从向量数据库进行自动检索¶

本指南展示了如何在 LlamaIndex 中执行**自动检索**。

许多流行的向量数据库除了支持用于语义搜索的查询字符串外，还支持一组元数据过滤器。给定一个自然语言查询，我们首先使用 LLM 推断出一组元数据过滤器以及传递给向量数据库的正确查询字符串（两者都可以为空）。然后，这个完整的查询包会在向量数据库上执行。

这使得检索形式更加动态、富有表现力，超越了传统的 Top-K 语义搜索。给定查询的相关上下文可能只需要根据元数据标签进行过滤，或者需要在过滤后的集合中结合过滤和语义搜索，或者仅仅进行原始语义搜索。

我们将使用 Elasticsearch 演示一个示例，但自动检索也已通过许多其他向量数据库（例如 Pinecone、Weaviate 等）实现。

设置¶

我们首先定义导入。

如果您在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%pip install llama-index-vector-stores-elasticsearch

%pip install llama-index-vector-stores-elasticsearch

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!pip install llama-index

!pip install llama-index

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import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

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# set up OpenAI
import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai

openai.api_key = os.environ["OPENAI_API_KEY"]

# set up OpenAI import os import getpass os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") import openai openai.api_key = os.environ["OPENAI_API_KEY"]

定义一些样本数据¶

我们将包含文本块的一些样本节点插入到向量数据库中。请注意，每个 TextNode 不仅包含文本，还包含元数据，例如 category 和 country。这些元数据字段将在底层向量数据库中按原样进行转换/存储。

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from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.elasticsearch import ElasticsearchStore

from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.elasticsearch import ElasticsearchStore

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from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text=(
            "A bunch of scientists bring back dinosaurs and mayhem breaks"
            " loose"
        ),
        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
    ),
    TextNode(
        text=(
            "Leo DiCaprio gets lost in a dream within a dream within a dream"
            " within a ..."
        ),
        metadata={
            "year": 2010,
            "director": "Christopher Nolan",
            "rating": 8.2,
        },
    ),
    TextNode(
        text=(
            "A psychologist / detective gets lost in a series of dreams within"
            " dreams within dreams and Inception reused the idea"
        ),
        metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},
    ),
    TextNode(
        text=(
            "A bunch of normal-sized women are supremely wholesome and some"
            " men pine after them"
        ),
        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
    ),
    TextNode(
        text="Toys come alive and have a blast doing so",
        metadata={"year": 1995, "genre": "animated"},
    ),
]

from llama_index.core.schema import TextNode nodes = [ TextNode( text=( "A bunch of scientists bring back dinosaurs and mayhem breaks" " loose" ), metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"}, ), TextNode( text=( "Leo DiCaprio gets lost in a dream within a dream within a dream" " within a ..." ), metadata={ "year": 2010, "director": "Christopher Nolan", "rating": 8.2, }, ), TextNode( text=( "A psychologist / detective gets lost in a series of dreams within" " dreams within dreams and Inception reused the idea" ), metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6}, ), TextNode( text=( "A bunch of normal-sized women are supremely wholesome and some" " men pine after them" ), metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3}, ), TextNode( text="Toys come alive and have a blast doing so", metadata={"year": 1995, "genre": "animated"}, ), ]

使用 Elasticsearch 向量存储构建向量索引¶

在这里，我们将数据加载到向量存储中。如上所述，每个节点的文本和元数据都将被转换为 Elasticsearch 中相应的表示形式。我们现在可以对 Elasticsearch 中的这些数据运行语义查询以及元数据过滤。

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vector_store = ElasticsearchStore(
    index_name="auto_retriever_movies", es_url="http://localhost:9200"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

vector_store = ElasticsearchStore( index_name="auto_retriever_movies", es_url="http://localhost:9200" ) storage_context = StorageContext.from_defaults(vector_store=vector_store)

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index = VectorStoreIndex(nodes, storage_context=storage_context)

index = VectorStoreIndex(nodes, storage_context=storage_context)

定义 VectorIndexAutoRetriever¶

我们定义核心 VectorIndexAutoRetriever 模块。该模块接受 VectorStoreInfo，其中包含向量存储集合及其支持的元数据过滤器的结构化描述。然后，此信息将用于自动检索提示中，LLM 在其中推断元数据过滤器。

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from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo


vector_store_info = VectorStoreInfo(
    content_info="Brief summary of a movie",
    metadata_info=[
        MetadataInfo(
            name="genre",
            description="The genre of the movie",
            type="string or list[string]",
        ),
        MetadataInfo(
            name="year",
            description="The year the movie was released",
            type="integer",
        ),
        MetadataInfo(
            name="director",
            description="The name of the movie director",
            type="string",
        ),
        MetadataInfo(
            name="rating",
            description="A 1-10 rating for the movie",
            type="float",
        ),
    ],
)
retriever = VectorIndexAutoRetriever(
    index, vector_store_info=vector_store_info
)

from llama_index.core.retrievers import VectorIndexAutoRetriever from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo vector_store_info = VectorStoreInfo( content_info="电影简介", metadata_info=[ MetadataInfo( name="genre", description="电影类型", type="string or list[string]", ), MetadataInfo( name="year", description="电影上映年份", type="integer", ), MetadataInfo( name="director", description="电影导演姓名", type="string", ), MetadataInfo( name="rating", description="电影的 1-10 评分", type="float", ), ], ) retriever = VectorIndexAutoRetriever( index, vector_store_info=vector_store_info )

运行一些样本数据¶

我们尝试运行一些样本数据。注意元数据过滤器是如何推断的——这有助于更精确的检索！

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retriever.retrieve(
    "What are 2 movies by Christopher Nolan were made before 2020?"
)

retriever.retrieve( "What are 2 movies by Christopher Nolan were made before 2020?" )

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retriever.retrieve("Has Andrei Tarkovsky directed any science fiction movies")

retriever.retrieve("Has Andrei Tarkovsky directed any science fiction movies")

INFO:llama_index.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using query str: science fiction
Using query str: science fiction
INFO:llama_index.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using filters: {'director': 'Andrei Tarkovsky'}
Using filters: {'director': 'Andrei Tarkovsky'}
INFO:llama_index.indices.vector_store.retrievers.auto_retriever.auto_retriever:Using top_k: 2
Using top_k: 2
INFO:elastic_transport.transport:POST http://localhost:9200/auto_retriever_movies/_search [status:200 duration:0.042s]
POST http://localhost:9200/auto_retriever_movies/_search [status:200 duration:0.042s]

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[]