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ai-ml

Build FastAPI AI/ML services with LangChain and ONNX model export.

Updated Feb 2, 2026
Search Tags:#fastapi#python#langchain#ai#llm#ml#onnx
One-click install
npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill ai-ml-jnzader-vault
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-ml
Source: https://github.com/JNZader-Vault/project-starter-framework/tree/main/.ai-config/skills/data-ai/ai-ml
Command: npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill ai-ml-jnzader-vault

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, uvicorn[standard], pydantic, pydantic-settings, openai, anthropic, langchain, langchain-openai, langchain-anthropic, sentence-transformers, torch, transformers, onnx, onnxruntime, structlog, transformers, datasets, optimum.onnxruntime, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a robust framework for building AI/ML services, streamlining the development of applications that leverage large language models, natural language processing, and machine learning pipelines.

Core Features & Use Cases

  • AI Service Development: Build AI-powered APIs using FastAPI, integrating with OpenAI and Anthropic.
  • LLM Integration: Utilize LangChain for complex LLM interactions, including SQL agents for database querying.
  • Model Export & Training: Supports ONNX model export for efficient inference and provides a pipeline for training custom classification models.
  • Use Case: Develop a customer support chatbot that can answer questions by querying a company's internal SQL database.

Quick Start

Use the ai-ml skill to create a FastAPI service that can generate text responses using OpenAI.

Frequently Asked Questions about ai-ml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a FastAPI service that integrates LangChain with OpenAI and Anthropic?▼

You can build a FastAPI service that integrates LangChain with OpenAI and Anthropic by using this framework to structure APIs, manage complex LLM interactions, and handle natural language processing pipelines efficiently.

What is the best way to export machine learning models to ONNX for inference?▼

The best way to export machine learning models to ONNX for inference is using a pipeline that supports ONNX export directly, ensuring scalable and efficient deployment for AI-driven applications.

Can I use LangChain to create an SQL agent that queries a database via FastAPI?▼

Yes, you can use LangChain to create an SQL agent that queries a database via FastAPI, enabling use cases like customer support chatbots that answer questions by querying internal SQL databases.

Does this approach support training custom classification models locally?▼

Yes, this approach supports training custom classification models locally by providing a dedicated machine learning pipeline that leverages libraries like PyTorch, Transformers, and Datasets.

How do I deploy large language model APIs without managing complex infrastructure?▼

You can deploy large language model APIs without managing complex infrastructure by using FastAPI and Uvicorn to serve the models, combined with ONNX runtime for optimized and efficient inference execution.

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