QVeris Python SDK

pip install qveris

export QVERIS_API_KEY="your-api-key"
export QVERIS_BASE_URL="https://qveris.cn/api/v1"

from qveris import QverisClient, QverisConfig

client = QverisClient(QverisConfig(
    api_key="your-api-key",
    base_url="https://qveris.cn/api/v1",
))

import asyncio
import math
from qveris import QverisClient, QverisConfig

def matches_type(kind, value):
    return {
        "string": lambda: isinstance(value, str),
        "integer": lambda: isinstance(value, int) and not isinstance(value, bool),
        "number": lambda: isinstance(value, (int, float)) and not isinstance(value, bool) and math.isfinite(value),
        "boolean": lambda: isinstance(value, bool),
        "array": lambda: isinstance(value, list),
        "object": lambda: isinstance(value, dict),
    }.get(kind, lambda: False)()

def supports_request(candidate, params):
    if candidate.params is None:
        return False
    definitions = {p.name: p for p in candidate.params}
    if len(definitions) != len(candidate.params):
        return False
    return all(not p.required or p.name in params for p in candidate.params) and all(
        (p := definitions.get(name)) is not None
        and matches_type(p.type, value)
        and (p.enum is None or any(allowed == value and
             isinstance(allowed, bool) == isinstance(value, bool) for allowed in p.enum))
        for name, value in params.items()
    )

async def main():
    client = QverisClient(QverisConfig(base_url="https://qveris.cn/api/v1"))
    try:
        # 1. 用自然语言发现能力（免费）
        discovered = await client.discover("天气预报 API", limit=5)
        params = {"city": "北京"}
        tool = next(
            (candidate for candidate in discovered.results
             if supports_request(candidate, params)),
            None,
        )

        # 2. 仅在 Discover 未提供选择所需契约时 Inspect
        if tool is None:
            details = await client.inspect(
                [candidate.tool_id for candidate in discovered.results[:3]],
                search_id=discovered.search_id,
            )
            tool = next(
                (candidate for candidate in details.results
                 if supports_request(candidate, params)),
                None,
            )
        if tool is None:
            raise RuntimeError("没有候选能力提供兼容的当前参数契约")

        # 3. 样例只作模板；覆盖为本次请求的真实业务值
        result = await client.call(
            tool.tool_id,
            params,
            search_id=discovered.search_id,
            max_response_size=20480,
        )
        print(result.success, result.result)

        # 4. 审计最终扣费结果
        usage = await client.usage(execution_id=result.execution_id, summary=True)
        ledger = await client.ledger(summary=True, limit=5)
        print(usage.total, ledger.total)
    finally:
        await client.close()

asyncio.run(main())

export OPENAI_API_KEY="..."
export OPENAI_BASE_URL="https://your-openai-compatible-endpoint/v1"   # 你的 OpenAI 兼容服务地址

import asyncio
from qveris import Agent, Message, QverisConfig

async def main():
    async with Agent(config=QverisConfig(base_url="https://qveris.cn/api/v1")) as agent:
        messages = [Message(role="user", content="查一下北京现在的天气。")]
        async for event in agent.run(messages):
            if event.type == "content" and event.content:
                print(event.content, end="", flush=True)

asyncio.run(main())

async with Agent(config=QverisConfig(base_url="https://qveris.cn/api/v1")) as agent:
    answer = await agent.run_to_completion(
        [Message(role="user", content="找一个股票报价能力并查询 AAPL。")]
    )
    print(answer)

agent = Agent(budget_credits=25)

status = agent.budget_status()   # {"limit": 25, "spent": 12.0, "remaining": 13.0}，或 None

from qveris import Agent, QverisConfig, AgentConfig

agent = Agent(
    config=QverisConfig(base_url="https://qveris.cn/api/v1", max_iterations=20),
    agent_config=AgentConfig(model="gpt-4o", temperature=0.2),
)

result = await client.call(
    "tool.id",
    {"symbol": "AAPL"},
    respond_with="summary",
    compatibility_mode="legacy_optional_fields",  # 已弃用；可能提交两次
)

