mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
|
import logging
|
||
|
|
import time
|
||
|
|
from typing import Any, List, Optional
|
||
|
|
|
||
|
|
import requests
|
||
|
|
from langchain_core.embeddings import Embeddings
|
||
|
|
from langchain_core.pydantic_v1 import BaseModel, Extra
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class OVHCloudEmbeddings(BaseModel, Embeddings):
|
||
|
|
"""
|
||
|
|
Usage:
|
||
|
|
OVH_AI_ENDPOINTS_ACCESS_TOKEN="your-token" python3 langchain_embedding.py
|
||
|
|
NB: Make sure you are using a valid token.
|
||
|
|
In the contrary, document indexing will be long due to rate-limiting.
|
||
|
|
"""
|
||
|
|
|
||
|
|
""" OVHcloud AI Endpoints Access Token"""
|
||
|
|
access_token: Optional[str] = None
|
||
|
|
|
||
|
|
""" OVHcloud AI Endpoints model name for embeddings generation"""
|
||
|
|
model_name: str = ""
|
||
|
|
|
||
|
|
""" OVHcloud AI Endpoints region"""
|
||
|
|
region: str = "kepler"
|
||
|
|
|
||
|
|
class Config:
|
||
|
|
"""Configuration for this pydantic object."""
|
||
|
|
|
||
|
|
extra = Extra.forbid
|
||
|
|
|
||
|
|
def __init__(self, **kwargs: Any):
|
||
|
|
super().__init__(**kwargs)
|
||
|
|
if self.access_token is None:
|
||
|
|
logger.warning(
|
||
|
|
"No access token provided indexing will be slow due to rate limiting."
|
||
|
|
)
|
||
|
|
if self.model_name == "":
|
||
|
|
raise ValueError("Model name is required for OVHCloud embeddings.")
|
||
|
|
if self.region == "":
|
||
|
|
raise ValueError("Region is required for OVHCloud embeddings.")
|
||
|
|
|
||
|
|
def _generate_embedding(self, text: str) -> List[float]:
|
||
|
|
"""Generate embeddings from OVHCLOUD AIE.
|
||
|
|
Args:
|
||
|
|
text (str): The text to embed.
|
||
|
|
Returns:
|
||
|
|
List[float]: Embeddings for the text.
|
||
|
|
"""
|
||
|
|
headers = {
|
||
|
|
"content-type": "text/plain",
|
||
|
|
"Authorization": f"Bearer {self.access_token}",
|
||
|
|
}
|
||
|
|
|
||
|
|
session = requests.session()
|
||
|
|
while True:
|
||
|
|
response = session.post(
|
||
|
|
f"https://{self.model_name}.endpoints.{self.region}.ai.cloud.ovh.net/api/text2vec",
|
||
|
|
headers=headers,
|
||
|
|
data=text,
|
||
|
|
)
|
||
|
|
if response.status_code != 200:
|
||
|
|
if response.status_code == 429:
|
||
|
|
"""Rate limit exceeded, wait for reset"""
|
||
|
|
reset_time = int(response.headers.get("RateLimit-Reset", 0))
|
||
|
|
logger.info("Rate limit exceeded. Waiting %d seconds.", reset_time)
|
||
|
|
if reset_time > 0:
|
||
|
|
time.sleep(reset_time)
|
||
|
|
continue
|
||
|
|
else:
|
||
|
|
"""Rate limit reset time has passed, retry immediately"""
|
||
|
|
continue
|
||
|
|
|
||
|
|
""" Handle other non-200 status codes """
|
||
|
|
raise ValueError(
|
||
|
|
"Request failed with status code: {status_code}, {text}".format(
|
||
|
|
status_code=response.status_code, text=response.text
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return response.json()
|
||
|
|
|
||
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||
|
|
"""Create a retry decorator for PremAIEmbeddings.
|
||
|
|
Args:
|
||
|
|
texts (List[str]): The list of texts to embed.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
List[List[float]]: List of embeddings, one for each input text.
|
||
|
|
"""
|
||
|
|
return [self._generate_embedding(text) for text in texts]
|
||
|
|
|
||
|
|
def embed_query(self, text: str) -> List[float]:
|
||
|
|
"""Embed a single query text.
|
||
|
|
Args:
|
||
|
|
text (str): The text to embed.
|
||
|
|
Returns:
|
||
|
|
List[float]: Embeddings for the text.
|
||
|
|
"""
|
||
|
|
return self._generate_embedding(text)
|