example
```json
{
"process_id": "int - OSPF process ID",
"networks": "List[Dict] - Each dict has 'ip' and 'mask' keys",
"area": "str - OSPF area ID"
}
```
## Step 2: Generate Parameters
After user confirms the template, use `generate_template_params` to generate device-specific parameters.
**IMPORTANT:** Wait for user confirmation before executing.
## Step 3: Execute Configuration
After user confirms parameters, use `execute_template_based_config` to execute.
## Critical Rules
- ⚠️ MUST wait for confirmation after each step
- ⚠️ DO NOT skip confirmation steps
- ✅ Proceed to next step only after user confirmation
- ❌ Stop if user cancels at any point
## Benefits
- **70-80% token savings** for multi-device configurations
- **Human review** at every critical step
- **Template reusability** across similar configurations
- **Preview capabilities** before execution
"""
```
### 5. LangGraph State Machine
```python
# gns3server/agent/gns3_copilot/workflows/template_config_graph.py
from langgraph.graph import StateGraph, END
from typing import TypedDict, Literal
class TemplateConfigState(TypedDict):
"""State for template-based configuration workflow."""
messages: list[BaseMessage]
current_step: Literal[
"idle",
"generating_template",
"template_review",
"generating_params",
"params_review",
"executing",
"completed",
"cancelled"
]
project_id: str
confirmed_template: dict | None
confirmed_params: dict | None
user_confirmation: str | None
execution_results: dict | None
def should_generate_params(state: TemplateConfigState) -> str:
"""Check if template was confirmed."""
if state.get("user_confirmation") == "template_confirmed":
return "generate_params"
return "end"
def should_execute(state: TemplateConfigState) -> str:
"""Check if params were confirmed."""
if state.get("user_confirmation") == "params_confirmed":
return "execute"
return "end"
# Build workflow graph
workflow = StateGraph(TemplateConfigState)
# Add nodes
workflow.add_node("generate_template", generate_template_node)
workflow.add_node("generate_params", generate_params_node)
workflow.add_node("execute", execute_config_node)
# Add conditional edges
workflow.add_conditional_edges(
"generate_template",
should_generate_params,
{
"generate_params": "generate_params",
"end": END
}
)
workflow.add_conditional_edges(
"generate_params",
should_execute,
{
"execute": "execute",
"end": END
}
)
workflow.add_edge("execute", END)
```
---
## UI/UX Design
### Template Review Interface
```
┌────────────────────────────────────────────────────────────────┐
│ 📋 AI-Generated Configuration Template │
│ ────────────────────────────────────────────────────────────── │
│ │
│ Device Type: Cisco IOS │
│ Description: OSPF basic configuration │
│ │
│ Template Content: │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ router ospf {{ process_id }} │ │
│ │ {% for network in networks %} │ │
│ │ network {{ network.ip }} {{ network.mask }} area {{ area }} │ │
│ │ {% endfor %} │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
│ Parameter Schema: │
│ • process_id: int - OSPF process ID │
│ • networks: List[Dict] - Network configurations │
│ - ip: str - Network address │
│ - mask: str - Wildcard mask │
│ • area: str - OSPF area ID │
│ │
│ Example Output: │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ router ospf 1 │ │
│ │ network 192.168.1.0 0.0.0.255 area 0 │ │
│ │ network 10.0.0.0 0.255.255.255 area 0 │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
│ [✓ Confirm & Continue] [✏️ Request Modification] [❌ Cancel] │
└────────────────────────────────────────────────────────────────┘
```
### Parameter Review Interface
```
┌────────────────────────────────────────────────────────────────┐
│ 📊 Configuration Parameters Preview │
│ ────────────────────────────────────────────────────────────── │
│ │
│ Total Devices: 3 │
│ Template: OSPF basic configuration │
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Device: R1 │ │
│ │ ─────────────────────────────────────────────────────── │ │
│ │ • process_id: 1 │ │
│ │ • area: 0 │ │
│ │ • networks: │ │
│ │ - 192.168.1.0/24 → area 0 │ │
│ │ - 10.0.0.0/8 → area 0 │ │
│ │ │ │
│ │ Rendered Configuration: │ │
│ │ router ospf 1 │ │
│ │ network 192.168.1.0 0.0.0.255 area 0 │ │
│ │ network 10.0.0.0 0.255.255.255 area 0 │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Device: R2 │ │
│ │ ... │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
│ [✓ Execute Configuration] [✏️ Modify Parameters] │
│ [👁️ Preview All] [❌ Cancel] │
└────────────────────────────────────────────────────────────────┘
```
---
## 🔥 Large-Scale Topology Support (1000+ Nodes)
### Overview
One of the most powerful use cases for the template-based configuration system is **rapid provisioning of large-scale network topologies**. This section details optimizations for environments with **hundreds to thousands of nodes**.
### Challenge: Traditional AI Approach at Scale
```
Problem: Configure 1000 routers with OSPF
Traditional AI Approach:
- AI generates config for each router: 150 tokens × 1000 = 150,000 tokens
- Serial or limited parallel execution: ~30-50 minutes
- High cost, slow execution, poor scalability
```
### Solution: Direct Execution Mode
The key innovation is allowing users to **modify and directly execute** templates without requiring AI re-analysis:
```
Template-Based Direct Execution:
1. AI generates template once: ~150 tokens
2. User reviews and modifies if needed
3. User clicks "⚡ Confirm & Execute"
4. Rule engine generates params for 1000 devices: 0 tokens
5. Parallel execution (50-100 concurrent): ~5 minutes
6. Total: 150 tokens, 5 minutes
```
### Enhanced HITL Workflow for Scale
```
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Template (Once) │
│ │
│ User: "Configure OSPF on all 1000 routers" │
│ │
│ AI generates template: ~150 tokens │
│ router ospf {{ process_id }} │
│ {% for network in networks %} │
│ network {{ network.ip }} {{ network.mask }} area {{ area }} │
│ {% endfor %} │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 1: Template Review │
│ │
│ User can: │
│ - Review template syntax │
│ - Modify template directly │
│ - See preview with sample data │
│ │
│ Actions: [✓ Confirm & Continue] [⚡ Confirm & Execute*] │
│ [✏️ Modify] [❌ Cancel] │
│ │
│ * "Confirm & Execute" = Skip AI, go to rule engine │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2A: Rule Engine (0 tokens) OR Step 2B: AI (5000 tokens)│
│ │
│ If user chose "⚡ Confirm & Execute": │
│ → Rule engine analyzes template │
│ → Extracts device names from topology │
│ → Auto-assigns IPs and parameters │
│ → Generates 1000 device param sets: 0 tokens │
│ │
│ If user chose "✓ Confirm & Continue": │
│ → AI analyzes template │
│ → Generates parameters: ~5000 tokens │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 2: Parameter Review │
│ │
│ For 1000 devices, show SUMMARY: │
│ - Total devices: 1000 │
│ - Configuration patterns: 3 unique patterns │
│ - Sample configs (first 3 devices) │
│ - IP addressing scheme used │
│ │
│ Actions: [⚡ Execute All*] [✓ Review & Modify] [❌ Cancel] │
│ │
│ * "Execute All" = Start parallel execution │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Parallel Batch Execution │
│ │
│ Configuration execution: │
│ - Batch size: 50 devices (configurable) │
│ - Batches: 20 total (1000 / 50) │
│ - Parallel execution within each batch │
│ - Real-time progress updates via SSE │
│ - Estimated time: 3-5 minutes │
│ │
│ Progress updates: │
│ Batch 1/20: Configuring devices 1-50... │
│ Batch 2/20: Configuring devices 51-100... │
│ ... │
│ Complete: 998 success, 2 failed │
└─────────────────────────────────────────────────────────────┘
```
### Rule Engine: Intelligent Parameter Generation
```python
# gns3server/agent/gns3_copilot/config_templates/param_generator.py
def generate_params_for_large_topology(
template: str,
topology_info: dict,
addressing_scheme: str = "sequential"
) -> dict:
"""
Generate parameters for 1000+ devices using rule-based logic.
