英特尔视觉数据管理系统 (VDMS)

本笔记本介绍了如何开始使用 VDMS 作为向量存储。

英特尔的视觉数据管理系统 (VDMS) 是一种存储解决方案，用于高效访问大型“视觉”数据，旨在通过搜索存储为图的视觉元数据来查找相关的视觉数据，并支持对视觉数据进行机器友好的增强以实现更快的访问，从而实现云规模。VDMS 在 MIT 许可下获得许可。有关 VDMS 的更多信息，请访问此页面，并在此处查找 LangChain API 参考。

VDMS 支持

K 近邻搜索
欧几里得距离 (L2) 和内积 (IP)
用于索引和计算距离的库：FaissFlat (默认), FaissHNSWFlat, FaissIVFFlat, Flinng, TileDBDense, TileDBSparse
用于文本、图像和视频的嵌入
向量和元数据搜索

设置

要访问 VDMS 向量存储，您需要安装 langchain-vdms 集成包，并通过公开可用的 Docker 镜像部署 VDMS 服务器。为了简单起见，本笔记本将在本地主机上使用端口 55555 部署 VDMS 服务器。

%pip install -qU "langchain-vdms>=0.1.3"
!docker run --no-healthcheck --rm -d -p 55555:55555 --name vdms_vs_test_nb intellabs/vdms:latest
!sleep 5

Note: you may need to restart the kernel to use updated packages.
c464076e292613df27241765184a673b00c775cecb7792ef058591c2cbf0bde8

凭证

您可以无需任何凭证即可使用 VDMS。

如果您想获得模型调用的自动追踪，您还可以通过取消注释下方内容来设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

初始化

使用 VDMS 客户端连接到 VDMS 向量存储，使用 FAISS IndexFlat 索引 (默认) 和欧几里得距离 (默认) 作为相似性搜索的距离度量。

选择嵌入模型

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

from langchain_vdms.vectorstores import VDMS, VDMS_Client

collection_name = "test_collection_faiss_L2"

vdms_client = VDMS_Client(host="localhost", port=55555)

vector_store = VDMS(
    client=vdms_client,
    embedding=embeddings,
    collection_name=collection_name,
    engine="FaissFlat",
    distance_strategy="L2",
)

管理向量存储

向向量存储添加项目

import logging

logging.basicConfig()
logging.getLogger("langchain_vdms.vectorstores").setLevel(logging.INFO)

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
    id=1,
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
    id=2,
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
    id=3,
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
    id=4,
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
    id=5,
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
    id=6,
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
    id=7,
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
    id=8,
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
    id=9,
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
    id=10,
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]

doc_ids = [str(i) for i in range(1, 11)]
vector_store.add_documents(documents=documents, ids=doc_ids)

API 参考:文档

['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']

如果多次提供相同的 id，add_documents 不会检查 id 是否唯一。因此，请使用 upsert 在添加之前删除现有的 id 条目。

vector_store.upsert(documents, ids=doc_ids)

{'succeeded': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'],
 'failed': []}

更新向量存储中的项目

updated_document_1 = Document(
    page_content="I had chocolate chip pancakes and fried eggs for breakfast this morning.",
    metadata={"source": "tweet"},
    id=1,
)

updated_document_2 = Document(
    page_content="The weather forecast for tomorrow is sunny and warm, with a high of 82 degrees.",
    metadata={"source": "news"},
    id=2,
)

vector_store.update_documents(
    ids=doc_ids[:2],
    documents=[updated_document_1, updated_document_2],
    batch_size=2,
)

从向量存储中删除项目

vector_store.delete(ids=doc_ids[-1])

True

查询向量存储

一旦您的向量存储创建完成并且添加了相关文档，您很可能希望在链或代理的运行期间查询它。

直接查询

执行简单的相似性搜索可以按如下方式完成

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": ["==", "tweet"]},
)
for doc in results:
    print(f"* ID={doc.id}: {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0063 seconds
``````output
* ID=3: Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* ID=8: LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]

如果您想执行相似性搜索并接收相应的分数，您可以运行

results = vector_store.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, filter={"source": ["==", "news"]}
)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0460 seconds
``````output
* [SIM=0.753577] The weather forecast for tomorrow is sunny and warm, with a high of 82 degrees. [{'source': 'news'}]

