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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from abc import ABC
import dashscope
from openai import OpenAI
from FlagEmbedding import FlagModel
import torch
import numpy as np
from rag.utils import num_tokens_from_string
flag_model = FlagModel("BAAI/bge-large-zh-v1.5",
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=torch.cuda.is_available())
class Base(ABC):
def __init__(self, key, model_name):
pass
def encode(self, texts: list, batch_size=32):
raise NotImplementedError("Please implement encode method!")
def encode_queries(self, text: str):
raise NotImplementedError("Please implement encode method!")
class HuEmbedding(Base):
def __init__(self, key="", model_name=""):
"""
If you have trouble downloading HuggingFace models, -_^ this might help!!
For Linux:
export HF_ENDPOINT=https://hf-mirror.com
For Windows:
Good luck
^_-
"""
self.model = flag_model
def encode(self, texts: list, batch_size=32):
token_count = 0
for t in texts: token_count += num_tokens_from_string(t)
res = []
for i in range(0, len(texts), batch_size):
res.extend(self.model.encode(texts[i:i + batch_size]).tolist())
return np.array(res), token_count
def encode_queries(self, text: str):
token_count = num_tokens_from_string(text)
return self.model.encode_queries([text]).tolist()[0], token_count
class OpenAIEmbed(Base):
def __init__(self, key, model_name="text-embedding-ada-002"):
self.client = OpenAI(api_key=key)
self.model_name = model_name
def encode(self, texts: list, batch_size=32):
res = self.client.embeddings.create(input=texts,
model=self.model_name)
return np.array([d.embedding for d in res.data]), res.usage.total_tokens
def encode_queries(self, text):
res = self.client.embeddings.create(input=[text],
model=self.model_name)
return np.array(res.data[0].embedding), res.usage.total_tokens
class QWenEmbed(Base):
def __init__(self, key, model_name="text_embedding_v2"):
dashscope.api_key = key
self.model_name = model_name
def encode(self, texts: list, batch_size=10):
import dashscope
res = []
token_count = 0
texts = [txt[:2048] for txt in texts]
for i in range(0, len(texts), batch_size):
resp = dashscope.TextEmbedding.call(
model=self.model_name,
input=texts[i:i+batch_size],
text_type="document"
)
embds = [[] for _ in range(len(resp["output"]["embeddings"]))]
for e in resp["output"]["embeddings"]:
embds[e["text_index"]] = e["embedding"]
res.extend(embds)
token_count += resp["usage"]["total_tokens"]
return np.array(res), token_count
def encode_queries(self, text):
resp = dashscope.TextEmbedding.call(
model=self.model_name,
input=text[:2048],
text_type="query"
)
return np.array(resp["output"]["embeddings"][0]["embedding"]), resp["usage"]["total_tokens"]
from zhipuai import ZhipuAI
class ZhipuEmbed(Base):
def __init__(self, key, model_name="embedding-2"):
self.client = ZhipuAI(api_key=key)
self.model_name = model_name
def encode(self, texts: list, batch_size=32):
res = self.client.embeddings.create(input=texts,
model=self.model_name)
return np.array([d.embedding for d in res.data]), res.usage.total_tokens
def encode_queries(self, text):
res = self.client.embeddings.create(input=text,
model=self.model_name)
return np.array(res["data"][0]["embedding"]), res.usage.total_tokens |