Key features:
- Extract device numbering from names (R1, R2, ... R1000)
- Auto-assign IP addresses sequentially
- Group devices by type and apply patterns
- Zero AI token consumption
"""
nodes = topology_info.get("nodes", [])
# Group by device type
devices_by_type = group_by_device_type(nodes)
# Result: {"router": [R1, R2, ..., R500], "switch": [SW1, ..., SW500]}
device_params = []
for device_type, type_nodes in devices_by_type.items():
for idx, node in enumerate(type_nodes, start=1):
device_name = node.get("name")
# Extract device number from name
device_num = extract_device_number(device_name, idx)
# R1 → 1, Router-100 → 100, DeviceX → fallback to idx
# Generate parameters using rules
params = {
"device_name": device_name,
"process_id": 1,
"area": "0",
"router_id": f"1.1.1.{device_num}",
"networks": [
{
"ip": f"192.168.{device_num}.0",
"mask": "0.0.0.255"
}
],
"loopback": {
"ip": f"10.{device_num}.1.1",
"mask": "255.255.255.255"
}
}
device_params.append(params)
return {
"device_params": device_params,
"total_devices": len(device_params),
"generation_method": "rule_engine",
"addressing_scheme": addressing_scheme
}
# Example: 1000 devices configured in < 1 second
# Token cost: 0 (pure rule-based logic)
```
### Batch Parallel Execution
```python
# gns3server/agent/gns3_copilot/tools_v2/config_tools_nornir.py
class ExecuteTemplateBasedConfig(BaseTool):
"""Optimized for large-scale parallel execution."""
def _run(self, tool_input: str | dict) -> dict:
"""Execute configuration with dynamic batching."""
device_params = data.get("device_params", [])
total_devices = len(device_params)
# 🔥 Dynamic batch sizing based on device count
if total_devices <= 10:
batch_size = 10
elif total_devices <= 50:
batch_size = 20
elif total_devices <= 100:
batch_size = 30
elif total_devices <= 500:
batch_size = 50
else: # 500+ devices
batch_size = 100 # High concurrency for large topologies
results = {
"total_devices": total_devices,
"batch_size": batch_size,
"total_batches": (total_devices + batch_size - 1) // batch_size,
"batches": []
}
# Process in batches with progress tracking
for batch_num in range(0, total_devices, batch_size):
batch_end = min(batch_num + batch_size, total_devices)
batch_params = device_params[batch_num:batch_end]
# Render configs for this batch
batch_configs = [
{
"device_name": p["device_name"],
"config_commands": renderer.render(template, p)
}
for p in batch_params
]
# Execute batch in parallel using Nornir
batch_result = self._execute_batch_parallel(
project_id,
batch_configs,
batch_num // batch_size + 1
)
results["batches"].append(batch_result)
# Yield progress for SSE streaming
yield_progress({
"type": "batch_complete",
"batch": batch_num // batch_size + 1,
"progress": int((batch_end / total_devices) * 100)
})
return results
```
### Real-Time Progress Streaming
```typescript
// Frontend: Large-scale configuration progress UI
class LargeScaleConfigProgress {
displayProgress() {
// Show progress bar for 1000 devices
return `
<div class="config-progress">
<h3>⚙️ Configuring 1000 Devices</h3>
<div class="progress-bar">
<div class="progress-fill" style="width: 0%"></div>
</div>
<div class="stats">
<div class="stat success">
<span class="icon">✅</span>
<span class="label">Success:</span>
<span class="value" id="success-count">0</span>
</div>
<div class="stat failed">
<span class="icon">❌</span>
<span class="label">Failed:</span>
<span class="value" id="failed-count">0</span>
</div>
<div class="stat progress">
<span class="icon">📊</span>
<span class="label">Progress:</span>
<span class="value" id="progress-text">0%</span>
</div>
<div class="stat time">
<span class="icon">⏱️</span>
<span class="label">ETA:</span>
<span class="value" id="eta">~5 min</span>
</div>
</div>
<div class="current-batch">
<span id="batch-info">Preparing...</span>
</div>
</div>
`;
}
updateProgress(data) {
// Update progress bar
const fill = document.querySelector('.progress-fill');
fill.style.width = `${data.progress}%`;
// Update stats
document.getElementById('success-count').textContent = data.success;
document.getElementById('failed-count').textContent = data.failed;
document.getElementById('progress-text').textContent = `${data.progress}%`;
document.getElementById('batch-info').textContent =
`Batch ${data.batch}/20: Configuring devices ${data.range}...`;
}
}
```
### Configuration Summary for Large Topologies
For 1000 devices, showing full configurations is impractical. Instead, provide **intelligent summaries**:
```python
class ConfigSummaryGenerator:
"""Generate summaries for large-scale configurations."""
def generate_summary(self, template: str, device_params: list) -> dict:
"""
Generate configuration summary for 1000+ devices.
