FastDeploy/test/ce/server/test_base_chat.py

#!/bin/env python3
# -*- coding: utf-8 -*-
# @author DDDivano
# encoding=utf-8 vi:ts=4:sw=4:expandtab:ft=python

import json

from core import TEMPLATE, URL, build_request_payload, send_request


def test_stream_response():
    data = {
        "stream": True,
        "messages": [
            {"role": "system", "content": "你是一个知识渊博的 AI 助手"},
            {"role": "user", "content": "讲讲爱因斯坦的相对论"},
        ],
        "max_tokens": 10,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload, stream=True)

    output = ""
    for line in resp.iter_lines(decode_unicode=True):
        if line.strip() == "" or not line.startswith("data: "):
            continue
        line = line[len("data: ") :]
        if line.strip() == "[DONE]":
            break
        chunk = json.loads(line)
        delta = chunk.get("choices", [{}])[0].get("delta", {})
        output += delta.get("content", "")

    print("Stream输出:", output)
    assert "相对论" in output or len(output) > 0


def test_system_prompt_effect():
    data = {
        "stream": False,
        "messages": [
            {"role": "system", "content": "请用一句话回答"},
            {"role": "user", "content": "什么是人工智能？"},
        ],
        "max_tokens": 30,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload).json()
    content = resp["choices"][0]["message"]["content"]
    print("内容输出:", content)
    assert len(content) < 50


def test_logprobs_enabled():
    data = {
        "stream": False,
        "logprobs": True,
        "top_logprobs": 5,
        "messages": [{"role": "user", "content": "非洲的首都是？"}],
        "max_tokens": 3,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload).json()
    logprob_data = resp["choices"][0].get("logprobs")
    print("LogProbs:", logprob_data)
    assert logprob_data is not None
    content_logprobs = logprob_data.get("content", [])
    assert isinstance(content_logprobs, list)
    assert all("token" in item for item in content_logprobs)


def test_stop_sequence():
    data = {
        "stream": False,
        "stop": ["然后"],
        "messages": [
            {"role": "user", "content": "请输出：这是第一段。然后这是第二段。"},
        ],
        "max_tokens": 20,
        "top_p": 0,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload).json()
    content = resp["choices"][0]["message"]["content"]
    print("截断输出:", content)
    assert "第二段" not in content


def test_sampling_parameters():
    data = {
        "stream": False,
        "temperature": 0,
        "top_p": 0,
        "messages": [
            {"role": "user", "content": "1+1=？,直接回答答案"},
        ],
        "max_tokens": 50,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload).json()
    answer = resp["choices"][0]["message"]["content"]
    print("Sampling输出:", answer)
    assert any(ans in answer for ans in ["2", "二"])


def test_multi_turn_conversation():
    data = {
        "stream": False,
        "messages": [
            {"role": "user", "content": "牛顿是谁？"},
            {"role": "assistant", "content": "牛顿是一位物理学家。"},
            {"role": "user", "content": "他提出了什么理论？"},
        ],
        "max_tokens": 30,
    }
    payload = build_request_payload(TEMPLATE, data)
    resp = send_request(URL, payload).json()
    content = resp["choices"][0]["message"]["content"]
    print("多轮记忆:", content)
    assert "三大运动定律" in content or "万有引力" in content


def test_bad_words_filtering():
    banned_tokens = ["你", "我", "年龄"]

    data = {
        "stream": False,
        "messages": [
            {"role": "system", "content": "你是一个助手，回答简洁清楚"},
            {"role": "user", "content": "请输出我的年龄是10岁"},
        ],
        "top_p": 0,
        "max_tokens": 69,
        "bad_words": banned_tokens,
    }

    payload = build_request_payload(TEMPLATE, data)
    response = send_request(URL, payload).json()

    content = response["choices"][0]["message"]["content"]
    print("生成内容:", content)

    for word in banned_tokens:
        assert word not in content, f"bad_word '{word}' 不应出现在生成结果中"

    print("test_bad_words_filtering 通过：生成结果未包含被禁词")