诺米客

Nomic模型是由 Nomic AI 开发的一系列高级文本和图像嵌入解决方案，旨在将各种形式的数据转换为密集的数字向量，以捕捉其语义。

Milvus 通过 NomicEmbeddingFunction 类与 Nomic 的嵌入模型集成。该类提供使用 Nomic 嵌入模型对文档和查询进行编码的方法，并将嵌入结果返回为与 Milvus 索引兼容的密集向量。要使用该功能，请从Nomic Atlas 获取 API 密钥。

要使用该功能，请安装必要的依赖项：

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

然后，实例化 NomicEmbeddingFunction：

# Before accessing the Nomic Atlas API, configure your Nomic API token
import nomic
nomic.login('YOUR_NOMIC_API_KEY')

# Import Nomic embedding function
from pymilvus.model.dense import NomicEmbeddingFunction

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

参数：

• model_name (字符串）

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

要为文档创建 Embeddings，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)

# Print embeddings
print("Embeddings:", docs_embeddings)
# Print dimension and shape of embeddings
print("Dim:", ef.dim, docs_embeddings[0].shape)

预期输出类似于下图：

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,)

要为查询创建嵌入式编码，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,)