Generated markdown tutorials from Jupyter Notebooks

Generated from: couchbase-examples/vector-search-cookbook

Author: actions-user

tutorial/markdown/generated/vector-search-cookbook/autovec-structured-autovec_langchain.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-couchbase-capella-autovectorization-workflows-with-structured-data-and-langchain"
-title: Auto-Vectorization of Structured Data with Couchbase Capella AI Dataplane
+title: Structured Vectorization with AI Data Plane (formerly AI Services)
 short_title: Auto-Vectorization with Couchbase and Semantic Search using LangChain
 description:
-  - Learn how to use Couchbase Capella's AI Dataplane auto-vectorization feature to automatically convert your structured data into vector embeddings. 
+  - Learn how to use Couchbase Capella's AI Data Plane (formerly AI Services) auto-vectorization feature to automatically convert your structured data into vector embeddings. 
   - To learn about the auto-vectorization of unstuctured data read the following [tutorial](tutorial-couchbase-autovectorization-workdlows-with-unstructured-data-and-langchain).
   - This tutorial demonstrates how to set up automated embedding generation workflows and perform semantic search using LangChain.
 content_type: tutorial
@@ -57,7 +57,7 @@ Now, before we actually create embeddings for the documents, we need to deploy a
 > **⚠️ IMPORTANT:** The model **must** be deployed in the **same region** as your database cluster for workflows to function properly. Failing to match regions will prevent the workflow from working and may require cluster redeployment.
 
 ## Selecting the Model 
-1. To select the model, you first need to navigate to the "<B>AI Dataplane</B>" tab, then select "<B>Models</B>" and click on "<B>Deploy New Model</B>".
+1. To select the model, you first need to navigate to the "<B>AI Data Plane (formerly AI Services)</B>" tab, then select "<B>Models</B>" and click on "<B>Deploy New Model</B>".
 
    <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec-structured/img/importing_model.png?raw=true" width="950px" height="500px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 
@@ -67,7 +67,7 @@ Now, before we actually create embeddings for the documents, we need to deploy a
 
 ## Access Control to the Model
 
-1. After deploying the model, go to the "<B>Models</B>" tab in the <B>AI Dataplane</B> and click on "<B>Setup Access</B>".
+1. After deploying the model, go to the "<B>Models</B>" tab in the <B>AI Data Plane (formerly AI Services)</B> and click on "<B>Setup Access</B>".
 
     <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec-structured/img/model_setup_access.png?raw=true" width="1100px" height="400px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 
@@ -84,7 +84,7 @@ Now, before we actually create embeddings for the documents, we need to deploy a
 
 Now, we are at the step that will help us create the embeddings/vectors. To proceed with the vectorization process, please follow the steps below. For more details, refer to the [data processing documentation](https://docs.couchbase.com/ai/build/vectorization-service/data-processing.html).
 
-1. For deploying the autovectorization, you need to go to the <B>`AI Dataplane`</B> tab, then click on <B>`Workflows`</B>, and then click on <B>`Create New Workflow`</B>.
+1. For deploying the autovectorization, you need to go to the <B>`AI Data Plane (formerly AI Services)`</B> tab, then click on <B>`Workflows`</B>, and then click on <B>`Create New Workflow`</B>.
 
    <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec-structured/img/workflow.png?raw=true" width="1000px" height="500px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 

tutorial/markdown/generated/vector-search-cookbook/autovec_unstructured-autovec_unstructured.md

@@ -1,10 +1,10 @@
 ---
 # frontmatter
 path: "/tutorial-couchbase-autovectorization-workflows-with-unstructured-data-and-langchain"
-title: Auto-Vectorization on Unstructured Data Stored in S3 Buckets Using Couchbase Capella AI Dataplane 
+title: S3 Auto-Vectorization with AI Data Plane (formerly AI Services)
 short_title: Auto-Vectorization on Unstructured Data Stored in S3 Buckets
 description: 
-  - Learn how to use Couchbase Capella's AI Dataplane Auto-Vectorization feature to automatically process unstructured data from S3 buckets.
+  - Learn how to use Couchbase Capella's AI Data Plane (formerly AI Services) Auto-Vectorization feature to automatically process unstructured data from S3 buckets.
   - Configure workflows to chunk and vectorize documents (PDFs, images, etc.) and import them into Capella collections.
   - Perform semantic vector search using LangChain and the generated embeddings.
 content_type: tutorial
@@ -39,7 +39,7 @@ When running Couchbase using [Capella](https://cloud.couchbase.com/sign-in), the
 # Deploying the Model
 Now, before we actually create embeddings for the documents, we need to deploy a model that will create the embeddings for us. Make sure the model is deployed in the same region as that of database for workflows to work. To know more about model services click [here](https://docs.couchbase.com/ai/build/model-service/deploy-embed-model.html).
 ## Selecting the Model 
-1. To select the model, you first need to navigate to the "<B>AI Dataplane</B>" tab, then select "<B>Models</B>" and click on "<B>Deploy New Model</B>".
+1. To select the model, you first need to navigate to the "<B>AI Data Plane (formerly AI Services)</B>" tab, then select "<B>Models</B>" and click on "<B>Deploy New Model</B>".
 
