python调用大模型api来进行对话

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pip install openai

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OPENAI_API_KEY=sk-xxxxx

OPENAI_BASE_URL=https://api.openai.com/v1

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importos

fromflaskimportFlask, jsonify

fromopenaiimportOpenAI

config=configparser.ConfigParser()

config.read("config.cfg", encoding="utf-8")

OPENAI_API_KEY=config.get("default","OPENAI_API_KEY", fallback=None)

OPENAI_BASE_URL=config.get("default","OPENAI_BASE_URL", fallback=None)

@app.route("/gpt_test")

defgpt_test():

"""

简单调用一次 GPT，返回一个固定问题的回答

"""

ifnotOPENAI_API_KEY:

returnjsonify({"error":"OPENAI_API_KEY 未配置"}),500

try:

# 这里用的是 chat.completions.create 风格

resp=client.chat.completions.create(

model="gpt-4.1-mini",# 或者你有的任意模型，比如 gpt-4.1, gpt-4o 等

messages=[

{"role":"system","content":"你是一个简洁回答的助手。"},

{"role":"user","content":"简单用一句话介绍一下你自己。"},

],

)

answer=resp.choices[0].message.content

returnjsonify({"answer": answer})

exceptException as e:

print("GPT 调用异常:",repr(e))

returnjsonify({"error":str(e)}),500

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pip install dashscope

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ALIYUN_API_KEY=config.get("default","ALIYUN_API_KEY", fallback=None)

@app.route("/llm_test/")

defllm_test():

"""测试与大模型的对话功能"""

try:

messages=[

{'role':'system','content':'You are a helpful assistant.'},

{'role':'user','content':'你是谁？'}

]

answer=chat_with_model(messages)

returnjsonify({"answer": answer})

exceptException as e:

print("LLM error:",repr(e))

returnjsonify({"error":str(e)}),500

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frompathlibimportPath

# run.py 所在目录

BASE_DIR=Path(__file__).resolve().parent

PROMPT_DIR=BASE_DIR/"app"/"prompt_store"

PROMPT_LATEST_FILE=PROMPT_DIR/"doc-llm-latest.md"

defload_latest_prompt()->str|None:

"""读取 doc-llm-latest.md 的内容"""

try:

with PROMPT_LATEST_FILE.open("r", encoding="utf-8") as f:

returnf.read()

exceptFileNotFoundError:

print(f"[WARN] Prompt file not found: {PROMPT_LATEST_FILE}")

returnNone

exceptException as e:

print(f"[ERROR] Failed to read prompt: {e!r}")

returnNone

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@app.route("/llm_with_prompt/")

defllm_with_prompt():

"""使用最新的Prompt与大模型对话"""

prompt=load_latest_prompt()

ifnotprompt:

returnjsonify({"error":"No prompt available"}),500

try:

messages=[

{

'role':'system',

'content': prompt

},

{

'role':'user',

'content':"请用一两句话，概括一下这个文档测试规范的核心目标。"

}

]

answer=chat_with_model(messages)

returnjsonify({"answer": answer})

exceptException as e:

print("LLM with prompt error:",repr(e))

returnjsonify({"error":str(e)}),500