Google Vertex AI 向量搜索

此笔记本展示了如何使用与Google Cloud Vertex AI Vector Search向量数据库相关的功能。

Google Vertex AI Vector Search（以前称为 Vertex AI Matching Engine）提供业界领先的高规模低延迟向量数据库。这些向量数据库通常被称为向量相似性匹配或近似最近邻 (ANN) 服务。

注意：Langchain API 预期已创建端点和已部署索引。索引创建时间可能长达一个小时。

要了解如何创建索引，请参阅部分创建索引并将其部署到端点
如果您已部署索引，请跳至从文本创建向量存储

创建索引并将其部署到端点

本节演示如何创建新索引并将其部署到端点

# TODO : Set values as per your requirements
# Project and Storage Constants
PROJECT_ID = "<my_project_id>"
REGION = "<my_region>"
BUCKET = "<my_gcs_bucket>"
BUCKET_URI = f"gs://{BUCKET}"

# The number of dimensions for the textembedding-gecko@003 is 768
# If other embedder is used, the dimensions would probably need to change.
DIMENSIONS = 768

# Index Constants
DISPLAY_NAME = "<my_matching_engine_index_id>"
DEPLOYED_INDEX_ID = "<my_matching_engine_endpoint_id>"

# Create a bucket.
! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI

使用VertexAIEmbeddings 作为嵌入模型

from google.cloud import aiplatform
from langchain_google_vertexai import VertexAIEmbeddings

aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)

embedding_model = VertexAIEmbeddings(model_name="textembedding-gecko@003")

创建空索引

注意：创建索引时，应从“BATCH_UPDATE”或“STREAM_UPDATE”中指定“index_update_method”。

批量索引用于在一段时间内存储的数据的批次更新索引，例如每周或每月处理的系统。流式索引用于在将新数据添加到数据存储时更新索引数据，例如，如果您有书店并希望尽快在线显示新库存。您选择哪种类型很重要，因为设置和要求不同。

请参阅官方文档，以获取有关配置索引的更多详细信息

# NOTE : This operation can take upto 30 seconds
my_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
    display_name=DISPLAY_NAME,
    dimensions=DIMENSIONS,
    approximate_neighbors_count=150,
    distance_measure_type="DOT_PRODUCT_DISTANCE",
    index_update_method="STREAM_UPDATE",  # allowed values BATCH_UPDATE , STREAM_UPDATE
)

创建端点

# Create an endpoint
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
    display_name=f"{DISPLAY_NAME}-endpoint", public_endpoint_enabled=True
)

将索引部署到端点

# NOTE : This operation can take upto 20 minutes
my_index_endpoint = my_index_endpoint.deploy_index(
    index=my_index, deployed_index_id=DEPLOYED_INDEX_ID
)

my_index_endpoint.deployed_indexes

从文本创建向量存储

注意：如果您有现有的索引和端点，可以使用以下代码加载它们

# TODO : replace 1234567890123456789 with your acutial index ID
my_index = aiplatform.MatchingEngineIndex("1234567890123456789")

# TODO : replace 1234567890123456789 with your acutial endpoint ID
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint("1234567890123456789")

from langchain_google_vertexai import (
    VectorSearchVectorStore,
    VectorSearchVectorStoreDatastore,
)

创建简单的向量存储（无过滤器）

# Input texts
texts = [
    "The cat sat on",
    "the mat.",
    "I like to",
    "eat pizza for",
    "dinner.",
    "The sun sets",
    "in the west.",
]

# Create a Vector Store
vector_store = VectorSearchVectorStore.from_components(
    project_id=PROJECT_ID,
    region=REGION,
    gcs_bucket_name=BUCKET,
    index_id=my_index.name,
    endpoint_id=my_index_endpoint.name,
    embedding=embedding_model,
    stream_update=True,
)

