pptagent/llms.py

import base64
import re
import threading
from dataclasses import dataclass
from typing import Optional, Union

import torch
from oaib import Auto
from openai import AsyncOpenAI, OpenAI
from openai.types.chat import ChatCompletion

from pptagent.utils import get_json_from_response, get_logger, tenacity_decorator

logger = get_logger(__name__)


@dataclass
class LLM:
    """
    A wrapper class to interact with a language model.
    """

    model: str
    base_url: Optional[str] = None
    api_key: Optional[str] = None
    timeout: int = 360

    def __post_init__(self):
        self.client = OpenAI(
            base_url=self.base_url, api_key=self.api_key, timeout=self.timeout
        )

    @tenacity_decorator
    def __call__(
        self,
        content: str,
        images: Optional[Union[str, list[str]]] = None,
        system_message: Optional[str] = None,
        history: Optional[list] = None,
        return_json: bool = False,
        return_message: bool = False,
        **client_kwargs,
    ) -> Union[str, dict, list, tuple]:
        """
        Call the language model with a prompt and optional images.

        Args:
            content (str): The prompt content.
            images (str or list[str]): An image file path or list of image file paths.
            system_message (str): The system message.
            history (list): The conversation history.
            return_json (bool): Whether to return the response as JSON.
            return_message (bool): Whether to return the message.
            **client_kwargs: Additional keyword arguments to pass to the client.

        Returns:
            Union[str, Dict, List, Tuple]: The response from the model.
        """
        if history is None:
            history = []
        system, message = self.format_message(content, images, system_message)
        try:
            completion = self.client.chat.completions.create(
                model=self.model, messages=system + history + message, **client_kwargs
            )
        except Exception as e:
            logger.warning("Error in LLM call: %s", e)
            raise e
        response = completion.choices[0].message.content
        message.append({"role": "assistant", "content": response})
        return self.__post_process__(response, message, return_json, return_message)

    def __post_process__(
        self,
        response: str,
        message: list,
        return_json: bool = False,
        return_message: bool = False,
    ) -> Union[str, dict, tuple]:
        """
        Process the response based on return options.

        Args:
            response (str): The raw response from the model.
            message (List): The message history.
            return_json (bool): Whether to return the response as JSON.
            return_message (bool): Whether to return the message.

        Returns:
            Union[str, Dict, Tuple]: Processed response.
        """
        response = response.strip()
        if return_json:
            response = get_json_from_response(response)
        if return_message:
            response = (response, message)
        return response

    def __repr__(self) -> str:
        repr_str = f"{self.__class__.__name__}(model={self.model}"
        if self.base_url is not None:
            repr_str += f", base_url={self.base_url}"
        return repr_str + ")"

    def test_connection(self) -> bool:
        """
        Test the connection to the LLM.

        Returns:
            bool: True if connection is successful, False otherwise.
        """
        try:
            self.client.models.list()
            return True
        except Exception as e:
            logger.warning(
                "Connection test failed: %s\nLLM: %s: %s, %s",
                e,
                self.model,
                self.base_url,
                self.api_key,
            )
            return False

    def format_message(
        self,
        content: str,
        images: Optional[Union[str, list[str]]] = None,
        system_message: Optional[str] = None,
    ) -> tuple[list, list]:
        """
        Format messages for OpenAI server call.

        Args:
            content (str): The prompt content.
            images (str or list[str]): An image file path or list of image file paths.
            system_message (str): The system message.

        Returns:
            Tuple[List, List]: Formatted system and user messages.
        """
        if isinstance(images, str):
            images = [images]
        if system_message is None:
            if content.startswith("You are"):
                system_message, content = content.split("\n", 1)
            else:
                system_message = "You are a helpful assistant"
        system = [
            {
                "role": "system",
                "content": [{"type": "text", "text": system_message}],
            }
        ]
        message = [{"role": "user", "content": [{"type": "text", "text": content}]}]
        if images is not None:
            for image in images:
                try:
                    with open(image, "rb") as f:
                        message[0]["content"].append(
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": f"data:image/jpeg;base64,{base64.b64encode(f.read()).decode('utf-8')}"
                                },
                            }
                        )
                except Exception as e:
                    logger.error("Failed to load image %s: %s", image, e)
        return system, message

    def gen_image(self, prompt: str, n: int = 1, **kwargs) -> str:
        """
        Generate an image from a prompt.
        """
        return (
            self.client.images.generate(model=self.model, prompt=prompt, n=n, **kwargs)
            .data[0]
            .b64_json
        )

    def get_embedding(
        self,
        text: str,
        encoding_format: str = "float",
        to_tensor: bool = True,
        **kwargs,
    ) -> torch.Tensor | list[float]:
        """
        Get the embedding of a text.
        """
        result = self.client.embeddings.create(
            model=self.model, input=text, encoding_format=encoding_format, **kwargs
        )
        embeddings = [embedding.embedding for embedding in result.data]
        if to_tensor:
            embeddings = torch.tensor(embeddings)
        return embeddings

    def to_async(self) -> "AsyncLLM":
        """
        Convert the LLM to an asynchronous LLM.
        """
        return AsyncLLM(
            model=self.model,
            base_url=self.base_url,
            api_key=self.api_key,
            timeout=self.timeout,
        )


@dataclass
class AsyncLLM(LLM):
    use_batch: bool = False
    """
    Asynchronous wrapper class for language model interaction.
    """

    def __post_init__(self):
        """
        Initialize the AsyncLLM.

