ベクトル埋め込み技術

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("BAAI/bge-m3")

sentences = [
    "That is a happy person",
    "That is a happy dog",
    "That is a very happy person",
    "Today is a sunny day"
]
embeddings = model.encode(sentences)
print(embeddings)
# [[-0.01178016  0.00884024 -0.05844684 ...  0.00750248 -0.04790139
#   0.00330675]
# [-0.03470375 -0.00886354 -0.05242309 ...  0.00899352 -0.02396279
#   0.02985837]
# [-0.01356584  0.01900942 -0.05800966 ...  0.00523864 -0.05689549
#   0.00077098]
# [-0.02149693  0.02998871 -0.05638731 ...  0.01443702 -0.02131325
#  -0.00112451]]
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# torch.Size([4, 4])

from transformers import AutoTokenizer, AutoModel
import torch

# モデルとトークナイザーのロード
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-m3")
model = AutoModel.from_pretrained("BAAI/bge-m3")

# 入力の準備
texts = ["これはサンプルテキストです"]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

# 埋め込みの生成
with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state[:, 0]  # [CLS]トークンの出力を使用
    print(embeddings)
    # tensor([[-1.4136,  0.7477, -0.9914,  ...,  0.0937, -0.0362, -0.1650]])
print(embeddings.shape)
# torch.Size([1, 1024])

import requests
from typing import List

class RemoteEmbedding():
    def __init__(
        self,
        base_url: str,
        api_key: str,
        model: str,
        dimensions: int = 1024,
        **kwargs,
    ):
        self._base_url = base_url
        self._api_key = api_key
        self._model = model
        self._dimensions = dimensions

    """
        OpenAI compatible embedding API. Tongyi, Baichuan, Doubao, etc.
    """

    def embed_documents(
        self,
        texts: List[str],
    ) -> List[List[float]]:
        """Embed search docs.

        Args:
            texts: List of text to embed.

        Returns:
            List of embeddings.
        """
        res = requests.post(
            f"{self._base_url}",
            headers={"Authorization": f"Bearer {self._api_key}"},
            json={
                "input": texts,
                "model": self._model,
                "encoding_format": "float",
                "dimensions": self._dimensions,
            },
        )
        data = res.json()
        embeddings = []
        try:
            for d in data["data"]:
                embeddings.append(d["embedding"][: self._dimensions])
            return embeddings
        except Exception as e:
            print(data)
            print("Error", e)
            raise e

    def embed_query(self, text: str, **kwargs) -> List[float]:
        """Embed query text.

        Args:
            text: Text to embed.

        Returns:
            Embedding.
        """
        return self.embed_documents([text])[0]

embedding = RemoteEmbedding(
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings", # https://bailian.console.aliyun.com/#/model-market/detail/text-embedding-v3?tabKey=sdk を参照
  api_key="your-api-key", # あなたのAPI Keyを入力してください
  model="text-embedding-v3",
)

print("Embedding result:", embedding.embed_query("今天天气不错"), "\n")
# Embedding result: [-0.03573227673768997, 0.0645645260810852, ...]
print("Embedding results:", embedding.embed_documents(["今天天气不错", "明天呢?"]), "\n")
# Embedding results: [[-0.03573227673768997, 0.0645645260810852, ...], [-0.05443647876381874, 0.07368793338537216, ...]]

import dashscope
from dashscope import TextEmbedding

# API Keyの設定
dashscope.api_key = "your-api-key"

# 入力テキストの準備
texts = ["これは最初の文です", "これは2番目の文です"]

# 埋め込みサービスの呼び出し
response = TextEmbedding.call(
    model="text-embedding-v3",
    input=texts
)

# 埋め込み結果の取得
if response.status_code == 200:
    print(response.output['embeddings'])
# [{"embedding": [-0.03193652629852295, 0.08152323216199875, ...]}, {"embedding": [...]}]

from PIL import Image
from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# 入力画像の準備
image = Image.open("path_to_your_image.jpg")
texts = ["これは最初の文です", "これは2番目の文です"]

# 埋め込みサービスの呼び出し
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)

# 埋め込み結果の取得
if outputs.status_code == 200:
    print(outputs.output['embeddings'])
# [{"embedding": [-0.03193652629852295, 0.08152323216199875, ...]}, {"embedding": [...]}]