File size: 1,702 Bytes
167596f | 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 | import sys
import os
if sys.version_info < (3, 9):
pass
else:
pass
import pipmaster as pm # Pipmaster for dynamic library install
# install specific modules
if not pm.is_installed("openai"):
pm.install("openai")
from openai import (
AsyncOpenAI,
APIConnectionError,
RateLimitError,
APITimeoutError,
)
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from lightrag.utils import (
wrap_embedding_func_with_attrs,
)
import numpy as np
@wrap_embedding_func_with_attrs(embedding_dim=2048)
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=60),
retry=retry_if_exception_type(
(RateLimitError, APIConnectionError, APITimeoutError)
),
)
async def nvidia_openai_embed(
texts: list[str],
model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1",
# refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
base_url: str = "https://integrate.api.nvidia.com/v1",
api_key: str = None,
input_type: str = "passage", # query for retrieval, passage for embedding
trunc: str = "NONE", # NONE or START or END
encode: str = "float", # float or base64
) -> np.ndarray:
if api_key:
os.environ["OPENAI_API_KEY"] = api_key
openai_async_client = (
AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
)
response = await openai_async_client.embeddings.create(
model=model,
input=texts,
encoding_format=encode,
extra_body={"input_type": input_type, "truncate": trunc},
)
return np.array([dp.embedding for dp in response.data])
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