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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
274 lines
8.5 KiB
Python
274 lines
8.5 KiB
Python
#!/bin/env python3
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# -*- coding: utf-8 -*-
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# @author DDDivano
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# encoding=utf-8 vi:ts=4:sw=4:expandtab:ft=python
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"""
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some basic check for fd web api
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"""
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import json
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from core import TEMPLATE, URL, build_request_payload, get_token_list, send_request
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def test_stream_response():
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data = {
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"stream": True,
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"messages": [
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{"role": "system", "content": "你是一个知识渊博的 AI 助手"},
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{"role": "user", "content": "讲讲爱因斯坦的相对论"},
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],
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"max_tokens": 10,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload, stream=True)
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output = ""
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for line in resp.iter_lines(decode_unicode=True):
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if line.strip() == "" or not line.startswith("data: "):
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continue
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line = line[len("data: ") :]
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if line.strip() == "[DONE]":
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break
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chunk = json.loads(line)
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delta = chunk.get("choices", [{}])[0].get("delta", {})
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output += delta.get("content", "")
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print("Stream输出:", output)
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assert "相对论" in output or len(output) > 0
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def test_system_prompt_effect():
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data = {
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"stream": False,
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"messages": [
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{"role": "system", "content": "请用一句话回答"},
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{"role": "user", "content": "什么是人工智能？"},
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],
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"max_tokens": 30,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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content = resp["choices"][0]["message"]["content"]
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print("内容输出:", content)
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assert len(content) < 50
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def test_logprobs_enabled():
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data = {
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"stream": False,
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"logprobs": True,
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"top_logprobs": 5,
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"messages": [{"role": "user", "content": "非洲的首都是？"}],
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"max_tokens": 3,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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logprob_data = resp["choices"][0].get("logprobs")
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print("LogProbs:", logprob_data)
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assert logprob_data is not None
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content_logprobs = logprob_data.get("content", [])
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assert isinstance(content_logprobs, list)
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assert all("token" in item for item in content_logprobs)
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def test_stop_sequence():
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data = {
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"stream": False,
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"stop": ["。"],
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"messages": [
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{
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"role": "user",
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"content": "你要严格按照我接下来的话输出，输出冒号后面的内容，请输出：这是第一段。果冻这是第二段啦啦啦啦啦。",
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},
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],
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"max_tokens": 20,
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"top_p": 0,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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content = resp["choices"][0]["message"]["content"]
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token_list = get_token_list(resp)
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print("截断输出:", content)
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assert "第二段" not in content
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assert "第二段" not in token_list
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assert "。" in token_list, "没有找到。符号"
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def test_stop_sequence1():
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"""
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不加stop看看是否有影响
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"""
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data = {
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"stream": False,
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"messages": [
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{
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"role": "user",
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"content": "你要严格按照我接下来的话输出，输出冒号后面的内容，请输出：这是第一段。果冻这是第二段啦啦啦啦啦。",
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},
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],
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"max_tokens": 20,
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"top_p": 0,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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content = resp["choices"][0]["message"]["content"]
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print("截断输出:", content)
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assert "第二段" in content
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def test_stop_sequence2():
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"""
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stop token长度测试
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"""
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data = {
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"stream": False,
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"stop": ["这是第二段啦啦"],
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"messages": [
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{
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"role": "user",
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"content": "你要严格按照我接下来的话输出，输出冒号后面的内容，请输出：这是第一段。果冻这是第二段啦啦啦啦啦。",
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},
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],
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"max_tokens": 50,
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"top_p": 0,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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content = resp["choices"][0]["message"]["content"]
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# token_list = get_token_list(resp)
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print("截断输出:", content)
