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Couchbase is an award-winning distributed NoSQL cloud database that delivers unmatched versatility, performance, scalability, and financial value for all of your cloud, mobile, AI, and edge computing applications.
If you want to see a detailed usage example, see Couchbase Vector Store.

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Installation and setup

Install the langchain-couchbase package along with embedding dependencies: 
pip install langchain-couchbase langchain-openai

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Vector store

Couchbase provides two different vector store implementations for LangChain:
Vector StoreIndex TypeMinimum VersionBest For
CouchbaseSearchVectorStoreSearch Vector IndexCouchbase Server 7.6+Hybrid searches combining vector similarity with Full-Text Search (FTS) and geospatial searches
CouchbaseQueryVectorStoreHyperscale Vector Index or Composite Vector IndexCouchbase Server 8.0+Large-scale pure vector searches or searches combining vector similarity with scalar filters

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CouchbaseSearchVectorStore

from langchain_couchbase import CouchbaseSearchVectorStore
from langchain_openai import OpenAIEmbeddings

import getpass
import os

# Get credentials
COUCHBASE_CONNECTION_STRING = getpass.getpass(
    "Enter the connection string for the Couchbase cluster: "
)
DB_USERNAME = getpass.getpass("Enter the username for the Couchbase cluster: ")
DB_PASSWORD = getpass.getpass("Enter the password for the Couchbase cluster: ")
OPENAI_API_KEY = getpass.getpass("Enter your OpenAI API key: ")

os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

# Create Couchbase connection object
from datetime import timedelta

from couchbase.auth import PasswordAuthenticator
from couchbase.cluster import Cluster
from couchbase.options import ClusterOptions

auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)
options = ClusterOptions(auth)
options.apply_profile("wan_development")
cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)

# Wait until the cluster is ready for use.
cluster.wait_until_ready(timedelta(seconds=5))

# Set up embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

# Create vector store
vector_store = CouchbaseSearchVectorStore(
    cluster=cluster,
    bucket_name="my_bucket",
    scope_name="_default",
    collection_name="_default",
    embedding=embeddings,
    index_name="my_search_index",
)

# Add documents
texts = ["Couchbase is a NoSQL database", "LangChain is a framework for LLM applications"]
vector_store.add_texts(texts)

# Search
query = "What is Couchbase?"
docs = vector_store.similarity_search(query)
API Reference: CouchbaseSearchVectorStore

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CouchbaseQueryVectorStore

from langchain_couchbase import CouchbaseQueryVectorStore
from langchain_openai import OpenAIEmbeddings

# (After setting up cluster connection as shown above)

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

vector_store = CouchbaseQueryVectorStore(
    cluster=cluster,
    bucket_name="my_bucket",
    scope_name="_default",
    collection_name="_default",
    embedding=embeddings,
    index_name="my_vector_index",
)

# Create index (if needed)
vector_store.create_index(
    index_type=IndexType.HYPERSCALE,
    index_description="IVF,SQ8",
    index_name="my_vector_index",
)

# Add documents and search
vector_store.add_documents([
    Document(page_content="Couchbase is a NoSQL database", metadata={"source": "couchbase"}),
    Document(page_content="LangChain is a framework for LLM applications", metadata={"source": "langchain"}),
])
docs = vector_store.similarity_search("What is Couchbase?")
API Reference: CouchbaseQueryVectorStore

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Document loader

See a usage example. 
from langchain_community.document_loaders.couchbase import CouchbaseLoader

connection_string = "couchbase://localhost"  # valid Couchbase connection string
db_username = (
    "Administrator"  # valid database user with read access to the bucket being queried
)
db_password = "Password"  # password for the database user

# query is a valid SQL++ query
query = """
    SELECT h.* FROM `travel-sample`.inventory.hotel h
        WHERE h.country = 'United States'
        LIMIT 1
        """

loader = CouchbaseLoader(
    connection_string,
    db_username,
    db_password,
    query,
)

docs = loader.load()

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LLM caches

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CouchbaseCache

Use Couchbase as a cache for prompts and responses. To import this cache: 
from langchain_couchbase.cache import CouchbaseCache
To use this cache with your LLMs: 
from langchain_core.globals import set_llm_cache

cluster = couchbase_cluster_connection_object

set_llm_cache(
    CouchbaseCache(
        cluster=cluster,
        bucket_name=BUCKET_NAME,
        scope_name=SCOPE_NAME,
        collection_name=COLLECTION_NAME,
    )
)
API Reference: CouchbaseCache

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CouchbaseSemanticCache

Semantic caching allows users to retrieve cached prompts based on the semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore. The CouchbaseSemanticCache needs a Search Index defined to work. Please look at the usage example on how to set up the index. To import this cache: 
from langchain_couchbase.cache import CouchbaseSemanticCache
To use this cache with your LLMs: 
from langchain_core.globals import set_llm_cache

# use any embedding provider...
from langchain_openai.Embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
cluster = couchbase_cluster_connection_object

set_llm_cache(
    CouchbaseSemanticCache(
        cluster=cluster,
        embedding = embeddings,
        bucket_name="my_bucket",
        scope_name="_default",
        collection_name="_default",
        index_name="my_search_index",
    )
)
API Reference: CouchbaseSemanticCache
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