# Add vectors and mapped text chunks to your vectore store
vector_store.add_texts(texts=texts)

可选：您还可以创建向量并存储数据存储中的块

# NOTE : This operation can take upto 20 mins
vector_store = VectorSearchVectorStoreDatastore.from_components(
    project_id=PROJECT_ID,
    region=REGION,
    index_id=my_index.name,
    endpoint_id=my_index_endpoint.name,
    embedding=embedding_model,
    stream_update=True,
)

vector_store.add_texts(texts=texts, is_complete_overwrite=True)

# Try running a simialarity search
vector_store.similarity_search("pizza")

创建具有元数据过滤器的向量存储

# Input text with metadata
record_data = [
    {
        "description": "A versatile pair of dark-wash denim jeans."
        "Made from durable cotton with a classic straight-leg cut, these jeans"
        " transition easily from casual days to dressier occasions.",
        "price": 65.00,
        "color": "blue",
        "season": ["fall", "winter", "spring"],
    },
    {
        "description": "A lightweight linen button-down shirt in a crisp white."
        " Perfect for keeping cool with breathable fabric and a relaxed fit.",
        "price": 34.99,
        "color": "white",
        "season": ["summer", "spring"],
    },
    {
        "description": "A soft, chunky knit sweater in a vibrant forest green. "
        "The oversized fit and cozy wool blend make this ideal for staying warm "
        "when the temperature drops.",
        "price": 89.99,
        "color": "green",
        "season": ["fall", "winter"],
    },
    {
        "description": "A classic crewneck t-shirt in a soft, heathered blue. "
        "Made from comfortable cotton jersey, this t-shirt is a wardrobe essential "
        "that works for every season.",
        "price": 19.99,
        "color": "blue",
        "season": ["fall", "winter", "summer", "spring"],
    },
    {
        "description": "A flowing midi-skirt in a delicate floral print. "
        "Lightweight and airy, this skirt adds a touch of feminine style "
        "to warmer days.",
        "price": 45.00,
        "color": "white",
        "season": ["spring", "summer"],
    },
]

# Parse and prepare input data

texts = []
metadatas = []
for record in record_data:
    record = record.copy()
    page_content = record.pop("description")
    texts.append(page_content)
    if isinstance(page_content, str):
        metadata = {**record}
        metadatas.append(metadata)

# Inspect metadatas
metadatas

# NOTE : This operation can take more than 20 mins
vector_store = VectorSearchVectorStore.from_components(
    project_id=PROJECT_ID,
    region=REGION,
    gcs_bucket_name=BUCKET,
    index_id=my_index.name,
    endpoint_id=my_index_endpoint.name,
    embedding=embedding_model,
)

vector_store.add_texts(texts=texts, metadatas=metadatas, is_complete_overwrite=True)

from google.cloud.aiplatform.matching_engine.matching_engine_index_endpoint import (
    Namespace,
    NumericNamespace,
)

# Try running a simple similarity search

# Below code should return 5 results
vector_store.similarity_search("shirt", k=5)

# Try running a similarity search with text filter
filters = [Namespace(name="season", allow_tokens=["spring"])]

# Below code should return 4 results now
vector_store.similarity_search("shirt", k=5, filter=filters)

# Try running a similarity search with combination of text and numeric filter
filters = [Namespace(name="season", allow_tokens=["spring"])]
numeric_filters = [NumericNamespace(name="price", value_float=40.0, op="LESS")]

# Below code should return 2 results now
vector_store.similarity_search(
    "shirt", k=5, filter=filters, numeric_filter=numeric_filters
)

使用向量存储作为检索器

# Initialize the vectore_store as retriever
retriever = vector_store.as_retriever()

# perform simple similarity search on retriever
retriever.invoke("What are my options in breathable fabric?")

# Try running a similarity search with text filter
filters = [Namespace(name="season", allow_tokens=["spring"])]

retriever.search_kwargs = {"filter": filters}

# perform similarity search with filters on retriever
retriever.invoke("What are my options in breathable fabric?")

# Try running a similarity search with combination of text and numeric filter
filters = [Namespace(name="season", allow_tokens=["spring"])]
numeric_filters = [NumericNamespace(name="price", value_float=40.0, op="LESS")]


retriever.search_kwargs = {"filter": filters, "numeric_filter": numeric_filters}

retriever.invoke("What are my options in breathable fabric?")

