Class LLMMessage

langroid/language_models/base.py:272–361

Class representing an entry in the msg-history sent to the LLM API. It could be one of these: - a user message - an LLM ("Assistant") response - a fn-call or tool-call-list from an OpenAI-compatible LLM API response - a result or results from executing a fn or tool-call(s)

class LLMMessage(BaseModel):
    """
    Class representing an entry in the msg-history sent to the LLM API.
    It could be one of these:
    - a user message
    - an LLM ("Assistant") response
    - a fn-call or tool-call-list from an OpenAI-compatible LLM API response
    - a result or results from executing a fn or tool-call(s)
    """

    role: Role
    name: Optional[str] = None
    tool_call_id: Optional[str] = None  # which OpenAI LLM tool this is a response to
    tool_id: str = ""  # used by OpenAIAssistant
    content: str
    files: List[FileAttachment] = []
    function_call: Optional[LLMFunctionCall] = None
    tool_calls: Optional[List[OpenAIToolCall]] = None
    timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
    # link to corresponding chat document, for provenance/rewind purposes
    chat_document_id: str = ""

    def api_dict(self, model: str, has_system_role: bool = True) -> Dict[str, Any]:
        """
        Convert to dictionary for API request, keeping ONLY
        the fields that are expected in an API call!
        E.g., DROP the tool_id, since it is only for use in the Assistant API,
            not the completion API.

        Args:
            has_system_role: whether the message has a system role (if not,
                set to "user" role)
        Returns:
            dict: dictionary representation of LLM message
        """
        d = self.model_dump()
        files: List[FileAttachment] = d.pop("files")
        if len(files) > 0 and self.role == Role.USER:
            # In there are files, then content is an array of
            # different content-parts
            d["content"] = [
                dict(
                    type="text",
                    text=self.content,
                )
            ] + [f.to_dict(model) for f in self.files]

        # if there is a key k = "role" with value "system", change to "user"
        # in case has_system_role is False
        if not has_system_role and "role" in d and d["role"] == "system":
            d["role"] = "user"
            if "content" in d:
                d["content"] = "[ADDITIONAL SYSTEM MESSAGE:]\n\n" + d["content"]
        # drop None values since API doesn't accept them
        dict_no_none = {k: v for k, v in d.items() if v is not None}
        if "name" in dict_no_none and dict_no_none["name"] == "":
            # OpenAI API does not like empty name
            del dict_no_none["name"]