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Redis 向量存储

本笔记本介绍了如何开始使用 Redis 向量存储。

Redis 是一种流行的开源内存数据结构存储，可用作数据库、缓存、消息代理和队列。它现在包含向量相似度搜索功能，使其适用于作为向量存储使用。

什么是 Redis？​

大多数开发者都熟悉 Redis。其核心是 Redis，一个键值对家族中的 NoSQL 数据库，可用作缓存、消息代理、流处理和主数据库。开发者选择 Redis 是因为其速度快、拥有庞大的客户端库生态系统，并且多年来已被主要企业部署。

除了这些传统用例，Redis 还提供额外的功能，例如搜索和查询功能，允许用户在 Redis 中创建二级索引结构。这使得 Redis 能够以缓存的速度成为向量数据库。

Redis 作为向量数据库​

Redis 使用压缩的倒排索引进行快速索引，占用内存少。它还支持许多高级功能，例如：

  • 在 Redis 哈希和 JSON 中索引多个字段
  • 向量相似度搜索（使用 HNSW (ANN) 或 FLAT (KNN)）
  • 向量范围搜索（例如，查找查询向量半径范围内的所有向量）
  • 增量索引，无性能损失
  • 文档排序（使用 tf-idf，可选用户提供权重）
  • 字段加权
  • 带有 AND、OR 和 NOT 运算符的复杂布尔查询
  • 前缀匹配、模糊匹配和精确短语查询
  • 支持 双元音音素匹配
  • 自动完成建议（带模糊前缀建议）
  • 在多种语言中基于词干的查询扩展（使用 Snowball）
  • 支持中文分词和查询（使用 Friso）
  • 数字过滤器和范围
  • 使用 Redis 地理空间索引进行地理空间搜索
  • 强大的聚合引擎
  • 支持所有 utf-8 编码文本
  • 检索完整文档、选定字段或仅文档 ID
  • 结果排序（例如，按创建日期）

客户端​

由于 Redis 不仅仅是一个向量数据库，因此通常有需要使用 LangChain 集成之外的 Redis 客户端的用例。您可以使用任何标准 Redis 客户端库来运行搜索和查询命令，但最简单的方法是使用一个封装了搜索和查询 API 的库。以下是一些示例，但您可以在此处找到更多客户端库。

项目语言许可证作者星级
jedisJavaMITRedisStars
redisvlPythonMITRedisStars
redis-pyPythonMITRedisStars
node-redisNode.jsMITRedisStars
nredisstack.NETMITRedisStars

部署选项​

有多种方式可以部署带有 RediSearch 的 Redis。最简单的入门方式是使用 Docker，但还有许多其他部署选项，例如：

Redis 连接 URL 方案​

有效的 Redis URL 方案有：

  1. redis:// - 连接到独立 Redis，未加密
  2. rediss:// - 连接到独立 Redis，使用 TLS 加密
  3. redis+sentinel:// - 通过 Redis Sentinel 连接到 Redis 服务器，未加密
  4. rediss+sentinel:// - 通过 Redis Sentinel 连接到 Redis 服务器，两次连接都使用 TLS 加密

有关其他连接参数的更多信息，请参阅 redis-py 文档。

设置​

要使用 RedisVectorStore，您需要安装 langchain-redis 伴侣包以及本笔记本中使用的其他包。

%pip install -qU langchain-redis langchain-huggingface sentence-transformers scikit-learn
Note: you may need to restart the kernel to use updated packages.

