Redis Vector Store
This notebook covers how to get started with the Redis vector store.
Redis is a popular open-source, in-memory data structure store that can be used as a database, cache, message broker, and queue. It now includes vector similarity search capabilities, making it suitable for use as a vector store.
What is Redis?โ
Most developers are familiar with Redis
. At its core, Redis
is a NoSQL Database in the key-value family that can used as a cache, message broker, stream processing and a primary database. Developers choose Redis
because it is fast, has a large ecosystem of client libraries, and has been deployed by major enterprises for years.
On top of these traditional use cases, Redis
provides additional capabilities like the Search and Query capability that allows users to create secondary index structures within Redis
. This allows Redis
to be a Vector Database, at the speed of a cache.
Redis as a Vector Databaseโ
Redis
uses compressed, inverted indexes for fast indexing with a low memory footprint. It also supports a number of advanced features such as:
- Indexing of multiple fields in Redis hashes and
JSON
- Vector similarity search (with
HNSW
(ANN) orFLAT
(KNN)) - Vector Range Search (e.g. find all vectors within a radius of a query vector)
- Incremental indexing without performance loss
- Document ranking (using tf-idf, with optional user-provided weights)
- Field weighting
- Complex boolean queries with
AND
,OR
, andNOT
operators - Prefix matching, fuzzy matching, and exact-phrase queries
- Support for double-metaphone phonetic matching
- Auto-complete suggestions (with fuzzy prefix suggestions)
- Stemming-based query expansion in many languages (using Snowball)
- Support for Chinese-language tokenization and querying (using Friso)
- Numeric filters and ranges
- Geospatial searches using Redis geospatial indexing
- A powerful aggregations engine
- Supports for all
utf-8
encoded text - Retrieve full documents, selected fields, or only the document IDs
- Sorting results (for example, by creation date)
Clientsโ
Since Redis
is much more than just a vector database, there are often use cases that demand the usage of a Redis
client besides just the LangChain
integration. You can use any standard Redis
client library to run Search and Query commands, but it's easiest to use a library that wraps the Search and Query API. Below are a few examples, but you can find more client libraries here.
Project | Language | License | Author | Stars |
---|---|---|---|---|
jedis | Java | MIT | Redis | |
redisvl | Python | MIT | Redis | |
redis-py | Python | MIT | Redis | |
node-redis | Node.js | MIT | Redis | |
nredisstack | .NET | MIT | Redis |
Deployment optionsโ
There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such as
- Redis Cloud
- Docker (Redis Stack)
- Cloud marketplaces: AWS Marketplace, Google Marketplace, or Azure Marketplace
- On-premise: Redis Enterprise Software
- Kubernetes: Redis Enterprise Software on Kubernetes
Redis connection Url schemasโ
Valid Redis Url schemas are:
redis://
- Connection to Redis standalone, unencryptedrediss://
- Connection to Redis standalone, with TLS encryptionredis+sentinel://
- Connection to Redis server via Redis Sentinel, unencryptedrediss+sentinel://
- Connection to Redis server via Redis Sentinel, booth connections with TLS encryption
More information about additional connection parameters can be found in the redis-py documentation.
Setupโ
To use the RedisVectorStore, you'll need to install the langchain-redis
partner package, as well as the other packages used throughout this notebook.
%pip install -qU langchain-redis langchain-huggingface sentence-transformers scikit-learn
Note: you may need to restart the kernel to use updated packages.
Credentialsโ
Redis connection credentials are passed as part of the Redis Connection URL. Redis Connection URLs are versatile and can accommodate various Redis server topologies and authentication methods. These URLs follow a specific format that includes the connection protocol, authentication details, host, port, and database information. The basic structure of a Redis Connection URL is:
[protocol]://[auth]@[host]:[port]/[database]
Where:
- protocol can be redis for standard connections, rediss for SSL/TLS connections, or redis+sentinel for Sentinel connections.
- auth includes username and password (if applicable).
- host is the Redis server hostname or IP address.
- port is the Redis server port.
- database is the Redis database number.
