Rerankers 概述

在信息检索和生成式 AI 领域，重排序器是一个重要工具，它可以优化初始搜索结果的顺序。重排序器与传统的嵌入模型不同，它接受查询和文档作为输入，直接返回相似度分数而不是嵌入向量。这个分数表示输入查询和文档之间的相关性。

重排序器通常在第一阶段检索之后使用，第一阶段检索通常通过向量近似最近邻（ANN）技术完成。虽然 ANN 搜索在获取一大批潜在相关结果方面效率很高，但它们可能不会总是根据与查询的实际语义相近性来优先排序结果。在这种情况下，重排序器用于通过更深层的上下文分析来优化结果顺序，通常利用先进的机器学习模型，如 BERT 或其他基于 Transformer 的模型。通过这样做，重排序器可以显著提高呈现给用户的最终结果的准确性和相关性。

PyMilvus 模型库集成了重排序功能，可以优化初始搜索返回结果的顺序。在您从 Milvus 中检索到最近的嵌入向量后，您可以利用这些重排序工具来精炼搜索结果，以提高搜索结果的精确度。

重排序功能	API 或开源
BGE	开源
Cross Encoder	开源
Voyage	API
Cohere	API
Jina AI	API

在使用开源重排序器之前，请确保下载并安装所有必需的依赖项和模型。
对于基于 API 的重排序器，请从提供商获取 API 密钥，并在相应的环境变量或参数中设置它。

示例 1：使用 BGE 重排序功能根据查询对文档进行重排序

在此示例中，我们演示如何基于特定查询使用 BGE 重排序器对搜索结果进行重排序。

要在 PyMilvus model 库中使用重排序器，首先安装 PyMilvus 模型库以及包含所有必要重排序实用程序的模型子包：

pip install pymilvus[model]
# or pip install "pymilvus[model]" for zsh.

要使用 BGE 重排序器，首先导入 BGERerankFunction 类：

from pymilvus.model.reranker import BGERerankFunction

然后，创建一个 BGERerankFunction 实例用于重排序：

bge_rf = BGERerankFunction(
    model_name="BAAI/bge-reranker-v2-m3",  # Specify the model name. Defaults to `BAAI/bge-reranker-v2-m3`.
    device="cpu" # Specify the device to use, e.g., 'cpu' or 'cuda:0'
)

要基于查询对文档进行重排序，请使用以下代码：

query = "What event in 1956 marked the official birth of artificial intelligence as a discipline?"

documents = [
    "In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence.",
    "The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals.",
    "In 1951, British mathematician and computer scientist Alan Turing also developed the first program designed to play chess, demonstrating an early example of AI in game strategy.",
    "The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems."
]

bge_rf(query, documents)

预期输出类似于以下内容：

[RerankResult(text="The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals.", score=0.9911615761470803, index=1),
 RerankResult(text="In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence.", score=0.0326971950177779, index=0),
 RerankResult(text='The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems.', score=0.006514905766152258, index=3),
 RerankResult(text='In 1951, British mathematician and computer scientist Alan Turing also developed the first program designed to play chess, demonstrating an early example of AI in game strategy.', score=0.0042116724917325935, index=2)]

示例 2：使用重排序器提高搜索结果的相关性

在本指南中，我们将探讨如何利用 PyMilvus 中的 search() 方法进行相似性搜索，以及如何使用重排序器来提高搜索结果的相关性。我们的演示将使用以下数据集：

entities = [
    {'doc_id': 0, 'doc_vector': [-0.0372721,0.0101959,...,-0.114994], 'doc_text': "In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence."}, 
    {'doc_id': 1, 'doc_vector': [-0.00308882,-0.0219905,...,-0.00795811], 'doc_text': "The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals."}, 
    {'doc_id': 2, 'doc_vector': [0.00945078,0.00397605,...,-0.0286199], 'doc_text': 'In 1951, British mathematician and computer scientist Alan Turing also developed the first program designed to play chess, demonstrating an early example of AI in game strategy.'}, 
    {'doc_id': 3, 'doc_vector': [-0.0391119,-0.00880096,...,-0.0109257], 'doc_text': 'The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems.'}
]

