Structured Outputs (JSON Mode)

Quick Start

SDK

from litellm import completion
import os 

os.environ["OPENAI_API_KEY"] = ""

response = completion(
  model="gpt-4o-mini",
  response_format={ "type": "json_object" },
  messages=[
    {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
    {"role": "user", "content": "Who won the world series in 2020?"}
  ]
)
print(response.choices[0].message.content)

PROXY

curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "response_format": { "type": "json_object" },
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant designed to output JSON."
      },
      {
        "role": "user",
        "content": "Who won the world series in 2020?"
      }
    ]
  }'

Check Model Support

Call litellm.get_supported_openai_params to check if a model/provider supports response_format.

from litellm import get_supported_openai_params

params = get_supported_openai_params(model="anthropic.claude-3", custom_llm_provider="bedrock")

assert "response_format" in params

Pass in 'json_schema'

To use Structured Outputs, simply specify

response_format: { "type": "json_schema", "json_schema": … , "strict": true }

Works for:

• OpenAI models
• Azure OpenAI models
• Google AI Studio - Gemini models
• Vertex AI models (Gemini + Anthropic)
• Bedrock Models
• Anthropic API Models
• Groq Models
• Ollama Models

SDK

import os
from litellm import completion 
from pydantic import BaseModel

# add to env var 
os.environ["OPENAI_API_KEY"] = ""

messages = [{"role": "user", "content": "List 5 important events in the XIX century"}]

class CalendarEvent(BaseModel):
  name: str
  date: str
  participants: list[str]

class EventsList(BaseModel):
    events: list[CalendarEvent]

resp = completion(
    model="gpt-4o-2024-08-06",
    messages=messages,
    response_format=EventsList
)

print("Received={}".format(resp))

PROXY

1. Add openai model to config.yaml

model_list:
  - model_name: "gpt-4o"
    litellm_params:
      model: "gpt-4o-2024-08-06"

2. Start proxy with config.yaml

litellm --config /path/to/config.yaml

3. Call with OpenAI SDK / Curl!

Just replace the 'base_url' in the openai sdk, to call the proxy with 'json_schema' for openai models

OpenAI SDK

from pydantic import BaseModel
from openai import OpenAI

client = OpenAI(
    api_key="anything", # 👈 PROXY KEY (can be anything, if master_key not set)
    base_url="http://0.0.0.0:4000" # 👈 PROXY BASE URL
)

class Step(BaseModel):
    explanation: str
    output: str

class MathReasoning(BaseModel):
    steps: list[Step]
    final_answer: str

completion = client.beta.chat.completions.parse(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."},
        {"role": "user", "content": "how can I solve 8x + 7 = -23"}
    ],
    response_format=MathReasoning,
)

math_reasoning = completion.choices[0].message.parsed

Curl

curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "math_reasoning",
        "schema": {
          "type": "object",
          "properties": {
            "steps": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "explanation": { "type": "string" },
                  "output": { "type": "string" }
                },
                "required": ["explanation", "output"],
                "additionalProperties": false
              }
            },
            "final_answer": { "type": "string" }
          },
          "required": ["steps", "final_answer"],
          "additionalProperties": false
        },
        "strict": true
      }
    }
  }'

Validate JSON Schema

Not all vertex models support passing the json_schema to them (e.g. gemini-1.5-flash). To solve this, LiteLLM supports client-side validation of the json schema.

litellm.enable_json_schema_validation=True

If litellm.enable_json_schema_validation=True is set, LiteLLM will validate the json response using jsonvalidator.

See Code

SDK

# !gcloud auth application-default login - run this to add vertex credentials to your env
import litellm, os
from litellm import completion 
from pydantic import BaseModel 


messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ]

litellm.enable_json_schema_validation = True
litellm.set_verbose = True # see the raw request made by litellm

class CalendarEvent(BaseModel):
  name: str
  date: str
  participants: list[str]

resp = completion(
    model="gemini/gemini-1.5-pro",
    messages=messages,
    response_format=CalendarEvent,
)

print("Received={}".format(resp))

PROXY

1. Create config.yaml

model_list:
  - model_name: "gemini-1.5-flash"
    litellm_params:
      model: "gemini/gemini-1.5-flash"
      api_key: os.environ/GEMINI_API_KEY

litellm_settings:
  enable_json_schema_validation: True

2. Start proxy

litellm --config /path/to/config.yaml

3. Test it!

curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -d '{
    "model": "gemini-1.5-flash",
    "messages": [
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
    "response_format": { 
        "type": "json_object",
        "response_schema": { 
            "type": "json_schema",
            "json_schema": {
              "name": "math_reasoning",
              "schema": {
                "type": "object",
                "properties": {
                  "steps": {
                    "type": "array",
                    "items": {
                      "type": "object",
                      "properties": {
                        "explanation": { "type": "string" },
                        "output": { "type": "string" }
                      },
                      "required": ["explanation", "output"],
                      "additionalProperties": false
                    }
                  },
                  "final_answer": { "type": "string" }
                },
                "required": ["steps", "final_answer"],
                "additionalProperties": false
              },
              "strict": true
            },
        }
    },
  }'