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from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_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: str
n_threads: Optional[int] = None
device: Optional[str] = "cpu"
gpt4all_kwargs: Optional[dict] = {}
client: Any #: :meta private:
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that GPT4All library is installed."""
try:
from gpt4all import Embed4All
values["client"] = Embed4All(
model_name=values["model_name"],
n_threads=values.get("n_threads"),
device=values.get("device"),
**values.get("gpt4all_kwargs"),
)
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]
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