如果您想使用嵌入执行相似性搜索，您可以运行

results = vector_store.similarity_search_by_vector(
    embedding=embeddings.embed_query("I love green eggs and ham!"), k=1
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0044 seconds
``````output
* The weather forecast for tomorrow is sunny and warm, with a high of 82 degrees. [{'source': 'news'}]

通过转换为检索器进行查询

您还可以将向量存储转换为检索器，以便在您的链中更轻松地使用。

retriever = vector_store.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 3},
)
results = retriever.invoke("Stealing from the bank is a crime")
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0042 seconds
``````output
* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]
* The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]
* Is the new iPhone worth the price? Read this review to find out. [{'source': 'website'}]

retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={
        "k": 1,
        "score_threshold": 0.0,  # >= score_threshold
    },
)
results = retriever.invoke("Stealing from the bank is a crime")
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0042 seconds
``````output
* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]

retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 1, "fetch_k": 10},
)
results = retriever.invoke(
    "Stealing from the bank is a crime", filter={"source": ["==", "news"]}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

INFO:langchain_vdms.vectorstores:VDMS mmr search took 0.0042 secs
``````output
* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]

删除集合

之前，我们根据文档的 id 删除了文档。在这里，由于未提供 ID，因此所有文档都将被删除。

print("Documents before deletion: ", vector_store.count())

vector_store.delete(collection_name=collection_name)

print("Documents after deletion: ", vector_store.count())

Documents before deletion:  10
Documents after deletion:  0

用于检索增强生成的用法

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分

使用其他引擎进行相似性搜索

VDMS 支持各种用于索引和计算距离的库：FaissFlat (默认), FaissHNSWFlat, FaissIVFFlat, Flinng, TileDBDense 和 TileDBSparse。默认情况下，向量存储使用 FaissFlat。下面我们展示一些使用其他引擎的示例。

使用 Faiss HNSWFlat 和欧几里得距离进行相似性搜索

在这里，我们使用 Faiss IndexHNSWFlat 索引和 L2 作为相似性搜索的距离度量，将文档添加到 VDMS。我们搜索与查询相关的三个文档 (k=3)，并返回文档以及分数。

db_FaissHNSWFlat = VDMS.from_documents(
    documents,
    client=vdms_client,
    ids=doc_ids,
    collection_name="my_collection_FaissHNSWFlat_L2",
    embedding=embeddings,
    engine="FaissHNSWFlat",
    distance_strategy="L2",
)
# Query
k = 3
query = "LangChain provides abstractions to make working with LLMs easy"
docs_with_score = db_FaissHNSWFlat.similarity_search_with_score(query, k=k, filter=None)

for res, score in docs_with_score:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

INFO:langchain_vdms.vectorstores:Descriptor set my_collection_FaissHNSWFlat_L2 created
INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.1272 seconds
``````output
* [SIM=0.716791] Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* [SIM=0.936718] LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
* [SIM=1.834110] Is the new iPhone worth the price? Read this review to find out. [{'source': 'website'}]

使用 Faiss IVFFlat 和内积 (IP) 距离进行相似性搜索

我们使用 Faiss IndexIVFFlat 索引和 IP 作为相似性搜索的距离度量，将文档添加到 VDMS。我们搜索与查询相关的三个文档 (k=3)，并返回文档以及分数。

db_FaissIVFFlat = VDMS.from_documents(
    documents,
    client=vdms_client,
    ids=doc_ids,
    collection_name="my_collection_FaissIVFFlat_IP",
    embedding=embeddings,
    engine="FaissIVFFlat",
    distance_strategy="IP",
)

k = 3
query = "LangChain provides abstractions to make working with LLMs easy"
docs_with_score = db_FaissIVFFlat.similarity_search_with_score(query, k=k, filter=None)
for res, score in docs_with_score:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

INFO:langchain_vdms.vectorstores:Descriptor set my_collection_FaissIVFFlat_IP created
INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0052 seconds
``````output
* [SIM=0.641605] Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* [SIM=0.531641] LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
* [SIM=0.082945] Is the new iPhone worth the price? Read this review to find out. [{'source': 'website'}]

