# Couchbase
## Prerequisite
### Requirements
1. Couchbase Cluster (Self Managed or Capella) version **7.6+** with [Search Service](https://docs.couchbase.com/server/current/search/search.html).
2. Capella Setup: To know more about connecting to your Capella cluster, please follow the [instructions](https://docs.couchbase.com/cloud/get-started/connect.html?_gl=1*1yhpmel*_gcl_au*MTMzNDE3NTQxLjE3MzY5MjA5MzQ.).
Specifically, you need to do the following:
* Create the [database credentials](https://docs.couchbase.com/cloud/clusters/manage-database-users.html?_gl=1*19zk7vq*_gcl_au*MTMzNDE3NTQxLjE3MzY5MjA5MzQ.) to access cluster.
* [Allow access](https://docs.couchbase.com/cloud/clusters/allow-ip-address.html?_gl=1*19zk7vq*_gcl_au*MTMzNDE3NTQxLjE3MzY5MjA5MzQ.) to the Cluster from the IP on which the application is running.
Self Managed Setup:
* Follow [Couchbase Installation Options](https://developer.couchbase.com/tutorial-couchbase-installation-options) for installing the latest Couchbase Database Server Instance. Make sure to add the Search Service.
3. Search Index Creation on the Full Text Service in Couchbase.
### Importing Search Index
#### [Couchbase Capella](https://github.com/FlowiseAI/FlowiseDocs/blob/main/en/integrations/langchain/vector-stores/\(https:/docs.couchbase.com/cloud/search/import-search-index.html)
Follow these steps to import a Search Index in Capella:
* Copy the index definition to a new file named `index.json`.
* Import the file in Capella following the instructions in the documentation.
* Click Create Index to finalize the index creation.
#### [Couchbase Server](https://github.com/FlowiseAI/FlowiseDocs/blob/main/en/integrations/langchain/vector-stores/\(https:/docs.couchbase.com/server/current/search/import-search-index.html)
Follow these steps for Couchbase Server:
* Navigate to Search → Add Index → Import.
* Copy the provided Index definition into the Import screen.
* Click Create Index to finalize the index creation.
You may also create a vector index using Search UI on both [Couchbase Capella](https://docs.couchbase.com/cloud/vector-search/create-vector-search-index-ui.html?_gl=1*1rglcpj*_gcl_au*MTMzNDE3NTQxLjE3MzY5MjA5MzQ.) and [Couchbase Self Managed Server](https://docs.couchbase.com/server/current/vector-search/create-vector-search-index-ui.html?_gl=1*t7aeet*_gcl_au*MTMzNDE3NTQxLjE3MzY5MjA5MzQ.).
### Index Definition
Here, we are creating the index `vector-index` on the documents. The Vector field is set to `embedding` with 1536 dimensions and the text field set to `text`. We are also indexing and storing all the fields under `metadata` in the document as a dynamic mapping to account for varying document structures. The similarity metric is set to `dot_product`. If there is a change in these parameters, please adapt the index accordingly.
```json
{
"name": "vector-index",
"type": "fulltext-index",
"params": {
"doc_config": {
"docid_prefix_delim": "",
"docid_regexp": "",
"mode": "scope.collection.type_field",
"type_field": "type"
},
"mapping": {
"default_analyzer": "standard",
"default_datetime_parser": "dateTimeOptional",
"default_field": "_all",
"default_mapping": {
"dynamic": true,
"enabled": false
},
"default_type": "_default",
"docvalues_dynamic": false,
"index_dynamic": true,
"store_dynamic": false,
"type_field": "_type",
"types": {
"_default._default": {
"dynamic": true,
"enabled": true,
"properties": {
"embedding": {
"enabled": true,
"dynamic": false,
"fields": [
{
"dims": 1536,
"index": true,
"name": "embedding",
"similarity": "dot_product",
"type": "vector",
"vector_index_optimized_for": "recall"
}
]
},
"metadata": {
"dynamic": true,
"enabled": true
},
"text": {
"enabled": true,
"dynamic": false,
"fields": [
{
"index": true,
"name": "text",
"store": true,
"type": "text"
}
]
}
}
}
}
},
"store": {
"indexType": "scorch",
"segmentVersion": 16
}
},
"sourceType": "gocbcore",
"sourceName": "pdf-chat",
"sourceParams": {},
"planParams": {
"maxPartitionsPerPIndex": 64,
"indexPartitions": 16,
"numReplicas": 0
}
}
```
## Setup
1. Add a new **Couchbase** node on canvas and fill in the Bucket Name, Scope Name, Collection Name and Index Name
2. Add new credential and fill in the parameters:
* Couchbase Connection String
* Cluster Username
* Cluster Password
3. Add additional nodes to canvas and start the upsert process
* **Document** can be connected with any node under [**Document Loader**](https://docs.flowiseai.com/integrations/langchain/document-loaders) category
* **Embeddings** can be connected with any node under [**Embeddings** ](https://docs.flowiseai.com/integrations/langchain/embeddings)category
5. Verify from the Couchbase UI to see if data has been successfully upserted!
## Resources
* LangChain Couchbase vectorstore integrations
* [Python](https://python.langchain.com/docs/integrations/vectorstores/couchbase/)
* [NodeJS](https://js.langchain.com/docs/integrations/vectorstores/couchbase/)
* Refer to the [Couchbase Documentation](https://docs.couchbase.com/home/index.html) to learn about Couchbase.
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