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https://github.com/hwchase17/langchain.git
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Update GPT4ALL integration (#4567)
# Update GPT4ALL integration GPT4ALL have completely changed their bindings. They use a bit odd implementation that doesn't fit well into base.py and it will probably be changed again, so it's a temporary solution. Fixes #3839, #4628
This commit is contained in:
Alexey Nominas
GitHub
parent
e2d7677526
commit
c9e2a01875
@ -27,7 +27,7 @@
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}
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],
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"source": [
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"%pip install pygpt4all > /dev/null"
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"%pip install gpt4all > /dev/null"
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]
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},
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{
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@ -64,7 +64,7 @@
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"source": [
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"### Specify Model\n",
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"\n",
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"To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pygpt4all\n",
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"To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/gpt4all\n",
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"\n",
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"For full installation instructions go [here](https://gpt4all.io/index.html).\n",
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"\n",
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@ -102,7 +102,7 @@
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"\n",
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"# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"# # Example model. Check https://github.com/nomic-ai/pygpt4all for the latest models.\n",
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"# # Example model. Check https://github.com/nomic-ai/gpt4all for the latest models.\n",
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"# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'\n",
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"\n",
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"# # send a GET request to the URL to download the file. Stream since it's large\n",
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@ -126,7 +126,8 @@
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"callbacks = [StreamingStdOutCallbackHandler()]\n",
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"# Verbose is required to pass to the callback manager\n",
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"llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)\n",
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"# If you want to use GPT4ALL_J model add the backend parameter\n",
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"# If you want to use a custom model add the backend parameter\n",
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"# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\n",
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"llm = GPT4All(model=local_path, backend='gptj', callbacks=callbacks, verbose=True)"
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]
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},
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@ -12,7 +12,7 @@ from langchain.llms.utils import enforce_stop_tokens
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class GPT4All(LLM):
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r"""Wrapper around GPT4All language models.
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To use, you should have the ``pygpt4all`` python package installed, the
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To use, you should have the ``gpt4all`` python package installed, the
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pre-trained model file, and the model's config information.
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Example:
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@ -28,7 +28,7 @@ class GPT4All(LLM):
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model: str
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"""Path to the pre-trained GPT4All model file."""
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backend: str = Field("llama", alias="backend")
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backend: Optional[str] = Field(None, alias="backend")
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n_ctx: int = Field(512, alias="n_ctx")
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"""Token context window."""
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@ -88,6 +88,10 @@ class GPT4All(LLM):
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streaming: bool = False
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"""Whether to stream the results or not."""
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context_erase: float = 0.5
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"""Leave (n_ctx * context_erase) tokens
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starting from beginning if the context has run out."""
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client: Any = None #: :meta private:
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class Config:
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@ -95,86 +99,55 @@ class GPT4All(LLM):
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extra = Extra.forbid
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def _llama_default_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"n_predict": self.n_predict,
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"n_threads": self.n_threads,
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"repeat_last_n": self.repeat_last_n,
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"repeat_penalty": self.repeat_penalty,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temp": self.temp,
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}
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def _gptj_default_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"n_predict": self.n_predict,
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"n_threads": self.n_threads,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temp": self.temp,
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}
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@staticmethod
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def _llama_param_names() -> Set[str]:
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"""Get the identifying parameters."""
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def _model_param_names() -> Set[str]:
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return {
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"seed",
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"n_ctx",
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"n_parts",
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"f16_kv",
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"logits_all",
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"vocab_only",
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"use_mlock",
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"embedding",
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"n_predict",
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"top_k",
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"top_p",
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"temp",
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"n_batch",
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"repeat_penalty",
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"repeat_last_n",
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"context_erase",
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}
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@staticmethod
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def _gptj_param_names() -> Set[str]:
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"""Get the identifying parameters."""
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return set()
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@staticmethod
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def _model_param_names(backend: str) -> Set[str]:
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if backend == "llama":
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return GPT4All._llama_param_names()
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else:
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return GPT4All._gptj_param_names()
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def _default_params(self) -> Dict[str, Any]:
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if self.backend == "llama":
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return self._llama_default_params()
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else:
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return self._gptj_default_params()
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return {
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"n_ctx": self.n_ctx,
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"n_predict": self.n_predict,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temp": self.temp,
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"n_batch": self.n_batch,
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"repeat_penalty": self.repeat_penalty,
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"repeat_last_n": self.repeat_last_n,
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"context_erase": self.context_erase,
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}
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that the python package exists in the environment."""
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try:
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backend = values["backend"]
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if backend == "llama":
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from pygpt4all import GPT4All as GPT4AllModel
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elif backend == "gptj":
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from pygpt4all import GPT4All_J as GPT4AllModel
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else:
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raise ValueError(f"Incorrect gpt4all backend {cls.backend}")
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from gpt4all import GPT4All as GPT4AllModel
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full_path = values["model"]
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model_path, delimiter, model_name = full_path.rpartition("/")
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model_path += delimiter
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model_kwargs = {
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k: v
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for k, v in values.items()
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if k in GPT4All._model_param_names(backend)
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}
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values["client"] = GPT4AllModel(
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model_path=values["model"],
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**model_kwargs,
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model_name=model_name,
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model_path=model_path or None,
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model_type=values["backend"],
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allow_download=False,
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)
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values["backend"] = values["client"].model.model_type
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except ImportError:
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raise ValueError(
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"Could not import pygpt4all python package. "
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"Please install it with `pip install pygpt4all`."
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"Could not import gpt4all python package. "
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"Please install it with `pip install gpt4all`."
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)
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return values
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@ -185,9 +158,7 @@ class GPT4All(LLM):
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"model": self.model,
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**self._default_params(),
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**{
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k: v
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for k, v in self.__dict__.items()
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if k in self._model_param_names(self.backend)
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k: v for k, v in self.__dict__.items() if k in self._model_param_names()
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},
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}
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