Intel 的可视化数据管理系统 （VDMS）
此笔记本介绍了如何开始使用 VDMS 作为矢量存储。
英特尔的可视化数据管理系统 （VDMS） 是一种存储解决方案，用于高效访问大“视觉”数据，旨在通过存储为图形的视觉元数据搜索相关的视觉数据，并实现对视觉数据的机器友好增强以实现更快的访问，从而实现云规模。VDMS 根据 MIT 获得许可。有关更多信息
VDMS，请访问此页面，并在此处找到 LangChain API 参考。
VDMS 支持：
- K 最近邻搜索
- 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
- 文本、图像和视频的嵌入
- 向量和元数据搜索
设置
要访问 VDMS 矢量存储，您需要安装langchain-vdms集成、通过公开可用的 Docker 映像打包和部署 VDMS 服务器。
为简单起见，此笔记本将使用端口 55555 在本地主机上部署 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
凭据
您可以使用VDMS没有任何凭据。
要启用模型调用的自动跟踪，请设置您的 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 矢量存储，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量度。
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",
)
管理矢量存储
将项目添加到向量存储
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 id，add_documents不检查 ID 是否唯一。因此，请使用upsert以在添加之前删除现有 ID 条目。
vector_store.upsert(documents, ids=doc_ids)
{'succeeded': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'],
'failed': []}
更新 vector store 中的项
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 vector store 中删除项目
vector_store.delete(ids=doc_ids[-1])
True
查询向量存储
创建矢量存储并添加相关文档后，您很可能希望在链或代理运行期间对其进行查询。
直接查询
可以按如下方式执行简单的相似性搜索：
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'}]
如果要执行相似性搜索并接收相应的分数，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'}]
如果要使用嵌入执行相似性搜索，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 retriever 进行查询
您还可以将 vector store 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
以前，我们根据其id.在这里，由于未提供 ID，因此将删除所有文档。
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
有关如何使用此向量存储进行检索增强生成 （RAG） 的指南，请参阅以下部分：
使用其他引擎的相似性搜索
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，vectorstore 使用 FaissFlat。下面我们展示了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 中。我们搜索 3 个文档 （k=3） 中，并返回分数和文档。
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 中。我们搜索 3 个文档 （k=3） 中，并返回分数和文档。
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，我们将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量度将文档添加到 VDMS 中。我们搜索 3 个文档 （k=3） 中，并返回分数和文档。
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'}]
筛选元数据
在处理集合之前缩小集合范围可能会有所帮助。
例如，可以使用get_by_constraints方法。字典用于筛选元数据。在这里，我们检索文档，其中langchain_id = "2"并将其从 vector store 中删除。
注意： id作为附加元数据生成为整数，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'}]
这里我们使用id以筛选 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