Vespa向量存储演示¶

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

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%pip install llama-index-vector-stores-vespa llama-index pyvespa

%pip install llama-index-vector-stores-vespa llama-index pyvespa

设置API密钥¶

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import os
import openai

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

import os import openai os.environ["OPENAI_API_KEY"] = "sk-..." openai.api_key = os.environ["OPENAI_API_KEY"]

加载文档，构建VectorStoreIndex¶

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from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.vespa import VespaVectorStore
from IPython.display import Markdown, display

from llama_index.core import VectorStoreIndex from llama_index.vector_stores.vespa import VespaVectorStore from IPython.display import Markdown, display

定义一些示例数据¶

让我们插入一些文档。

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from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
            "year": 1994,
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
            "year": 1972,
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
            "theme": "Fiction",
            "year": 2010,
        },
    ),
    TextNode(
        text="To Kill a Mockingbird",
        metadata={
            "author": "Harper Lee",
            "theme": "Mafia",
            "year": 1960,
        },
    ),
    TextNode(
        text="1984",
        metadata={
            "author": "George Orwell",
            "theme": "Totalitarianism",
            "year": 1949,
        },
    ),
    TextNode(
        text="The Great Gatsby",
        metadata={
            "author": "F. Scott Fitzgerald",
            "theme": "The American Dream",
            "year": 1925,
        },
    ),
    TextNode(
        text="Harry Potter and the Sorcerer's Stone",
        metadata={
            "author": "J.K. Rowling",
            "theme": "Fiction",
            "year": 1997,
        },
    ),
]

from llama_index.core.schema import TextNode nodes = [ TextNode( text="The Shawshank Redemption", metadata={ "author": "Stephen King", "theme": "Friendship", "year": 1994, }, ), TextNode( text="The Godfather", metadata={ "director": "Francis Ford Coppola", "theme": "Mafia", "year": 1972, }, ), TextNode( text="Inception", metadata={ "director": "Christopher Nolan", "theme": "Fiction", "year": 2010, }, ), TextNode( text="To Kill a Mockingbird", metadata={ "author": "Harper Lee", "theme": "Mafia", "year": 1960, }, ), TextNode( text="1984", metadata={ "author": "George Orwell", "theme": "Totalitarianism", "year": 1949, }, ), TextNode( text="The Great Gatsby", metadata={ "author": "F. Scott Fitzgerald", "theme": "The American Dream", "year": 1925, }, ), TextNode( text="Harry Potter and the Sorcerer's Stone", metadata={ "author": "J.K. Rowling", "theme": "Fiction", "year": 1997, }, ), ]

初始化VespaVectorStore¶

为了让开始变得非常简单，我们提供了一个模板Vespa应用程序，该应用程序在初始化向量存储时将被部署。

这是一个巨大的抽象，有无尽的机会来定制和自定义Vespa应用程序以满足您的需求。但现在，让我们保持简单，使用默认模板进行初始化。

In [ ]:

from llama_index.core import StorageContext

vector_store = VespaVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)

from llama_index.core import StorageContext vector_store = VespaVectorStore() storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex(nodes, storage_context=storage_context)

删除文档¶

In [ ]:

node_to_delete = nodes[0].node_id
node_to_delete

node_to_delete = nodes[0].node_id node_to_delete

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vector_store.delete(ref_doc_id=node_to_delete)

vector_store.delete(ref_doc_id=node_to_delete)

查询¶

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from llama_index.core.vector_stores.types import (
    VectorStoreQuery,
    VectorStoreQueryMode,
)

from llama_index.core.vector_stores.types import ( VectorStoreQuery, VectorStoreQueryMode, )

In [ ]:

query = VectorStoreQuery(
    query_str="Great Gatsby",
    mode=VectorStoreQueryMode.TEXT_SEARCH,
    similarity_top_k=1,
)
result = vector_store.query(query)

query = VectorStoreQuery( query_str="Great Gatsby", mode=VectorStoreQueryMode.TEXT_SEARCH, similarity_top_k=1, ) result = vector_store.query(query)

In [ ]:

result

result

作为检索器¶

默认查询模式（文本搜索）¶

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retriever = index.as_retriever(vector_store_query_mode="default")
results = retriever.retrieve("Who directed inception?")
display(Markdown(f"**Retrieved nodes:**\n {results}"))

retriever = index.as_retriever(vector_store_query_mode="default") results = retriever.retrieve("Who directed inception?") display(Markdown(f"**Retrieved nodes:**\n {results}"))

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retriever = index.as_retriever(vector_store_query_mode="semantic_hybrid")
results = retriever.retrieve("Who wrote Harry Potter?")
display(Markdown(f"**Retrieved nodes:**\n {results}"))

retriever = index.as_retriever(vector_store_query_mode="semantic_hybrid") results = retriever.retrieve("Who wrote Harry Potter?") display(Markdown(f"**Retrieved nodes:**\n {results}"))

作为查询引擎¶

In [ ]:

query_engine = index.as_query_engine()
response = query_engine.query("Who directed inception?")
display(Markdown(f"**Response:** {response}"))

query_engine = index.as_query_engine() response = query_engine.query("Who directed inception?") display(Markdown(f"**Response:** {response}"))