凭证​

Redis 连接凭据作为 Redis 连接 URL 的一部分传递。Redis 连接 URL 功能多样，可以适应各种 Redis 服务器拓扑和身份验证方法。这些 URL 遵循特定的格式，包括连接协议、身份验证详细信息、主机、端口和数据库信息。Redis 连接 URL 的基本结构是：

[protocol]://[auth]@[host]:[port]/[database]

其中：

  • 协议可以是 redis（用于标准连接）、rediss（用于 SSL/TLS 连接）或 redis+sentinel（用于 Sentinel 连接）。
  • auth 包括用户名和密码（如果适用）。
  • host 是 Redis 服务器主机名或 IP 地址。
  • port 是 Redis 服务器端口。
  • database 是 Redis 数据库编号。

Redis 连接 URL 支持各种配置，包括：

  • 独立 Redis 服务器（带或不带身份验证）
  • Redis Sentinel 设置
  • SSL/TLS 加密连接
  • 不同的身份验证方法（仅密码或用户名-密码）

以下是不同配置的 Redis 连接 URL 示例：

# connection to redis standalone at localhost, db 0, no password
redis_url = "redis://:6379"
# connection to host "redis" port 7379 with db 2 and password "secret" (old style authentication scheme without username / pre 6.x)
redis_url = "redis://:secret@redis:7379/2"
# connection to host redis on default port with user "joe", pass "secret" using redis version 6+ ACLs
redis_url = "redis://joe:secret@redis/0"

# connection to sentinel at localhost with default group mymaster and db 0, no password
redis_url = "redis+sentinel://:26379"
# connection to sentinel at host redis with default port 26379 and user "joe" with password "secret" with default group mymaster and db 0
redis_url = "redis+sentinel://joe:secret@redis"
# connection to sentinel, no auth with sentinel monitoring group "zone-1" and database 2
redis_url = "redis+sentinel://redis:26379/zone-1/2"

# connection to redis standalone at localhost, db 0, no password but with TLS support
redis_url = "rediss://:6379"
# connection to redis sentinel at localhost and default port, db 0, no password
# but with TLS support for both Sentinel and Redis server
redis_url = "rediss+sentinel://"

使用 Docker 启动 Redis 实例​

要将 Redis 与 LangChain 结合使用，您需要一个正在运行的 Redis 实例。您可以使用 Docker 启动一个，命令如下：

docker run -d -p 6379:6379 redis/redis-stack:latest

在此示例中，我们将使用本地 Redis 实例。如果您使用的是远程实例，则需要相应地修改 Redis URL。

import os

REDIS_URL = os.getenv("REDIS_URL", "redis://:6379")
print(f"Connecting to Redis at: {REDIS_URL}")
Connecting to Redis at: redis://redis:6379

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"

我们来通过 ping Redis 检查它是否正常运行：

import redis

redis_client = redis.from_url(REDIS_URL)
redis_client.ping()
True

示例数据​

20 个新闻组数据集包含约 18000 篇关于 20 个主题的新闻组帖子。我们将使用一个子集进行演示，并重点关注两个类别：“alt.atheism”和“sci.space”。

from langchain.docstore.document import Document
from sklearn.datasets import fetch_20newsgroups

categories = ["alt.atheism", "sci.space"]
newsgroups = fetch_20newsgroups(
    subset="train", categories=categories, shuffle=True, random_state=42
)

# Use only the first 250 documents
texts = newsgroups.data[:250]
metadata = [
    {"category": newsgroups.target_names[target]} for target in newsgroups.target[:250]
]

len(texts)
API 参考：Document
250

初始化​

RedisVectorStore 实例可以通过多种方式初始化：

  • RedisVectorStore.__init__ - 直接初始化
  • RedisVectorStore.from_texts - 从文本列表（可选带元数据）初始化
  • RedisVectorStore.from_documents - 从 langchain_core.documents.Document 对象列表初始化
  • RedisVectorStore.from_existing_index - 从现有 Redis 索引初始化

下面我们将使用 RedisVectorStore.__init__ 方法，通过一个 RedisConfig 实例进行初始化。

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")

我们将使用 SentenceTransformer 模型来创建嵌入。此模型在本地运行，不需要 API 密钥。

from langchain_redis import RedisConfig, RedisVectorStore

config = RedisConfig(
    index_name="newsgroups",
    redis_url=REDIS_URL,
    metadata_schema=[
        {"name": "category", "type": "tag"},
    ],
)

vector_store = RedisVectorStore(embeddings, config=config)

