68f663f2f4
主要变更: - 移除Hermes智能体及相关回调服务 - 新增知识库RAG、同步、调度、规范化和索引任务服务 - 重构orchestrator服务，增强运行时聊天功能 - 更新前端聊天、政策制度、设置等页面样式和逻辑 - 更新expense_claims和document_intelligence服务 - 删除llm_wiki相关服务和测试文件 - 更新docker-compose配置和启动脚本
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
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, max_token_size=8192, model_name="nvidia_embedding_model"
|
|
)
|
|
@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])
|