Source code for langchain.llms.gpt4all
"""Wrapper for the GPT4All model."""
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class GPT4All(LLM):
    r"""Wrapper around GPT4All language models.
    To use, you should have the ``gpt4all`` python package installed, the
    pre-trained model file, and the model's config information.
    Example:
        .. code-block:: python
            from langchain.llms import GPT4All
            model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
            # Simplest invocation
            response = model("Once upon a time, ")
    """
    model: str
    """Path to the pre-trained GPT4All model file."""
    backend: Optional[str] = Field(None, alias="backend")
    n_ctx: int = Field(512, alias="n_ctx")
    """Token context window."""
    n_parts: int = Field(-1, alias="n_parts")
    """Number of parts to split the model into. 
    If -1, the number of parts is automatically determined."""
    seed: int = Field(0, alias="seed")
    """Seed. If -1, a random seed is used."""
    f16_kv: bool = Field(False, alias="f16_kv")
    """Use half-precision for key/value cache."""
    logits_all: bool = Field(False, alias="logits_all")
    """Return logits for all tokens, not just the last token."""
    vocab_only: bool = Field(False, alias="vocab_only")
    """Only load the vocabulary, no weights."""
    use_mlock: bool = Field(False, alias="use_mlock")
    """Force system to keep model in RAM."""
    embedding: bool = Field(False, alias="embedding")
    """Use embedding mode only."""
    n_threads: Optional[int] = Field(4, alias="n_threads")
    """Number of threads to use."""
    n_predict: Optional[int] = 256
    """The maximum number of tokens to generate."""
    temp: Optional[float] = 0.8
    """The temperature to use for sampling."""
    top_p: Optional[float] = 0.95
    """The top-p value to use for sampling."""
    top_k: Optional[int] = 40
    """The top-k value to use for sampling."""
    echo: Optional[bool] = False
    """Whether to echo the prompt."""
    stop: Optional[List[str]] = []
    """A list of strings to stop generation when encountered."""
    repeat_last_n: Optional[int] = 64
    "Last n tokens to penalize"
    repeat_penalty: Optional[float] = 1.3
    """The penalty to apply to repeated tokens."""
    n_batch: int = Field(1, alias="n_batch")
    """Batch size for prompt processing."""
    streaming: bool = False
    """Whether to stream the results or not."""
    context_erase: float = 0.5
    """Leave (n_ctx * context_erase) tokens
    starting from beginning if the context has run out."""
    allow_download: bool = False
    """If model does not exist in ~/.cache/gpt4all/, download it."""
    client: Any = None  #: :meta private:
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @staticmethod
    def _model_param_names() -> Set[str]:
        return {
            "n_ctx",
            "n_predict",
            "top_k",
            "top_p",
            "temp",
            "n_batch",
            "repeat_penalty",
            "repeat_last_n",
            "context_erase",
        }
    def _default_params(self) -> Dict[str, Any]:
        return {
            "n_ctx": self.n_ctx,
            "n_predict": self.n_predict,
            "top_k": self.top_k,
            "top_p": self.top_p,
            "temp": self.temp,
            "n_batch": self.n_batch,
            "repeat_penalty": self.repeat_penalty,
            "repeat_last_n": self.repeat_last_n,
            "context_erase": self.context_erase,
        }
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that the python package exists in the environment."""
        try:
            from gpt4all import GPT4All as GPT4AllModel
        except ImportError:
            raise ImportError(
                "Could not import gpt4all python package. "
                "Please install it with `pip install gpt4all`."
            )
        full_path = values["model"]
        model_path, delimiter, model_name = full_path.rpartition("/")
        model_path += delimiter
        values["client"] = GPT4AllModel(
            model_name,
            model_path=model_path or None,
            model_type=values["backend"],
            allow_download=values["allow_download"],
        )
        if values["n_threads"] is not None:
            # set n_threads
            values["client"].model.set_thread_count(values["n_threads"])
        try:
            values["backend"] = values["client"].model_type
        except AttributeError:
            # The below is for compatibility with GPT4All Python bindings <= 0.2.3.
            values["backend"] = values["client"].model.model_type
        return values
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            "model": self.model,
            **self._default_params(),
            **{
                k: v for k, v in self.__dict__.items() if k in self._model_param_names()
            },
        }
    @property
    def _llm_type(self) -> str:
        """Return the type of llm."""
        return "gpt4all"
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        r"""Call out to GPT4All's generate method.
        Args:
            prompt: The prompt to pass into the model.
            stop: A list of strings to stop generation when encountered.
        Returns:
            The string generated by the model.
        Example:
            .. code-block:: python
                prompt = "Once upon a time, "
                response = model(prompt, n_predict=55)
        """
        text_callback = None
        if run_manager:
            text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose)
        text = ""
        params = {**self._default_params(), **kwargs}
        for token in self.client.generate(prompt, **params):
            if text_callback:
                text_callback(token)
            text += token
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text