Redis Connection URLs support various configurations, including:
- Standalone Redis servers (with or without authentication)
- Redis Sentinel setups
- SSL/TLS encrypted connections
- Different authentication methods (password-only or username-password)
Below are examples of Redis Connection URLs for different configurations:
# 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 booth Sentinel and Redis server
redis_url = "rediss+sentinel://localhost"
Launching a Redis Instance with Dockerโ
To use Redis with LangChain, you need a running Redis instance. You can start one using Docker with:
docker run -d -p 6379:6379 redis/redis-stack:latest
For this example, we'll use a local Redis instance. If you're using a remote instance, you'll need to modify the Redis URL accordingly.
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
If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
Let's check that Redis is up an running by pinging it:
import redis
redis_client = redis.from_url(REDIS_URL)
redis_client.ping()
True
Sample Dataโ
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics. We'll use a subset for this demonstration and focus on two categories: 'alt.atheism' and '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)
250
Initializationโ
The RedisVectorStore instance can be initialized in several ways:
RedisVectorStore.__init__
- Initialize directlyRedisVectorStore.from_texts
- Initialize from a list of texts (optionally with metadata)RedisVectorStore.from_documents
- Initialize from a list oflangchain_core.documents.Document
objectsRedisVectorStore.from_existing_index
- Initialize from an existing Redis index
Below we will use the RedisVectorStore.__init__
method using a RedisConfig
instance.
- OpenAI
- HuggingFace
- Fake Embedding
pip install -qU langchain-openai
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
pip install -qU langchain-huggingface
from langchain_huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model="sentence-transformers/all-mpnet-base-v2")
pip install -qU langchain-core
from langchain_core.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=4096)
We'll use the SentenceTransformer model to create embeddings. This model runs locally and doesn't require an API key.
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)
Manage vector storeโ
Add items to vector storeโ
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']
Let's inspect the first document:
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'})
Delete items from vector storeโ
# Delete documents by passing one or more keys/ids
vector_store.index.drop_keys(ids[0])
1
Inspecting the created Indexโ
Once the Redis
VectorStore object has been constructed, an index will have been created in Redis if it did not already exist. The index can be inspected with both the rvl
and the redis-cli
command line tool. If you installed redisvl
above, you can use the rvl
command line tool to inspect the index.
# assumes you're running Redis locally (use --host, --port, --password, --username, to change this)
!rvl index listall --port 6379
[32m17:54:50[0m [34m[RedisVL][0m [1;30mINFO[0m Using Redis address from environment variable, REDIS_URL
[32m17:54:50[0m [34m[RedisVL][0m [1;30mINFO[0m Indices:
[32m17:54:50[0m [34m[RedisVL][0m [1;30mINFO[0m 1. newsgroups
The Redis
VectorStore implementation will attempt to generate index schema (fields for filtering) for any metadata passed through the from_texts
, from_texts_return_keys
, and from_documents
methods. This way, whatever metadata is passed will be indexed into the Redis search index allowing
for filtering on those fields.
