File size: 2,998 Bytes
5e4510c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | """
Adapted from SakanaAI/ShinkaEvolve (Apache-2.0 License)
Original source: https://github.com/SakanaAI/ShinkaEvolve/blob/main/shinka/llm/embedding.py
"""
import os
import openai
from typing import Union, List
import logging
logger = logging.getLogger(__name__)
M = 1_000_000
OPENAI_EMBEDDING_MODELS = [
"text-embedding-3-small",
"text-embedding-3-large",
]
AZURE_EMBEDDING_MODELS = [
"azure-text-embedding-3-small",
"azure-text-embedding-3-large",
]
OPENAI_EMBEDDING_COSTS = {
"text-embedding-3-small": 0.02 / M,
"text-embedding-3-large": 0.13 / M,
}
class EmbeddingClient:
def __init__(self, model_name: str = "text-embedding-3-small"):
"""
Initialize the EmbeddingClient.
Args:
model (str): The OpenAI embedding model name to use.
"""
self.client, self.model = self._get_client_model(model_name)
def _get_client_model(self, model_name: str) -> tuple[openai.OpenAI, str]:
if model_name in OPENAI_EMBEDDING_MODELS:
# Use OPENAI_EMBEDDING_API_KEY if set, otherwise fall back to OPENAI_API_KEY
# This allows users to use OpenRouter for LLMs while using OpenAI for embeddings
embedding_api_key = os.getenv("OPENAI_EMBEDDING_API_KEY") or os.getenv("OPENAI_API_KEY")
client = openai.OpenAI(api_key=embedding_api_key)
model_to_use = model_name
elif model_name in AZURE_EMBEDDING_MODELS:
# get rid of the azure- prefix
model_to_use = model_name.split("azure-")[-1]
client = openai.AzureOpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version=os.getenv("AZURE_API_VERSION"),
azure_endpoint=os.getenv("AZURE_API_ENDPOINT"),
)
else:
raise ValueError(f"Invalid embedding model: {model_name}")
return client, model_to_use
def get_embedding(self, code: Union[str, List[str]]) -> Union[List[float], List[List[float]]]:
"""
Computes the text embedding for a code string.
Args:
code (str, list[str]): The code as a string or list
of strings.
Returns:
list: Embedding vector for the code or None if an error
occurs.
"""
if isinstance(code, str):
code = [code]
single_code = True
else:
single_code = False
try:
response = self.client.embeddings.create(
model=self.model, input=code, encoding_format="float"
)
# Extract embedding from response
if single_code:
return response.data[0].embedding
else:
return [d.embedding for d in response.data]
except Exception as e:
logger.info(f"Error getting embedding: {e}")
if single_code:
return [], 0.0
else:
return [[]], 0.0
|