指导者

Instructor是一种根据指令调整的文本嵌入模型，只需提供任务指令，无需任何微调，就能生成适合任何任务（如分类、检索、聚类、文本评估等）和领域（如科学、金融等）的文本嵌入。

Milvus 通过 InstructorEmbeddingFunction 类与 Instructor 的嵌入模型集成。该类提供了使用 Instructor 嵌入模型对文档和查询进行编码的方法，并将嵌入作为与 Milvus 索引兼容的稠密向量返回。

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 InstructorEmbeddingFunction：

from pymilvus.model.dense import InstructorEmbeddingFunction

ef = InstructorEmbeddingFunction(
    model_name="hkunlp/instructor-xl", # Defaults to `hkunlp/instructor-xl`
    query_instruction="Represent the question for retrieval:",
    doc_instruction="Represent the document for retrieval:"
)

参数：

• model_name (字符串）

  用于编码的 Mistral AI 嵌入模型名称。默认值为hkunlp/instructor-xl 。更多信息，请参阅模型列表。

• query_instruction （字符串）

  特定于任务的指令，用于指导模型如何为查询或问题生成 Embeddings。

• doc_instruction （字符串）

  指导模型为文档生成嵌入的特定任务指令。

要为文档创建嵌入，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([ 1.08575663e-02, 3.87877878e-03, 3.18090729e-02, -8.12458917e-02,
       -4.68971021e-02, -5.85585833e-02, -5.95418774e-02, -8.55880603e-03,
       -5.54775111e-02, -6.08020350e-02, 1.76202394e-02, 1.06648318e-02,
       -5.89960292e-02, -7.46861771e-02, 6.60329172e-03, -4.25189249e-02,
       ...
       -1.26921125e-02, 3.01475357e-02, 8.25323071e-03, -1.88470203e-02,
        6.04814291e-03, -2.81618331e-02, 5.91602828e-03, 7.13866428e-02],
      dtype=float32)]
Dim: 768 (768,)

要为查询创建嵌入，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)

预期输出类似于下面的内容：

Embeddings: [array([ 1.21721877e-02, 1.88485277e-03, 3.01732980e-02, -8.10302645e-02,
       -6.13401756e-02, -3.98149453e-02, -5.18723316e-02, -6.76784338e-03,
       -6.59285188e-02, -5.38365729e-02, -5.13435388e-03, -2.49210224e-02,
       -5.74403182e-02, -7.03031123e-02, 6.63730130e-03, -3.42259370e-02,
       ...
        7.36595877e-03, 2.85532661e-02, -1.55952033e-02, 2.13342719e-02,
        1.51187545e-02, -2.82798670e-02, 2.69396193e-02, 6.16136603e-02],
      dtype=float32)]
Dim 768 (768,)