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Redis 向量儲存

本筆記本涵蓋如何開始使用 Redis 向量儲存。

Redis 是一種流行的開源、記憶體內資料結構儲存，可用作資料庫、快取、訊息代理和佇列。 它現在包含向量相似性搜尋功能，使其適合用作向量儲存。

什麼是 Redis？​

大多數開發人員都熟悉Redis。 在其核心，Redis 是 key-value 家族中的 NoSQL 資料庫，可用作快取、訊息代理、流處理和主要資料庫。 開發人員選擇 Redis 是因為它速度快、擁有龐大的客戶端程式庫生態系統，並且已被主要企業部署多年。

除了這些傳統的使用案例之外，Redis 還提供額外的功能，例如搜尋和查詢功能，允許使用者在 Redis 內建立二級索引結構。 這使得 Redis 能夠成為向量資料庫，並具有快取的速度。

Redis 作為向量資料庫​

Redis 使用壓縮的反向索引進行快速索引，且記憶體佔用量低。 它還支援許多高級功能，例如

  • 在 Redis hashes 和 JSON 中索引多個欄位
  • 向量相似性搜尋（使用 HNSW (ANN) 或 FLAT (KNN)）
  • 向量範圍搜尋（例如，查找查詢向量半徑內的所有向量）
  • 無需性能損失的增量索引
  • 文件排名（使用 tf-idf，帶有可選的使用者提供權重）
  • 欄位加權
  • 帶有 AND、OR 和 NOT 運算符的複雜布林查詢
  • 字首匹配、模糊匹配和精確短語查詢
  • 支援 double-metaphone 語音匹配
  • 自動完成建議（帶有模糊字首建議）
  • 在 多種語言中基於詞幹的查詢擴展（使用 Snowball）
  • 支援中文分詞和查詢（使用 Friso）
  • 數值過濾器和範圍
  • 使用 Redis 地理空間索引進行地理空間搜尋
  • 強大的聚合引擎
  • 支援所有 utf-8 編碼的文本
  • 檢索完整文件、選定的欄位或僅檢索文件 ID
  • 排序結果（例如，按建立日期）

客戶端​

由於 Redis 不僅僅是一個向量資料庫，因此經常有需要使用 Redis 客戶端而不僅僅是 LangChain 整合的使用案例。 您可以使用任何標準的 Redis 客戶端程式庫來運行搜尋和查詢命令，但最簡單的方法是使用封裝搜尋和查詢 API 的程式庫。 以下是一些示例，但您可以在此處找到更多客戶端程式庫。

專案語言許可證作者Stars
jedisJavaMITRedisStars
redisvlPythonMITRedisStars
redis-pyPythonMITRedisStars
node-redisNode.jsMITRedisStars
nredisstack.NETMITRedisStars

部署選項​

有多種使用 RediSearch 部署 Redis 的方法。 開始使用的最簡單方法是使用 Docker，但有很多潛在的部署選項，例如

Redis 連接 URL 模式​

有效的 Redis URL 模式為

  1. redis:// - 連接到 Redis standalone，未加密
  2. rediss:// - 連接到 Redis standalone，帶 TLS 加密
  3. redis+sentinel:// - 透過 Redis Sentinel 連接到 Redis 伺服器，未加密
  4. rediss+sentinel:// - 透過 Redis Sentinel 連接到 Redis 伺服器，兩者連接都帶 TLS 加密

有關其他連接參數的更多資訊，請參閱 redis-py 文件。

設定​

要使用 RedisVectorStore，您需要安裝 langchain-redis 合作夥伴套件，以及本筆記本中使用的其他套件。

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

憑證​

Redis 連接憑證作為 Redis 連接 URL 的一部分傳遞。 Redis 連接 URL 功能多樣，可以容納各種 Redis 伺服器拓撲和身份驗證方法。 這些 URL 遵循特定格式，包括連接協議、身份驗證詳細資訊、主機、端口和資料庫資訊。 Redis 連接 URL 的基本結構為

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

其中

  • protocol 可以是 redis 用於標準連接，rediss 用於 SSL/TLS 連接，或 redis+sentinel 用於 Sentinel 連接。
  • auth 包含使用者名稱和密碼（如果適用）。
  • host 是 Redis 伺服器的主機名稱或 IP 位址。
  • port 是 Redis 伺服器的連接埠。
  • database 是 Redis 資料庫編號。

Redis 連線 URL 支援各種設定，包括

  • 獨立 Redis 伺服器（具有或不具有身份驗證）
  • Redis Sentinel 設定
  • SSL/TLS 加密連線
  • 不同的身份驗證方法（僅密碼或使用者名稱-密碼）