在问答链中使用带有过滤器的检索器

from langchain_google_vertexai import VertexAI

llm = VertexAI(model_name="gemini-pro")

from langchain.chains import RetrievalQA

filters = [Namespace(name="season", allow_tokens=["spring"])]
numeric_filters = [NumericNamespace(name="price", value_float=40.0, op="LESS")]

retriever.search_kwargs = {"k": 2, "filter": filters, "numeric_filter": numeric_filters}

retrieval_qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True,
)

question = "What are my options in breathable fabric?"
response = retrieval_qa({"query": question})
print(f"{response['result']}")
print("REFERENCES")
print(f"{response['source_documents']}")

API 参考：RetrievalQA

读取、分块、向量化和索引 PDF

!pip install pypdf

from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

API 参考：PyPDFLoader | RecursiveCharacterTextSplitter

loader = PyPDFLoader("https://arxiv.org/pdf/1706.03762.pdf")
pages = loader.load()

text_splitter = RecursiveCharacterTextSplitter(
    # Set a really small chunk size, just to show.
    chunk_size=1000,
    chunk_overlap=20,
    length_function=len,
    is_separator_regex=False,
)
doc_splits = text_splitter.split_documents(pages)

texts = [doc.page_content for doc in doc_splits]
metadatas = [doc.metadata for doc in doc_splits]

texts[0]

# Inspect Metadata of 1st page
metadatas[0]

vector_store = VectorSearchVectorStore.from_components(
    project_id=PROJECT_ID,
    region=REGION,
    gcs_bucket_name=BUCKET,
    index_id=my_index.name,
    endpoint_id=my_index_endpoint.name,
    embedding=embedding_model,
)

vector_store.add_texts(texts=texts, metadatas=metadatas, is_complete_overwrite=True)

vector_store = VectorSearchVectorStore.from_components(
    project_id=PROJECT_ID,
    region=REGION,
    gcs_bucket_name=BUCKET,
    index_id=my_index.name,
    endpoint_id=my_index_endpoint.name,
    embedding=embedding_model,
)

向量存储概念指南
向量存储操作指南

Google Vertex AI 向量搜索

创建索引并将其部署到端点

使用VertexAIEmbeddings 作为嵌入模型

创建空索引

创建端点

将索引部署到端点

从文本创建向量存储

创建简单的向量存储（无过滤器）

可选：您还可以创建向量并存储数据存储中的块

创建具有元数据过滤器的向量存储

使用向量存储作为检索器

在问答链中使用带有过滤器的检索器

读取、分块、向量化和索引 PDF

此页面是否有帮助？

您也可以留下详细的反馈在 GitHub 上.

Google Vertex AI 向量搜索

创建索引并将其部署到端点​

使用VertexAIEmbeddings 作为嵌入模型​

创建空索引​

创建端点​

将索引部署到端点​

从文本创建向量存储​

创建简单的向量存储（无过滤器）​

可选：您还可以创建向量并存储数据存储中的块​

创建具有元数据过滤器的向量存储​

使用向量存储作为检索器​

在问答链中使用带有过滤器的检索器​

读取、分块、向量化和索引 PDF​

相关​

此页面是否有帮助？

您也可以留下详细的反馈 在 GitHub 上.

创建索引并将其部署到端点

使用VertexAIEmbeddings 作为嵌入模型

创建空索引

创建端点

将索引部署到端点

从文本创建向量存储

创建简单的向量存储（无过滤器）

可选：您还可以创建向量并存储数据存储中的块

创建具有元数据过滤器的向量存储

使用向量存储作为检索器

在问答链中使用带有过滤器的检索器

读取、分块、向量化和索引 PDF

相关

您也可以留下详细的反馈在 GitHub 上.