Shows:
- Pattern analysis (how many unique config patterns)
- Sample configs (first 3 devices)
- IP addressing scheme used
- Estimated total lines of configuration
"""
total_devices = len(device_params)
# Render all configs to analyze patterns
renderer = ConfigTemplateRenderer()
all_configs = {}
for params in device_params:
device_name = params["device_name"]
config = renderer.render(template, params)
all_configs[device_name] = config
# Analyze patterns
unique_patterns = {}
for device_name, config in all_configs.items():
pattern_hash = hash(tuple(config))
if pattern_hash not in unique_patterns:
unique_patterns[pattern_hash] = []
unique_patterns[pattern_hash].append(device_name)
# Generate summary
return {
"total_devices": total_devices,
"unique_patterns": len(unique_patterns),
"patterns": [
{
"count": len(devices),
"sample_devices": devices[:5] + ["..."] if len(devices) > 5 else devices,
"config_preview": all_configs[devices[0]][:5] # First 5 lines
}
for devices in unique_patterns.values()
],
"estimated_total_lines": sum(len(c) for c in all_configs.values()),
"examples": {
device_name: all_configs[device_name]
for device_name in list(all_configs.keys())[:3] # First 3 only
}
}
```
### Performance Benchmarks
#### Scenario: 1000 Router OSPF Configuration
| Metric | Traditional AI | Template + AI | Template + Direct |
|--------|---------------|---------------|-------------------|
| **Token Consumption** | 150,000 | 5,000 | **400** |
| **Execution Time** | 30-50 min | 10-15 min | **3-5 min** |
| **Cost (at $10/M tokens)** | $1.50 | $0.05 | **$0.004** |
| **User Control** | Low | Medium | **High** |
| **Parallel Execution** | Limited | Yes | **Yes (100 concurrent)** |
#### Scenario: 5000 Switch VLAN Configuration
| Metric | Traditional AI | Template + Direct |
|--------|---------------|-------------------|
| **Token Consumption** | 400,000 | **400** |
| **Execution Time** | 2-3 hours | **15-20 min** |
| **Cost** | $4.00 | **$0.004** |
| **Scalability** | Poor | **Excellent** |
### Addressing Schemes for Large Topologies
The rule engine supports multiple automatic addressing schemes:
```python
# 1. Sequential Addressing (Default)
# R1: 192.168.1.0/24, R2: 192.168.2.0/24, ..., R1000: 192.168.1000.0/24
# 2. VLAN-Based Addressing
# VLAN 100: 10.0.100.0/24, VLAN 101: 10.0.101.0/24, ...
# 3. Hierarchical Addressing
# Core routers: 10.0.0.0/24
# Distribution routers: 10.1.0.0/16
# Access switches: 10.100.0.0/16
# 4. Device Type Based
# Routers: 192.168.0.0/16
# Switches: 192.169.0.0/16
# Firewalls: 192.170.0.0/16
```
### Error Handling for Scale
For 1000+ devices, some failures are inevitable. The system provides:
```python
{
"total_devices": 1000,
"summary": {
"success": 987,
"failed": 13,
"skipped": 0
},
"failed_devices": [
{
"device_name": "R456",
"error": "Connection timeout",
"retry_available": true
},
...
],
"retry_suggestions": {
"auto_retry": True,
"retry_batch_size": 10,
"exponential_backoff": True
}
}
```
### Use Cases for Large-Scale Support
1. **Network Training Labs**: Provision 1000+ device labs for student training
2. **CI/CD Testing**: Automated topology setup for testing network automation scripts
3. **Disaster Recovery Drills**: Rapid deployment of large backup topologies
4. **Network Simulation**: Research environments with thousands of nodes
5. **Data Center Fabric**: Configure spine-leaf topologies with hundreds of leaf switches
---
## 🔥🔥 Node Creation Templates (Batch Topology Provisioning)
### Overview
Just as configuration templates enable rapid device configuration, **node creation templates** enable rapid topology provisioning. This is particularly valuable for:
- **Training labs**: Provision 100+ device labs in minutes
- **Testing environments**: Quickly spin up complex test topologies
- **Data center simulation**: Create spine-leaf fabrics with hundreds of nodes
- **Network research**: Deploy large-scale simulation topologies
### Current vs. Template-Based Node Creation
#### Scenario: Create 100 Routers
**Current Method:**
```
AI calls create_node tool 100 times:
- Token cost: 50 tokens/node × 100 = 5000 tokens
- Execution time: 5-10 minutes (serial/limited parallel)
- No batch operations
- Manual positioning required
```
**Template Method:**
```
1. AI generates node creation template: ~100 tokens
2. User reviews and confirms template
3. Rule engine creates nodes in parallel batches: 0 tokens
4. Total: 100 tokens, 30-60 seconds
```
**Savings:** 98% tokens, 90% time
### Node Creation Workflow
```
User Request: "Create a data center topology with 2 core routers,
10 aggregation switches, and 100 access switches"
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Node Creation Template │
│ │
│ AI Output: │
│ { │
│ "node_groups": [ │
│ { │
│ "node_type": "cisco_iosv", │
│ "count": 2, │
│ "name_pattern": "Core-R{{ id }}", │
│ "properties": {"ram": 4096, "cpus": 2}, │
│ "position": {"y": 100, "x_spacing": 600} │
│ }, │
│ { │
│ "node_type": "cisco_iosv_l2", │
│ "count": 10, │
│ "name_pattern": "Agg-SW{{ id }}", │
│ "position": {"grid": "2x5", "y": 300} │
│ }, │
│ { │
│ "node_type": "cisco_iosv_l2", │
│ "count": 100, │
│ "name_pattern": "Acc-SW{{ id }}", │
│ "position": {"grid": "10x10", "y": 600} │
│ } │
│ ], │
│ "layout": "auto_spine_leaf", │
│ "resource_limits": {"max_ram_mb": 120000} │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint: Node Template Review │
│ │
│ User sees: │
│ • Total nodes: 112 │
│ • Group breakdown: │
│ - 2x Core routers (Core-R1, Core-R2) │
│ - 10x Aggregation switches (Agg-SW1 - Agg-SW10) │
│ - 100x Access switches (Acc-SW1 - Acc-SW100) │
│ • Resource requirements: │
│ - RAM: ~120 GB │
│ - vCPUs: 112 │
│ • Layout preview (visual diagram) │
│ │
│ Actions: [⚡ Batch Create] [✏️ Modify] [❌ Cancel] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: Parallel Batch Node Creation (0 tokens) │
│ │
│ Process: │
│ - Validate resources │
│ - Create nodes in parallel batches (20-50 concurrent) │
│ - Auto-position nodes using layout strategy │
│ - Real-time progress streaming │
│ │
│ Progress: │
│ Batch 1/6: Creating 20 nodes... │
│ Batch 2/6: Creating 20 nodes... │
│ ... │
│ Complete: 112/112 nodes created successfully │
└─────────────────────────────────────────────────────────────┘
```
### Node Template Schema
```python
# gns3server/schemas/controller/node_template.py
class NodeCreationTemplate(BaseModel):
"""Template for batch node creation."""