数据集组件：

doc_id：每个文档的唯一标识符。
doc_vector：表示文档的向量嵌入。有关生成嵌入的指导，请参阅嵌入。
doc_text：文档的文本内容。

准备工作

在开始相似性搜索之前，您需要与 Milvus 建立连接，创建一个 Collection，并准备数据并将其插入到该 Collection 中。以下代码片段说明了这些准备步骤。

from pymilvus import MilvusClient, DataType

client = MilvusClient(
    uri="http://10.102.6.214:19530" # replace with your own Milvus server address
)

client.drop_collection('test_collection')

# define schema

schema = client.create_schema(auto_id=False, enabel_dynamic_field=True)

schema.add_field(field_name="doc_id", datatype=DataType.INT64, is_primary=True, description="document id")
schema.add_field(field_name="doc_vector", datatype=DataType.FLOAT_VECTOR, dim=384, description="document vector")
schema.add_field(field_name="doc_text", datatype=DataType.VARCHAR, max_length=65535, description="document text")

# define index params

index_params = client.prepare_index_params()

index_params.add_index(field_name="doc_vector", index_type="IVF_FLAT", metric_type="IP", params={"nlist": 128})

# create collection

client.create_collection(collection_name="test_collection", schema=schema, index_params=index_params)

# insert data into collection

client.insert(collection_name="test_collection", data=entities)

# Output:
# {'insert_count': 4, 'ids': [0, 1, 2, 3]}

进行相似性搜索

数据插入后，使用 search 方法执行相似性搜索。

# search results based on our query

res = client.search(
    collection_name="test_collection",
    data=[[-0.045217834, 0.035171617, ..., -0.025117004]], # replace with your query vector
    limit=3,
    output_fields=["doc_id", "doc_text"]
)

for i in res[0]:
    print(f'distance: {i["distance"]}')
    print(f'doc_text: {i["entity"]["doc_text"]}')

预期输出类似于以下内容：

distance: 0.7235960960388184
doc_text: The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals.
distance: 0.6269873976707458
doc_text: In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence.
distance: 0.5340118408203125
doc_text: The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems.

使用重排序器增强搜索结果

然后，通过重排序步骤提高搜索结果的相关性。在这个示例中，我们使用 PyMilvus 内置的 CrossEncoderRerankFunction 对结果进行重排序以提高准确性。

# use reranker to rerank search results

from pymilvus.model.reranker import CrossEncoderRerankFunction

ce_rf = CrossEncoderRerankFunction(
    model_name="cross-encoder/ms-marco-MiniLM-L-6-v2",  # Specify the model name.
    device="cpu" # Specify the device to use, e.g., 'cpu' or 'cuda:0'
)

reranked_results = ce_rf(
    query='What event in 1956 marked the official birth of artificial intelligence as a discipline?',
    documents=[
        "In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence.",
        "The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals.",
        "In 1951, British mathematician and computer scientist Alan Turing also developed the first program designed to play chess, demonstrating an early example of AI in game strategy.",
        "The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems."
    ],
    top_k=3
)

# print the reranked results
for result in reranked_results:
    print(f'score: {result.score}')
    print(f'doc_text: {result.text}')

预期输出类似于以下内容：

score: 6.250532627105713
doc_text: The Dartmouth Conference in 1956 is considered the birthplace of artificial intelligence as a field; here, John McCarthy and others coined the term 'artificial intelligence' and laid out its basic goals.
score: -2.9546022415161133
doc_text: In 1950, Alan Turing published his seminal paper, 'Computing Machinery and Intelligence,' proposing the Turing Test as a criterion of intelligence, a foundational concept in the philosophy and development of artificial intelligence.
score: -4.771512031555176
doc_text: The invention of the Logic Theorist by Allen Newell, Herbert A. Simon, and Cliff Shaw in 1955 marked the creation of the first true AI program, which was capable of solving logic problems, akin to proving mathematical theorems.

示例 1：使用 BGE 重排序功能根据查询对文档进行重排序​

示例 2：使用重排序器提高搜索结果的相关性​

准备工作​

进行相似性搜索​

使用重排序器增强搜索结果​

示例 1：使用 BGE 重排序功能根据查询对文档进行重排序

示例 2：使用重排序器提高搜索结果的相关性

准备工作

进行相似性搜索

使用重排序器增强搜索结果