In [ ]:

query_engine = index.as_query_engine(
    vector_store_query_mode="semantic_hybrid", verbose=True
)
response = query_engine.query(
    "When was the book about the wizard boy published and what was it called?"
)
display(Markdown(f"**Response:** {response}"))
display(Markdown(f"**Sources:** {response.source_nodes}"))

query_engine = index.as_query_engine( vector_store_query_mode="semantic_hybrid", verbose=True ) response = query_engine.query( "When was the book about the wizard boy published and what was it called?" ) display(Markdown(f"**Response:** {response}")) display(Markdown(f"**Sources:** {response.source_nodes}"))

使用元数据过滤器¶

注意：此元数据过滤是由llama-index在vespa之外完成的。为了使用本机且性能更高的过滤能力，您应该使用Vespa自己的过滤功能。

有关更多信息，请参阅Vespa文档。

In [ ]:

from llama_index.core.vector_stores import (
    FilterOperator,  # 过滤操作符
    FilterCondition,  # 过滤条件
    MetadataFilter,   # 元数据过滤器
    MetadataFilters,  # 元数据过滤器集合
)

# 定义一个过滤器，只允许具有主题“小说”或在1997年后发布的节点

filters = MetadataFilters(
    filters=[
        MetadataFilter(key="theme", value="Fiction"),  # 主题为“小说”
        MetadataFilter(key="year", value=1997, operator=FilterOperator.GT),  # 年份大于1997
    ],
    condition=FilterCondition.OR,  # 条件为“或”
)

retriever = index.as_retriever(filters=filters)
result = retriever.retrieve("Harry Potter")
display(Markdown(f"**Result:** {result}"))

from llama_index.core.vector_stores import ( FilterOperator, # 过滤操作符 FilterCondition, # 过滤条件 MetadataFilter, # 元数据过滤器 MetadataFilters, # 元数据过滤器集合 ) # 定义一个过滤器，只允许具有主题“小说”或在1997年后发布的节点 filters = MetadataFilters( filters=[ MetadataFilter(key="theme", value="Fiction"), # 主题为“小说” MetadataFilter(key="year", value=1997, operator=FilterOperator.GT), # 年份大于1997 ], condition=FilterCondition.OR, # 条件为“或” ) retriever = index.as_retriever(filters=filters) result = retriever.retrieve("Harry Potter") display(Markdown(f"**Result:** {result}"))

集成的抽象级别¶

为了让您能够轻松上手，我们提供了一个模板 Vespa 应用程序，该应用程序将在初始化向量存储时部署。这消除了首次设置 Vespa 的一些复杂性，但对于严肃的用例，我们强烈建议您阅读Vespa文档并根据您的需求定制应用程序。

模板¶

下面可以看到提供的模板 Vespa 应用程序：

from vespa.package import (
    ApplicationPackage,
    Field,
    Schema,
    Document,
    HNSW,
    RankProfile,
    Component,
    Parameter,
    FieldSet,
    GlobalPhaseRanking,
    Function,
)

hybrid_template = ApplicationPackage(
    name="hybridsearch",
    schema=[
        Schema(
            name="doc",
            document=Document(
                fields=[
                    Field(name="id", type="string", indexing=["summary"]),
                    Field(name="metadata", type="string", indexing=["summary"]),
                    Field(
                        name="text",
                        type="string",
                        indexing=["index", "summary"],
                        index="enable-bm25",
                        bolding=True,
                    ),
                    Field(
                        name="embedding",
                        type="tensor<float>(x[384])",
                        indexing=[
                            "input text",
                            "embed",
                            "index",
                            "attribute",
                        ],
                        ann=HNSW(distance_metric="angular"),
                        is_document_field=False,
                    ),
                ]
            ),
            fieldsets=[FieldSet(name="default", fields=["text", "metadata"])],
            rank_profiles=[
                RankProfile(
                    name="bm25",
                    inputs=[("query(q)", "tensor<float>(x[384])")],
                    functions=[Function(name="bm25sum", expression="bm25(text)")],
                    first_phase="bm25sum",
                ),
                RankProfile(
                    name="semantic",
                    inputs=[("query(q)", "tensor<float>(x[384])")],
                    first_phase="closeness(field, embedding)",
                ),
                RankProfile(
                    name="fusion",
                    inherits="bm25",
                    inputs=[("query(q)", "tensor<float>(x[384])")],
                    first_phase="closeness(field, embedding)",
                    global_phase=GlobalPhaseRanking(
                        expression="reciprocal_rank_fusion(bm25sum, closeness(field, embedding))",
                        rerank_count=1000,
                    ),
                ),
            ],
        )
    ],
    components=[
        Component(
            id="e5",
            type="hugging-face-embedder",
            parameters=[
                Parameter(
                    "transformer-model",
                    {
                        "url": "https://github.com/vespa-engine/sample-apps/raw/master/simple-semantic-search/model/e5-small-v2-int8.onnx"
                    },
                ),
                Parameter(
                    "tokenizer-model",
                    {
                        "url": "https://raw.githubusercontent.com/vespa-engine/sample-apps/master/simple-semantic-search/model/tokenizer.json"
                    },
                ),
            ],
        )
    ],
)

请注意，字段 id、metadata、text 和 embedding 对于集成工作是必需的。 模式名称必须是 doc，排名配置文件必须命名为 bm25、semantic 和 fusion。

除此之外，您可以根据需要自由修改，例如切换嵌入模型、添加更多字段或更改排名表达式。

有关更多详细信息，请查看hybrid search的 Pyvespa 示例笔记本。