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

docs/modules/models/llms/integrations/gpt4all.ipynb

@@ -27,7 +27,7 @@
                 }
             ],
             "source": [
-                "%pip install pygpt4all > /dev/null"
+                "%pip install gpt4all > /dev/null"
             ]
         },
         {
@@ -64,7 +64,7 @@
             "source": [
                 "### Specify Model\n",
                 "\n",
-                "To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pygpt4all\n",
+                "To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/gpt4all\n",
                 "\n",
                 "For full installation instructions go [here](https://gpt4all.io/index.html).\n",
                 "\n",
@@ -102,7 +102,7 @@
                 "\n",
                 "# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
                 "\n",
-                "# # Example model. Check https://github.com/nomic-ai/pygpt4all for the latest models.\n",
+                "# # Example model. Check https://github.com/nomic-ai/gpt4all for the latest models.\n",
                 "# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'\n",
                 "\n",
                 "# # send a GET request to the URL to download the file. Stream since it's large\n",
@@ -126,7 +126,8 @@
                 "callbacks = [StreamingStdOutCallbackHandler()]\n",
                 "# Verbose is required to pass to the callback manager\n",
                 "llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)\n",
-                "# If you want to use GPT4ALL_J model add the backend parameter\n",
+                "# If you want to use a custom model add the backend parameter\n",
+                "# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\n",
                 "llm = GPT4All(model=local_path, backend='gptj', callbacks=callbacks, verbose=True)"
             ]
         },

langchain/llms/gpt4all.py

@@ -12,7 +12,7 @@ from langchain.llms.utils import enforce_stop_tokens
 class GPT4All(LLM):
     r"""Wrapper around GPT4All language models.
 
-    To use, you should have the ``pygpt4all`` python package installed, the
+    To use, you should have the ``gpt4all`` python package installed, the
     pre-trained model file, and the model's config information.
 
     Example:
@@ -28,7 +28,7 @@ class GPT4All(LLM):
     model: str
     """Path to the pre-trained GPT4All model file."""
 
-    backend: str = Field("llama", alias="backend")
+    backend: Optional[str] = Field(None, alias="backend")
 
     n_ctx: int = Field(512, alias="n_ctx")
     """Token context window."""
@@ -88,6 +88,10 @@ class GPT4All(LLM):
     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."""
+
     client: Any = None  #: :meta private:
 
     class Config:
@@ -95,86 +99,55 @@ class GPT4All(LLM):
 
         extra = Extra.forbid
 
-    def _llama_default_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
+    @staticmethod
+    def _model_param_names() -> Set[str]:
         return {
-            "n_predict": self.n_predict,
-            "n_threads": self.n_threads,
-            "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,
+            "n_ctx",
+            "n_predict",
+            "top_k",
+            "top_p",
+            "temp",
+            "n_batch",
+            "repeat_penalty",
+            "repeat_last_n",
+            "context_erase",
         }
 
-    def _gptj_default_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
+    def _default_params(self) -> Dict[str, Any]:
         return {
+            "n_ctx": self.n_ctx,
             "n_predict": self.n_predict,
-            "n_threads": self.n_threads,
             "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,
         }
 
-    @staticmethod
-    def _llama_param_names() -> Set[str]:
-        """Get the identifying parameters."""
-        return {
-            "seed",
-            "n_ctx",
-            "n_parts",
-            "f16_kv",
-            "logits_all",
-            "vocab_only",
-            "use_mlock",
-            "embedding",
-        }
-
-    @staticmethod
-    def _gptj_param_names() -> Set[str]:
-        """Get the identifying parameters."""
-        return set()
-
-    @staticmethod
-    def _model_param_names(backend: str) -> Set[str]:
-        if backend == "llama":
-            return GPT4All._llama_param_names()
-        else:
-            return GPT4All._gptj_param_names()
-
-    def _default_params(self) -> Dict[str, Any]:
-        if self.backend == "llama":
-            return self._llama_default_params()
-        else:
-            return self._gptj_default_params()
-
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that the python package exists in the environment."""
         try:
-            backend = values["backend"]
-            if backend == "llama":
-                from pygpt4all import GPT4All as GPT4AllModel
-            elif backend == "gptj":
-                from pygpt4all import GPT4All_J as GPT4AllModel
-            else:
-                raise ValueError(f"Incorrect gpt4all backend {cls.backend}")
-
-            model_kwargs = {
-                k: v
-                for k, v in values.items()
-                if k in GPT4All._model_param_names(backend)
-            }
+            from gpt4all import GPT4All as GPT4AllModel
+
+            full_path = values["model"]
+            model_path, delimiter, model_name = full_path.rpartition("/")
+            model_path += delimiter
+
             values["client"] = GPT4AllModel(
-                model_path=values["model"],
-                **model_kwargs,
+                model_name=model_name,
+                model_path=model_path or None,
+                model_type=values["backend"],
+                allow_download=False,
             )
+            values["backend"] = values["client"].model.model_type
 
         except ImportError:
             raise ValueError(
-                "Could not import pygpt4all python package. "
-                "Please install it with `pip install pygpt4all`."
+                "Could not import gpt4all python package. "
+                "Please install it with `pip install gpt4all`."
             )
         return values
 
@@ -185,9 +158,7 @@ class GPT4All(LLM):
             "model": self.model,
             **self._default_params(),
             **{
-                k: v
-                for k, v in self.__dict__.items()
-                if k in self._model_param_names(self.backend)
+                k: v for k, v in self.__dict__.items() if k in self._model_param_names()
             },
         }