    <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec_unstructured/img/importing_model.png?raw=true" width="950px" height="500px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 
@@ -49,7 +49,7 @@ Now, before we actually create embeddings for the documents, we need to deploy a
 
 ## Access Control to the Model
 
-1. After deploying the model, go to the "<B>Models</B>" tab in the <B>AI Dataplane</B> and click on "<B>Setup Access</B>".
+1. After deploying the model, go to the "<B>Models</B>" tab in the <B>AI Data Plane (formerly AI Services)</B> and click on "<B>Setup Access</B>".
 
     <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec_unstructured/img/model_setup_access.png?raw=true" width="1100px" height="400px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 
@@ -64,7 +64,7 @@ Now, before we actually create embeddings for the documents, we need to deploy a
 # Data upload from S3 bucket to Couchbase (with chunking and vectorization)
 
 In order to import unstructured data from the S3 bucket, you need to create a workflow that connects to your S3 bucket and chunks your unstructured data before importing it into the collections. To do so, please follow the steps mentioned below:
-1) Let's start by creating a new workflow. This can be done by clicking on the <B>`AI Dataplane`</B> tab, then click on <B>`Workflows`</B>, and then click on <B>`Create New Workflow`</B>.
+1) Let's start by creating a new workflow. This can be done by clicking on the <B>`AI Data Plane (formerly AI Services)`</B> tab, then click on <B>`Workflows`</B>, and then click on <B>`Create New Workflow`</B>.
 
    <img src="https://github.com/couchbase-examples/vector-search-cookbook/blob/main/autovec_unstructured/img/workflow.png?raw=true" width="1000px" height="500px" style="padding: 5px; border-radius: 10px 20px 30px 40px; border: 2px solid #555;">
 

tutorial/markdown/generated/vector-search-cookbook/capella-model-services-llamaindex-search_based-RAG_with_Capella_Model_Services_and_LlamaIndex.md

@@ -34,7 +34,7 @@ This notebook demonstrates how to build a RAG system using:
 - The [BBC News dataset](https://huggingface.co/datasets/RealTimeData/bbc_news_alltime) containing news articles
 - Couchbase Capella as the vector store
 - LlamaIndex framework for the RAG pipeline
-- Capella AI Dataplane for embeddings and text generation
+- Capella AI Data Plane (formerly AI Services) for embeddings and text generation
 
 Semantic search goes beyond simple keyword matching by understanding the context and meaning behind the words in a query, making it an essential tool for applications that require intelligent information retrieval. This tutorial will equip you with the knowledge to create a fully functional RAG system using Capella Model Services and LlamaIndex.
 
@@ -437,9 +437,9 @@ try:
 
     # Configure LlamaIndex to use this LLM
     Settings.llm = llm
-    logging.info("Successfully created the LLM in Capella AI Dataplane")
+    logging.info("Successfully created the LLM in Capella AI Data Plane (formerly AI Services)")
 except Exception as e:
-    raise ValueError(f"Error creating LLM in Capella AI Dataplane: {str(e)}")
+    raise ValueError(f"Error creating LLM in Capella AI Data Plane (formerly AI Services): {str(e)}")
 ```
 
 # Creating the Vector Store Index
@@ -492,9 +492,9 @@ except RecursionError as e:
     raise RuntimeError(f"Error performing semantic search: {e}")
 ```
 
-## Caching in Capella AI Dataplane
+## Caching in Capella AI Data Plane (formerly AI Services)
 
-To optimize performance and reduce costs, Capella AI Dataplane employs two caching mechanisms:
+To optimize performance and reduce costs, Capella AI Data Plane (formerly AI Services) employs two caching mechanisms:
 
 1. Semantic Cache
 
@@ -560,6 +560,6 @@ The key components of our RAG system include:
 
 1. **Couchbase Capella** as the vector database for storing and retrieving document embeddings
 2. **LlamaIndex** as the framework for connecting our data to the LLM
-3. **Capella AI Dataplane** for generating embeddings and LLM responses
+3. **Capella AI Data Plane (formerly AI Services)** for generating embeddings and LLM responses
 
 This approach allows us to enhance the capabilities of large language models by grounding their responses in specific, up-to-date information from our knowledge base.