Elasticsearch 向量存储

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

import getpass
import os

import openai

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

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

    - 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".

from llama_index.vector_stores.elasticsearch import ElasticsearchStore

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

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.
)

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)
)

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

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)

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

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

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

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.

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.

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.

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, 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