Elasticsearch 向量存储¶

Elasticsearch 是一个构建在 Apache Lucene 之上的分布式、RESTful 搜索和分析引擎。它提供多种检索选项，包括密集向量检索、稀疏向量检索、关键词搜索和混合搜索。

注册免费试用 Elastic Cloud 或运行一个本地服务器，如下所述。

需要 Elasticsearch 8.9.0 或更高版本以及 AIOHTTP。

In [ ]

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

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

In [ ]

import getpass
import os

import openai

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

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

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

运行并连接到 Elasticsearch¶

设置用于 Elasticsearch 实例的两种方式

Elastic Cloud¶

Elastic Cloud 是一项托管式 Elasticsearch 服务。注册免费试用。

本地¶

在本地运行 Elasticsearch 以开始使用。最简单的方法是使用官方的 Elasticsearch Docker 镜像。有关更多信息，请参阅 Elasticsearch Docker 文档。

docker run -p 9200:9200 \
  -e "discovery.type=single-node" \
  -e "xpack.security.enabled=false" \
  -e "xpack.license.self_generated.type=trial" \
  docker.elastic.co/elasticsearch/elasticsearch:8.13.2

配置 ElasticsearchStore¶

ElasticsearchStore 类用于连接到 Elasticsearch 实例。它需要以下参数

    - index_name: Name of the Elasticsearch index. Required.
    - es_client: Optional. Pre-existing Elasticsearch client.
    - es_url: Optional. Elasticsearch URL.
    - es_cloud_id: Optional. Elasticsearch cloud ID.
    - es_api_key: Optional. Elasticsearch API key.
    - es_user: Optional. Elasticsearch username.
    - es_password: Optional. Elasticsearch password.
    - text_field: Optional. Name of the Elasticsearch field that stores the text.
    - vector_field: Optional. Name of the Elasticsearch field that stores the
                embedding.
    - batch_size: Optional. Batch size for bulk indexing. Defaults to 200.
    - distance_strategy: Optional. Distance strategy to use for similarity search.
                    Defaults to "COSINE".

示例：本地连接¶

from llama_index.vector_stores.elasticsearch import ElasticsearchStore

es = ElasticsearchStore(
    index_name="my_index",
    es_url="http://localhost:9200",
)

示例：使用用户名和密码连接到 Elastic Cloud¶

from llama_index.vector_stores.elasticsearch import ElasticsearchStore

es = ElasticsearchStore(
    index_name="my_index",
    es_cloud_id="<cloud-id>", # found within the deployment page
    es_user="elastic"
    es_password="<password>" # provided when creating deployment. Alternatively can reset password.
)

示例：使用 API 密钥连接到 Elastic Cloud¶

from llama_index.stores.elasticsearch import ElasticsearchStore

es = ElasticsearchStore(
    index_name="my_index",
    es_cloud_id="<cloud-id>", # found within the deployment page
    es_api_key="<api-key>" # create an API key within Kibana (Security -> API Keys)
)

示例数据¶

In [ ]

from llama_index.core.schema import TextNode

movies = [
    TextNode(
        text="The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.",
        metadata={"title": "Pulp Fiction"},
    ),
    TextNode(
        text="When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.",
        metadata={"title": "The Dark Knight"},
    ),
    TextNode(
        text="An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.",
        metadata={"title": "Fight Club"},
    ),
    TextNode(
        text="A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.",
        metadata={"title": "Inception"},
    ),
    TextNode(
        text="A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.",
        metadata={"title": "The Matrix"},
    ),
    TextNode(
        text="Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.",
        metadata={"title": "Se7en"},
    ),
    TextNode(
        text="An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.",
        metadata={"title": "The Godfather", "theme": "Mafia"},
    ),
]

from llama_index.core.schema import TextNode movies = [ TextNode( text="The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", metadata={"title": "Pulp Fiction"}, ), TextNode( text="When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.", metadata={"title": "The Dark Knight"}, ), TextNode( text="An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.", metadata={"title": "Fight Club"}, ), TextNode( text="A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.", metadata={"title": "Inception"}, ), TextNode( text="A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.", metadata={"title": "The Matrix"}, ), TextNode( text="Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.", metadata={"title": "Se7en"}, ), TextNode( text="An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.", metadata={"title": "The Godfather", "theme": "Mafia"}, ), ]

