mirror of
https://github.com/hwchase17/langchain.git
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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
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel, ConfigDict, model_validator
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class GPT4AllEmbeddings(BaseModel, Embeddings):
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"""GPT4All embedding models.
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To use, you should have the gpt4all python package installed
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Example:
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.. code-block:: python
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from langchain_community.embeddings import GPT4AllEmbeddings
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model_name = "all-MiniLM-L6-v2.gguf2.f16.gguf"
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gpt4all_kwargs = {'allow_download': 'True'}
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embeddings = GPT4AllEmbeddings(
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model_name=model_name,
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gpt4all_kwargs=gpt4all_kwargs
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)
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"""
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model_name: Optional[str] = None
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n_threads: Optional[int] = None
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device: Optional[str] = "cpu"
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gpt4all_kwargs: Optional[dict] = {}
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client: Any #: :meta private:
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model_config = ConfigDict(protected_namespaces=())
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@model_validator(mode="before")
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@classmethod
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def validate_environment(cls, values: Dict) -> Any:
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"""Validate that GPT4All library is installed."""
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try:
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from gpt4all import Embed4All
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values["client"] = Embed4All(
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model_name=values.get("model_name"),
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n_threads=values.get("n_threads"),
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device=values.get("device"),
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**(values.get("gpt4all_kwargs") or {}),
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)
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except ImportError:
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raise ImportError(
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"Could not import gpt4all library. "
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"Please install the gpt4all library to "
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"use this embedding model: pip install gpt4all"
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)
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using GPT4All.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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embeddings = [self.client.embed(text) for text in texts]
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return [list(map(float, e)) for e in embeddings]
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using GPT4All.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self.embed_documents([text])[0]
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