mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-19 20:50:00 +08:00
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
from abc import ABC
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from openai import OpenAI
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from FlagEmbedding import FlagModel
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import torch
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import os
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import numpy as np
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class Base(ABC):
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def encode(self, texts: list, batch_size=32):
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raise NotImplementedError("Please implement encode method!")
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class HuEmbedding(Base):
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def __init__(self):
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"""
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If you have trouble downloading HuggingFace models, -_^ this might help!!
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For Linux:
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export HF_ENDPOINT=https://hf-mirror.com
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For Windows:
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Good luck
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^_-
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"""
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self.model = FlagModel("BAAI/bge-large-zh-v1.5",
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query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
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use_fp16=torch.cuda.is_available())
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def encode(self, texts: list, batch_size=32):
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res = []
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for i in range(0, len(texts), batch_size):
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res.extend(self.model.encode(texts[i:i + batch_size]).tolist())
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return np.array(res)
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class GptEmbed(Base):
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def __init__(self):
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self.client = OpenAI(api_key=os.envirement["OPENAI_API_KEY"])
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def encode(self, texts: list, batch_size=32):
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res = self.client.embeddings.create(input=texts,
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model="text-embedding-ada-002")
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return [d["embedding"] for d in res["data"]]
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class QWenEmbd(Base):
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def encode(self, texts: list, batch_size=32, text_type="document"):
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# export DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY
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import dashscope
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from http import HTTPStatus
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res = []
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for txt in texts:
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resp = dashscope.TextEmbedding.call(
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model=dashscope.TextEmbedding.Models.text_embedding_v2,
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input=txt[:2048],
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text_type=text_type
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)
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res.append(resp["output"]["embeddings"][0]["embedding"])
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return res
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