Lantern 向量存储库（自动检索器）¶

本指南展示如何在 LlamaIndex 中执行自动检索功能。

除语义搜索的查询字符串外，许多主流向量数据库还支持一组元数据过滤器。给定自然语言查询时，我们首先使用大语言模型（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（两者均可为空）。随后将这个完整的查询组合体在向量数据库中执行。

这种方法支持比 top-k 语义搜索更动态、更具表现力的检索形式。特定查询的相关上下文可能仅需对元数据标签进行过滤，或需要在过滤后的集合中结合使用过滤+语义搜索，亦或仅需原始语义搜索。

我们以 Lantern 为例进行演示，但自动检索功能也已实现在许多其他向量数据库中（如 Pinecone、Chroma、Weaviate 等）。

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

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

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

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!pip install llama-index psycopg2-binary asyncpg

!pip install llama-index psycopg2-binary asyncpg

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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

os.environ["OPENAI_API_KEY"] = "<your-api-key>"

import openai

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

# set up OpenAI import os os.environ["OPENAI_API_KEY"] = "" import openai openai.api_key = os.environ["OPENAI_API_KEY"]

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import psycopg2
from sqlalchemy import make_url

connection_string = "postgresql://postgres:postgres@localhost:5432"

url = make_url(connection_string)

db_name = "postgres"
conn = psycopg2.connect(connection_string)
conn.autocommit = True

import psycopg2 from sqlalchemy import make_url connection_string = "postgresql://postgres:postgres@localhost:5432" url = make_url(connection_string) db_name = "postgres" conn = psycopg2.connect(connection_string) conn.autocommit = True

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from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.lantern import LanternVectorStore

from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.lantern import LanternVectorStore

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from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text=(
            "Michael Jordan is a retired professional basketball player,"
            " widely regarded as one of the greatest basketball players of all"
            " time."
        ),
        metadata={
            "category": "Sports",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Angelina Jolie is an American actress, filmmaker, and"
            " humanitarian. She has received numerous awards for her acting"
            " and is known for her philanthropic work."
        ),
        metadata={
            "category": "Entertainment",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Elon Musk is a business magnate, industrial designer, and"
            " engineer. He is the founder, CEO, and lead designer of SpaceX,"
            " Tesla, Inc., Neuralink, and The Boring Company."
        ),
        metadata={
            "category": "Business",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Rihanna is a Barbadian singer, actress, and businesswoman. She"
            " has achieved significant success in the music industry and is"
            " known for her versatile musical style."
        ),
        metadata={
            "category": "Music",
            "country": "Barbados",
        },
    ),
    TextNode(
        text=(
            "Cristiano Ronaldo is a Portuguese professional footballer who is"
            " considered one of the greatest football players of all time. He"
            " has won numerous awards and set multiple records during his"
            " career."
        ),
        metadata={
            "category": "Sports",
            "country": "Portugal",
        },
    ),
]

from llama_index.core.schema import TextNode nodes = [ TextNode( text=( "Michael Jordan is a retired professional basketball player," " widely regarded as one of the greatest basketball players of all" " time." ), metadata={ "category": "Sports", "country": "United States", }, ), TextNode( text=( "Angelina Jolie is an American actress, filmmaker, and" " humanitarian. She has received numerous awards for her acting" " and is known for her philanthropic work." ), metadata={ "category": "Entertainment", "country": "United States", }, ), TextNode( text=( "Elon Musk is a business magnate, industrial designer, and" " engineer. He is the founder, CEO, and lead designer of SpaceX," " Tesla, Inc., Neuralink, and The Boring Company." ), metadata={ "category": "Business", "country": "United States", }, ), TextNode( text=( "Rihanna is a Barbadian singer, actress, and businesswoman. She" " has achieved significant success in the music industry and is" " known for her versatile musical style." ), metadata={ "category": "Music", "country": "Barbados", }, ), TextNode( text=( "Cristiano Ronaldo is a Portuguese professional footballer who is" " considered one of the greatest football players of all time. He" " has won numerous awards and set multiple records during his" " career." ), metadata={ "category": "Sports", "country": "Portugal", }, ), ]

使用 Lantern 向量存储构建向量索引¶

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转换为 Lantern 中的对应表示形式。现在我们可以对该数据执行语义查询，并通过 Lantern 进行元数据过滤。

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vector_store = LanternVectorStore.from_params(
    database=db_name,
    host=url.host,
    password=url.password,
    port=url.port,
    user=url.username,
    table_name="famous_people",
    embed_dim=1536,  # openai embedding dimension
    m=16,  # HNSW M parameter
    ef_construction=128,  # HNSW ef construction parameter
    ef=64,  # HNSW ef search parameter
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

vector_store = LanternVectorStore.from_params( database=db_name, host=url.host, password=url.password, port=url.port, user=url.username, table_name="famous_people", embed_dim=1536, # openai embedding dimension m=16, # HNSW M parameter ef_construction=128, # HNSW ef construction parameter ef=64, # HNSW ef search parameter ) 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¶

我们定义了核心模块 VectorIndexAutoRetriever。该模块接收包含向量存储集合结构化描述及其支持的元数据过滤器的 VectorStoreInfo 参数。这些信息将用于自动检索提示中，供大语言模型（LLM）推断元数据过滤器。

In [ ]:

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


vector_store_info = VectorStoreInfo(
    content_info="brief biography of celebrities",
    metadata_info=[
        MetadataInfo(
            name="category",
            type="str",
            description=(
                "Category of the celebrity, one of [Sports, Entertainment,"
                " Business, Music]"
            ),
        ),
        MetadataInfo(
            name="country",
            type="str",
            description=(
                "Country of the celebrity, one of [United States, Barbados,"
                " Portugal]"
            ),
        ),
    ],
)
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="brief biography of celebrities", metadata_info=[ MetadataInfo( name="category", type="str", description=( "Category of the celebrity, one of [Sports, Entertainment," " Business, Music]" ), ), MetadataInfo( name="country", type="str", description=( "Country of the celebrity, one of [United States, Barbados," " Portugal]" ), ), ], ) retriever = VectorIndexAutoRetriever( index, vector_store_info=vector_store_info )

处理示例数据¶

我们尝试对一些示例数据进行处理。请注意元数据过滤器是如何被自动推断的——这有助于实现更精确的检索！

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retriever.retrieve("Tell me about two celebrities from United States")

retriever.retrieve("Tell me about two celebrities from United States")