管理向量存储​

向向量存储添加项目​

ids = vector_store.add_texts(texts, metadata)

print(ids[0:10])
['newsgroups:f1e788ee61fe410daa8ef941dd166223', 'newsgroups:80b39032181f4299a359a9aaed6e2401', 'newsgroups:99a3efc1883647afba53d115b49e6e92', 'newsgroups:503a6c07cd71418eb71e11b42589efd7', 'newsgroups:7351210e32d1427bbb3c7426cf93a44f', 'newsgroups:4e79fdf67abe471b8ee98ba0e8a1a055', 'newsgroups:03559a1d574e4f9ca0479d7b3891402e', 'newsgroups:9a1c2a7879b8409a805db72feac03580', 'newsgroups:3578a1e129f5435f9743cf803413f37a', 'newsgroups:9f68baf4d6b04f1683d6b871ce8ad92d']

我们来检查第一个文档

texts[0], metadata[0]
('From: bil@okcforum.osrhe.edu (Bill Conner)\nSubject: Re: Not the Omni!\nNntp-Posting-Host: okcforum.osrhe.edu\nOrganization: Okcforum Unix Users Group\nX-Newsreader: TIN [version 1.1 PL6]\nLines: 18\n\nCharley Wingate (mangoe@cs.umd.edu) wrote:\n: \n: >> Please enlighten me.  How is omnipotence contradictory?\n: \n: >By definition, all that can occur in the universe is governed by the rules\n: >of nature. Thus god cannot break them. Anything that god does must be allowed\n: >in the rules somewhere. Therefore, omnipotence CANNOT exist! It contradicts\n: >the rules of nature.\n: \n: Obviously, an omnipotent god can change the rules.\n\nWhen you say, "By definition", what exactly is being defined;\ncertainly not omnipotence. You seem to be saying that the "rules of\nnature" are pre-existent somehow, that they not only define nature but\nactually cause it. If that\'s what you mean I\'d like to hear your\nfurther thoughts on the question.\n\nBill\n',
 {'category': 'alt.atheism'})

从向量存储删除项目​

# Delete documents by passing one or more keys/ids
vector_store.index.drop_keys(ids[0])
1

检查已创建的索引​

一旦 Redis 向量存储对象构建完成，如果 Redis 中尚不存在索引，则会创建一个索引。可以使用 rvl 和 redis-cli 命令行工具检查索引。如果您安装了 redisvl，可以使用 rvl 命令行工具检查索引。

# assumes you're running Redis locally (use --host, --port, --password, --username, to change this)
!rvl index listall --port 6379
17:54:50 [RedisVL] INFO   Using Redis address from environment variable, REDIS_URL
17:54:50 [RedisVL] INFO   Indices:
17:54:50 [RedisVL] INFO   1. newsgroups

Redis 向量存储实现将尝试为通过 from_texts、from_texts_return_keys 和 from_documents 方法传递的任何元数据生成索引模式（用于过滤的字段）。这样，无论传递什么元数据，都将被索引到 Redis 搜索索引中，从而允许对这些字段进行过滤。

下面我们展示了从我们上面定义的元数据创建的字段：

!rvl index info -i newsgroups --port 6379
17:54:50 [RedisVL] INFO   Using Redis address from environment variable, REDIS_URL


Index Information:
╭──────────────┬────────────────┬────────────────┬─────────────────┬────────────╮
│ Index Name   │ Storage Type   │ Prefixes       │ Index Options   │   Indexing │
├──────────────┼────────────────┼────────────────┼─────────────────┼────────────┤
│ newsgroups   │ HASH           │ ['newsgroups'] │ []              │          0 │
╰──────────────┴────────────────┴────────────────┴─────────────────┴────────────╯
Index Fields:
╭───────────┬─────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮
│ Name      │ Attribute   │ Type   │ Field Option   │ Option Value   │ Field Option   │ Option Value   │ Field Option   │   Option Value │ Field Option    │ Option Value   │
├───────────┼─────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤
│ text      │ text        │ TEXT   │ WEIGHT         │ 1              │                │                │                │                │                 │                │
│ embedding │ embedding   │ VECTOR │ algorithm      │ FLAT           │ data_type      │ FLOAT32        │ dim            │            768 │ distance_metric │ COSINE         │
│ category  │ category    │ TAG    │ SEPARATOR      │ |              │                │                │                │                │                 │                │
╰───────────┴─────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯
!rvl stats -i newsgroups --port 6379
17:54:51 [RedisVL] INFO   Using Redis address from environment variable, REDIS_URL