# Node groups to create
node_groups: List[NodeGroupTemplate] = Field(
...,
description="Groups of nodes with same template"
)
# Layout strategy
layout: Literal[
"auto_grid", # Automatic grid layout
"auto_spine_leaf", # Spine-Leaf topology
"auto_star", # Star topology
"auto_mesh", # Mesh topology
"manual" # Manual coordinates
] = Field(default="auto_grid")
# Resource constraints
resource_limits: Optional[ResourceLimits] = Field(None)
# Auto-link configuration
auto_link: Optional[AutoLinkConfig] = Field(
None,
description="Automatically create links between nodes"
)
class NodeGroupTemplate(BaseModel):
"""Template for a group of similar nodes."""
# Node type and count
node_type: str = Field(..., description="GNS3 node template type")
count: int = Field(..., ge=1, le=10000)
# Naming convention
name_pattern: str = Field(
...,
description="Name pattern with {{ id }} placeholder, e.g., 'R{{ id }}'"
)
id_start: int = Field(default=1, description="Starting ID number")
# Node properties
properties: Dict[str, Any] = Field(
default_factory=dict,
description="Node properties (RAM, CPUs, adapters, etc.)"
)
# Positioning
position: Optional[PositionSpec] = Field(None)
class PositionSpec(BaseModel):
"""Position specification for node group."""
strategy: Literal[
"auto", # Auto-calculate
"grid", # Grid arrangement
"circle", # Circular arrangement
"hierarchical", # Hierarchical layout
"random" # Random distribution
] = Field(default="auto")
# Grid parameters
grid_rows: Optional[int] = Field(None)
grid_cols: Optional[int] = Field(None)
# Positioning
x_start: Optional[int] = Field(None, description="Starting X coordinate")
y_start: Optional[int] = Field(None, description="Starting Y coordinate")
x_spacing: int = Field(default=200, description="Horizontal spacing")
y_spacing: int = Field(default=150, description="Vertical spacing")
class AutoLinkConfig(BaseModel):
"""Automatic link creation between node groups."""
links: List[LinkPattern] = Field(
...,
description="Link patterns to create"
)
class LinkPattern(BaseModel):
"""Pattern for creating links between node groups."""
from_group: str = Field(..., description="Source node group name")
to_group: str = Field(..., description="Destination node group name")
link_type: str = Field(default="ethernet")
count: int = Field(default=1, description="Links per node pair")
strategy: Literal[
"mesh", # Full mesh between groups
"linear", # Linear connection
"paired", # One-to-one pairing
"custom" # Custom pattern
] = Field(default="mesh")
```
### Auto-Linking: Create Topologies with Connections
Node creation templates can also automatically create links:
```python
# Example: Create spine-leaf topology with links
{
"node_groups": [
{
"name": "spine",
"node_type": "cisco_iosv",
"count": 4,
"name_pattern": "Spine{{ id }}",
"position": {"y": 100, "x_spacing": 400}
},
{
"name": "leaf",
"node_type": "cisco_iosv_l2",
"count": 48,
"name_pattern": "Leaf{{ id }}",
"position": {"grid": "6x8", "y": 400}
}
],
"auto_link": {
"links": [
{
"from_group": "spine",
"to_group": "leaf",
"strategy": "mesh", # Each spine connects to all leafs
"count": 1
}
]
}
}
# Result: 4 spine switches, 48 leaf switches, 192 links (4×48)
# Created in ~2-3 minutes
```
### Batch Node Creation Tool
```python
# gns3server/agent/gns3_copilot/tools_v2/node_template_tools.py
class ExecuteBatchNodeCreation(BaseTool):
"""
Batch create nodes from template.
Features:
- Parallel creation (20-50 concurrent)
- Automatic positioning and layout
- Resource validation before creation
- Progress streaming via SSE
- Error isolation (single failure doesn't stop others)
"""
name = "execute_batch_node_creation"
description = "Batch create nodes from template (0 token cost)"
def _run(self, tool_input: str | dict) -> dict:
"""Execute batch node creation."""
data = json.loads(tool_input) if isinstance(tool_input, str) else tool_input
project_id = data.get("project_id")
node_template = data.get("node_template")
total_nodes = sum(g["count"] for g in node_template["node_groups"])
# Dynamic batch sizing based on scale
if total_nodes <= 50:
batch_size = 10
elif total_nodes <= 200:
batch_size = 20
elif total_nodes <= 500:
batch_size = 30
else: # 500+ nodes
batch_size = 50
results = {
"total_nodes": total_nodes,
"batch_size": batch_size,
"groups": [],
"auto_links": []
}
# Check resource availability
if node_template.get("resource_limits"):
availability = self._check_resources(project_id, node_template["resource_limits"])
if not availability["available"]:
return {
"error": "Insufficient resources",
"details": availability["shortfall"]
}
# Create each node group
for group in node_template["node_groups"]:
group_result = self._create_node_group(
project_id,
group,
batch_size,
node_template["layout"]
)
results["groups"].append(group_result)
# Yield progress for SSE streaming
yield_progress({
"type": "group_complete",
"group_name": group.get("name", "unknown"),
"progress": group_result["created"]
})
# Create auto-links if specified
if node_template.get("auto_link"):
links_result = self._create_auto_links(
project_id,
node_template["auto_link"],
results["groups"]
)
results["auto_links"] = links_result
return results
def _create_node_group(
self,
project_id: str,
group_template: dict,
batch_size: int,
layout_strategy: str
) -> dict:
"""Create a group of nodes with same template."""
count = group_template["count"]
name_pattern = group_template["name_pattern"]
id_start = group_template.get("id_start", 1)
properties = group_template.get("properties", {})
# Generate node specifications
nodes_to_create = []
for i in range(count):
node_id = id_start + i
node_name = name_pattern.replace("{{ id }}", str(node_id))
# Calculate position
position = self._calculate_position(
i, count, layout_strategy, group_template
)
nodes_to_create.append({
"name": node_name,
"node_type": group_template["node_type"],
"properties": properties,
"x": position["x"],
"y": position["y"]
})
# Create in batches
created_nodes = []
failed_nodes = []
for batch_start in range(0, count, batch_size):
batch_end = min(batch_start + batch_size, count)
batch_nodes = nodes_to_create[batch_start:batch_end]
# Parallel creation
batch_results = await self._create_batch_parallel(
project_id, batch_nodes
)
for result in batch_results:
if result["status"] == "success":
created_nodes.append(result)
else:
failed_nodes.append(result)
# Progress update
yield_progress({
"type": "batch_complete",
"progress": int((batch_end / count) * 100),
"created": len(created_nodes),
"failed": len(failed_nodes)
})
return {
"node_type": group_template["node_type"],
"total": count,
"created": len(created_nodes),
"failed": len(failed_nodes),
"nodes": created_nodes,
"errors": failed_nodes
}
def _calculate_position(
self,
index: int,
total: int,
layout: str,
group_spec: dict
) -> dict:
"""Calculate node position based on layout strategy."""
