https://github.com/cameronking4/deepseek-r1-structured-outputs
This project demonstrates how to get structured outputs from Deepseek R1 reasoning model and do tool calling using an OpenAI summarization step. It's made using Next.js serverless routes and uses shadcn/ui for the frontend.
https://github.com/cameronking4/deepseek-r1-structured-outputs
deepseek deepseek-r1 openai structured-outputs tool-calling tool-use
Last synced: 2 months ago
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This project demonstrates how to get structured outputs from Deepseek R1 reasoning model and do tool calling using an OpenAI summarization step. It's made using Next.js serverless routes and uses shadcn/ui for the frontend.
- Host: GitHub
- URL: https://github.com/cameronking4/deepseek-r1-structured-outputs
- Owner: cameronking4
- Created: 2025-01-26T09:47:21.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-26T10:26:52.000Z (8 months ago)
- Last Synced: 2025-01-26T10:28:17.102Z (8 months ago)
- Topics: deepseek, deepseek-r1, openai, structured-outputs, tool-calling, tool-use
- Language: TypeScript
- Homepage: https://deepseek-r1-structured-outputs.vercel.app/
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DeepSeek Reasoning + OpenAI summarization
https://github.com/user-attachments/assets/b6c07eea-7eba-4621-b2e5-f0bb97a5b964
As of 1/26/2025, DeepSeek's R1 model does not support structured outputs and tool calling.. This project is a proof of concept for how to use DeepSeek's reasoning model to analyze questions in detail and then use GPT's summarization power to create a structured output and make tool calls while keeping the cost down.
## Project Description
A Next.js application that combines DeepSeek's reasoning capabilities with GPT's summarization power. This project demonstrates how to:
1. Use DeepSeek's reasoning stage and handoff to OpenAI
2. Process responses in multiple formats (basic, structured, tool-calling)
3. Swap GPT models on the client to experiement with summarization and cost
## Features
- Modern Next.js web interface with real-time interactions
- Multiple API endpoints for different response formats
- Dark/light theme support with Tailwind CSS
- Real-time response streaming
- Structured data outputs with JSON schema validation
- Cost-effective hybrid model approach
- Chat UI made using shadcn/ui
## Development
```
pnpm install
touch .env
pnpm dev
```
Environment variables:
```
DEEPSEEK_API_KEY=
OPENAI_API_KEY=
```
## API Routes
### Basic API (`/api/route.ts`)
- Simple question/answer format
- Uses DeepSeek for reasoning and GPT-3.5-turbo for summarization
- Returns plain text responses with token usage metrics
### Structured API (`/api/structured/route.ts`)
- Enhanced response format with JSON schema validation
- Structured output includes:
- One-sentence summary
- Bullet points
- Reasoning steps count
- Follow-up prompts
- Uses GPT-4o-mini for structured summarization
### Tool Calling API (`/api/tool-calling/route.ts`)
- Advanced response format with function calling capabilities
- Similar structure to structured API
- Optimized for integration with external tools and services
## Requirements
You'll need API keys for:
- DeepSeek API (for reasoning capabilities)
- OpenAI API (for summarization)
## How It Works
1. User submits a question through the web interface or API
2. The selected API route processes the request:
- DeepSeek model provides detailed reasoning
- GPT model creates summary after thinking phase and returns API response
3. Usage metrics and timing are tracked and returned as well
## Implementation Details
The code demonstrates several key patterns:
- Next.js App Router architecture
- React Server Components with client-side interactivity
- Tailwind CSS for styling with CSS variables
- Radix UI primitives for accessible components
- TypeScript for type safety
- Real-time response streaming
- Error handling and loading states