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# 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.
```bash
pip install langchain-couchbase
```
## Vector Store
See a [usage example](/docs/integrations/vectorstores/couchbase).
```python
from langchain_couchbase import CouchbaseVectorStore
```
## Document loader
See a [usage example](/docs/integrations/document_loaders/couchbase).
```python
from langchain_community.document_loaders.couchbase import CouchbaseLoader
```
## LLM Caches
### CouchbaseCache
Use Couchbase as a cache for prompts and responses.
See a [usage example](/docs/integrations/llm_caching/#couchbase-cache).
To import this cache:
```python
from langchain_couchbase.cache import CouchbaseCache
```
To use this cache with your LLMs:
```python
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,
)
)
```
### 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](/docs/integrations/vectorstores/couchbase) on how to set up the index.
See a [usage example](/docs/integrations/llm_caching/#couchbase-semantic-cache).
To import this cache:
```python
from langchain_couchbase.cache import CouchbaseSemanticCache
```
To use this cache with your LLMs:
```python
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,
)
)
```
## Chat Message History
Use Couchbase as the storage for your chat messages.
See a [usage example](/docs/integrations/memory/couchbase_chat_message_history).
To use the chat message history in your applications:
```python
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!")
``` |