Documentation Index
Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
Use this file to discover all available pages before exploring further.
The langchain-community package is no longer maintained. Examples that import from langchain_community may be outdated or broken. Use with caution.
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.
Installation and setup
Install the langchain-couchbase package along with embedding dependencies:
pip install langchain-couchbase langchain-openai
Vector store
Couchbase provides two different vector store implementations for LangChain:
| Vector Store | Index Type | Minimum Version | Best For |
|---|
CouchbaseSearchVectorStore | Search Vector Index | Couchbase Server 7.6+ | Hybrid searches combining vector similarity with Full-Text Search (FTS) and geospatial searches |
CouchbaseQueryVectorStore | Hyperscale Vector Index or Composite Vector Index | Couchbase Server 8.0+ | Large-scale pure vector searches or searches combining vector similarity with scalar filters |
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)
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?")
Document loader
See a .
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()
LLM caches
CouchbaseCache
Use Couchbase as a cache for prompts and responses.
To import this cache:
from langchain_couchbase.cache import CouchbaseCache
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,
)
)
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
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",
)
)