position = group_spec.get("position", {})
strategy = position.get("strategy", "auto")
if strategy == "grid":
# Grid layout
cols = position.get("grid_cols") or int(math.sqrt(total)) + 1
row = index // cols
col = index % cols
return {
"x": (position.get("x_start") or 100) + col * position.get("x_spacing", 200),
"y": (position.get("y_start") or 100) + row * position.get("y_spacing", 150)
}
elif strategy == "hierarchical" or layout == "auto_spine_leaf":
# Hierarchical: Core → Aggregation → Access
node_type = group_spec.get("node_type", "").lower()
if "core" in node_type or "spine" in node_type:
# Top layer
x = 100 + index * 600
y = 100
elif "agg" in node_type or "leaf" in node_type:
# Middle layer
cols = int(math.sqrt(total)) + 1
row = index // cols
col = index % cols
x = 100 + col * 300
y = 400 + row * 200
else:
# Bottom layer
x = 100 + (index % 20) * 150
y = 800 + (index // 20) * 150
return {"x": x, "y": y}
else: # auto or default
return {
"x": 100 + (index * 200) % 2000,
"y": 100 + (index // 10) * 150
}
async def _create_batch_parallel(
self,
project_id: str,
nodes: list[dict]
) -> list[dict]:
"""Create a batch of nodes in parallel."""
import asyncio
async def create_single(node_spec: dict) -> dict:
"""Create a single node."""
try:
# Call GNS3 create_node API
node_id = await self._call_gns3_create_node(
project_id, node_spec
)
return {
"name": node_spec["name"],
"status": "success",
"node_id": node_id,
"x": node_spec["x"],
"y": node_spec["y"]
}
except Exception as e:
return {
"name": node_spec["name"],
"status": "failed",
"error": str(e)
}
tasks = [create_single(node) for node in nodes]
return await asyncio.gather(*tasks)
def _create_auto_links(
self,
project_id: str,
auto_link_config: dict,
created_groups: list[dict]
) -> dict:
"""Automatically create links between node groups."""
links_created = []
for link_pattern in auto_link_config.get("links", []):
from_group_name = link_pattern["from_group"]
to_group_name = link_pattern["to_group"]
strategy = link_pattern.get("strategy", "mesh")
# Find the created nodes in each group
from_nodes = self._get_nodes_by_group(created_groups, from_group_name)
to_nodes = self._get_nodes_by_group(created_groups, to_group_name)
# Create links based on strategy
if strategy == "mesh":
# Full mesh: every from_node connects to every to_node
for from_node in from_nodes:
for to_node in to_nodes:
link_result = self._create_link(
project_id, from_node, to_node, link_pattern
)
links_created.append(link_result)
elif strategy == "paired":
# One-to-one pairing
for from_node, to_node in zip(from_nodes, to_nodes):
link_result = self._create_link(
project_id, from_node, to_node, link_pattern
)
links_created.append(link_result)
elif strategy == "linear":
# Linear chain
for i in range(min(len(from_nodes), len(to_nodes)) - 1):
link_result = self._create_link(
project_id, from_nodes[i], to_nodes[i + 1], link_pattern
)
links_created.append(link_result)
return {
"total_links": len(links_created),
"created": sum(1 for l in links_created if l["status"] == "success"),
"links": links_created
}
```
### Performance Benchmarks
#### Scenario: 100 Router Lab
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | 5,000 | **100** |
| **Execution Time** | 5-10 min | **30-60 sec** |
| **User Control** | Low | **High (preview before create)** |
| **Positioning** | Manual | **Automatic** |
#### Scenario: 500 Switch Data Center
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | 25,000 | **150** |
| **Execution Time** | 25-30 min | **2-3 min** |
| **Links Created** | Manual | **Auto (mesh, spine-leaf)** |
#### Scenario: 1000 Node Training Lab
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | 50,000 | **200** |
| **Execution Time** | 50-60 min | **4-6 min** |
| **Scalability** | Poor | **Excellent** |
### Complete Example: Enterprise Data Center
```python
# User Request
"""
Create an enterprise data center topology:
- 4 spine routers (high-end)
- 20 leaf switches (10G)
- 200 access switches (1G)
- 500 servers (VPCS)
Use spine-leaf architecture with full mesh connectivity.
All servers connect to access switches in pairs.
"""
# Generated Template
{
"node_groups": [
{
"name": "spine",
"node_type": "cisco_iosv",
"count": 4,
"name_pattern": "Spine-R{{ id }}",
"properties": {
"ram": 4096,
"cpus": 2,
"adapters": 8
},
"position": {
"strategy": "hierarchical",
"y": 100,
"x_spacing": 600
}
},
{
"name": "leaf",
"node_type": "cisco_iosv_l2",
"count": 20,
"name_pattern": "Leaf-SW{{ id }}",
"properties": {
"ram": 2048,
"cpus": 1,
"adapters": 16
},
"position": {
"strategy": "grid",
"grid_rows": 4,
"grid_cols": 5,
"y": 400,
"x_spacing": 300,
"y_spacing": 200
}
},
{
"name": "access",
"node_type": "cisco_iosv_l2",
"count": 200,
"name_pattern": "Acc-SW{{ id }}",
"properties": {
"ram": 1024,
"cpus": 1,
"adapters": 4
},
"position": {
"strategy": "grid",
"grid_rows": 10,
"grid_cols": 20,
"y": 800,
"x_spacing": 120,
"y_spacing": 100
}
},
{
"name": "server",
"node_type": "vpcs",
"count": 500,
"name_pattern": "Server-{{ id }}",
"properties": {},
"position": {
"strategy": "grid",
"grid_rows": 20,
"grid_cols": 25,
"y": 1200,
"x_spacing": 60,
"y_spacing": 60
}
}
],
"auto_link": {
"links": [
{
"from_group": "spine",
"to_group": "leaf",
"strategy": "mesh"
},
{
"from_group": "leaf",
"to_group": "access",
"strategy": "paired",
"count": 10
},
{
"from_group": "access",
"to_group": "server",
"strategy": "paired",
"count": 2
}
]
},
"layout": "auto_spine_leaf",
"resource_limits": {
"max_ram_mb": 750000,
"max_vcpus": 724
}
}
# Execution Result
{
"total_nodes": 724,
"created": 724,
"failed": 0,
"duration_sec": 285, # ~4.75 minutes
"links_created": 4280, # Auto-created
"groups": [
{"name": "spine", "created": 4, "failed": 0},
{"name": "leaf", "created": 20, "failed": 0},
{"name": "access", "created": 200, "failed": 0},
{"name": "server", "created": 500, "failed": 0}
]
}
```
### Combined Workflow: Node Creation + Configuration
The real power comes from combining both template systems:
```
1. Create topology with node templates
- 724 nodes created in ~5 minutes
- 4280 links auto-created
2. Configure devices with config templates
- Generate OSPF/BGP templates
- Configure 724 devices in ~5 minutes
Total: 724-node data center
- Created and configured in ~10 minutes
- Token cost: ~400 (vs ~100,000 with AI-only approach)
- 99.6% token savings
```
---
## 🔥🔥🔥 Link Creation Templates (Batch Topology Connectivity)
### Overview
Just as node and configuration templates enable rapid provisioning, **link creation templates** enable rapid connectivity setup. This completes the template trilogy for complete topology automation.
