Workflow:Vllm project Vllm Structured Output Generation
| Knowledge Sources | |
|---|---|
| Domains | LLMs, Inference, Structured_Output, Data_Engineering |
| Last Updated | 2026-02-08 13:00 GMT |
Overview
End-to-end process for generating constrained, structured text outputs (JSON, regex-matched strings, grammar-conforming text) from Large Language Models using vLLM's guided decoding.
Description
This workflow covers the procedure for enforcing structural constraints on LLM outputs during generation. vLLM supports multiple constraint types: choice lists (pick from predefined options), regular expressions, JSON schemas (including Pydantic model schemas), and context-free grammars. The guided decoding engine masks invalid tokens at each generation step, guaranteeing that the output conforms to the specified structure while maintaining natural language quality.
Usage
Execute this workflow when you need LLM outputs to conform to a specific format. Typical scenarios include extracting structured data from text (JSON extraction), classification tasks (choosing from fixed labels), form filling, SQL generation, code generation constrained to a grammar, and any pipeline where downstream processing requires a predictable output format.
Execution Steps
Step 1: Define the Output Structure
Specify the constraint that the generated output must satisfy. vLLM supports four constraint types that cover progressively more complex structural requirements.
Constraint types:
- Choice: A list of allowed output strings (e.g., ["Positive", "Negative"])
- Regex: A regular expression pattern the output must match
- JSON Schema: A JSON Schema object (often derived from a Pydantic model)
- Grammar: A context-free grammar in EBNF notation
Key considerations:
- Choice is simplest and most efficient for classification tasks
- JSON schema is the most common for data extraction pipelines
- Pydantic models can be converted to JSON schema via model_json_schema()
- Grammar support enables complex structured languages like SQL subsets
Step 2: Create StructuredOutputsParams
Wrap the chosen constraint into a StructuredOutputsParams object. This object is attached to the SamplingParams to activate guided decoding for the request.
Key considerations:
- Only one constraint type can be active per request
- The constraint is validated before generation begins
- Complex JSON schemas may slightly increase first-token latency
- Grammar compilation is cached across requests with the same grammar
Step 3: Configure Sampling Parameters
Create SamplingParams with the structured_outputs parameter set. Standard sampling parameters (temperature, max_tokens, stop conditions) still apply alongside the structural constraint.
Key considerations:
- Temperature still affects token selection within valid options
- max_tokens should be sufficient to complete the structured output
- Stop conditions can complement the structural constraint
- For JSON outputs, the generation naturally stops at the closing brace
Step 4: Initialize the LLM
Create the LLM instance. No special engine configuration is needed for structured outputs; the guided decoding backend is activated automatically when StructuredOutputsParams is provided.
Key considerations:
- Any text generation model can be used with structured outputs
- Instruction-tuned models generally produce better structured output quality
- The guided decoding backend selection is automatic
Step 5: Generate Constrained Output
Submit the prompt with the configured sampling parameters. The engine applies token masking at each generation step, only allowing tokens that keep the output on track to satisfy the constraint.
Key considerations:
- Each generated token is validated against the constraint in real time
- Invalid tokens are masked (probability set to zero) before sampling
- Generation may take slightly longer due to constraint checking overhead
- Batch inference with mixed constraint types is supported
Step 6: Parse and Validate Results
Extract the generated text and parse it according to the expected structure. For JSON outputs, parse into the target data structure. For regex/grammar outputs, verify the match is complete.
Key considerations:
- JSON outputs can be parsed directly with json.loads()
- Pydantic model validation can be applied for type checking
- Choice outputs will be exactly one of the specified options
- Regex outputs are guaranteed to match the specified pattern