from qveris import ToolExecutionResponse

def explain(result: ToolExecutionResponse) -> str:
    if not result.success:
        return f"失败：{result.error_message}"
    charged = result.billing.summary if result.billing else "无计费信息"
    return f"成功（{charged}）；剩余={result.remaining_credits}"

from qveris import QverisClient, QverisConfig
from qveris.client.tools import DISCOVER_TOOL_DEF, INSPECT_TOOL_DEF, CALL_TOOL_DEF

tools = [DISCOVER_TOOL_DEF, INSPECT_TOOL_DEF, CALL_TOOL_DEF]
client = QverisClient(QverisConfig(base_url="https://qveris.cn/api/v1"))

# ... 你的 LLM 产出一个工具调用 (func_name, func_args) ...
result, is_error, handled = await client.handle_tool_call(func_name, func_args)
if handled and not is_error:
    ...  # 把 result 回传给模型

pip install "qveris[langchain]" "langchain>=1.0" langchain-openai

import asyncio

from langchain.agents import create_agent
from qveris import QverisClient, QverisConfig
from qveris.integrations.langchain import get_qveris_tools

async def main():
    client = QverisClient(QverisConfig(base_url="https://qveris.cn/api/v1"))
    try:
        agent = create_agent("openai:gpt-4o-mini", tools=get_qveris_tools(client))
        result = await agent.ainvoke({"messages": [{"role": "user", "content": "查找股票报价工具并查询 AAPL。"}]})
        print(result)
    finally:
        await client.close()

asyncio.run(main())

import asyncio

from agents import Agent, Runner
from qveris import QverisClient, QverisConfig
from qveris.integrations.openai_agents import get_qveris_tools

async def main():
    client = QverisClient(QverisConfig(base_url="https://qveris.cn/api/v1"))
    try:
        agent = Agent(name="Assistant", tools=get_qveris_tools(client))
        result = await Runner.run(agent, "查找股票报价能力并查询 AAPL。")
        print(result.final_output)
    finally:
        await client.close()

asyncio.run(main())

from crewai import Agent
from qveris import QverisClient, QverisConfig
from qveris.integrations.crewai import aclose, get_qveris_tools

client = QverisClient(QverisConfig(base_url="https://qveris.cn/api/v1"))
agent = Agent(role="Researcher", goal="选择正确能力", backstory="工具专家", tools=get_qveris_tools(client))
# Crew(...).kickoff()
aclose(client)

# 以下片段假设已配置 QverisClient 以及框架所需的 model_client / llm；
# 只需选择与你的 Agent 框架对应的 Adapter。
# AutoGen AssistantAgent
from autogen_agentchat.agents import AssistantAgent
from qveris.integrations.autogen import get_qveris_tools
agent = AssistantAgent("assistant", model_client=model_client, tools=get_qveris_tools(client))

# LlamaIndex FunctionAgent
from llama_index.core.agent.workflow import FunctionAgent
from qveris.integrations.llamaindex import get_qveris_tools
agent = FunctionAgent(tools=get_qveris_tools(client), llm=llm)

# Pydantic AI Agent
from pydantic_ai import Agent
from qveris.integrations.pydantic_ai import get_qveris_tools
agent = Agent("openai:gpt-4o-mini", tools=get_qveris_tools(client))

from typing import AsyncGenerator, List
from openai.types.chat import ChatCompletionToolParam
from qveris import Agent
from qveris.config import AgentConfig
from qveris.llm.base import LLMProvider
from qveris.types import ChatResponse, Message, StreamEvent

class MyProvider(LLMProvider):
    async def chat_stream(self, messages: List[Message], tools: List[ChatCompletionToolParam], config: AgentConfig) -> AsyncGenerator[StreamEvent, None]:
        ...

    async def chat(self, messages: List[Message], tools: List[ChatCompletionToolParam], config: AgentConfig) -> ChatResponse:
        ...

agent = Agent(llm_provider=MyProvider())