Statistics:
╭─────────────────────────────┬────────────╮
│ Stat Key                    │ Value      │
├─────────────────────────────┼────────────┤
│ num_docs                    │ 249        │
│ num_terms                   │ 16178      │
│ max_doc_id                  │ 250        │
│ num_records                 │ 50394      │
│ percent_indexed             │ 1          │
│ hash_indexing_failures      │ 0          │
│ number_of_uses              │ 2          │
│ bytes_per_record_avg        │ 38.2743    │
│ doc_table_size_mb           │ 0.0263586  │
│ inverted_sz_mb              │ 1.83944    │
│ key_table_size_mb           │ 0.00932026 │
│ offset_bits_per_record_avg  │ 10.6699    │
│ offset_vectors_sz_mb        │ 0.089057   │
│ offsets_per_term_avg        │ 1.38937    │
│ records_per_doc_avg         │ 202.386    │
│ sortable_values_size_mb     │ 0          │
│ total_indexing_time         │ 72.444     │
│ total_inverted_index_blocks │ 16207      │
│ vector_index_sz_mb          │ 3.01776    │
╰─────────────────────────────┴────────────╯

查询向量存储​

创建向量存储并添加相关文档后，您很可能希望在运行链或代理时对其进行查询。

直接查询​

执行简单的相似性搜索可以按如下方式完成

query = "Tell me about space exploration"
results = vector_store.similarity_search(query, k=2)

print("Simple Similarity Search Results:")
for doc in results:
    print(f"Content: {doc.page_content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    print()
Simple Similarity Search Results:
Content: From: aa429@freenet.carleton.ca (Terry Ford)
Subject: A flawed propulsion system: Space Shuttle
X-Ad...
Metadata: {'category': 'sci.space'}

Content: From: nsmca@aurora.alaska.edu
Subject: Space Design Movies?
Article-I.D.: aurora.1993Apr23.124722.1
...
Metadata: {'category': 'sci.space'}

如果您想执行相似性搜索并接收相应的分数，您可以运行

# Similarity search with score and filter
scored_results = vector_store.similarity_search_with_score(query, k=2)

print("Similarity Search with Score Results:")
for doc, score in scored_results:
    print(f"Content: {doc.page_content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    print(f"Score: {score}")
    print()
Similarity Search with Score Results:
Content: From: aa429@freenet.carleton.ca (Terry Ford)
Subject: A flawed propulsion system: Space Shuttle
X-Ad...
Metadata: {'category': 'sci.space'}
Score: 0.569670975208

Content: From: nsmca@aurora.alaska.edu
Subject: Space Design Movies?
Article-I.D.: aurora.1993Apr23.124722.1
...
Metadata: {'category': 'sci.space'}
Score: 0.590400338173