        Args:
            model (str): The model name.
            base_url (str): The base URL for the API.
            api_key (str): API key for authentication. Defaults to environment variable.
        """
        self.client = AsyncOpenAI(
            base_url=self.base_url,
            api_key=self.api_key,
            timeout=self.timeout,
        )
        if threading.current_thread() == threading.main_thread():
            self.batch = Auto(
                base_url=self.base_url,
                api_key=self.api_key,
                timeout=self.timeout,
                loglevel=0,
            )
        else:
            logger.warning("Auto initialization skipped because it's not the main thread.")

    @tenacity_decorator
    async def __call__(
        self,
        content: str,
        images: Optional[Union[str, list[str]]] = None,
        system_message: Optional[str] = None,
        history: Optional[list] = None,
        return_json: bool = False,
        return_message: bool = False,
        **client_kwargs,
    ) -> Union[str, dict, tuple]:
        """
        Asynchronously call the language model with a prompt and optional images.

        Args:
            content (str): The prompt content.
            images (str or list[str]): An image file path or list of image file paths.
            system_message (str): The system message.
            history (list): The conversation history.
            return_json (bool): Whether to return the response as JSON.
            return_message (bool): Whether to return the message.
            **client_kwargs: Additional keyword arguments to pass to the client.

        Returns:
            Union[str, Dict, List, Tuple]: The response from the model.
        """
        if self.use_batch and threading.current_thread() is threading.main_thread():
            self.batch = Auto(
                base_url=self.base_url,
                api_key=self.api_key,
                timeout=self.timeout,
                loglevel=0,
            )
        elif self.use_batch:
            logger.warning(
                "Warning: AsyncLLM is not running in the main thread, may cause race condition."
            )
        if history is None:
            history = []
        system, message = self.format_message(content, images, system_message)
        try:
            if self.use_batch:
                await self.batch.add(
                    "chat.completions.create",
                    model=self.model,
                    messages=system + history + message,
                    **client_kwargs,
                )
                completion = await self.batch.run()
                if "result" not in completion or len(completion["result"]) != 1:
                    raise ValueError(
                        f"The length of completion result should be 1, but got {completion}.\nRace condition may have occurred if multiple values are returned.\nOr, there was an error in the LLM call, use the synchronous version to check."
                    )
                completion = ChatCompletion(**completion["result"][0])
            else:
                completion = await self.client.chat.completions.create(
                    model=self.model,
                    messages=system + history + message,
                    **client_kwargs,
                )

        except Exception as e:
            logger.warning("Error in AsyncLLM call: %s", e)
            raise e
        response = completion.choices[0].message.content
        message.append({"role": "assistant", "content": response})
        return self.__post_process__(response, message, return_json, return_message)

    def __getstate__(self):
        state = self.__dict__.copy()
        state["client"] = None
        state["batch"] = None
        return state

    def __setstate__(self, state: dict):
        self.__dict__.update(state)
        self.client = AsyncOpenAI(
            base_url=self.base_url,
            api_key=self.api_key,
            timeout=self.timeout,
        )
        self.batch = Auto(
            base_url=self.base_url,
            api_key=self.api_key,
            timeout=self.timeout,
            loglevel=0,
        )

    async def test_connection(self) -> bool:
        """
        Test the connection to the LLM asynchronously.

        Returns:
            bool: True if connection is successful, False otherwise.
        """
        try:
            await self.client.models.list()
            return True
        except Exception as e:
            logger.warning(
                "Async connection test failed: %s\nLLM: %s: %s, %s",
                e,
                self.model,
                self.base_url,
                self.api_key,
            )
            return False

    async def gen_image(self, prompt: str, n: int = 1, **kwargs) -> str:
        """
        Generate an image from a prompt asynchronously.

        Args:
            prompt (str): The text prompt to generate an image from.
            n (int): Number of images to generate.
            **kwargs: Additional keyword arguments for image generation.

        Returns:
            str: Base64-encoded image data.
        """
        response = await self.client.images.generate(
            model=self.model, prompt=prompt, n=n, response_format="b64_json", **kwargs
        )
        return response.data[0].b64_json

    async def get_embedding(
        self,
        text: str,
        to_tensor: bool = True,
        **kwargs,
    ) -> torch.Tensor | list[float]:
        """
        Get the embedding of a text asynchronously.

        Args:
            text (str): The text to get embeddings for.
            **kwargs: Additional keyword arguments.

        Returns:
            List[float]: The embedding vector.
        """
        response = await self.client.embeddings.create(
            model=self.model,
            input=text,
            encoding_format="float",
            **kwargs,
        )
        embeddings = [embedding.embedding for embedding in response.data]
        if to_tensor:
            embeddings = torch.tensor(embeddings)
        return embeddings

    def to_sync(self) -> LLM:
        """
        Convert the AsyncLLM to a synchronous LLM.
        """
        return LLM(model=self.model, base_url=self.base_url, api_key=self.api_key)


def get_model_abbr(llms: Union[LLM, list[LLM]]) -> str:
    """
    Get abbreviated model names from LLM instances.

    Args:
        llms: A single LLM instance or a list of LLM instances.

    Returns:
        str: Abbreviated model names joined with '+'.
    """
    # Convert single LLM to list for consistent handling
    if isinstance(llms, LLM):
        llms = [llms]

    try:
        # Attempt to extract model names before version numbers
        return "+".join(re.search(r"^(.*?)-\d{2}", llm.model).group(1) for llm in llms)
    except Exception:
        # Fallback: return full model names if pattern matching fails
        return "+".join(llm.model for llm in llms)