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Endpoint Based

EndpointChatLLM

Bases: ChatLLM

A ChatLLM that uses an endpoint to generate responses. This expects an OpenAI API compatible endpoint.

Attributes:

Name Type Description
endpoint_url str

The url of a OpenAI API compatible endpoint.
Source code in libs\kotaemon\kotaemon\llms\chats\endpoint_based.py 
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class EndpointChatLLM(ChatLLM):
    """
    A ChatLLM that uses an endpoint to generate responses. This expects an OpenAI API
    compatible endpoint.

    Attributes:
        endpoint_url (str): The url of a OpenAI API compatible endpoint.
    """

    endpoint_url: str = Param(
        help="URL of the OpenAI API compatible endpoint", required=True
    )

    def run(
        self, messages: str | BaseMessage | list[BaseMessage], **kwargs
    ) -> LLMInterface:
        """
        Generate response from messages
        Args:
            messages (str | BaseMessage | list[BaseMessage]): history of messages to
                generate response from
            **kwargs: additional arguments to pass to the OpenAI API
        Returns:
            LLMInterface: generated response
        """
        if isinstance(messages, str):
            input_ = [HumanMessage(content=messages)]
        elif isinstance(messages, BaseMessage):
            input_ = [messages]
        else:
            input_ = messages

        def decide_role(message: BaseMessage):
            if isinstance(message, SystemMessage):
                return "system"
            elif isinstance(message, AIMessage):
                return "assistant"
            else:
                return "user"

        request_json = {
            "messages": [{"content": m.text, "role": decide_role(m)} for m in input_]
        }

        response = requests.post(self.endpoint_url, json=request_json).json()

        content = ""
        candidates = []
        if response["choices"]:
            candidates = [
                each["message"]["content"]
                for each in response["choices"]
                if each["message"]["content"]
            ]
            content = candidates[0]

        return LLMInterface(
            content=content,
            candidates=candidates,
            completion_tokens=response["usage"]["completion_tokens"],
            total_tokens=response["usage"]["total_tokens"],
            prompt_tokens=response["usage"]["prompt_tokens"],
        )

    def invoke(
        self, messages: str | BaseMessage | list[BaseMessage], **kwargs
    ) -> LLMInterface:
        """Same as run"""
        return self.run(messages, **kwargs)

    async def ainvoke(
        self, messages: str | BaseMessage | list[BaseMessage], **kwargs
    ) -> LLMInterface:
        return self.invoke(messages, **kwargs)

run 

1
run(messages, **kwargs)

Generate response from messages Args: messages (str | BaseMessage | list[BaseMessage]): history of messages to generate response from **kwargs: additional arguments to pass to the OpenAI API Returns: LLMInterface: generated response

Source code in libs\kotaemon\kotaemon\llms\chats\endpoint_based.py 
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def run(
    self, messages: str | BaseMessage | list[BaseMessage], **kwargs
) -> LLMInterface:
    """
    Generate response from messages
    Args:
        messages (str | BaseMessage | list[BaseMessage]): history of messages to
            generate response from
        **kwargs: additional arguments to pass to the OpenAI API
    Returns:
        LLMInterface: generated response
    """
    if isinstance(messages, str):
        input_ = [HumanMessage(content=messages)]
    elif isinstance(messages, BaseMessage):
        input_ = [messages]
    else:
        input_ = messages

    def decide_role(message: BaseMessage):
        if isinstance(message, SystemMessage):
            return "system"
        elif isinstance(message, AIMessage):
            return "assistant"
        else:
            return "user"

    request_json = {
        "messages": [{"content": m.text, "role": decide_role(m)} for m in input_]
    }

    response = requests.post(self.endpoint_url, json=request_json).json()

    content = ""
    candidates = []
    if response["choices"]:
        candidates = [
            each["message"]["content"]
            for each in response["choices"]
            if each["message"]["content"]
        ]
        content = candidates[0]

    return LLMInterface(
        content=content,
        candidates=candidates,
        completion_tokens=response["usage"]["completion_tokens"],
        total_tokens=response["usage"]["total_tokens"],
        prompt_tokens=response["usage"]["prompt_tokens"],
    )

invoke 

1
invoke(messages, **kwargs)

Same as run

Source code in libs\kotaemon\kotaemon\llms\chats\endpoint_based.py 
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def invoke(
    self, messages: str | BaseMessage | list[BaseMessage], **kwargs
) -> LLMInterface:
    """Same as run"""
    return self.run(messages, **kwargs)