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Redis 向量存储

这个笔记本介绍了如何开始使用Redis向量存储。

Redis 是一个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性搜索功能，使其适合作为向量存储使用。

什么是 Redis？​

大多数开发者都熟悉 Redis。从根本上说，Redis 是键值型 NoSQL 数据库，可用作缓存、消息代理、流处理工具以及主数据库。Redis 因其速度快、拥有丰富的客户端库生态系统，并且多年来已被大型企业广泛部署，因而受到开发者的青睐。

除了这些传统用例之外，Redis 还提供了额外的功能，例如搜索和查询功能，允许用户在 Redis 内创建二级索引结构。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来实现快速索引，并具有较低的内存占用。它还支持许多高级功能，例如：

  • 在 Redis 哈希中对多个字段建立索引以及 JSON
  • 向量相似性搜索（使用 HNSW (ANN) 或 FLAT (KNN)）
  • 向量范围搜索（例如，查找查询向量半径内的所有向量）
  • 增量索引且无性能损失
  • 文档排序（使用 tf-idf，可选择性提供用户权重）
  • 字段加权
  • 使用 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
  • 支持双音素匹配（double-metaphone phonetic matching）
  • 自动补全建议（支持模糊前缀匹配）
  • 基于词干提取的查询扩展，支持多种语言（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
  • 一个强大的聚合引擎
  • 支持所有utf-8编码的文本
  • 检索完整的文档、选定的字段或仅文档ID
  • 按创建日期等条件对结果进行排序

客户端​

由于 Redis 远不只是一个向量数据库，因此经常会出现需要使用 Redis 客户端而不仅仅是 LangChain 集成的用例。你可以使用任何标准的 Redis 客户端库来运行搜索和查询命令，但使用封装了搜索和查询 API 的库会更加简便。以下是一些示例，但你也可以在这里找到更多客户端库：这里。

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

部署选项​

使用 RediSearch 部署 Redis 有多种方式。开始使用的最简单方法是使用 Docker，但还有其他许多潜在的部署选项，例如

Redis 连接 URL 架构​

有效的 Redis URL 模式包括：

  1. redis:// - 连接到 Redis 独立实例，未加密
  2. rediss:// - 连接到启用 TLS 加密的 Redis 独立实例
  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，两个连接均使用 TLS 加密

有关其他连接参数的更多信息，请参见redis-py 文档。

设置​

要使用 RedisVectorStore，您需要安装 langchain-redis 合作伙伴包，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.

Credentials​

Redis 连接凭据作为 Redis 连接 URL 的一部分传递。Redis 连接 URL 功能强大，可适应各种 Redis 服务器拓扑结构和认证方法。这些 URL 遵循特定格式，包括连接协议、认证详情、主机、端口和数据库信息。 Redis 连接 URL 的基本结构如下：

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

位置:

  • 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）。
  • 认证包括用户名和密码（如适用）。
  • host 是 Redis 服务器的 hostname 或 IP 地址。
  • 端口是 Redis 服务器端口。
  • database 是 Redis 数据库编号。

Redis 连接 URL 支持多种配置，包括：

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

以下是不同配置下的 Redis 连接 URL 示例：

# connection to redis standalone at localhost, db 0, no password
redis_url = "redis://localhost: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://localhost: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://localhost: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://localhost"

启动使用 Docker 的 Redis 实例​

要使用 Redis 与 LangChain，您需要一个正在运行的 Redis 实例。您可以使用 Docker 启动它：

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

对于本示例，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://localhost:6379")
print(f"Connecting to Redis at: {REDIS_URL}")
Connecting to Redis at: redis://redis:6379

要启用对您的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"

让我们通过 ping 命令检查 Redis 是否正在运行：

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条关于20个主题的帖子。我们将使用其中的一个子集进行演示，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:文档
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（可选择包含元数据）初始化
  • RedisVectorStore.from_documents - 从 langchain_core.documents.Document 对象列表初始化
  • RedisVectorStore.from_existing_index - 从现有的 Redis 索引初始化

下面我们将使用 RedisVectorStore.__init__ 方法配合一个 RedisConfig 实例。

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。该模型在本地运行，无需 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​

添加项到向量存储​

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']

让我们检查第一个文档：

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-existant 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'})

删除向量存储中的项​

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

检查创建的索引​

一旦 Redis VectorStore 对象构建完成，如果 Redis 中尚不存在该索引，则会自动创建一个索引。该索引可以通过 rvl和redis-cli命令行工具进行检查。如果您在上述步骤中安装了 redisvl，则可以使用 rvl命令行工具来检查该索引。

# 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

The Redis VectorStore 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架构（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。

下面展示我们根据上述定义的元数据创建了哪些字段

!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 ​

一旦您的向量存储已经创建并添加了相关文档，您很可能在运行链或代理的过程中希望对其进行查询。

查询直接​

简单进行相似性搜索可以按照以下方式进行：

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'}

如果您想要执行相似性搜索并接收相应的评分，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

查询通过转换为检索器​

您也可以将向量存储转换为检索器，以便在链条中更方便地使用。

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')]

使用检索增强生成​

对于如何使用此向量存储进行检索增强生成（RAG）的指南，请参见以下部分：

Redis 特定功能​

Redis 为向量搜索提供了一些独特功能：

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'}

链用法​

下面的代码展示了如何在简单的 RAG 链中将向量存储用作检索器：

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.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")
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 VectorStore 时拥有相同的元数据索引，您需要传入相同的 index_schema，可以是 YAML 文件的路径或字典。以下内容展示了如何从索引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，请访问 API 参考文档：https://python.langchain.com/api_reference/redis/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html