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

Vector Store

See a usage example.

from langchain_couchbase import CouchbaseVectorStore

API Reference:CouchbaseVectorStore

Document loader

See a usage example.

from langchain_community.document_loaders.couchbase import CouchbaseLoader

API Reference:CouchbaseLoader

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

API Reference: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:set_llm_cache

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

API Reference: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:set_llm_cache

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