> ## Documentation Index
> Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Couchbase

> [Couchbase](http://couchbase.com/) 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.

<CodeGroup>
  ```bash pip theme={null}
  pip install langchain-couchbase
  ```

  ```bash uv theme={null}
  uv add langchain-couchbase
  ```
</CodeGroup>

## Vector Store

See a [usage example](/oss/python/integrations/vectorstores/couchbase).

```python theme={null}
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](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.vectorstores.search_vector_store)

## Document loader

See a [usage example](/oss/python/integrations/document_loaders/couchbase).

```python theme={null}
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](/oss/python/integrations/llm_caching/#couchbase-caches).

To import this cache:

```python theme={null}
from langchain_couchbase.cache import CouchbaseCache
```

To use this cache with your LLMs:

```python theme={null}
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](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#langchain_couchbase.cache.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](/oss/python/integrations/vectorstores/couchbase) on how to set up the index.

See a [usage example](/oss/python/integrations/llm_caching/#couchbase-caches).

To import this cache:

```python theme={null}
from langchain_couchbase.cache import CouchbaseSemanticCache
```

To use this cache with your LLMs:

```python theme={null}
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](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#langchain_couchbase.cache.CouchbaseSemanticCache)

## Chat Message History

Use Couchbase as the storage for your chat messages.

See a [usage example](/oss/python/integrations/memory/couchbase_chat_message_history).

To use the chat message history in your applications:

```python theme={null}
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](https://couchbase-ecosystem.github.io/langchain-couchbase/langchain_couchbase.html#module-langchain_couchbase.chat_message_histories)