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venv/Lib/site-packages/langchain_community/embeddings/gpt4all.py

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+from typing import Any, Dict, List, Optional
+
+from langchain_core.embeddings import Embeddings
+from pydantic import BaseModel, ConfigDict, model_validator
+
+
+class GPT4AllEmbeddings(BaseModel, Embeddings):
+    """GPT4All embedding models.
+
+    To use, you should have the gpt4all python package installed
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.embeddings import GPT4AllEmbeddings
+
+            model_name = "all-MiniLM-L6-v2.gguf2.f16.gguf"
+            gpt4all_kwargs = {'allow_download': 'True'}
+            embeddings = GPT4AllEmbeddings(
+                model_name=model_name,
+                gpt4all_kwargs=gpt4all_kwargs
+            )
+    """
+
+    model_name: Optional[str] = None
+    n_threads: Optional[int] = None
+    device: Optional[str] = "cpu"
+    gpt4all_kwargs: Optional[dict] = {}
+    client: Any  #: :meta private:
+
+    model_config = ConfigDict(protected_namespaces=())
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_environment(cls, values: Dict) -> Any:
+        """Validate that GPT4All library is installed."""
+        try:
+            from gpt4all import Embed4All
+
+            values["client"] = Embed4All(
+                model_name=values.get("model_name"),
+                n_threads=values.get("n_threads"),
+                device=values.get("device"),
+                **(values.get("gpt4all_kwargs") or {}),
+            )
+        except ImportError:
+            raise ImportError(
+                "Could not import gpt4all library. "
+                "Please install the gpt4all library to "
+                "use this embedding model: pip install gpt4all"
+            )
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of documents using GPT4All.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+
+        embeddings = [self.client.embed(text) for text in texts]
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed a query using GPT4All.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents([text])[0]