Update dependencies. Remove custom gpt4all_j wrapper.

gpt4all_j.py

@@ -1,160 +0,0 @@
-"""Wrapper for the GPT4All-J 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
-
-
-class GPT4All_J(LLM):
-    r"""Wrapper around GPT4All-J language models.
-
-    To use, you should have the ``pygpt4all`` python package installed, the
-    pre-trained model file, and the model's config information.
-
-    Example:
-        .. code-block:: python
-
-            from langchain.llms import GPT4All_J
-            model = GPT4All_J(model="./models/gpt4all-model.bin")
-
-            # Simplest invocation
-            response = model("Once upon a time, ")
-    """
-
-    model: str
-    """Path to the pre-trained GPT4All model file."""
-
-    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."""
-
-    client: Any = None  #: :meta private:
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
-        return {
-            "seed": self.seed,
-            "n_predict": self.n_predict,
-            "n_threads": self.n_threads,
-            "n_batch": self.n_batch,
-            "repeat_last_n": self.repeat_last_n,
-            "repeat_penalty": self.repeat_penalty,
-            "top_k": self.top_k,
-            "top_p": self.top_p,
-            "temp": self.temp,
-        }
-
-    @staticmethod
-    def _llama_param_names() -> Set[str]:
-        """Get the identifying parameters."""
-        return {}
-
-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that the python package exists in the environment."""
-        try:
-            from pygpt4all.models.gpt4all_j import GPT4All_J as GPT4AllModel
-            
-            llama_keys = cls._llama_param_names()
-            model_kwargs = {k: v for k, v in values.items() if k in llama_keys}
-            values["client"] = GPT4AllModel(
-                model_path=values["model"],
-                **model_kwargs,
-            )
-
-        except ImportError:
-            raise ValueError(
-                "Could not import pygpt4all python package. "
-                "Please install it with `pip install pygpt4all`."
-            )
-        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 GPT4All_J._llama_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,
-    ) -> 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)
-        """
-        if run_manager:
-            text_callback = partial(run_manager.on_llm_new_token, verbose=self.verbose)
-            text = self.client.generate(
-                prompt,
-                new_text_callback=text_callback
-            )
-        else:
-            text = self.client.generate(prompt)
-        if stop is not None:
-            text = enforce_stop_tokens(text, stop)
-        return text

privateGPT.py

@@ -1,8 +1,8 @@
-from gpt4all_j import GPT4All_J
 from langchain.chains import RetrievalQA
 from langchain.embeddings import LlamaCppEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import Chroma
+from langchain.llms import GPT4All
 
 def main():        
     # Load stored vectorstore
@@ -12,14 +12,28 @@ def main():
     retriever = db.as_retriever()
     # Prepare the LLM
     callbacks = [StreamingStdOutCallbackHandler()]
-    llm = GPT4All_J(model='./models/ggml-gpt4all-j-v1.3-groovy.bin', callbacks=callbacks, verbose=False)
+    llm = GPT4All(model='./models/ggml-gpt4all-j-v1.3-groovy.bin', backend='gptj', callbacks=callbacks, verbose=False)
-    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
     # Interactive questions and answers
     while True:
-        query = input("Enter a query: ")
+        query = input("\nEnter a query: ")
         if query == "exit":
             break
-        qa.run(query)    
+        
+        # Get the answer from the chain
+        res = qa(query)    
+        answer, docs = res['result'], res['source_documents']
+
+        # Print the result
+        print("\n\n> Question:")
+        print(query)
+        print("\n> Answer:")
+        print(answer)
+        
+        # Print the relevant sources used for the answer
+        for document in docs:
+            print("\n> " + document.metadata["source"] + ":")
+            print(document.page_content)
 
 if __name__ == "__main__":
     main()

requirements.txt

@@ -1,5 +1,4 @@
-langchain==0.0.154
+langchain==0.0.162
-pygptj==1.0.10
+pygpt4all==1.1.0
-pygpt4all==1.0.1
+chromadb==0.3.22
-chromadb==0.3.21
+llama-cpp-python==0.1.47
-llama-cpp-python==0.1.41