mirror of
https://github.com/hwchase17/langchain.git
synced 2025-10-31 07:41:40 +00:00
24605bcdb6
- [x] **PR message**:
- **Description:** Fixes warning messages raised due to missing
`protected_namespaces` parameter in `ConfigDict`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/27609
- **Dependencies:** No dependencies
- **Twitter handle:** @gawbul
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
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]
|