LlamaIndex

Develop AI applications with Couchbase and Python using LlamaIndex integrations, which include document loading and vector store.

LlamaIndex overview & features

The CouchbaseReader integrates Couchbase with LlamaIndex, allowing data to be loaded from a Couchbase cluster into documents. Users can connect with or without a pre-configured client using connection strings and credentials. Data loading supports SQL++ queries and customizable fields for both text and metadata. Options for lazy or complete data loading provide flexibility, making it ideal for AI applications needing efficient data retrieval and integration.

LlamaIndex resources

Docs
LlamaIndex readers integration loads documents from Couchbase

  • Data Loading from Couchbase: Load documents into LlamaIndex using SQL++ queries to retrieve the required data from Couchbase.
  • Flexible Configuration Options: Connect with Couchbase through credentials or an existing client instance.
  • Field Selection Control: Specify text and metadata fields to structure the loaded documents as needed.
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Blogs
Latest updates on LlamaIndex with Couchbase
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Frequently asked questions

  • What does Couchbase integration with LlamaIndex enable?

    It allows users to fetch data from Couchbase using SQL++ queries and load it into LlamaIndex for processing and indexing.

  • How do I connect Couchbase to LlamaIndex?

    You can connect by providing Couchbase credentials or by passing an initialized Couchbase client to the CouchbaseReader.

  • Can I choose specific fields to include in the documents?

    Yes, you can specify which text fields and metadata fields to include when loading data from Couchbase into LlamaIndex.

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