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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

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!")

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