使用 FLINNG 和 IP 距离进行相似性搜索

在本节中，我们使用过滤器识别近邻组 (FLINNG) 索引和 IP 作为相似性搜索的距离度量，将文档添加到 VDMS。我们搜索与查询相关的三个文档 (k=3)，并返回文档以及分数。

db_Flinng = VDMS.from_documents(
    documents,
    client=vdms_client,
    ids=doc_ids,
    collection_name="my_collection_Flinng_IP",
    embedding=embeddings,
    engine="Flinng",
    distance_strategy="IP",
)
# Query
k = 3
query = "LangChain provides abstractions to make working with LLMs easy"
docs_with_score = db_Flinng.similarity_search_with_score(query, k=k, filter=None)
for res, score in docs_with_score:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

INFO:langchain_vdms.vectorstores:Descriptor set my_collection_Flinng_IP created
INFO:langchain_vdms.vectorstores:VDMS similarity search took 0.0042 seconds
``````output
* [SIM=0.000000] I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]
* [SIM=0.000000] I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]
* [SIM=0.000000] I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]

基于元数据进行过滤

在处理集合之前缩小范围可能会很有帮助。

例如，可以使用 get_by_constraints 方法基于元数据过滤集合。字典用于过滤元数据。在这里，我们检索 langchain_id = "2" 的文档并将其从向量存储中删除。

注意: id 作为额外的元数据以整数形式生成，而 langchain_id (内部 ID) 是每个条目的唯一字符串。

response, response_array = db_FaissIVFFlat.get_by_constraints(
    db_FaissIVFFlat.collection_name,
    limit=1,
    include=["metadata", "embeddings"],
    constraints={"langchain_id": ["==", "2"]},
)

# Delete id=2
db_FaissIVFFlat.delete(collection_name=db_FaissIVFFlat.collection_name, ids=["2"])

print("Deleted entry:")
for doc in response:
    print(f"* ID={doc.id}: {doc.page_content} [{doc.metadata}]")

Deleted entry:
* ID=2: The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]

response, response_array = db_FaissIVFFlat.get_by_constraints(
    db_FaissIVFFlat.collection_name,
    include=["metadata"],
)
for doc in response:
    print(f"* ID={doc.id}: {doc.page_content} [{doc.metadata}]")

* ID=10: I have a bad feeling I am going to get deleted :( [{'source': 'tweet'}]
* ID=9: The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]
* ID=8: LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
* ID=7: The top 10 soccer players in the world right now. [{'source': 'website'}]
* ID=6: Is the new iPhone worth the price? Read this review to find out. [{'source': 'website'}]
* ID=5: Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]
* ID=4: Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]
* ID=3: Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* ID=1: I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]

在这里，我们使用 id 来过滤 ID 范围，因为它是一个整数。

response, response_array = db_FaissIVFFlat.get_by_constraints(
    db_FaissIVFFlat.collection_name,
    include=["metadata", "embeddings"],
    constraints={"source": ["==", "news"]},
)
for doc in response:
    print(f"* ID={doc.id}: {doc.page_content} [{doc.metadata}]")

* ID=9: The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]
* ID=4: Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]

停止 VDMS 服务器

!docker kill vdms_vs_test_nb

vdms_vs_test_nb

API 参考

TODO: 添加 API 参考

向量存储概念指南
向量存储操作指南

设置​

凭证​

初始化​

管理向量存储​

向向量存储添加项目​

更新向量存储中的项目​

从向量存储中删除项目​

查询向量存储​

直接查询​

通过转换为检索器进行查询​

删除集合​

用于检索增强生成的用法​

使用其他引擎进行相似性搜索​

使用 Faiss HNSWFlat 和欧几里得距离进行相似性搜索​

使用 Faiss IVFFlat 和内积 (IP) 距离进行相似性搜索​

使用 FLINNG 和 IP 距离进行相似性搜索​

基于元数据进行过滤​

停止 VDMS 服务器​

API 参考​

相关内容​

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设置

凭证

初始化