Update dependencies. Remove custom gpt4all_j wrapper.
This commit is contained in:
Iván Martínez
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92244a90b4
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bdd8c8748b
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gpt4all_j.py
160
gpt4all_j.py
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"""Wrapper for the GPT4All-J model."""
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from functools import partial
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from typing import Any, Dict, List, Mapping, Optional, Set
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from pydantic import Extra, Field, root_validator
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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class GPT4All_J(LLM):
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r"""Wrapper around GPT4All-J language models.
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To use, you should have the ``pygpt4all`` 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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.. code-block:: python
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from langchain.llms import GPT4All_J
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model = GPT4All_J(model="./models/gpt4all-model.bin")
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# Simplest invocation
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response = model("Once upon a time, ")
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"""
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model: str
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"""Path to the pre-trained GPT4All model file."""
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n_threads: Optional[int] = Field(4, alias="n_threads")
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"""Number of threads to use."""
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n_predict: Optional[int] = 256
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"""The maximum number of tokens to generate."""
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temp: Optional[float] = 0.8
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"""The temperature to use for sampling."""
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top_p: Optional[float] = 0.95
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"""The top-p value to use for sampling."""
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top_k: Optional[int] = 40
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"""The top-k value to use for sampling."""
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echo: Optional[bool] = False
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"""Whether to echo the prompt."""
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stop: Optional[List[str]] = []
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"""A list of strings to stop generation when encountered."""
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repeat_last_n: Optional[int] = 64
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"Last n tokens to penalize"
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repeat_penalty: Optional[float] = 1.3
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"""The penalty to apply to repeated tokens."""
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n_batch: int = Field(1, alias="n_batch")
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"""Batch size for prompt processing."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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client: Any = None #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {
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"seed": self.seed,
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"n_predict": self.n_predict,
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"n_threads": self.n_threads,
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"n_batch": self.n_batch,
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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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@staticmethod
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def _llama_param_names() -> Set[str]:
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"""Get the identifying parameters."""
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return {}
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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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from pygpt4all.models.gpt4all_j import GPT4All_J as GPT4AllModel
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llama_keys = cls._llama_param_names()
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model_kwargs = {k: v for k, v in values.items() if k in llama_keys}
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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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)
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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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)
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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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 GPT4All_J._llama_param_names()
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},
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}
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@property
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def _llm_type(self) -> str:
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"""Return the type of llm."""
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return "gpt4all"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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) -> str:
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r"""Call out to GPT4All's generate method.
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Args:
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prompt: The prompt to pass into the model.
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stop: A list of strings to stop generation when encountered.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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prompt = "Once upon a time, "
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response = model(prompt, n_predict=55)
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"""
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if run_manager:
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text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose)
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text = self.client.generate(
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prompt,
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new_text_callback=text_callback
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)
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else:
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text = self.client.generate(prompt)
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if stop is not None:
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text = enforce_stop_tokens(text, stop)
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return text
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@ -1,8 +1,8 @@
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from gpt4all_j import GPT4All_J
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from langchain.chains import RetrievalQA
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from langchain.embeddings import LlamaCppEmbeddings
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain.vectorstores import Chroma
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from langchain.llms import GPT4All
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def main():
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# Load stored vectorstore
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@ -12,14 +12,28 @@ def main():
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retriever = db.as_retriever()
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# Prepare the LLM
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callbacks = [StreamingStdOutCallbackHandler()]
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llm = GPT4All_J(model='./models/ggml-gpt4all-j-v1.3-groovy.bin', callbacks=callbacks, verbose=False)
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
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llm = GPT4All(model='./models/ggml-gpt4all-j-v1.3-groovy.bin', backend='gptj', callbacks=callbacks, verbose=False)
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qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
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# Interactive questions and answers
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while True:
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query = input("Enter a query: ")
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query = input("\nEnter a query: ")
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if query == "exit":
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break
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qa.run(query)
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# Get the answer from the chain
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res = qa(query)
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answer, docs = res['result'], res['source_documents']
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# Print the result
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print("\n\n> Question:")
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print(query)
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print("\n> Answer:")
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print(answer)
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# Print the relevant sources used for the answer
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for document in docs:
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print("\n> " + document.metadata["source"] + ":")
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print(document.page_content)
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if __name__ == "__main__":
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main()
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@ -1,5 +1,4 @@
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langchain==0.0.154
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pygptj==1.0.10
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pygpt4all==1.0.1
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chromadb==0.3.21
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llama-cpp-python==0.1.41
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langchain==0.0.162
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pygpt4all==1.1.0
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chromadb==0.3.22
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llama-cpp-python==0.1.47
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