response_format interface.
Define object schemas using Pydantic (Python) or Zod (JavaScript) to extract structured information from unstructured text.
1. Using Pydantic or Zod (Recommended)
This approach provides type hinting and automatic validation.from portkey_ai import Portkey
from pydantic import BaseModel
class Step(BaseModel):
explanation: str
output: str
class MathReasoning(BaseModel):
steps: list[Step]
final_answer: str
portkey = Portkey(
api_key="PORTKEY_API_KEY"
)
# Use .parse() for automatic parsing
completion = portkey.chat.completions.parse(
model="@anthropic-testing/claude-sonnet-4-6",
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
)
print(completion.choices[0].message.parsed)
import { Portkey } from 'portkey-ai';
import { z } from 'zod';
import { zodResponseFormat } from "openai/helpers/zod";
const MathReasoning = z.object({
steps: z.array(z.object({ explanation: z.string(), output: z.string() })),
final_answer: z.string()
});
const portkey = new Portkey({
apiKey: "PORTKEY_API_KEY",
});
async function runMathTutor() {
try {
const completion = await portkey.chat.completions.create({
model: "@anthropic-testing/claude-sonnet-4-6",
messages: [
{ role: "system", content: "You are a helpful math tutor." },
{ role: "user", content: "Solve 8x + 7 = -23" }
],
response_format: zodResponseFormat(MathReasoning, "MathReasoning")
});
console.log(JSON.parse(completion.choices[0].message.content));
} catch (error) {
console.error("Error running math tutor:", error);
}
}
runMathTutor();
import json, re
from anthropic import Anthropic
from pydantic import BaseModel
class Step(BaseModel):
explanation: str
output: str
class MathReasoning(BaseModel):
steps: list[Step]
final_answer: str
def parse(client, model, messages, response_model):
schema = response_model.model_json_schema()
msg = client.messages.create(
model=model,
max_tokens=1024,
system=f"Return ONLY JSON matching this schema:\n{json.dumps(schema)}",
messages=messages
)
text = msg.content[0].text
json_text = re.search(r"\{.*\}", text, re.S).group(0)
return response_model.model_validate(json.loads(json_text))
client = Anthropic(
auth_token="PORTKEY_API_KEY",
base_url="https://api.portkey.ai"
)
completion = parse(
client,
"@anthropic-testing/claude-sonnet-4-6",
[{"role": "user", "content": "how can I solve 8x + 7 = -23"}],
MathReasoning
)
print(completion)
import Anthropic from "@anthropic-ai/sdk";
import { z } from "zod";
const Step = z.object({
explanation: z.string(),
output: z.string(),
});
const MathReasoning = z.object({
steps: z.array(Step),
final_answer: z.string(),
});
const client = new Anthropic({
authToken: "PORTKEY_API_KEY",
baseURL: "https://api.portkey.ai",
});
async function main() {
const completion = await client.messages.create({
model: "@anthropic-testing/claude-sonnet-4-6",
max_tokens: 1024,
system: "Return ONLY valid JSON matching the schema: {steps:[{explanation:string,output:string}], final_answer:string}",
messages: [
{ role: "user", content: "how can I solve 8x + 7 = -23" }
],
});
const text = completion.content[0].text;
const jsonText = text.match(/\{[\s\S]*\}/)[0];
const result = MathReasoning.parse(JSON.parse(jsonText));
console.log(result);
}
main().catch(console.error);
curl https://api.portkey.ai/v1/chat/completions \
-H "x-portkey-api-key: $PORTKEY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@anthropic-provider/claude-3-5-sonnet-20241022",
"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
}
}
}
}'
2. Using Raw JSON Schema
For cross-language compatibility or dynamic schemas, pass a standard JSON schema directly.from portkey_ai import Portkey
portkey = Portkey(
api_key="PORTKEY_API_KEY"
)
completion = portkey.chat.completions.create(
model="@anthropic-provider/claude-3-5-sonnet-20241022",
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_schema",
"json_schema": {
"name": "event_extraction",
"schema": {
"type": "object",
"properties": {
"location": { "type": "string" },
"date": { "type": "string" },
"participants": { "type": "array", "items": { "type": "string" } }
},
"required": ["location", "date", "participants"],
"additionalProperties": False
},
"strict": True
}
}
)
print(completion.choices[0].message.content)
import { Portkey } from "portkey-ai";
const portkey = new Portkey({
apiKey: "PORTKEY_API_KEY",
});
async function main() {
try {
const completion = await portkey.chat.completions.create({
model: "@anthropic-provider/claude-3-5-sonnet-20241022",
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_schema",
json_schema: {
name: "event_extraction",
schema: {
type: "object",
properties: {
location: { type: "string" },
date: { type: "string" },
participants: { type: "array", items: { type: "string" } }
},
required: ["location", "date", "participants"],
additionalProperties: false
},
strict: true
}
}
});
console.log(completion.choices[0].message.content);
} catch (error) {
console.error("Error extracting event:", error);
}
}
main();
from anthropic import Anthropic
import json
client = Anthropic(
auth_token="PORTKEY_API_KEY",
base_url="https://api.portkey.ai" )
schema = {
"type": "object",
"properties": {
"location": {"type": "string"},
"date": {"type": "string"},
"participants": {
"type": "array",
"items": {"type": "string"}
}
},
"required": ["location", "date", "participants"],
"additionalProperties": False
}
prompt = f"""
Extract the event information from the sentence.
Return ONLY valid JSON matching this schema:
{json.dumps(schema, indent=2)}
Sentence:
Alice and Bob are going to a science fair on Friday.
"""
response = client.messages.create(
model="@anthropic-testing/claude-sonnet-4-6",
max_tokens=200,
system="You are an information extraction system. Only return valid JSON.",
messages=[
{
"role": "user",
"content": prompt
}
]
)
print(response.content[0].text)
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({
authToken: "PORTKEY_API_KEY",
baseURL: "https://api.portkey.ai",
});
async function main() {
const message = await client.messages.create({
model: "@anthropic-testing/claude-sonnet-4-6",
max_tokens: 1024,
messages: [{ role: "user", content: "Alice and Bob are going to a science fair on Friday." }],
response_format: {
type: "json_schema",
json_schema: {
name: "event_extraction",
schema: {
type: "object",
properties: {
location: { type: "string" },
date: { type: "string" },
participants: { type: "array", items: { type: "string" } }
},
required: ["location", "date", "participants"],
additionalProperties: false
},
strict: true
}
}
});
console.log(message.content);
}
main().catch(console.error);
curl https://api.portkey.ai/v1/chat/completions \
-H "x-portkey-api-key: $PORTKEY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "@anthropic-provider/claude-3-5-sonnet-20241022",
"messages": [
{ "role": "system", "content": "Extract event information." },
{ "role": "user", "content": "Alice and Bob are going to a science fair on Friday." }
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "event_extraction",
"schema": {
"type": "object",
"properties": {
"location": { "type": "string" },
"date": { "type": "string" },
"participants": { "type": "array", "items": { "type": "string" } }
},
"required": ["location", "date", "participants"],
"additionalProperties": false
},
"strict": true
}
}
}'