### Current vs. Template-Based Link Creation
#### Scenario: Create Full-Mesh Network (100 Routers)
**Current Method:**
```
AI calls create_link tool 4950 times (100×99/2):
- Token cost: 30 tokens/link × 4950 = ~150,000 tokens
- Execution time: 30-40 minutes (serial/limited parallel)
- Manual port management
- Error-prone
```
**Template Method:**
```
1. AI generates link template: ~200 tokens
2. User reviews link patterns and topology preview
3. Rule engine creates links in parallel batches: 0 tokens
4. Total: 200 tokens, 2-3 minutes
```
**Savings:** 99.9% tokens, 95% time
### Link Creation Workflow
```
User Request: "Create full-mesh connectivity between all routers"
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Link Creation Template │
│ │
│ AI Output: │
│ { │
│ "link_patterns": [ │
│ { │
│ "from_nodes": {"tag": "router"}, │
│ "to_nodes": {"tag": "router"}, │
│ "strategy": "full_mesh", │
│ "port_allocation": "round_robin" │
│ } │
│ ], │
│ "total_links": 4950 │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint: Link Template Review │
│ │
│ User sees: │
│ • Total links: 4,950 │
│ • Topology type: Full Mesh │
│ • Port allocation strategy: Round-robin │
│ • Topology preview (visual graph) │
│ • Port utilization estimates │
│ │
│ Sample links (first 10): │
│ • R1:Gi0/0 → R2:Gi0/0 │
│ • R1:Gi0/1 → R3:Gi0/0 │
│ • ... │
│ │
│ Actions: [⚡ Batch Create] [👁️ Detailed Preview] [✏️ Modify] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: Detailed Preview (Optional) │
│ │
│ • Port assignment per node │
│ • Bandwidth calculations │
│ • Redundancy analysis │
│ • Link naming scheme │
│ │
│ [⚡ Confirm Create All] [🔧 Adjust Ports] [⬅️ Back] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Parallel Batch Link Creation (0 tokens) │
│ │
│ Process: │
│ - Validate port availability │
│ - Allocate ports using strategy │
│ - Create links in parallel batches (50-100 concurrent) │
│ - Handle conflicts automatically │
│ - Real-time progress streaming │
│ │
│ Progress: │
│ Batch 1/50: Creating 99 links... │
│ Batch 2/50: Creating 99 links... │
│ ... │
│ Complete: 4,950/4,950 links created successfully │
└─────────────────────────────────────────────────────────────┘
```
### Link Template Schema (Simplified)
```python
class LinkCreationTemplate(BaseModel):
"""Template for batch link creation."""
# Link patterns
link_patterns: List[LinkPattern]
# Port allocation strategy
port_allocation: PortAllocationStrategy
class LinkPattern(BaseModel):
"""Pattern for creating links between node groups."""
from_nodes: NodeSelector # Source nodes
to_nodes: NodeSelector # Destination nodes
strategy: Literal[
"one_to_one", # 1:1 pairing
"one_to_many", # Star topology
"many_to_many", # Full mesh
"sequential", # Linear chain
"ring" # Ring topology
]
port_allocation: PortAllocationStrategy
class NodeSelector(BaseModel):
"""Select nodes for linking."""
selector_type: Literal["group", "name_pattern", "tag", "all"]
group_name: Optional[str]
name_pattern: Optional[str] # "R*", "Core-*"
tag: Optional[str]
class PortAllocationStrategy(BaseModel):
"""How to allocate ports for links."""
strategy: Literal[
"round_robin", # Distribute evenly
"sequential", # Use in order
"optimized", # Smart allocation
"auto" # Automatic selection
]
on_conflict: Literal[
"skip", # Skip if port unavailable
"use_next", # Use next available port
"fail" # Fail on conflict
] = "use_next"
```
### Common Topology Patterns
The system includes pre-built topology patterns:
#### 1. Spine-Leaf (Data Center)
```
Pattern: Full mesh between spine and leaf layers
Example: 4 Spine × 48 Leaf
- Links: 4 × 48 = 192 links
- Each spine: 48 downlinks
- Each leaf: 4 uplinks
```
#### 2. Three-Tier Hierarchical
```
Core ↔ Aggregation ↔ Access
Example: 2 Core × 10 Agg × 100 Access
- Core-Agg: Full mesh (2×10 = 20 links)
- Agg-Access: Paired (10×10 = 100 links)
- Total: 120 links
```
#### 3. Ring Topology
```
Sequential connection with wrap-around
Example: 10 routers in ring
- Links: 10 (each node connects to 2 neighbors)
- Pattern: R1→R2→R3→...→R10→R1
```
#### 4. Full Mesh
```
All nodes connected to all nodes
Example: 10 routers
- Links: 45 (10×9/2)
- Every node connects to every other node
```
#### 5. Star Topology
```
Center node connects to all edge nodes
Example: 1 Core + 20 Edge
- Links: 20
- Center degree: 20
- Edge degree: 1
```
### Performance Benchmarks
#### Scenario: Spine-Leaf Data Center (8 Spine × 100 Leaf)
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | 30,000 | **200** |
| **Execution Time** | 15-20 min | **2-3 min** |
| **Links Created** | Manual | **Auto (800 links)** |
| **Port Management** | Manual | **Auto (round-robin)** |
#### Scenario: Full Mesh (100 Routers)
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | 150,000 | **200** |
| **Execution Time** | 30-40 min | **2-3 min** |
| **Links Created** | 4,950 | **4,950** |
| **Error Rate** | High (manual) | **Low (validated)** |
#### Scenario: Large-Scale Data Center
**Topology:**
- 8 Spine routers
- 100 Leaf switches (48-port each)
- 2000 Servers
- Redundant connections
**Link Creation:**
- Spine-Leaf: 8 × 100 = 800 links
- Leaf-Server: 2000 × 2 = 4000 links
- **Total: 4,800 links**
| Metric | Current Method | Template Method |
|--------|---------------|-----------------|
| **Token Consumption** | ~150,000 | **300** |
| **Execution Time** | 45-60 min | **5-8 min** |
| **Savings** | - | **99.8% tokens, 90% time** |
### Intelligent Port Allocation
The system includes smart port allocation algorithms:
```python
# Example: Optimized allocation for multi-adapter switches
Strategy: "optimized"
Considerations:
- Port speed matching (10G ports for spine-leaf, 1G for servers)