检索示例¶

本节展示了通过 ElasticsearchStore 可用的不同检索选项，并通过 VectorStoreIndex 使用它们。

In [ ]

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

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

我们首先定义一个辅助函数来检索并打印用户查询输入的结

In [ ]

def print_results(results):
    for rank, result in enumerate(results, 1):
        print(
            f"{rank}. title={result.metadata['title']} score={result.get_score()} text={result.get_text()}"
        )


def search(
    vector_store: ElasticsearchStore, nodes: list[TextNode], query: str
):
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    index = VectorStoreIndex(nodes, storage_context=storage_context)

    print(">>> Documents:")
    retriever = index.as_retriever()
    results = retriever.retrieve(query)
    print_results(results)

    print("\n>>> Answer:")
    query_engine = index.as_query_engine()
    response = query_engine.query(query)
    print(response)

def print_results(results): for rank, result in enumerate(results, 1): print( f"{rank}. title={result.metadata['title']} score={result.get_score()} text={result.get_text()}" ) def search( vector_store: ElasticsearchStore, nodes: list[TextNode], query: str ): storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex(nodes, storage_context=storage_context) print(">>> Documents:") retriever = index.as_retriever() results = retriever.retrieve(query) print_results(results) print("\n>>> Answer:") query_engine = index.as_query_engine() response = query_engine.query(query) print(response)

密集检索¶

这里我们使用 OpenAI 的嵌入进行搜索。

In [ ]

from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy

dense_vector_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_dense",
    retrieval_strategy=AsyncDenseVectorStrategy(),
)

search(dense_vector_store, movies, "which movie involves dreaming?")

from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy dense_vector_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_dense", retrieval_strategy=AsyncDenseVectorStrategy(), ) search(dense_vector_store, movies, "which movie involves dreaming?")

>>> Documents:
1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.

>>> Answer:
Inception

这也是默认的检索策略

In [ ]

default_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_default",
)

search(default_store, movies, "which movie involves dreaming?")

default_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_default", ) search(default_store, movies, "which movie involves dreaming?")

>>> Documents:
1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.

>>> Answer:
Inception

稀疏检索¶

对于此示例，您首先需要在 Elasticsearch 部署中部署 ELSER 模型第二版。

In [ ]

from llama_index.vector_stores.elasticsearch import AsyncSparseVectorStrategy

sparse_vector_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_sparse",
    retrieval_strategy=AsyncSparseVectorStrategy(model_id=".elser_model_2"),
)

search(sparse_vector_store, movies, "which movie involves dreaming?")

from llama_index.vector_stores.elasticsearch import AsyncSparseVectorStrategy sparse_vector_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_sparse", retrieval_strategy=AsyncSparseVectorStrategy(model_id=".elser_model_2"), ) search(sparse_vector_store, movies, "which movie involves dreaming?")

>>> Documents:
1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.

>>> Answer:
Inception

关键词检索¶

要使用经典的全文搜索，可以使用 BM25 策略。

In [ ]

from llama_index.vector_stores.elasticsearch import AsyncBM25Strategy

bm25_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_bm25",
    retrieval_strategy=AsyncBM25Strategy(),
)

search(bm25_store, movies, "joker")

from llama_index.vector_stores.elasticsearch import AsyncBM25Strategy bm25_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_bm25", retrieval_strategy=AsyncBM25Strategy(), ) search(bm25_store, movies, "joker")

>>> Documents:
1. title=The Dark Knight score=1.0 text=When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.

>>> Answer:
The Joker is a menacing character who wreaks havoc and chaos on the people of Gotham, posing a significant challenge for Batman to combat injustice.

