Usage

langchain-couchbase is the official Couchbase integration for LangChain.

Vector Store

Use Couchbase as a vector store for your documents:

See a complete vector store usage example.

import getpass

# Constants for the connection
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: ")

# 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)
cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)

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

vector_store = CouchbaseSearchVectorStore(
    cluster=cluster,
    bucket_name=BUCKET_NAME,
    scope_name=SCOPE_NAME,
    collection_name=COLLECTION_NAME,
    embedding=my_embeddings,
    index_name=SEARCH_INDEX_NAME,
)

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

# Search
query = "What is Couchbase?"
docs = vectorstore.similarity_search(query)

Cache

Use Couchbase as a cache for LLM responses:

See a complete cache usage example.

from langchain_couchbase.cache import CouchbaseCache
from langchain_core.globals import set_llm_cache

cluster = couchbase_cluster_connection_object

cache = CouchbaseCache(
    cluster=cluster,
    bucket_name=BUCKET_NAME,
    scope_name=SCOPE_NAME,
    collection_name=COLLECTION_NAME,
)

set_llm_cache(cache)

Semantic Cache

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.

See a complete semantic cache usage example.

from langchain_couchbase.cache import CouchbaseSemanticCache
from langchain_core.globals import set_llm_cache
from langchain.embeddings import OpenAIEmbeddings

# use any embedding provider...
embeddings = OpenAIEmbeddings()

# Setup cache
cluster = couchbase_cluster_connection_object

cache = CouchbaseSemanticCache(
            cluster=cluster,
            embedding=embeddings,
            bucket_name=BUCKET_NAME,
            scope_name=SCOPE_NAME,
            collection_name=COLLECTION_NAME,
            index_name=INDEX_NAME,
    )

# Set as global cache
set_llm_cache(cache)

Chat Message History

Use Couchbase as the storage for your chat messages.

See a complete chat message history usage example.

from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory

message_history = CouchbaseChatMessageHistory(
    cluster=cluster,
    bucket_name=BUCKET_NAME,
    scope_name=SCOPE_NAME,
    collection_name=COLLECTION_NAME,
    session_id="test-session",
)

# Add messages
message_history.add_user_message("Hello!")
message_history.add_ai_message("Hi there! How can I help you today?")

# Get messages
messages = message_history.messages