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https://github.com/hwchase17/langchain.git
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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
75 lines
2.2 KiB
Python
75 lines
2.2 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, 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_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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