- Physical adapter separation (redundancy across modules)
- Load balancing (distribute connections evenly)
- Future expansion planning (reserve ports)
Result:
- Spine-Leaf: Use 10G ports on adapter 0-3
- Leaf-Server: Use 1G ports on adapter 4-7
- Redundant paths: Use different physical adapters
```
### Combined Workflow: Complete Topology Provisioning
```
Step 1: Node Creation Template
- 2108 nodes created in ~4 minutes
- Token cost: ~200
Step 2: Link Creation Template
- 20,780 links created in ~6 minutes
- Token cost: ~300
Step 3: Configuration Template
- 2108 devices configured in ~5 minutes
- Token cost: ~200
TOTAL: Large Data Center
- 2,108 nodes + 20,780 links
- Created, linked, and configured in ~15 minutes
- Token cost: ~700 (vs ~250,000 with AI-only)
- 99.7% token savings
```
### Use Cases
1. **Data Center Fabric:** Spine-Leaf with thousands of links
2. **ISP Backbone:** Full-mesh core routers
3. **Campus Network:** Three-tier hierarchical
4. **Ring Topology:** Metropolitan area networks
5. **Research Networks:** Custom experimental topologies
---
## Implementation Phases
### Phase 1: Core MVP (Minimum Viable Product)
**Status:** 📋 Planned
**Estimated Effort:** 3-5 days
**Tasks:**
1. ✅ Create `ConfigTemplateRenderer` class
2. ✅ Create `TemplateSessionManager` class
3. ✅ Implement `GenerateConfigTemplate` tool
4. ✅ Implement `GenerateTemplateParams` tool
5. ✅ Implement `ExecuteTemplateBasedConfig` tool
6. ✅ Create `config_templates/` package structure
7. ✅ Update system prompts with template workflow
8. ✅ Basic error handling and validation
**Deliverables:**
- Working three-step HITL workflow
- Template rendering for Cisco IOS devices
- Basic CLI/API responses
- Unit tests for core components
### Phase 2: Enhanced User Experience & Direct Execution
**Status:** 💡 Proposed
**Estimated Effort:** 2-3 days
**Tasks:**
1. Enhanced UI for template/parameter review
2. Configuration preview functionality
3. Template modification and retry logic
4. 🔥 **Direct execution mode** (skip AI, use rule engine)
5. Progress indicators for multi-device configs
6. Improved error messages and recovery
**Deliverables:**
- User-friendly review interfaces
- Preview-before-execute capability
- **Rule-based parameter generation (0 token cost)**
- User documentation
### Phase 2.5: Node Creation Templates
**Status:** 💡 Proposed
**Estimated Effort:** 2-3 days
**Tasks:**
1. 🔥🔥 Implement `GenerateNodeTemplate` tool
2. 🔥🔥 Implement `ExecuteBatchNodeCreation` tool
3. 🔥🔥 Create `NodeCreationTemplate` schema
4. 🔥🔥 Implement automatic positioning algorithms
5. 🔥🔥 Implement auto-linking functionality
6. Resource validation before creation
**Deliverables:**
- **Batch node creation with 0 token cost**
- **Auto-positioning (grid, spine-leaf, star, mesh)**
- **Auto-linking (mesh, paired, linear)**
- Progress streaming for large batches
### Phase 2.75: Link Creation Templates
**Status:** 💡 Proposed
**Estimated Effort:** 2-3 days
**Tasks:**
1. 🔥🔥🔥 Implement `GenerateLinkTemplate` tool
2. 🔥🔥🔥 Implement `ExecuteBatchLinkCreation` tool
3. 🔥🔥🔥 Create `LinkCreationTemplate` schema
4. 🔥🔥🔥 Implement topology pattern library (Spine-Leaf, Ring, Mesh, Star, etc.)
5. 🔥🔥🔥 Implement intelligent port allocation algorithms
6. Port availability validation and conflict handling
**Deliverables:**
- **Batch link creation with 0 token cost**
- **5+ pre-built topology patterns**
- **Smart port allocation (round-robin, optimized)**
- **Port conflict detection and auto-resolution**
- Progress streaming for thousands of links
### Phase 3: Template Library & Large-Scale Support
**Status:** 💡 Proposed
**Estimated Effort:** 2-3 days
**Tasks:**
1. Template persistence and storage
2. Pre-built template library (OSPF, BGP, VLAN, NAT, etc.)
3. 🔥 **Batch parallel execution** (dynamic batching for 100+ devices)
4. 🔥 **Rule engine enhancements** (intelligent parameter generation)
5. 🔥 **Real-time progress streaming** via SSE
6. Template versioning and history
**Deliverables:**
- 20+ pre-built templates
- **Support for 1000+ device configurations**
- **Parallel execution with 50-100 concurrent connections**
- Template management API
### Phase 4: Advanced Features & Optimization
**Status:** 💡 Proposed
**Estimated Effort:** 3-4 days
**Tasks:**
1. Multi-vendor template support (Huawei, H3C, Juniper)
2. 🔥 **Intelligent addressing schemes** (sequential, VLAN-based, hierarchical)
3. 🔥 **Configuration summary generation** (pattern analysis for large topologies)
4. Configuration diff and comparison
5. Template analytics and usage statistics
6. 🔥 **Performance optimization** (caching, connection pooling)
**Deliverables:**
- Multi-vendor template ecosystem
- **Optimized for 10,000+ node topologies**
- Advanced configuration management
- Analytics dashboard
---
## Technical Considerations
### Jinja2 Configuration
```python
# Network device configurations require special handling
Environment(
# Preserve indentation for config hierarchy
trim_l_blocks=True, # Remove block left whitespace
trim_r_blocks=True, # Remove block right whitespace
lstrip_blocks=True, # Strip leading whitespace from lines
# Don't escape configuration commands
autoescape=False,
# Custom filters for network operations
filters={
'to_cidr': lambda ip, mask: f"{ip}/{mask}",
'ip_network': lambda ip: ipaddr.IPv4Network(ip),
# Add more as needed
}
)
```
### Security Considerations
1. **Template Validation:**
- Validate template syntax before rendering
- Check for dangerous operations (file I/O, system calls)
- Sandbox Jinja2 environment
2. **Parameter Validation:**
- Type checking for all parameters
- Range validation (IP addresses, VLAN IDs, etc.)
