{"model": "gpt-4o-2024-08-06","messages": [{"role": "system","content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function."},{"role": "user","content": "look up all my orders in may of last year that were fulfilled but not delivered on time"}],"tools": [{"type": "function","function": {"name": "query","description": "Execute a query.","strict": true, # 启用结构化输出"parameters": {"type": "object","properties": {"table_name": {"type": "string","enum": ["orders"]},"columns": {"type": "array","items": {"type": "string","enum": ["id","status","expected_delivery_date","delivered_at","shipped_at","ordered_at","canceled_at"]}},"conditions": {"type": "array","items": {"type": "object","properties": {"column": {"type": "string"},"operator": {"type": "string","enum": ["=", ">", "<", ">=", "<=", "!="]},"value": {"anyOf": [{"type": "string"},{"type": "number"},{"type": "object","properties": {"column_name": {"type": "string"}},"required": ["column_name"],"additionalProperties": false}]}},"required": ["column", "operator", "value"],"additionalProperties": false}},"order_by": {"type": "string","enum": ["asc", "desc"]}},"required": ["table_name", "columns", "conditions", "order_by"],"additionalProperties": false}}}]}

{  "table_name": "orders",  "columns": ["id", "status", "expected_delivery_date", "delivered_at"],  "conditions": [    {      "column": "status",      "operator": "=",      "value": "fulfilled"    },    {      "column": "ordered_at",      "operator": ">=",      "value": "2023-05-01"    },    {      "column": "ordered_at",      "operator": "<",      "value": "2023-06-01"    },    {      "column": "delivered_at",      "operator": ">",      "value": {        "column_name": "expected_delivery_date"      }    }  ],  "order_by": "asc"}

{  "model": "gpt-4o-2024-08-06",  "messages": [    {      "role": "system",      "content": "You are a helpful math tutor."    },    {      "role": "user",      "content": "solve 8x + 31 = 2"    }  ],  "response_format": {    "type": "json_schema", # 规范输出    "json_schema": {      "name": "math_response",      "strict": true,      "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      }    }  }}

{  "steps": [    {      "explanation": "Subtract 31 from both sides to isolate the term with x.",      "output": "8x + 31 - 31 = 2 - 31"    },    {      "explanation": "This simplifies to 8x = -29.",      "output": "8x = -29"    },    {      "explanation": "Divide both sides by 8 to solve for x.",      "output": "x = -29 / 8"    }  ],  "final_answer": "x = -29 / 8"}

from enum import Enumfrom typing import Unionfrom pydantic import BaseModelimport openaifrom openai import OpenAI class Table(str, Enum):    orders = "orders"    customers = "customers"    products = "products" class Column(str, Enum):    id = "id"    status = "status"    expected_delivery_date = "expected_delivery_date"    delivered_at = "delivered_at"    shipped_at = "shipped_at"    ordered_at = "ordered_at"    canceled_at = "canceled_at" class Operator(str, Enum):    eq = "="    gt = ">"    lt = "<"    le = "<="    ge = ">="    ne = "!=" class OrderBy(str, Enum):    asc = "asc"    desc = "desc" class DynamicValue(BaseModel ):    column_name: str class Condition(BaseModel):    column: str    operator: Operator    value: Union[str, int, DynamicValue] class Query(BaseModel):    table_name: Table    columns: list[Column]    conditions: list[Condition]    order_by: OrderBy client = OpenAI() completion = client.beta.chat.completions.parse(    model="gpt-4o-2024-08-06",    messages=[        {            "role": "system",            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",        },        {            "role": "user",            "content": "look up all my orders in may of last year that were fulfilled but not delivered on time",        },    ],    tools=[        openai.pydantic_function_tool(Query),    ],) print(completion.choices[0].message.tool_calls[0].function.parsed_arguments)