Workflow:Sgl project Sglang Structured Output Generation
| Knowledge Sources | |
|---|---|
| Domains | LLM_Inference, Structured_Generation, Constrained_Decoding |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
End-to-end process for generating structured outputs (JSON, regex-constrained text) from large language models using SGLang's constrained decoding capabilities.
Description
This workflow demonstrates how to use SGLang's built-in constrained generation to force model outputs to conform to specific formats. SGLang supports regex-based constraints, Pydantic model schemas (automatically converted to regex), and JSON schema constraints. The constrained decoding engine uses a compressed finite state machine to guide token selection, ensuring that every generated output is valid according to the specified format. This eliminates the need for post-processing validation or retry logic.
Usage
Execute this workflow when you need model outputs in a guaranteed structured format such as JSON objects, specific field formats (IP addresses, dates), or enum-constrained values. Common use cases include data extraction, form filling, API response generation, and structured information retrieval.
Execution Steps
Step 1: Launch the SGLang Server
Start a server or initialize an Engine with the desired language model. The structured output backend is included by default and requires no additional configuration.
Key considerations:
- Any SGLang-supported model can be used for structured generation
- The constrained decoding backend is loaded automatically
- Both server mode and offline Engine mode support structured outputs
Step 2: Define the Output Schema
Specify the desired output structure using one of three methods: a raw regex pattern string, a Pydantic BaseModel class definition, or a JSON schema dictionary. For Pydantic models, SGLang automatically converts the schema to a regex pattern using the outlines backend.
Key considerations:
- Regex patterns offer the most fine-grained control
- Pydantic models provide type-safe schema definitions with validation
- JSON schema can be passed directly via the OpenAI API response_format parameter
- Complex nested structures are supported
Step 3: Construct the Prompt
Build the prompt that instructs the model to generate output in the desired format. Include the schema description or example in the prompt to guide the model's generation toward the constrained format.
Key considerations:
- Clear instructions improve generation quality within constraints
- The model should understand what format is expected
- Including an example of the desired output in the prompt is helpful
Step 4: Execute Constrained Generation
Submit the generation request with the constraint parameter. For the frontend language API, use the regex parameter in sgl.gen(). For the OpenAI-compatible API, use the response_format parameter with json_schema type. The constrained decoding engine masks invalid tokens at each step, guaranteeing format compliance.
Key considerations:
- Frontend API: sgl.gen("output", regex=pattern) for regex constraints
- OpenAI API: response_format with type "json_schema" for JSON constraints
- The constraint applies token-level masking — only valid continuations are sampled
- Generation speed is slightly reduced due to token masking overhead
Step 5: Parse and Validate the Output
Parse the generated text into the target data structure. Since the output is guaranteed to match the constraint, parsing should always succeed. For Pydantic schemas, the output can be directly loaded into the model class for type-safe access.
Key considerations:
- JSON outputs can be parsed with json.loads() without error handling
- Pydantic model outputs can be validated with Model.model_validate_json()
- Token-level log probabilities are available for confidence scoring