- Device-specific validation
3. **Command Filtering:**
- Apply existing `command_filter.py` checks
- Integrate with forbidden commands list
- Maintain audit logging
### Error Handling Strategy
```python
class TemplateExecutionError(Exception):
"""Base class for template execution errors."""
pass
class TemplateSyntaxError(TemplateExecutionError):
"""Template has invalid Jinja2 syntax."""
pass
class ParameterValidationError(TemplateExecutionError):
"""Parameters don't match template schema."""
pass
class RenderingError(TemplateExecutionError):
"""Error during template rendering."""
pass
# Error response format
{
"error": "error_type",
"message": "Human-readable error message",
"details": {
"template": "...",
"params": {...},
"traceback": "..." # Only in development
},
"suggestions": [
"Check template syntax",
"Verify parameter types",
"Review device compatibility"
]
}
```
---
## Testing Strategy
### Unit Tests
```python
# tests/test_template_renderer.py
def test_simple_template_rendering():
template = "interface {{ name }}\n ip address {{ ip }} {{ mask }}"
params = {"name": "GigabitEthernet0/0", "ip": "192.168.1.1", "mask": "255.255.255.0"}
renderer = ConfigTemplateRenderer()
result = renderer.render(template, params)
assert result == [
"interface GigabitEthernet0/0",
"ip address 192.168.1.1 255.255.255.0"
]
def test_loop_template_rendering():
template = "{% for n in networks %}network {{ n }}\n{% endfor %}"
params = {"networks": ["192.168.1.0", "192.168.2.0"]}
renderer = ConfigTemplateRenderer()
result = renderer.render(template, params)
assert result == ["network 192.168.1.0", "network 192.168.2.0"]
def test_conditional_template_rendering():
template = "{% if ospf %}router ospf 1\n{% endif %}exit"
params = {"ospf": True}
renderer = ConfigTemplateRenderer()
result = renderer.render(template, params)
assert "router ospf 1" in result
```
### Integration Tests
```python
# tests/test_template_workflow_integration.py
def test_full_template_workflow():
"""Test complete HITL workflow from template to execution."""
# Step 1: Generate template
template_tool = GenerateConfigTemplate()
template_result = template_tool._run({
"project_id": test_project_id,
"device_type": "cisco_ios",
"requirement": "Configure OSPF"
})
assert "template_content" in template_result
# Step 2: Generate params
params_tool = GenerateTemplateParams()
params_result = params_tool._run({
"project_id": test_project_id,
"confirmed_template": template_result
})
assert "device_params" in params_result
# Step 3: Execute
execute_tool = ExecuteTemplateBasedConfig()
exec_result = execute_tool._run({
"project_id": test_project_id,
"confirmed_template": template_result["template_content"],
"confirmed_params": params_result["device_params"]
})
assert "results" in exec_result
```
### End-to-End Tests
```python
# tests/test_e2e_template_config.py
def test_ospf_configuration_10_routers():
"""Test OSPF configuration on 10 routers."""
# Setup: Create GNS3 project with 10 routers
project_id = create_test_project(device_count=10)
# Execute workflow
result = run_template_workflow(
project_id=project_id,
requirement="Configure OSPF on all routers"
)
# Verify
assert result["status"] == "success"
assert len(result["configured_devices"]) == 10
assert all(["ospf" in dev["config"] for dev in result["configured_devices"]])
```
---
## Success Metrics
### Token Savings
- **Target:** 70%+ reduction in token usage for multi-device configurations
- **Measurement:** Compare token usage before/after for same tasks
### User Adoption
- **Target:** 60%+ of configuration tasks use template workflow
- **Measurement:** Track tool usage statistics
### Error Reduction
- **Target:** 50%+ reduction in configuration errors
- **Measurement:** Compare error rates before/after HITL
### User Satisfaction
- **Target:** 4.5+ star rating (5-star scale)
- **Measurement:** Post-task user surveys
---
## Risks and Mitigations
| Risk | Impact | Mitigation |
|------|--------|------------|
| AI generates invalid Jinja2 syntax | High | Add template validation, provide syntax feedback |
| Users find HITL workflow too slow | Medium | Add "quick confirm" option, template reuse |
| Template reuse causes stale configs | Medium | Template versioning, checksum validation |
| Multi-vendor complexity | High | Phase 1: Cisco only, Phase 4: expand |
| Session state management bugs | Medium | Comprehensive testing, state cleanup |
---
## Open Questions
1. **Template Storage:** Should templates be stored per-user or shared globally?
2. **Template Validation:** How strict should template validation be?
3. **Backward Compatibility:** Should existing direct-config tools remain available?
4. **Template Sharing:** Should users be able to share templates in a marketplace?
5. **Performance:** How to handle template rendering for 100+ devices?
---
## Dependencies
### Required Python Packages
```txt
jinja2>=3.1.0
langchain>=0.1.0
langgraph>=0.0.20
```
### Integration Points
- `gns3server/agent/gns3_copilot/tools_v2/config_tools_nornir.py` (existing)
- `gns3server/agent/gns3_copilot/prompts/lab_automation_assistant_prompt.py` (update)
- `gns3server/agent/gns3_copilot/gns3_client/gns3_topology_reader.py` (existing)
- `gns3server/agent/gns3_copilot/utils/command_filter.py` (existing)
---
## Timeline
### Sprint 1: Foundation (Week 1-2)
- Core rendering engine
- Three LangChain tools
- Basic session management
- System prompt updates
### Sprint 2: User Experience (Week 3)
- Review interfaces
- Preview functionality
- Error handling
- Documentation
### Sprint 3: Enhancement (Week 4-5)
- Template library
- Caching mechanisms
- Multi-vendor support
- Testing and QA
### Sprint 4: Polish (Week 6)
- Performance optimization
- Bug fixes
- User feedback integration
- Release preparation
---
## References
- [Jinja2 Documentation](https://jinja.palletsprojects.com/)
- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)
- [Existing Config Tools](../implemented/multi-vendor-device-support.md)
- [Command Security](../implemented/command-security.md)
---
## Changelog
| Date | Version | Changes |
|------|---------|---------|
| 2026-03-20 | 0.3 | Added node creation templates section with batch topology provisioning, auto-linking, automatic positioning; Combined node creation + configuration workflows for rapid 1000+ node data center deployment |
| 2026-03-20 | 0.2 | Added large-scale topology support section (1000+ nodes), direct execution mode, batch parallel execution, rule engine optimizations |
| 2026-03-20 | 0.1 | Initial roadmap document created |
---
**Document Status:** 💡 Proposed - Awaiting Implementation
**Next Review:** After Phase 1 completion
---
*For questions or feedback about this roadmap, please open an issue or contact the AI Copilot team.*
Reference in New Issue View Git Blame Copy Permalink