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，以便在您的链中更方便地使用。

retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 2})
retriever.invoke("What planet in the solar system has the largest number of moons?")
[Document(metadata={'category': 'sci.space'}, page_content='Subject: Re: Comet in Temporary Orbit Around Jupiter?\nFrom: Robert Coe <bob@1776.COM>\nDistribution: world\nOrganization: 1776 Enterprises, Sudbury MA\nLines: 23\n\njgarland@kean.ucs.mun.ca writes:\n\n> >> Also, perihelions of Gehrels3 were:\n> >> \n> >> April  1973     83 jupiter radii\n> >> August 1970     ~3 jupiter radii\n> > \n> > Where 1 Jupiter radius = 71,000 km = 44,000 mi = 0.0005 AU.  So the\n> > 1970 figure seems unlikely to actually be anything but a perijove.\n> > Is that the case for the 1973 figure as well?\n> > -- \n> Sorry, _perijoves_...I\'m not used to talking this language.\n\nHmmmm....  The prefix "peri-" is Greek, not Latin, so it\'s usually used\nwith the Greek form of the name of the body being orbited.  (That\'s why\nit\'s "perihelion" rather than "perisol", "perigee" rather than "periterr",\nand "pericynthion" rather than "perilune".)  So for Jupiter I\'d expect it\nto be something like "perizeon".)   :^)\n\n   ___            _                                           -  Bob\n   /__) _   /    / ) _   _\n(_/__) (_)_(_)  (___(_)_(/_______________________________________ bob@1776.COM\nRobert K. Coe ** 14 Churchill St, Sudbury, Massachusetts 01776 ** 508-443-3265\n'),
 Document(metadata={'category': 'sci.space'}, page_content='From: pyron@skndiv.dseg.ti.com (Dillon Pyron)\nSubject: Re: Why not give $1 billion to first year-long moon residents?\nLines: 42\nNntp-Posting-Host: skndiv.dseg.ti.com\nReply-To: pyron@skndiv.dseg.ti.com\nOrganization: TI/DSEG VAX Support\n\n\nIn article <1qve4kINNpas@sal-sun121.usc.edu>, schaefer@sal-sun121.usc.edu (Peter Schaefer) writes:\n>In article <1993Apr19.130503.1@aurora.alaska.edu>, nsmca@aurora.alaska.edu writes:\n>|> In article <6ZV82B2w165w@theporch.raider.net>, gene@theporch.raider.net (Gene Wright) writes:\n>|> > With the continuin talk about the "End of the Space Age" and complaints \n>|> > by government over the large cost, why not try something I read about \n>|> > that might just work.\n>|> > \n>|> > Announce that a reward of $1 billion would go to the first corporation \n>|> > who successfully keeps at least 1 person alive on the moon for a year. \n>|> > Then you\'d see some of the inexpensive but not popular technologies begin \n>|> > to be developed. THere\'d be a different kind of space race then!\n>|> > \n>|> > --\n>|> >   gene@theporch.raider.net (Gene Wright)\n>|> > theporch.raider.net  615/297-7951 The MacInteresteds of Nashville\n>|> ====\n>|> If that were true, I\'d go for it.. I have a few friends who we could pool our\n>|> resources and do it.. Maybe make it a prize kind of liek the "Solar Car Race"\n>|> in Australia..\n>|> Anybody game for a contest!\n>|> \n>|> ==\n>|> Michael Adams, nsmca@acad3.alaska.edu -- I\'m not high, just jacked\n>\n>\n>Oh gee, a billion dollars!  That\'d be just about enough to cover the cost of the\n>feasability study!  Happy, Happy, JOY! JOY!\n>\n\nFeasability study??  What a wimp!!  While you are studying, others would be\ndoing.  Too damn many engineers doing way too little engineering.\n\n"He who sits on his arse sits on his fortune"  - Sir Richard Francis Burton\n--\nDillon Pyron                      | The opinions expressed are those of the\nTI/DSEG Lewisville VAX Support    | sender unless otherwise stated.\n(214)462-3556 (when I\'m here)     |\n(214)492-4656 (when I\'m home)     |Texans: Vote NO on Robin Hood.  We need\npyron@skndiv.dseg.ti.com          |solutions, not gestures.\nPADI DM-54909                     |\n\n')]

检索增强生成的使用​

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

Redis 特定功能​

Redis 为向量搜索提供了一些独特的功能：

带元数据过滤的相似度搜索​

我们可以根据元数据过滤搜索结果

from redisvl.query.filter import Tag

query = "Tell me about space exploration"