混合检索¶

通过设置一个标志，可以启用密集检索和关键词搜索的结合，实现混合检索。

In [ ]

from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy

hybrid_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_hybrid",
    retrieval_strategy=AsyncDenseVectorStrategy(hybrid=True),
)

search(hybrid_store, movies, "which movie involves dreaming?")

from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy hybrid_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_hybrid", retrieval_strategy=AsyncDenseVectorStrategy(hybrid=True), ) search(hybrid_store, movies, "which movie involves dreaming?")

>>> Documents:
1. title=Inception score=0.36787944117144233 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.

>>> Answer:
"Inception" is the movie that involves dreaming.

元数据过滤器¶

我们还可以根据文档的元数据对查询引擎应用过滤器。

In [ ]

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

metadata_store = ElasticsearchStore(
    es_url="http://localhost:9200",  # for Elastic Cloud authentication see above
    index_name="movies_metadata",
)
storage_context = StorageContext.from_defaults(vector_store=metadata_store)
index = VectorStoreIndex(movies, storage_context=storage_context)

# Metadata filter
filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]
)
retriever = index.as_retriever(filters=filters)

results = retriever.retrieve("What is inception about?")
print_results(results)

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters metadata_store = ElasticsearchStore( es_url="http://localhost:9200", # for Elastic Cloud authentication see above index_name="movies_metadata", ) storage_context = StorageContext.from_defaults(vector_store=metadata_store) index = VectorStoreIndex(movies, storage_context=storage_context) # Metadata filter filters = MetadataFilters( filters=[ExactMatchFilter(key="theme", value="Mafia")] ) retriever = index.as_retriever(filters=filters) results = retriever.retrieve("What is inception about?") print_results(results)

1. title=The Godfather score=1.0 text=An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.

自定义过滤器和覆盖查询¶

当前，Elasticsearch 实现仅支持 LlamaIndex 提供的 ExactMatchFilters。Elasticsearch 本身支持广泛的过滤器，包括范围过滤器、地理过滤器等。要使用这些过滤器，可以将它们作为字典列表传递给 es_filter 参数。

In [ ]

def custom_query(query, query_str):
    print("custom query", query)
    return query


query_engine = index.as_query_engine(
    vector_store_kwargs={
        "es_filter": [{"match": {"title": "matrix"}}],
        "custom_query": custom_query,
    }
)
query_engine.query("what is this movie about?")

def custom_query(query, query_str): print("custom query", query) return query query_engine = index.as_query_engine( vector_store_kwargs={ "es_filter": [{"match": {"title": "matrix"}}], "custom_query": custom_query, } ) query_engine.query("what is this movie about?")

