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Issue: the current [Cache](https://python.langchain.com/docs/integrations/llm_caching/) page has an inconsistent heading. Mixed terms are used; mixed casing; and mixed `selecting`. Excessively long titles make right-side ToC hard to read and unnecessarily long. Changes: consitent and more-readable ToC
111 lines
2.9 KiB
Plaintext
111 lines
2.9 KiB
Plaintext
# Couchbase
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>[Couchbase](http://couchbase.com/) is an award-winning distributed NoSQL cloud database
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> that delivers unmatched versatility, performance, scalability, and financial value
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> for all of your cloud, mobile, AI, and edge computing applications.
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## Installation and Setup
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We have to install the `langchain-couchbase` package.
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```bash
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pip install langchain-couchbase
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```
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## Vector Store
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See a [usage example](/docs/integrations/vectorstores/couchbase).
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```python
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from langchain_couchbase import CouchbaseVectorStore
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```
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## Document loader
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See a [usage example](/docs/integrations/document_loaders/couchbase).
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```python
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from langchain_community.document_loaders.couchbase import CouchbaseLoader
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```
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## LLM Caches
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### CouchbaseCache
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Use Couchbase as a cache for prompts and responses.
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See a [usage example](/docs/integrations/llm_caching/#couchbase-caches).
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To import this cache:
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```python
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from langchain_couchbase.cache import CouchbaseCache
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```
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To use this cache with your LLMs:
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```python
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from langchain_core.globals import set_llm_cache
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cluster = couchbase_cluster_connection_object
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set_llm_cache(
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CouchbaseCache(
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cluster=cluster,
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bucket_name=BUCKET_NAME,
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scope_name=SCOPE_NAME,
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collection_name=COLLECTION_NAME,
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)
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)
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```
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### CouchbaseSemanticCache
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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.
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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.
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See a [usage example](/docs/integrations/llm_caching/#couchbase-caches).
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To import this cache:
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```python
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from langchain_couchbase.cache import CouchbaseSemanticCache
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```
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To use this cache with your LLMs:
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```python
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from langchain_core.globals import set_llm_cache
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# use any embedding provider...
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from langchain_openai.Embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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cluster = couchbase_cluster_connection_object
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set_llm_cache(
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CouchbaseSemanticCache(
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cluster=cluster,
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embedding = embeddings,
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bucket_name=BUCKET_NAME,
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scope_name=SCOPE_NAME,
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collection_name=COLLECTION_NAME,
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index_name=INDEX_NAME,
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)
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)
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```
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## Chat Message History
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Use Couchbase as the storage for your chat messages.
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See a [usage example](/docs/integrations/memory/couchbase_chat_message_history).
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To use the chat message history in your applications:
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```python
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from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory
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message_history = CouchbaseChatMessageHistory(
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cluster=cluster,
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bucket_name=BUCKET_NAME,
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scope_name=SCOPE_NAME,
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collection_name=COLLECTION_NAME,
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session_id="test-session",
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)
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message_history.add_user_message("hi!")
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``` |