# Create a RedisVL filter expression
filter_condition = Tag("category") == "sci.space"

filtered_results = vector_store.similarity_search(query, k=2, filter=filter_condition)

print("Filtered Similarity Search Results:")
for doc in filtered_results:
    print(f"Content: {doc.page_content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    print()
Filtered Similarity Search Results:
Content: From: aa429@freenet.carleton.ca (Terry Ford)
Subject: A flawed propulsion system: Space Shuttle
X-Ad...
Metadata: {'category': 'sci.space'}

Content: From: nsmca@aurora.alaska.edu
Subject: Space Design Movies?
Article-I.D.: aurora.1993Apr23.124722.1
...
Metadata: {'category': 'sci.space'}

最大边际相关性搜索有助于获得多样化的结果

# Maximum marginal relevance search with filter
mmr_results = vector_store.max_marginal_relevance_search(
    query, k=2, fetch_k=10, filter=filter_condition
)

print("Maximum Marginal Relevance Search Results:")
for doc in mmr_results:
    print(f"Content: {doc.page_content[:100]}...")
    print(f"Metadata: {doc.metadata}")
    print()
Maximum Marginal Relevance Search Results:
Content: From: aa429@freenet.carleton.ca (Terry Ford)
Subject: A flawed propulsion system: Space Shuttle
X-Ad...
Metadata: {'category': 'sci.space'}

Content: From: moroney@world.std.com (Michael Moroney)
Subject: Re: Vulcan? (No, not the guy with the ears!)
...
Metadata: {'category': 'sci.space'}

链式使用​

以下代码展示了如何在一个简单的 RAG 链中将向量存储用作检索器：

pip install -qU "langchain[google-genai]"
import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
  os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-2.0-flash", model_provider="google_genai")
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Prompt
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:""",
        ),
    ]
)


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain.invoke("Describe the Space Shuttle program?")
'The Space Shuttle program was a NASA initiative that enabled reusable spacecraft to transport astronauts and cargo to and from low Earth orbit. It conducted a variety of missions, including satellite deployment, scientific research, and assembly of the International Space Station, and typically carried a crew of five astronauts. Although it achieved many successes, the program faced criticism for its safety concerns and the complexity of its propulsion system.'

连接到现有索引​

为了在使用 Redis 向量存储时索引相同的元数据。您需要将相同的 index_schema 作为 YAML 文件路径或字典传递。以下展示了如何从索引中获取模式并连接到现有索引。

# write the schema to a yaml file
vector_store.index.schema.to_yaml("redis_schema.yaml")
# now we can connect to our existing index as follows

new_rdvs = RedisVectorStore(
    embeddings,
    redis_url=REDIS_URL,
    schema_path="redis_schema.yaml",
)

results = new_rdvs.similarity_search("Space Shuttle Propulsion System", k=3)
print(results[0])
18:19:58 redisvl.index.index INFO   Index already exists, not overwriting.
page_content='From: aa429@freenet.carleton.ca (Terry Ford)
Subject: A flawed propulsion system: Space Shuttle
X-Added: Forwarded by Space Digest
Organization: [via International Space University]
Original-Sender: isu@VACATION.VENARI.CS.CMU.EDU
Distribution: sci
Lines: 13



For an essay, I am writing about the space shuttle and a need for a better
propulsion system.  Through research, I have found that it is rather clumsy 
(i.e. all the checks/tests before launch), the safety hazards ("sitting
on a hydrogen bomb"), etc..  If you have any beefs about the current
space shuttle program Re: propulsion, please send me your ideas.

Thanks a lot.

--
Terry Ford [aa429@freenet.carleton.ca]
Nepean, Ontario, Canada.
' metadata={'category': 'sci.space'}
# compare the two schemas to verify they are the same
new_rdvs.index.schema == vector_store.index.schema
True

清理向量存储​

# Clear vector store
vector_store.index.delete(drop=True)

API 参考​

有关所有 RedisVectorStore 功能和配置的详细文档，请参阅 API 参考：https://python.langchain.ac.cn/api_reference/redis/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html