以下是不同設定的 Redis 連線 URL 範例

# connection to redis standalone at localhost, db 0, no password
redis_url = "redis://127.0.0.1:6379"
# 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://127.0.0.1:26379"
# 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://127.0.0.1:6379"
# connection to redis sentinel at localhost and default port, db 0, no password
# but with TLS support for booth Sentinel and Redis server
redis_url = "rediss+sentinel://127.0.0.1"

使用 Docker 啟動 Redis 執行個體​

若要將 Redis 與 LangChain 搭配使用，您需要一個正在運行的 Redis 執行個體。您可以使用 Docker 啟動一個：

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

在本範例中，我們將使用本機 Redis 執行個體。如果您使用的是遠端執行個體，則需要相應地修改 Redis URL。

import os

REDIS_URL = os.getenv("REDIS_URL", "redis://127.0.0.1:6379")
print(f"Connecting to Redis at: {REDIS_URL}")
Connecting to Redis at: redis://redis:6379

如果您想要取得模型呼叫的自動追蹤，您也可以設定您的 LangSmith API 金鑰，取消註解下面這行即可

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

讓我們透過 ping 來檢查 Redis 是否已啟動並正在運行

import redis

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

範例資料​

20 newsgroups 資料集包含大約 18000 篇關於 20 個主題的新聞組文章。我們將使用一個子集進行此示範，並專注於兩個類別：「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 參考：Document
250

初始化​

RedisVectorStore 實例可以透過多種方式初始化

  • RedisVectorStore.__init__ - 直接初始化
  • RedisVectorStore.from_texts - 從文字清單初始化（可選擇包含中繼資料）
  • RedisVectorStore.from_documents - 從 langchain_core.documents.Document 物件清單初始化
  • RedisVectorStore.from_existing_index - 從現有的 Redis 索引初始化

下面我們將使用 RedisVectorStore.__init__ 方法，並使用 RedisConfig 實例。

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")

我們將使用 SentenceTransformer 模型來建立嵌入。此模型在本機運行，不需要 API 金鑰。

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)

管理向量儲存區​

將項目新增至向量儲存區​

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']

讓我們檢查第一個文件

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'})

從向量儲存區刪除項目​

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

檢查已建立的索引​

一旦建構了 Redis VectorStore 物件，如果 Redis 中還不存在索引，則會建立一個索引。可以使用 rvl 和 redis-cli 命令列工具來檢查索引。如果您安裝了上面的 redisvl，您可以使用 rvl 命令列工具來檢查索引。

# 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 VectorStore 實作將嘗試為透過 from_texts、from_texts_return_keys 和 from_documents 方法傳遞的任何中繼資料產生索引結構描述（用於篩選的欄位）。這樣，傳遞的任何中繼資料都會被索引到 Redis 搜尋索引中，允許對這些欄位進行篩選。

下面我們展示了從我們上面定義的中繼資料中建立的欄位

!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    │
╰─────────────────────────────┴────────────╯

查詢向量儲存區​

一旦建立了您的向量儲存區並新增了相關文件，您很可能會希望在執行您的鏈或代理時查詢它。

直接查詢​

可以如下執行簡單的相似度搜尋

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'}

如果您想執行相似度搜尋並接收相應的分數，您可以運行

# 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

透過轉換為檢索器來查詢​

您也可以將向量儲存區轉換為檢索器，以便在您的鏈中更輕鬆地使用。

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')]

用於檢索增強生成​

有關如何將此向量儲存區用於檢索增強生成 (RAG) 的指南，請參閱以下章節

Redis 特定功能​

Redis 為向量搜尋提供了一些獨特的功能

具有中繼資料篩選的相似度搜尋​

我們可以根據中繼資料篩選我們的搜尋結果

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'}

最大邊際相關性搜尋有助於獲得多樣化的結果

# 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'}

鏈使用​

下面的程式碼顯示了如何在簡單的 RAG 鏈中使用向量儲存區作為檢索器

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_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")
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.'

連接到現有索引​

為了在使用 Redis VectorStore 時能索引相同的 metadata，您需要傳入相同的 index_schema，可以是一個 yaml 檔案的路徑，或是一個字典。 以下展示如何從一個索引取得 schema，並連接到現有的索引。

# 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

清除向量儲存區​

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

API 參考​

如需所有 RedisVectorStore 功能和配置的詳細文件，請前往 API 參考：https://langchain-python.dev.org.tw/api_reference/redis/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html

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