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assert "啦啦啦" not in content
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# def test_stop_sequence3():
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# """
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# stop token 数量测试
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# """
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# data = {
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# "stream": False,
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# "stop": ["。", "果冻", "果", "冻", "第二", "二"],
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# "messages": [
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# {
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# "role": "user",
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# "content": "你要严格按照我接下来的话输出，输出冒号后面的内容，请输出：这是第一段。果冻这是第二段啦啦啦啦啦。",
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# },
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# ],
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# "max_tokens": 50,
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# "top_p": 0,
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# }
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# payload = build_request_payload(TEMPLATE, data)
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# resp = send_request(URL, payload).json()
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# content = resp["choices"][0]["message"]["content"]
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# print("截断输出:", content)
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# assert "啦啦啦" not in content
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def test_sampling_parameters():
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data = {
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"stream": False,
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"temperature": 0,
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"top_p": 0,
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"messages": [
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{"role": "user", "content": "1+1=？,直接回答答案"},
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],
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"max_tokens": 50,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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answer = resp["choices"][0]["message"]["content"]
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print("Sampling输出:", answer)
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assert any(ans in answer for ans in ["2", "二"])
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def test_multi_turn_conversation():
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data = {
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"stream": False,
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"messages": [
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{"role": "user", "content": "牛顿是谁？"},
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{"role": "assistant", "content": "牛顿是一位物理学家。"},
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{"role": "user", "content": "他提出了什么理论？"},
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],
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"max_tokens": 30,
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}
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payload = build_request_payload(TEMPLATE, data)
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resp = send_request(URL, payload).json()
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content = resp["choices"][0]["message"]["content"]
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print("多轮记忆:", content)
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assert "三大运动定律" in content or "万有引力" in content
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def test_bad_words_filtering():
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banned_tokens = ["香蕉"]
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data = {
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"stream": False,
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"messages": [
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{"role": "system", "content": "你是一个助手，回答简洁清楚"},
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{"role": "user", "content": "请输出冒号后面的字: 我爱吃果冻，和苹果，香蕉，和荔枝"},
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],
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"top_p": 0,
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"max_tokens": 69,
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"bad_words": banned_tokens,
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}
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payload = build_request_payload(TEMPLATE, data)
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response = send_request(URL, payload).json()
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content = response["choices"][0]["message"]["content"]
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print("生成内容:", content)
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token_list = get_token_list(response)
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for word in banned_tokens:
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assert word not in token_list, f"bad_word '{word}' 不应出现在生成结果中"
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print("test_bad_words_filtering 正例验证通过")
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def test_bad_words_filtering1():
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banned_tokens = ["和", "呀"]
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data = {
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"stream": False,
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"messages": [
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{"role": "system", "content": "你是一个助手，回答简洁清楚"},
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{"role": "user", "content": "请输出冒号后面的字: 我爱吃果冻，和苹果，香蕉，和荔枝"},
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],
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"top_p": 0,
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"max_tokens": 69,
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"bad_words": banned_tokens,
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}
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payload = build_request_payload(TEMPLATE, data)
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response = send_request(URL, payload).json()
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content = response["choices"][0]["message"]["content"]
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print("生成内容:", content)
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for word in banned_tokens:
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assert word not in content, f"bad_word '{word}' 不应出现在生成结果中"
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print("test_bad_words_filtering1 通过：生成结果未包含被禁词")
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# 正例验证
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word = "呀"
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data = {
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"stream": False,
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"messages": [
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{"role": "system", "content": "你是一个助手，回答简洁清楚"},
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{"role": "user", "content": "请输出冒号后面的字，一模一样: 我爱吃果冻，苹果，香蕉，和荔枝呀呀呀"},
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],
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"top_p": 0,
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"max_tokens": 69,
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}
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payload = build_request_payload(TEMPLATE, data)
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response = send_request(URL, payload).json()
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content = response["choices"][0]["message"]["content"]
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print("生成内容:", content)
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token_list = get_token_list(response)
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assert word in token_list, f"'{word}' 应出现在生成结果中"
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print("test_bad_words_filtering1 正例验证通过")
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