Couchbase

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.

Installation and Setup

We have to install the langchain-couchbase package.

pip install langchain-couchbase

uv add langchain-couchbase

Vector Store

See a usage example.

from langchain_couchbase import CouchbaseSearchVectorStore

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)

API Reference: CouchbaseSearchVectorStore

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()

LLM Caches

CouchbaseCache

Use Couchbase as a cache for prompts and responses. See a usage example. 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

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. See a usage example. 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=BUCKET_NAME,
        scope_name=SCOPE_NAME,
        collection_name=COLLECTION_NAME,
        index_name=INDEX_NAME,
    )
)

API Reference: CouchbaseSemanticCache

Chat Message History

Use Couchbase as the storage for your chat messages. See a usage example. To use the chat message history in your applications:

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",
)

message_history.add_user_message("hi!")

API Reference: CouchbaseChatMessageHistory

快速开始

核心组件

进阶使用

在生产环境使用

Installation and Setup

Vector Store

Document loader

LLM Caches

CouchbaseCache

CouchbaseSemanticCache

Chat Message History

​Installation and Setup

​Vector Store

​Document loader

​LLM Caches

​CouchbaseCache

​CouchbaseSemanticCache

​Chat Message History

Installation and Setup

Vector Store

Document loader

LLM Caches

CouchbaseCache

CouchbaseSemanticCache

Chat Message History