hier-config-gpt

An enhanced hierarchical configuration library that integrates Large Language Model (LLM) capabilities for advanced network configuration analysis and remediation.

Overview

hier-config-gpt extends the powerful hier-config library by adding AI-driven custom remediation workflows. It addresses complex network configuration edge cases that fall outside standard negation and idempotency workflows by leveraging LLMs to dynamically generate remediation plans.

Key Features

• Multi-Provider LLM Support: Works with OpenAI GPT, Anthropic Claude, and Ollama (self-hosted) models
• Intelligent Remediation: Automatically generates complex configuration remediation steps
• Quorum Mode: Optional consensus mechanism across multiple LLM providers for increased reliability
• Response Caching: Built-in caching to reduce API costs and improve performance
• Rate Limiting: Token bucket algorithm to prevent API throttling
• Configurable Prompts: Customize prompt templates for your specific needs
• Production Ready: Comprehensive error handling, retry logic, and logging

Installation

Basic Installation

pip install hier-config-gpt

Install with Specific Provider(s)

# OpenAI GPT models
pip install hier-config-gpt[openai]

# Anthropic Claude models
pip install hier-config-gpt[anthropic]

# Ollama (self-hosted) models
pip install hier-config-gpt[ollama]

# All providers
pip install hier-config-gpt[all]

Quick Start

Basic Example with OpenAI

import os
from hier_config import HConfig, Platform
from hier_config.models import MatchRule
from hier_config_gpt import GPTWorkflowRemediation
from hier_config_gpt.models import GPTRemediationRule, GPTRemediationExample
from hier_config_gpt.clients import ChatGPTClient

# Load configurations
running_config = open("running_config.conf").read()
generated_config = open("desired_config.conf").read()

# Initialize workflow
wfr = GPTWorkflowRemediation(
    running_config=HConfig.from_text(Platform.CISCO_IOS, running_config),
    generated_config=HConfig.from_text(Platform.CISCO_IOS, generated_config)
)

# Define remediation rule
description = """When remediating an access-list on Cisco IOS devices:
1. Resequence the access-list so each sequence number is a multiple of 10
2. Add a temporary 'permit any' statement at sequence 1
3. Apply the required changes from the generated configuration
4. Remove the temporary permit statement
"""

lineage = (MatchRule(startswith="ip access-list"),)
example = GPTRemediationExample(
    running_config="ip access-list extended TEST\n  12 permit ip host 10.0.0.1 any",
    remediation_config="ip access-list resequence TEST 10 10\nip access-list extended TEST\n  1 permit ip any any\n  no 10\n  10 permit ip host 10.0.0.2 any\n  no 1"
)

gpt_rule = GPTRemediationRule(
    description=description,
    lineage=lineage,
    example=example
)

# Add rule and set up client
wfr.add_gpt_rule(gpt_rule)
client = ChatGPTClient(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
wfr.set_gpt_client(client)

# Generate remediation plan
remediation = wfr.gpt_remediation_config()
print(remediation)

Using Anthropic Claude

from hier_config_gpt.clients import ClaudeGPTClient

client = ClaudeGPTClient(
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    model="claude-3-5-sonnet-20241022"
)
wfr.set_gpt_client(client)

Using Ollama (Self-Hosted)

from hier_config_gpt.clients import OllamaGPTClient

client = OllamaGPTClient(
    host="http://localhost:11434",
    model="llama3.2"
)
wfr.set_gpt_client(client)

Advanced Features

Response Caching

Reduce API costs and improve performance with built-in caching:

from hier_config_gpt.clients import ChatGPTClient, CachedGPTClient, ResponseCache

# Create base client
base_client = ChatGPTClient(api_key=os.getenv("OPENAI_API_KEY"))

# Wrap with caching (1 hour TTL)
cache = ResponseCache(ttl_seconds=3600)
client = CachedGPTClient(base_client, cache=cache)

wfr.set_gpt_client(client)

Rate Limiting

Prevent API throttling with automatic rate limiting:

from hier_config_gpt.clients import ChatGPTClient, RateLimitedGPTClient

# Create base client
base_client = ChatGPTClient(api_key=os.getenv("OPENAI_API_KEY"))

# Wrap with rate limiting (60 requests per minute)
client = RateLimitedGPTClient(
    base_client,
    max_requests=60,
    time_window_seconds=60.0
)

wfr.set_gpt_client(client)

Combining Caching and Rate Limiting

from hier_config_gpt.clients import (
    ChatGPTClient,
    CachedGPTClient,
    RateLimitedGPTClient,
    ResponseCache
)

# Create layered client: rate limiting -> caching -> base client
base_client = ChatGPTClient(api_key=os.getenv("OPENAI_API_KEY"))
cached_client = CachedGPTClient(base_client, cache=ResponseCache())
client = RateLimitedGPTClient(cached_client, max_requests=60)

wfr.set_gpt_client(client)

Quorum Mode (Multi-Provider Consensus)

Use multiple LLM providers with majority voting for critical operations:

from hier_config_gpt.clients import (
    ChatGPTClient,
    ClaudeGPTClient,
    OllamaGPTClient,
    MultiProviderGPTClient
)

# Create multiple provider clients
openai_client = ChatGPTClient(api_key=os.getenv("OPENAI_API_KEY"))
claude_client = ClaudeGPTClient(api_key=os.getenv("ANTHROPIC_API_KEY"))
ollama_client = OllamaGPTClient()

# Create quorum client (requires majority agreement)
client = MultiProviderGPTClient(
    providers=[openai_client, claude_client, ollama_client],
    enable_quorum=True
)

wfr.set_gpt_client(client)

Custom Prompt Templates

Customize the prompt structure for your specific needs:

from hier_config_gpt import PromptTemplate, GPTWorkflowRemediation

# Define custom template
custom_template = """
Generate network commands to transform the configuration.

CURRENT STATE:
{running_config}

DESIRED STATE:
{generated_config}

RULES:
{description}

EXAMPLE:
Running: {example_running_config}
Remediation: {example_remediation_config}

Return JSON with "plan" array of command strings.
"""

# Use custom template
template = PromptTemplate(template=custom_template)
wfr = GPTWorkflowRemediation(
    running_config=running,
    generated_config=generated,
    prompt_template=template
)

Configuration Timeouts

All clients support configurable timeouts:

# OpenAI with 30-second timeout
client = ChatGPTClient(
    api_key=os.getenv("OPENAI_API_KEY"),
    timeout=30.0
)

# Claude with custom timeout
client = ClaudeGPTClient(
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    timeout=45.0
)

Use Cases

• Access List Resequencing: Automatically handle complex ACL resequencing with temporary permit statements
• Interface Configuration: Generate safe interface configuration changes with proper ordering
• Routing Protocol Updates: Handle complex routing protocol transitions
• VLAN Reconfiguration: Manage VLAN changes across multiple switches
• QoS Policy Updates: Coordinate policy-map and class-map changes

Documentation

Full documentation is available at hier-config-gpt.readthedocs.io

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Security

For security considerations and best practices, see SECURITY.md.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Credits

• Built on top of hier-config by James Williams
• Supports OpenAI GPT, Anthropic Claude, and Ollama

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: ReadTheDocs