Below we show what fields were created from the metadata we defined above
!rvl index info -i newsgroups --port 6379
[32m17:54:50[0m [34m[RedisVL][0m [1;30mINFO[0m Using Redis address from environment variable, REDIS_URL
Index Information:
โญโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโฎ
โ Index Name โ Storage Type โ Prefixes โ Index Options โ Indexing โ
โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ newsgroups โ HASH โ ['newsgroups'] โ [] โ 0 โ
โฐโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโฏ
Index Fields:
โญโโโโโโโโโโโโฌโโโโโโโโโโโโโโฌโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโฎ
โ Name โ Attribute โ Type โ Field Option โ Option Value โ Field Option โ Option Value โ Field Option โ Option Value โ Field Option โ Option Value โ
โโโโโโโโโโโโโผโโโโโโโโโโโโโโผโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโค
โ text โ text โ TEXT โ WEIGHT โ 1 โ โ โ โ โ โ โ
โ embedding โ embedding โ VECTOR โ algorithm โ FLAT โ data_type โ FLOAT32 โ dim โ 768 โ distance_metric โ COSINE โ
โ category โ category โ TAG โ SEPARATOR โ | โ โ โ โ โ โ โ
โฐโโโโโโโโโโโโดโโโโโโโโโโโโโโดโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโฏ
!rvl stats -i newsgroups --port 6379
[32m17:54:51[0m [34m[RedisVL][0m [1;30mINFO[0m Using Redis address from environment variable, REDIS_URL
Statistics:
โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโฎ
โ Stat Key โ Value โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ num_docs โ 249 โ
โ num_terms โ 16178 โ
โ max_doc_id โ 250 โ
โ num_records โ 50394 โ
โ percent_indexed โ 1 โ
โ hash_indexing_failures โ 0 โ
โ number_of_uses โ 2 โ
โ bytes_per_record_avg โ 38.2743 โ
โ doc_table_size_mb โ 0.0263586 โ
โ inverted_sz_mb โ 1.83944 โ
โ key_table_size_mb โ 0.00932026 โ
โ offset_bits_per_record_avg โ 10.6699 โ
โ offset_vectors_sz_mb โ 0.089057 โ
โ offsets_per_term_avg โ 1.38937 โ
โ records_per_doc_avg โ 202.386 โ
โ sortable_values_size_mb โ 0 โ
โ total_indexing_time โ 72.444 โ
โ total_inverted_index_blocks โ 16207 โ
โ vector_index_sz_mb โ 3.01776 โ
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโฏ
Query vector storeโ
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
Query directlyโ
Performing a simple similarity search can be done as follows:
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'}
If you want to execute a similarity search and receive the corresponding scores you can run:
# 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
Query by turning into retrieverโ
You can also transform the vector store into a retriever for easier usage in your chains.
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')]
Usage for retrieval-augmented generationโ
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
- Tutorials: working with external knowledge
- How-to: Question and answer with RAG
- Retrieval conceptual docs
Redis-specific functionalityโ
Redis offers some unique features for vector search:
Similarity search with metadata filteringโ
We can filter our search results based on metadata:
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'}
Maximum marginal relevance searchโ
Maximum marginal relevance search helps in getting diverse results:
# 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'}
Chain usageโ
The code below shows how to use the vector store as a retriever in a simple RAG chain:
- OpenAI
- Anthropic
- Azure
- Cohere
- NVIDIA
- FireworksAI
- Groq
- MistralAI
- TogetherAI
pip install -qU langchain-openai
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
pip install -qU langchain-anthropic
import getpass
import os
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
pip install -qU langchain-openai
import getpass
import os
os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)
pip install -qU langchain-google-vertexai
import getpass
import os
os.environ["GOOGLE_API_KEY"] = getpass.getpass()
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model="gemini-1.5-flash")
pip install -qU langchain-cohere
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass()
from langchain_cohere import ChatCohere
llm = ChatCohere(model="command-r-plus")
pip install -qU langchain-nvidia-ai-endpoints
import getpass
import os
os.environ["NVIDIA_API_KEY"] = getpass.getpass()
from langchain import ChatNVIDIA
llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
pip install -qU langchain-fireworks
import getpass
import os
os.environ["FIREWORKS_API_KEY"] = getpass.getpass()
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")
pip install -qU langchain-groq
import getpass
import os
os.environ["GROQ_API_KEY"] = getpass.getpass()
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama3-8b-8192")
pip install -qU langchain-mistralai
import getpass
import os
os.environ["MISTRAL_API_KEY"] = getpass.getpass()
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest")
pip install -qU langchain-openai
import getpass
import os
os.environ["TOGETHER_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)
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.'
Connect to an existing Indexโ
In order to have the same metadata indexed when using the Redis
VectorStore. You will need to have the same index_schema
passed in either as a path to a yaml file or as a dictionary. The following shows how to obtain the schema from an index and connect to an existing index.
# 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
Cleanup vector storeโ
# Clear vector store
vector_store.index.delete(drop=True)
API referenceโ
For detailed documentation of all RedisVectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html
Relatedโ
- Vector store conceptual guide
- Vector store how-to guides