mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-22 19:09:57 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import logging
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import time
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from typing import Any, List
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import requests
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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logger = logging.getLogger(__name__)
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class OVHCloudEmbeddings(BaseModel, Embeddings):
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"""
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OVHcloud AI Endpoints Embeddings.
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"""
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""" OVHcloud AI Endpoints Access Token"""
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access_token: str = ""
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""" OVHcloud AI Endpoints model name for embeddings generation"""
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model_name: str = ""
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""" OVHcloud AI Endpoints region"""
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region: str = "kepler"
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class Config:
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extra = "forbid"
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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if self.access_token == "":
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raise ValueError("Access token is required for OVHCloud embeddings.")
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if self.model_name == "":
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raise ValueError("Model name is required for OVHCloud embeddings.")
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if self.region == "":
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raise ValueError("Region is required for OVHCloud embeddings.")
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def _generate_embedding(self, text: str) -> List[float]:
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"""Generate embeddings from OVHCLOUD AIE.
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Args:
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text (str): The text to embed.
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Returns:
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List[float]: Embeddings for the text.
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"""
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headers = {
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"content-type": "text/plain",
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"Authorization": f"Bearer {self.access_token}",
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}
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session = requests.session()
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while True:
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response = session.post(
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f"https://{self.model_name}.endpoints.{self.region}.ai.cloud.ovh.net/api/text2vec",
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headers=headers,
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data=text,
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)
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if response.status_code != 200:
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if response.status_code == 429:
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"""Rate limit exceeded, wait for reset"""
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reset_time = int(response.headers.get("RateLimit-Reset", 0))
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logger.info("Rate limit exceeded. Waiting %d seconds.", reset_time)
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if reset_time > 0:
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time.sleep(reset_time)
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continue
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else:
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"""Rate limit reset time has passed, retry immediately"""
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continue
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if response.status_code == 401:
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""" Unauthorized, retry with new token """
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raise ValueError("Unauthorized, retry with new token")
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""" Handle other non-200 status codes """
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raise ValueError(
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"Request failed with status code: {status_code}, {text}".format(
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status_code=response.status_code, text=response.text
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)
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)
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return response.json()
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Create a retry decorator for PremAIEmbeddings.
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Args:
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texts (List[str]): The list of texts to embed.
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Returns:
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List[List[float]]: List of embeddings, one for each input text.
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"""
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return [self._generate_embedding(text) for text in texts]
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def embed_query(self, text: str) -> List[float]:
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"""Embed a single query text.
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Args:
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text (str): The text to embed.
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Returns:
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List[float]: Embeddings for the text.
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"""
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return self._generate_embedding(text)
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