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BasedHardware
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omi-backend-patterns

Guide backend engineers in implementing Omi backend patterns with FastAPI, LangGraph, Firestore, Pinecone, and Redis.

13.1k|2.2k|Updated Mar 22, 2024
Search Tags:#LangGraph#FastAPI#conversations#Firestore#backend-patterns#API-design#memory-extraction
One-click install
npx skills add https://github.com/BasedHardware/omi --skill omi-backend-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omi-backend-patterns
Source: https://github.com/BasedHardware/omi/tree/main/.cursor/skills/omi-backend-patterns
Command: npx skills add https://github.com/BasedHardware/omi --skill omi-backend-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides guidance for backend engineers to implement robust Omi backend patterns, including conversation processing, memory extraction, chat routing with LangGraph, and integration with Firestore, Pinecone, and Redis.

Core Features & Use Cases

  • Patterns and best practices for building and extending the Omi backend
  • Guidance on API design, data flow, and system architecture across the backend stack
  • Use Case: When adding a new conversations endpoint, use this skill to ensure consistent routing, clean separation of concerns, and reliable storage

Quick Start

Use the omi-backend-patterns skill to guide backend integration tasks.

Frequently Asked Questions about omi-backend-patterns

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

FAQPage Schema
How do I structure FastAPI routers and modules for scalable backend APIs?▼

Scalable FastAPI backend APIs require thin routers, strict module hierarchy, and dependency injection. This pattern enforces clean separation of concerns, consistent error handling, and reliable storage integration across your backend architecture.

What's the best way to process conversations and extract memory in a FastAPI backend?▼

Conversation processing and memory extraction in a FastAPI backend use LangGraph for chat routing. This pattern integrates Firestore, Pinecone, and Redis to manage data flow and ensure reliable memory extraction from chat systems.

How does LangGraph integrate with Firestore and Redis for chat system routing?▼

LangGraph integrates with Firestore and Redis by applying modular routing patterns to chat systems. Firestore handles persistent conversation storage while Redis manages fast data access, ensuring a robust data flow architecture.

Can I use these backend patterns to add a new conversations endpoint with consistent API design?▼

Yes, you can use these backend patterns when adding new conversations endpoints. They ensure consistent routing, clean separation of concerns, and reliable storage integration across your FastAPI application architecture.

When should I apply dependency injection and module hierarchy in backend API design?▼

Apply dependency injection and module hierarchy in backend API design when building scalable chat systems. These patterns ensure consistent error handling and maintainable data flow across FastAPI, Firestore, and Pinecone integrations.

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