pinecone[major]: Update to pydantic v2

This commit is contained in:
Bagatur
2024-09-04 09:50:39 -07:00
parent 2888e34f53
commit b27bfa6717

View File

@@ -1,16 +1,19 @@
import logging import logging
from typing import Dict, Iterable, List, Optional from typing import Any, Dict, Iterable, List, Optional
import aiohttp import aiohttp
from langchain_core.embeddings import Embeddings from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
BaseModel,
Field,
SecretStr,
root_validator,
)
from langchain_core.utils import secret_from_env from langchain_core.utils import secret_from_env
from pinecone import Pinecone as PineconeClient # type: ignore from pinecone import Pinecone as PineconeClient # type: ignore[import-untyped]
from pydantic import (
BaseModel,
ConfigDict,
Field,
PrivateAttr,
SecretStr,
model_validator,
)
from typing_extensions import Self
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -29,8 +32,8 @@ class PineconeEmbeddings(BaseModel, Embeddings):
""" """
# Clients # Clients
_client: PineconeClient = Field(default=None, exclude=True) _client: PineconeClient = PrivateAttr(default=None)
_async_client: aiohttp.ClientSession = Field(default=None, exclude=True) _async_client: aiohttp.ClientSession = PrivateAttr(default=None)
model: str model: str
"""Model to use for example 'multilingual-e5-large'.""" """Model to use for example 'multilingual-e5-large'."""
# Config # Config
@@ -44,7 +47,7 @@ class PineconeEmbeddings(BaseModel, Embeddings):
dimension: Optional[int] = None dimension: Optional[int] = None
# #
show_progress_bar: bool = False show_progress_bar: bool = False
pinecone_api_key: Optional[SecretStr] = Field( pinecone_api_key: SecretStr = Field(
default_factory=secret_from_env( default_factory=secret_from_env(
"PINECONE_API_KEY", "PINECONE_API_KEY",
error_message="Pinecone API key not found. Please set the PINECONE_API_KEY " error_message="Pinecone API key not found. Please set the PINECONE_API_KEY "
@@ -56,12 +59,14 @@ class PineconeEmbeddings(BaseModel, Embeddings):
If not provided, will look for the PINECONE_API_KEY environment variable.""" If not provided, will look for the PINECONE_API_KEY environment variable."""
class Config: model_config = ConfigDict(
extra = "forbid" extra="forbid",
allow_population_by_field_name = True populate_by_name=True,
)
@root_validator(pre=True) @model_validator(mode="before")
def set_default_config(cls, values: dict) -> dict: @classmethod
def set_default_config(cls, values: dict) -> Any:
"""Set default configuration based on model.""" """Set default configuration based on model."""
default_config_map = { default_config_map = {
"multilingual-e5-large": { "multilingual-e5-large": {
@@ -79,23 +84,23 @@ class PineconeEmbeddings(BaseModel, Embeddings):
values[key] = value values[key] = value
return values return values
@root_validator(pre=False, skip_on_failure=True) @model_validator(mode="after")
def validate_environment(cls, values: dict) -> dict: def validate_environment(self) -> Self:
"""Validate that Pinecone version and credentials exist in environment.""" """Validate that Pinecone version and credentials exist in environment."""
api_key_str = values["pinecone_api_key"].get_secret_value() api_key_str = self.pinecone_api_key.get_secret_value()
client = PineconeClient(api_key=api_key_str, source_tag="langchain") client = PineconeClient(api_key=api_key_str, source_tag="langchain")
values["_client"] = client self._client = client
# initialize async client # initialize async client
if not values.get("_async_client"): if not (self._async_client or None):
values["_async_client"] = aiohttp.ClientSession( self._async_client = aiohttp.ClientSession(
headers={ headers={
"Api-Key": api_key_str, "Api-Key": api_key_str,
"Content-Type": "application/json", "Content-Type": "application/json",
"X-Pinecone-API-Version": "2024-07", "X-Pinecone-API-Version": "2024-07",
} }
) )
return values return self
def _get_batch_iterator(self, texts: List[str]) -> Iterable: def _get_batch_iterator(self, texts: List[str]) -> Iterable:
if self.batch_size is None: if self.batch_size is None: