Milvus 混合搜索检索器

Milvus 是一个开源向量数据库，旨在为嵌入相似性搜索和 AI 应用程序提供支持。Milvus 使非结构化数据搜索更容易访问，并提供一致的用户体验，无论部署环境如何。

这将帮助您开始使用 Milvus 混合搜索检索器，它结合了密集和稀疏向量搜索的优势。有关所有 MilvusCollectionHybridSearchRetriever 功能和配置的详细文档，请访问 API 参考。

另请参阅 Milvus 多向量搜索文档。

集成详情

检索器	自托管	云服务	包
MilvusCollectionHybridSearchRetriever	✅	❌	langchain_milvus

设置

如果您希望从单个查询中获取自动跟踪，您还可以通过取消注释以下内容来设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

此检索器位于 langchain-milvus 包中。本指南需要以下依赖项

%pip install --upgrade --quiet pymilvus[model] langchain-milvus langchain-openai

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever
from langchain_milvus.utils.sparse import BM25SparseEmbedding
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from pymilvus import (
    Collection,
    CollectionSchema,
    DataType,
    FieldSchema,
    WeightedRanker,
    connections,
)

API 参考：StrOutputParser | PromptTemplate | RunnablePassthrough | ChatOpenAI | OpenAIEmbeddings

启动 Milvus 服务

请参阅 Milvus 文档以启动 Milvus 服务。

启动 Milvus 后，您需要指定您的 Milvus 连接 URI。

CONNECTION_URI = "http://localhost:19530"

准备 OpenAI API 密钥

请参阅 OpenAI 文档以获取您的 OpenAI API 密钥，并将其设置为环境变量。

export OPENAI_API_KEY=<your_api_key>

准备密集和稀疏嵌入函数

让我们虚构 10 个关于小说的虚假描述。在实际生产中，它可能是大量文本数据。

texts = [
    "In 'The Whispering Walls' by Ava Moreno, a young journalist named Sophia uncovers a decades-old conspiracy hidden within the crumbling walls of an ancient mansion, where the whispers of the past threaten to destroy her own sanity.",
    "In 'The Last Refuge' by Ethan Blackwood, a group of survivors must band together to escape a post-apocalyptic wasteland, where the last remnants of humanity cling to life in a desperate bid for survival.",
    "In 'The Memory Thief' by Lila Rose, a charismatic thief with the ability to steal and manipulate memories is hired by a mysterious client to pull off a daring heist, but soon finds themselves trapped in a web of deceit and betrayal.",
    "In 'The City of Echoes' by Julian Saint Clair, a brilliant detective must navigate a labyrinthine metropolis where time is currency, and the rich can live forever, but at a terrible cost to the poor.",
    "In 'The Starlight Serenade' by Ruby Flynn, a shy astronomer discovers a mysterious melody emanating from a distant star, which leads her on a journey to uncover the secrets of the universe and her own heart.",
    "In 'The Shadow Weaver' by Piper Redding, a young orphan discovers she has the ability to weave powerful illusions, but soon finds herself at the center of a deadly game of cat and mouse between rival factions vying for control of the mystical arts.",
    "In 'The Lost Expedition' by Caspian Grey, a team of explorers ventures into the heart of the Amazon rainforest in search of a lost city, but soon finds themselves hunted by a ruthless treasure hunter and the treacherous jungle itself.",
    "In 'The Clockwork Kingdom' by Augusta Wynter, a brilliant inventor discovers a hidden world of clockwork machines and ancient magic, where a rebellion is brewing against the tyrannical ruler of the land.",
    "In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by a mysterious organization to transport a valuable artifact across a war-torn continent, but soon finds themselves pursued by deadly assassins and rival factions.",
    "In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers she has the ability to enter people's dreams, but soon finds herself trapped in a surreal world of nightmares and illusions, where the boundaries between reality and fantasy blur.",
]

我们将使用 OpenAI 嵌入生成密集向量，并使用 BM25 算法生成稀疏向量。

初始化密集嵌入函数并获取维度

dense_embedding_func = OpenAIEmbeddings()
dense_dim = len(dense_embedding_func.embed_query(texts[1]))
dense_dim

初始化稀疏嵌入函数。

请注意，稀疏嵌入的输出是一组稀疏向量，它表示输入文本的关键词的索引和权重。

sparse_embedding_func = BM25SparseEmbedding(corpus=texts)
sparse_embedding_func.embed_query(texts[1])

{0: 0.4270424944042204,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.2237754316221157,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331,
1.845826690498331}

创建 Milvus 集合并加载数据

初始化连接 URI 并建立连接

connections.connect(uri=CONNECTION_URI)

定义字段名称及其数据类型

pk_field = "doc_id"
dense_field = "dense_vector"
sparse_field = "sparse_vector"
text_field = "text"
fields = [
    FieldSchema(
        name=pk_field,
        dtype=DataType.VARCHAR,
        is_primary=True,
        auto_id=True,
        max_length=100,
    ),
    FieldSchema(name=dense_field, dtype=DataType.FLOAT_VECTOR, dim=dense_dim),
    FieldSchema(name=sparse_field, dtype=DataType.SPARSE_FLOAT_VECTOR),
    FieldSchema(name=text_field, dtype=DataType.VARCHAR, max_length=65_535),
]

使用定义的模式创建集合

schema = CollectionSchema(fields=fields, enable_dynamic_field=False)
collection = Collection(
    name="IntroductionToTheNovels", schema=schema, consistency_level="Strong"
)

为密集和稀疏向量定义索引

dense_index = {"index_type": "FLAT", "metric_type": "IP"}
collection.create_index("dense_vector", dense_index)
sparse_index = {"index_type": "SPARSE_INVERTED_INDEX", "metric_type": "IP"}
collection.create_index("sparse_vector", sparse_index)
collection.flush()

将实体插入集合并加载集合

entities = []
for text in texts:
    entity = {
        dense_field: dense_embedding_func.embed_documents([text])[0],
        sparse_field: sparse_embedding_func.embed_documents([text])[0],
        text_field: text,
    }
    entities.append(entity)
collection.insert(entities)
collection.load()

实例化

现在我们可以实例化我们的检索器，为稀疏和密集字段定义搜索参数

sparse_search_params = {"metric_type": "IP"}
dense_search_params = {"metric_type": "IP", "params": {}}
retriever = MilvusCollectionHybridSearchRetriever(
    collection=collection,
    rerank=WeightedRanker(0.5, 0.5),
    anns_fields=[dense_field, sparse_field],
    field_embeddings=[dense_embedding_func, sparse_embedding_func],
    field_search_params=[dense_search_params, sparse_search_params],
    top_k=3,
    text_field=text_field,
)

在此检索器的输入参数中，我们使用密集嵌入和稀疏嵌入对该集合的两个字段执行混合搜索，并使用 WeightedRanker 进行重排序。最后，将返回 3 个 top-K 文档。

用法

retriever.invoke("What are the story about ventures?")

[Document(page_content="In 'The Lost Expedition' by Caspian Grey, a team of explorers ventures into the heart of the Amazon rainforest in search of a lost city, but soon finds themselves hunted by a ruthless treasure hunter and the treacherous jungle itself.", metadata={'doc_id': '449281835035545843'}),
 Document(page_content="In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by a mysterious organization to transport a valuable artifact across a war-torn continent, but soon finds themselves pursued by deadly assassins and rival factions.", metadata={'doc_id': '449281835035545845'}),
 Document(page_content="In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers she has the ability to enter people's dreams, but soon finds herself trapped in a surreal world of nightmares and illusions, where the boundaries between reality and fantasy blur.", metadata={'doc_id': '449281835035545846'})]

在链中使用

初始化 ChatOpenAI 并定义提示模板

llm = ChatOpenAI()

PROMPT_TEMPLATE = """
Human: You are an AI assistant, and provides answers to questions by using fact based and statistical information when possible.
Use the following pieces of information to provide a concise answer to the question enclosed in <question> tags.

<context>
{context}
</context>

<question>
{question}
</question>

Assistant:"""

prompt = PromptTemplate(
    template=PROMPT_TEMPLATE, input_variables=["context", "question"]
)

定义一个用于格式化文档的函数

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

使用检索器和其他组件定义链

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

使用定义的链执行查询

rag_chain.invoke("What novels has Lila written and what are their contents?")

"Lila Rose has written 'The Memory Thief,' which follows a charismatic thief with the ability to steal and manipulate memories as they navigate a daring heist and a web of deceit and betrayal."

删除集合

collection.drop()

API 参考

有关所有 MilvusCollectionHybridSearchRetriever 功能和配置的详细文档，请访问 API 参考。

检索器概念指南
检索器操作指南

Milvus 混合搜索检索器

集成详情

设置

安装

启动 Milvus 服务

准备 OpenAI API 密钥

准备密集和稀疏嵌入函数

创建 Milvus 集合并加载数据

实例化

用法

在链中使用

API 参考

此页面是否有帮助？

您还可以留下详细的反馈在 GitHub 上.

集成详情​

设置​

安装​

启动 Milvus 服务​

准备 OpenAI API 密钥​

准备密集和稀疏嵌入函数​

创建 Milvus 集合并加载数据​

实例化​

用法​

在链中使用​

API 参考​

相关​

此页面是否有帮助？

您还可以留下详细的反馈 在 GitHub 上.

集成详情

设置

安装

启动 Milvus 服务

准备 OpenAI API 密钥

准备密集和稀疏嵌入函数

创建 Milvus 集合并加载数据

实例化

用法

在链中使用

API 参考

相关

您还可以留下详细的反馈在 GitHub 上.