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DA-1334 update: tutorial with the terminology of the search index #83

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VirajAgarwal1 merged 1 commit into from
Jan 12, 2026
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DA-1334 update: tutorial with the terminology of the search index #83

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11 changes: 6 additions & 5 deletions tutorial/markdown/nodejs/nodejs-langchain-pdf-chat/nodejs-langchain-pdf-chat.md
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,10 @@ description:
content_type: tutorial
filter: sdk
technology:
- fts
- vector search
- kv
tags:
- Search Vector Index
- Next.js
- LangChain
- OpenAI
Expand All @@ -37,7 +38,7 @@ This tutorial will demonstrate how to -
## Prerequisites

- [LTS Node.js Version](https://nodejs.org/en/download)
- Couchbase Cluster (Self Managed or Capella) version 7.6+ with [Search Service](https://docs.couchbase.com/server/current/fts/fts-introduction.html)
- Couchbase Cluster (Self Managed or Capella) version 7.6+ with [Search Service](https://docs.couchbase.com/server/current/search/search.html)

> Note that this tutorial is designed to work with the latest Node SDK version (4.3.0+) for Couchbase. It will not work with the older Node.js versions.

Expand Down Expand Up @@ -72,7 +73,7 @@ Specifically, you need to do the following:

#### Self Managed Setup

- Follow [Couchbase Installation Options](/tutorial-couchbase-installation-options) for installing the latest Couchbase Database Server Instance. Make sure to add the [Search Service](https://docs.couchbase.com/server/current/fts/fts-introduction.html)
- Follow [Couchbase Installation Options](/tutorial-couchbase-installation-options) for installing the latest Couchbase Database Server Instance. Make sure to add the [Search Service](https://docs.couchbase.com/server/current/search/search.html)

### Create Bucket

Expand Down Expand Up @@ -193,7 +194,7 @@ DB_PASSWORD=<password_for_couchbase_cluster>
DB_BUCKET=<name_of_bucket_to_store_documents>
DB_SCOPE=<name_of_scope_to_store_documents>
DB_COLLECTION=<name_of_collection_to_store_documents>
INDEX_NAME=<name_of_fts_index_with_vector_support>
INDEX_NAME=<name_of_search_index_with_vector_support>
```

> [OpenAI](https://openai.com) API Key is required for usage in generating embedding and querying LLM
Expand Down Expand Up @@ -254,7 +255,7 @@ When a user asks a question or provides a prompt:
- The app queries this search index using the user's query embedding. Couchbase's Vector Search calculates the [similarity](https://www.couchbase.com/blog/vector-similarity-search/) (e.g., dot product) between the query embedding and the indexed PDF embeddings, enabling fast retrieval of the nearest neighbor embeddings.
- The nearest neighbor embeddings represent the most semantically similar passages or sections from the PDF documents compared to the user's query.
- The app retrieves the text content associated with these nearest neighbor embeddings, providing the necessary context for generating a relevant response.
- Couchbase's Vector Search supports advanced indexing techniques, such as [scoped indexes](https://docs.couchbase.com/nodejs-sdk/current/howtos/full-text-searching-with-sdk.html#scoped-vs-global-indexes), [dynamic indexing](https://docs.couchbase.com/server/current/fts/fts-creating-index-from-REST-dynamic.html) and [hybrid search](https://docs.couchbase.com/nodejs-sdk/current/howtos/full-text-searching-with-sdk.html#combining-fts-and-vector-queries), allowing for efficient management, better scaling of the vector store and multiple types of search supported.
- Couchbase's Vector Search supports advanced indexing techniques, such as [scoped indexes](https://docs.couchbase.com/nodejs-sdk/current/howtos/full-text-searching-with-sdk.html#scoped-vs-global-indexes), [dynamic indexing](https://docs.couchbase.com/server/current/search/create-search-index-rest-api.html) and [hybrid search](https://docs.couchbase.com/nodejs-sdk/current/howtos/full-text-searching-with-sdk.html#combining-fts-and-vector-queries), allowing for efficient management, better scaling of the vector store and multiple types of search supported.
- The search index facilitates fast and accurate retrieval, enabling the app to provide context-aware and relevant responses to the user's queries, even when the phrasing or terminology differs from the PDF content.
- Couchbase's Vector Search integrates seamlessly with LangChain's [CouchbaseSearchVectorStore](https://js.langchain.com/docs/integrations/vectorstores/couchbase#create-vector-store) class, abstracting away the complexities of vector similarity calculations.

Expand Down