from openai import OpenAI
import json
client = OpenAI(
base_url="https://api.highwayapi.ai/openai",
api_key="<Your API Key>",
)
model = "qwen/qwen-2.5-72b-instruct"
# Ejemplo de salida estructurada con JSON Schema
# Este ejemplo crea un schema para extraer información de gastos
# Definir el prompt del sistema para el seguimiento de gastos
system_prompt = """You are an expense tracking assistant.
Extract expense information from the user's input and format it according to the provided schema."""
# Definir el JSON schema para la respuesta estructurada
response_format = {
"type": "json_schema",
"json_schema": {
"name": "expense_tracking_schema",
"schema": {
"type": "object",
"properties": {
"expenses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "Description of the expense"
},
"amount": {
"type": "number",
"description": "Amount spent in dollars"
},
"date": {
"type": "string",
"description": "When the expense occurred"
},
"category": {
"type": "string",
"description": "Category of expense (e.g., food, office, travel)"
}
},
"required": [
"description",
"amount"
]
}
},
"total": {
"type": "number",
"description": "Total amount of all expenses"
}
},
"required": [
"expenses",
"total"
],
},
},
}
chat_completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": """I spent $120 on dinner at an Italian restaurant last Friday with my colleagues.
Also bought office supplies for $45 on Monday.""",
},
],
max_tokens=1024,
temperature=0.8,
stream=False,
response_format=response_format,
)
response_content = chat_completion.choices[0].message.content
# Analizar y embellecer el JSON
try:
json_response = json.loads(response_content)
prettified_json = json.dumps(json_response, indent=2)
print(prettified_json)
except json.JSONDecodeError:
print("Could not parse response as JSON. Raw response:")
print(response_content)
