Nomic

Nomic 模型是由 Nomic AI 开发的一系列先进的文本和图像嵌入解决方案，旨在将各种形式的数据转换为捕获其语义含义的密集数值向量。

Milvus 通过 NomicEmbeddingFunction 类与 Nomic 的嵌入模型集成。此类提供了使用 Nomic 嵌入模型对文档和查询进行编码的方法，并返回与 Milvus 索引兼容的密集向量嵌入。要使用此功能，请从 Nomic Atlas 获取 API 密钥。

要使用此功能，请安装必要的依赖项：

pip install --upgrade pymilvus
pip install "pymilvus[model]"

然后，实例化 NomicEmbeddingFunction：

# 在访问 Nomic Atlas API 之前，配置您的 Nomic API 令牌
import nomic
nomic.login('YOUR_NOMIC_API_KEY')

# 导入 Nomic 嵌入函数
from pymilvus.model.dense import NomicEmbeddingFunction

ef = NomicEmbeddingFunction(
    model_name="nomic-embed-text-v1.5", # 默认为 `mistral-embed`
)

参数：

• model_name (string)

  用于编码的 Nomic 嵌入模型名称。默认值为 nomic-embed-text-v1.5。有关更多信息，请参考 Nomic 官方文档。

要为文档创建嵌入向量，请使用 encode_documents() 方法：

docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]

docs_embeddings = ef.encode_documents(docs)

# 打印嵌入向量
print("Embeddings:", docs_embeddings)
# 打印嵌入向量的维度和形状
print("Dim:", ef.dim, docs_embeddings[0].shape)

预期输出类似于以下内容：

Embeddings: [array([ 5.59997560e-02, 7.23266600e-02, -1.51977540e-01, -4.53491200e-02,
        6.49414060e-02, 4.33654800e-02, 2.26593020e-02, -3.51867680e-02,
        3.49998470e-03, 1.75571440e-03, -4.30297850e-03, 1.81274410e-02,
        ...
       -1.64337160e-02, -3.85437000e-02, 6.14318850e-02, -2.82745360e-02,
       -7.25708000e-02, -4.15563580e-04, -7.63320900e-03, 1.88446040e-02,
       -5.78002930e-02, 1.69830320e-02, -8.91876200e-03, -2.37731930e-02])]
Dim: 768 (768,)

要为查询创建嵌入向量，请使用 encode_queries() 方法：

queries = ["When was artificial intelligence founded",
           "Where was Alan Turing born?"]

query_embeddings = ef.encode_queries(queries)

print("Embeddings:", query_embeddings)
print("Dim", ef.dim, query_embeddings[0].shape)

预期输出类似于以下内容：

Embeddings: [array([ 3.24096680e-02, 7.35473600e-02, -1.63940430e-01, -4.45556640e-02,
        7.83081050e-02, 2.64587400e-02, 1.35898590e-03, -1.59606930e-02,
       -3.33557130e-02, 1.05056760e-02, -2.35290530e-02, 2.23388670e-02,
        ...
        7.67211900e-02, 4.54406740e-02, 9.70459000e-02, 4.00161740e-03,
       -3.12805180e-02, -7.05566400e-02, 5.04760740e-02, 5.22766100e-02,
       -3.87878400e-02, -3.03649900e-03, 5.90515140e-03, -1.95007320e-02])]
Dim 768 (768,)