custom query {'knn': {'filter': [{'match': {'title': 'matrix'}}], 'field': 'embedding', 'k': 2, 'num_candidates': 20, 'query_vector': [0.00446691969409585, -0.038953110575675964, -0.023963095620274544, -0.024891795590519905, -0.016729693859815598, 0.017200583592057228, -0.002360992832109332, -0.012622482143342495, -0.009980263188481331, -0.026108263060450554, 0.02950914017856121, 0.018626336008310318, -0.016154160723090172, -0.012099270708858967, 0.03777588531374931, 0.006209868937730789, 0.03539527207612991, -0.011746102944016457, 0.0029888467397540808, -0.022066453471779823, -0.02290359139442444, -0.011752642691135406, -0.018744058907032013, -0.015251620672643185, 0.0034074161667376757, 0.00014756205200683326, 0.022955913096666336, -0.02264198660850525, 0.002032350515946746, -0.021778685972094536, 0.012164671905338764, -0.015055416151881218, 0.006543416064232588, -0.009509372524917126, -0.008632993325591087, -0.006814832333475351, 0.011765723116695881, -0.01788076013326645, 0.00166691979393363, 0.002267795614898205, 0.015460905618965626, -0.016533490270376205, -0.014401402324438095, -0.0142836794257164, -0.020863065496087074, -0.01714826375246048, 0.027913343161344528, -0.032962337136268616, -0.016546569764614105, 0.0019947446417063475, 0.026304468512535095, 0.011706861667335033, -0.03733115270733833, -0.02812262810766697, -0.01879638060927391, 0.003232467221096158, -0.01393051166087389, -0.00879649631679058, 0.018469372764229774, -0.006402803119271994, 0.016481168568134308, 0.009149664081633091, -0.023492205888032913, -0.0024296643678098917, -0.00589921185746789, 0.0089338393881917, -0.016755854710936546, -0.0016309489728882909, -0.011726481840014458, -0.004267445299774408, 0.03162814676761627, 0.04190925508737564, 0.015513226389884949, 0.0019260731060057878, 0.027050044387578964, -0.03780204430222511, -0.038220614194869995, -0.008155561983585358, 0.01085010264068842, 0.021333957090973854, 0.026042861863970757, -0.008122861385345459, -0.021137751638889313, 0.02192256972193718, 0.0020094600040465593, -0.0013415474677458405, -0.0063570220954716206, 0.02903824858367443, 0.006232759449630976, 0.0072072409093379974, 0.00309185404330492, 0.0014167592162266374, 0.03691258653998375, 0.023034395650029182, -0.005048993043601513, 0.025336526334285736, 0.0038652264047414064, 0.009254306554794312, -0.007560409139841795, -0.02906440943479538, -0.00708951847627759, 0.0022220145910978317, -0.016808176413178444, -0.008305985480546951, -0.01105938758701086, -0.0062752701342105865, 0.011602219194173813, -0.011621839366853237, 0.04303416237235069, 0.0014069488970562816, -0.025179563090205193, 0.009293547831475735, 0.01714826375246048, -0.030110832303762436, -0.0007423065835610032, -0.023740731179714203, 0.019594278186559677, 0.0014004088006913662, 0.02074534446001053, -0.01884870044887066, 0.020876146852970123, 0.026814598590135574, 0.01534318272024393, -0.005971153266727924, 0.012328175827860832, -0.014074394479393959, -0.025192642584443092, -0.008855357766151428, 0.006697109900414944, -0.020091328769922256, 0.03050324134528637, -0.0019080876372754574, 0.0057128178887069225, 0.004175883252173662, -0.02783486247062683, 0.0035022483207285404, -0.0032242920715361834, 0.02075842395424843, -0.028253432363271713, -0.024237781763076782, 0.029352176934480667, 0.02979690581560135, -0.027573255822062492, -0.0004287883348297328, -0.02974458411335945, 0.004221664275974035, 0.01598411798477173, 0.02167404443025589, 0.020915387198328972, -0.00558855477720499, 0.011105168610811234, 0.009888701140880585, 0.007318423595279455, 0.0031539855990558863, 0.018992584198713303, 0.016729693859815598, -0.016559649258852005, -0.01837781071662903, -0.01570943184196949, 0.0026340438053011894, -0.01831240952014923, 0.0038815767038613558, -0.001384058385156095, -0.004784116987138987, 0.011281752027571201, 0.04175229370594025, 0.01274674478918314, -0.012328175827860832, -0.01595795713365078, -0.0022449051029980183, -0.011994628235697746, 0.03693874552845955, -0.030817169696092606, 0.01998668722808361, -0.007651971187442541, 0.009561693295836449, 0.01595795713365078, 0.019620439037680626, -0.013256876729428768, 0.012073109857738018, -0.011301372200250626, 0.0334070660173893, 0.028436556458473206, 0.024237781763076782, -0.030869489535689354, 0.023479124531149864, -0.013538102619349957, -0.009130043908953667, 0.00642569363117218, -0.0003075912536587566, -0.005300788674503565, 0.006984876003116369, 0.011092088185250759, -0.007822014391422272, -0.6659438610076904, -0.017449110746383667, 0.009332788176834583, 0.01096128486096859, 0.0040254597552120686, 0.007292263209819794, 0.01466300804167986, -0.006278540473431349, -0.01713518239557743, 0.02048373781144619, -0.007233401760458946, -0.004983591381460428, 0.012236613780260086, -0.01638960652053356, -0.027023883536458015, -0.01574867218732834, -0.0021141022443771362, -0.0309479720890522, -0.010941664688289165, 0.008842277340590954, -0.03544759377837181, 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