To build a GLM4 model, can ChatZhipuAI specify endpoint_url instead of using api_key?

[RyanOvO]

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Example Code

like this

endpoint_url = "http://127.0.0.1:8000/v1/chat/completions"

messages = [
    AIMessage(content="我将从美国到中国来旅游，出行前希望了解中国的城市"),
    AIMessage(content="欢迎问我任何问题。"),
]

llm = ChatGLM3(
    endpoint_url=endpoint_url,
    max_tokens=80000,
    prefix_messages=messages,
    top_p=0.9,
)

Description

None

System Info

None

[dosubot[bot]]

Hey @RyanOvO! I'm here to help you with any questions or issues you have. Let's tackle this together!

Yes, it is possible to configure ChatZhipuAI to build a GLM4 model using an endpoint_url instead of an api_key. You can achieve this by setting the api_base parameter during the instantiation of the ChatZhipuAI class.

Here is an example of how you can do this:

from langchain_community.chat_models import ChatZhipuAI

zhipuai_chat = ChatZhipuAI(
    temperature=0.5,
    api_base="http://127.0.0.1:8000/v1/chat/completions",
    model="glm-4",
    # other params...
)

messages = [
    ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
    ("human", "我喜欢编程。"),
]

response = zhipuai_chat.invoke(messages)
print(response)

In this example, replace "http://127.0.0.1:8000/v1/chat/completions" with the actual endpoint URL you want to use. The api_base parameter is used to specify the base URL for API requests ^[1].

To continue talking to Dosu, mention @dosu.

[RyanOvO]

it's not worked.
i try like this:

from langchain_community.chat_models import ChatZhipuAI
# app/server.py
from fastapi import FastAPI
from LLM import ChatGLM4_LLM
from langchain.prompts import ChatPromptTemplate
from langserve import add_routes

app = FastAPI()

model = ChatZhipuAI(
    temperature=0.5,
    api_base="http://127.0.0.1:5555/v1/chat/completions",
    model="glm-4",
    top_p = 0.95,
    top_k = 50,
    do_sample = True,
    max_length = 2500
)

text = '''

以下是患者输入的信息：

{topic}
'''

prompt = ChatPromptTemplate.from_template(text)
chain = prompt | model

add_routes(app, chain, path="/glm")

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=6006)

but it's error，like:

how i can solution?

[dosubot[bot]]

To resolve the pydantic.ValidationError indicating that the zhipuai_api_key is missing and successfully use the endpoint_url without requiring an api_key, you need to modify the code to make the zhipuai_api_key optional and handle the case where it is not provided.

Here is a modified version of the relevant parts of the code to achieve this:

1. Update the ChatZhipuAI class to make zhipuai_api_key optional and handle the absence of the API key:

from typing import Optional, List, Any, Dict
from pydantic import Field, root_validator
from langchain_community.chat_models.base import BaseChatModel
from langchain_community.chat_models.messages import BaseMessage, ChatResult, ChatGenerationChunk, AIMessageChunk
from langchain_community.chat_models.utils import _truncate_params, _get_jwt_token, _convert_delta_to_message_chunk, generate_from_stream
from langchain_community.callbacks import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun
import json
import httpx
from httpx_sse import aconnect_sse

class ChatZhipuAI(BaseChatModel):
    """ZhipuAI chat model integration."""

    zhipuai_api_key: Optional[str] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `ZHIPUAI_API_KEY` if not provided."""
    zhipuai_api_base: Optional[str] = Field(default=None, alias="api_base")
    """Base URL path for API requests, leave blank if not using a proxy or service emulator."""

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        stream: Optional[bool] = None,
        **kwargs: Any,
    ) -> ChatResult:
        """Generate a chat response."""
        should_stream = stream if stream is not None else self.streaming
        if should_stream:
            stream_iter = self._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return generate_from_stream(stream_iter)

        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {
            **params,
            **kwargs,
            "messages": message_dicts,
            "stream": False,
        }
        _truncate_params(payload)
        headers = {
            "Accept": "application/json",
        }
        if self.zhipuai_api_key:
            headers["Authorization"] = _get_jwt_token(self.zhipuai_api_key)

        async with httpx.AsyncClient(headers=headers, timeout=60) as client:
            response = await client.post(self.zhipuai_api_base, json=payload)  # type: ignore[arg-type]
            response.raise_for_status()
        return self._create_chat_result(response.json())

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        if self.zhipuai_api_base is None:
            raise ValueError("Did not find zhipu_api_base.")
        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {**params, **kwargs, "messages": message_dicts, "stream": True}
        _truncate_params(payload)
        headers = {
            "Accept": "application/json",
        }
        if self.zhipuai_api_key:
            headers["Authorization"] = _get_jwt_token(self.zhipuai_api_key)

        default_chunk_class = AIMessageChunk

        async with httpx.AsyncClient(headers=headers, timeout=60) as client:
            async with aconnect_sse(
                client, "POST", self.zhipuai_api_base, json=payload
            ) as event_source:
                async for sse in event_source.aiter_sse():
                    chunk = json.loads(sse.data)
                    if len(chunk["choices"]) == 0:
                        continue
                    choice = chunk["choices"][0]
                    chunk = _convert_delta_to_message_chunk(
                        choice["delta"], default_chunk_class
                    )
                    finish_reason = choice.get("finish_reason", None)

                    generation_info = (
                        {"finish_reason": finish_reason}
                        if finish_reason is not None
                        else None
                    )
                    chunk = ChatGenerationChunk(
                        message=chunk, generation_info=generation_info
                    )
                    yield chunk
                    if run_manager:
                        await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
                    if finish_reason is not None:
                        break

Additionally, ensure that the environment variables are correctly set and validated, similar to how it is done in the ZhipuAIEmbeddings class from the LangChain library:

@root_validator(pre=True)
def validate_environment(cls, values: Dict) -> Dict:
    """Validate that auth token exists in environment."""
    values["api_key"] = get_from_dict_or_env(values, "api_key", "ZHIPUAI_API_KEY")
    try:
        from zhipuai import ZhipuAI

        values["client"] = ZhipuAI(api_key=values["api_key"])
    except ImportError:
        raise ImportError(
            "Could not import zhipuai python package."
            "Please install it with `pip install zhipuai`."
        )
    return values

By making these changes, the zhipuai_api_key becomes optional, and the code will handle cases where the API key is not provided, allowing you to use the endpoint_url without requiring an api_key ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[RyanOvO]

ok，Will you consider supporting this feature in the official code?

[dosubot[bot]]

To support the feature of using an endpoint_url without requiring an api_key in the official code, it would require modifying the codebase to make the zhipuai_api_key optional while ensuring backward compatibility and maintaining security. Currently, there are no existing configurations or environment variables in the LangChain repository that allow for this setup ^[1].

If you would like to propose this feature, you can consider submitting a feature request to the LangChain repository. This would allow the maintainers to review and potentially integrate this functionality into the official codebase.

@eyurtsev, could you step in and provide your guidance on this matter?

To continue talking to Dosu, mention @dosu.

[Jimmy-L99]

@RyanOvO 请问还在基于GLM4做东西吗？貌似你也是在本地部署GLM，能交流下么