@claudeautopm/plugin-ai

Complete AI/ML plugin with OpenAI, Gemini, LangChain agents, RAG systems, model deployment, prompt engineering, and MLOps patterns

Overview

This plugin provides comprehensive AI and Machine Learning capabilities for ClaudeAutoPM, including expert agents for OpenAI, Gemini, and LangChain, production-ready commands for RAG systems and model deployment, and Context7-verified best practices for AI development.

Features

• 🤖 AI/ML Agents: OpenAI, Gemini, and LangGraph workflow experts
• 📚 RAG Systems: Complete retrieval-augmented generation scaffolding
• 🚀 Model Deployment: Production-ready AI model deployment infrastructure
• 📝 Prompt Engineering: Comprehensive prompt engineering standards
• 📊 MLOps: Experiment tracking and model registry with MLflow
• ✅ Context7-Verified: All patterns verified against official documentation

Installation

npm install @claudeautopm/plugin-ai

Agents

openai-python-expert

Expert in OpenAI Python SDK integration:

• GPT models (GPT-4, GPT-3.5)
• Chat completions and streaming
• Function calling and tool usage
• Embeddings and vector operations
• Vision and audio processing
• Production deployment patterns

Context7 Sources: /openai/openai-python (277 snippets, trust 9.1)

gemini-api-expert

Expert in Google Gemini API integration:

• Gemini Pro and Flash models
• Multimodal inputs (text, images, audio, video)
• Safety controls and content filtering
• Structured output generation
• Function calling
• Production deployment

Context7 Sources: /google/generative-ai-python

langgraph-workflow-expert

Expert in LangGraph workflow orchestration:

• State machines and conditional routing
• Multi-agent collaboration
• Graph-based AI workflows
• Human-in-the-loop patterns
• Tool integration
• Workflow monitoring

Context7 Sources: /langchain-ai/langgraph (verified patterns)

Commands

/ai:rag-setup

Generate production-ready RAG system with:

• Document ingestion and chunking
• Vector store setup (Chroma, FAISS, Pinecone)
• Embedding configuration (OpenAI, HuggingFace)
• Retrieval strategies (MMR, similarity, compression)
• RAG chain assembly with RunnablePassthrough
• Testing and monitoring

Usage:

/ai:rag-setup my-rag-project --vector-store chroma --retrieval-strategy mmr

/ai:model-deploy

Generate AI model deployment infrastructure with:

• FastAPI application with async endpoints
• MLflow model registry
• Prometheus monitoring
• Response caching and rate limiting
• Docker and Kubernetes configurations
• Error handling and retries

Usage:

/ai:model-deploy my-model --framework fastapi --monitoring prometheus

Rules

ai-model-standards

Comprehensive AI model development standards covering:

• Model selection and configuration
• Async operations and rate limiting
• Error handling with exponential backoff
• Response caching for efficiency
• Monitoring and metrics tracking
• HuggingFace and MLflow patterns

prompt-engineering-standards

Comprehensive prompt engineering standards covering:

• System prompts and role definition
• Few-shot learning patterns
• Chain-of-thought reasoning
• Structured output generation
• Context window management
• Prompt injection prevention
• RAG prompt patterns

Example Scripts

All example scripts demonstrate Context7-verified patterns:

openai-chat-example.py

Demonstrates:

• AsyncOpenAI client configuration
• Streaming chat completions
• Function calling with tools
• Error handling with exponential backoff
• Response caching with TTL

Run:

export OPENAI_API_KEY='your-key'
python scripts/examples/openai-chat-example.py

langchain-rag-example.py

Demonstrates:

• Document chunking with RecursiveCharacterTextSplitter
• Vector store creation with Chroma
• MMR retrieval for diverse results
• RAG chain with RunnablePassthrough.assign()
• Context boundary enforcement

Run:

export OPENAI_API_KEY='your-key'
python scripts/examples/langchain-rag-example.py

huggingface-inference-example.py

Demonstrates:

• Pipeline API for quick inference
• AutoTokenizer and AutoModel patterns
• Device management (CPU/GPU)
• Batch processing
• Model caching

Run:

python scripts/examples/huggingface-inference-example.py

mlflow-tracking-example.py

Demonstrates:

• Experiment organization
• Parameter and metric logging
• Model registry and versioning
• Run comparison
• Artifact storage

Run:

python scripts/examples/mlflow-tracking-example.py
mlflow ui  # View results

Context7 Integration

This plugin extensively uses Context7 for retrieving up-to-date documentation and best practices:

• OpenAI: 277 code snippets, trust score 9.1
• LangChain: 150 code snippets, trust score 9.2
• HuggingFace: 2,790 code snippets, trust score 9.6
• MLflow: 3,114 code snippets, trust score 9.1

All patterns are verified against official documentation to ensure correctness and follow industry best practices.

Quick Start

1. Install the plugin:

   npm install @claudeautopm/plugin-ai

2. Use an agent:

   # In Claude Code
   @openai-python-expert help me implement chat completions with streaming

3. Run a command:

   /ai:rag-setup my-knowledge-base

4. Try an example:

   export OPENAI_API_KEY='your-key'
   python scripts/examples/openai-chat-example.py

Best Practices

AsyncOpenAI Pattern

from openai import AsyncOpenAI

client = AsyncOpenAI(
    api_key=api_key,
    max_retries=3,
    timeout=60.0
)

response = await client.chat.completions.create(
    model="gpt-4",
    messages=messages
)

RAG with RunnablePassthrough

from langchain_core.runnables import RunnablePassthrough
from operator import itemgetter

rag_chain = (
    RunnablePassthrough.assign(
        context=itemgetter("question")
            | retriever
            | format_docs
    )
    | prompt
    | llm
    | StrOutputParser()
)

HuggingFace Pipeline

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    device=0 if torch.cuda.is_available() else -1
)

results = classifier(texts)

MLflow Tracking

import mlflow

with mlflow.start_run():
    mlflow.log_params(params)
    mlflow.log_metrics(metrics)
    mlflow.sklearn.log_model(model, "model")

Requirements

• Node.js: >=16.0.0
• npm: >=8.0.0
• Python: >=3.8 (for example scripts)
• API Keys: OpenAI, Gemini (as needed)

Dependencies

• Peer: @claudeautopm/plugin-core@^2.0.0
• Python (for scripts): openai, langchain, transformers, mlflow

Contributing

Contributions are welcome! Please ensure:

• All patterns follow Context7-verified best practices
• Documentation is comprehensive
• Examples are runnable
• Tests pass

License

MIT © ClaudeAutoPM Team

Support

• Issues: https://github.com/rafeekpro/ClaudeAutoPM/issues
• Documentation: https://github.com/rafeekpro/ClaudeAutoPM/tree/main/packages/plugin-ai

Resources

• OpenAI API Documentation
• LangChain Documentation
• HuggingFace Transformers
• MLflow Documentation
• Context7 Documentation

Version: 2.0.0 Schema Version: 2.0 Package Size: ~22 KB (gzipped)