Merge main into update-ruby-on-rails-tutorial

Resolved merge conflict in test/test-markdown-frontmatter.js by keeping
both PHP Laravel and Ruby on Rails test configuration changes.

Author: teetangh

test/test-markdown-frontmatter.js

@@ -4,15 +4,16 @@ const yaml = require('js-yaml')
 const chalk = require('chalk')
 
 // accepted data field values
-const sdk_languages = ['nodejs', 'scala', 'python', 'swift', 'csharp', 'objective-c', 'android-java', 'any', 'java', 'kotlin', 'dart', 'golang', 'ruby', 'c++']
+const sdk_languages = ['nodejs', 'scala', 'python', 'swift', 'csharp', 'objective-c', 'android-java', 'any', 'java', 'kotlin', 'dart', 'golang', 'ruby', 'php', 'c++']
 
-const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Airbyte', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'Rails', 'Couchbase ORM', 'LangChain', 'OpenAI', 'Streamlit', 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'TAVs', 'Custom Queries', 'Data API', 'FTS', 'GSI', 'Hugging Face', 'LlamaIndex', 'GraphQL', 'Semantic Kernel', 'Hyperscale Vector Index', 'Composite Vector Index', 'Search Vector Index']
+const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Airbyte', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'Rails', 'Couchbase ORM', 'Laravel', 'LangChain', 'OpenAI', 'Streamlit', 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'TAVs', 'Custom Queries', 'Data API', 'FTS', 'GSI', 'Hugging Face', 'LlamaIndex', 'GraphQL', 'Semantic Kernel', 'Hyperscale Vector Index', 'Composite Vector Index', 'Search Vector Index']
 
 const technologies = ['connectors', 'kv', 'query', 'capella', 'server', 'index', 'mobile', 'fts', 'sync gateway', 'eventing', 'analytics', 'udf', 'vector search', 'react', 'edge-server', 'app-services', 'hyperscale vector index', 'composite vector index']
 
 const content_types = ['quickstart', 'tutorial', 'learn']
 
 
+
 const test = (data, path) => {
   // test path
   if (!data.path) {

tutorial/markdown/generated/vector-search-cookbook/crewai-short-term-memory-gsi-CouchbaseStorage_Demo.md

@@ -1,18 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-crewai-short-term-memory-couchbase-with-fts"
-title: Implementing Short-Term Memory for CrewAI Agents with Couchbase using FTS Service
-short_title: CrewAI Short-Term Memory with Couchbase using FTS
+path: "/tutorial-crewai-short-term-memory-couchbase-with-search-vector-index"
+title: Implementing Short-Term Memory for CrewAI Agents with Couchbase Search Vector Index
+short_title: CrewAI Short-Term Memory with Couchbase Search Vector Index
 description:
-  - Learn how to implement short-term memory for CrewAI agents using Couchbase's vector search capabilities using FTS.
+  - Learn how to implement short-term memory for CrewAI agents using Couchbase's vector search capabilities with Search Vector Index.
   - This tutorial demonstrates how to store and retrieve agent interactions using semantic search.
   - You'll understand how to enhance CrewAI agents with memory capabilities using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
-  - FTS
+  - Search Vector Index
   - Artificial Intelligence
   - LangChain
   - CrewAI
@@ -22,28 +22,28 @@ length: 45 Mins
 ---
 
 
+
 <!--- *** WARNING ***: Autogenerated markdown file from jupyter notebook. ***DO NOT EDIT THIS FILE***. Changes should be made to the original notebook file. See commit message for source repo. -->
 
 
-[View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb)
+[View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/crewai-short-term-memory/search_based/CouchbaseStorage_Demo.ipynb)
 
-# CrewAI with Couchbase Short-Term Memory
+## Introduction
 
-This notebook demonstrates how to implement a custom storage backend for CrewAI's memory system using Couchbase and vector search. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-global-secondary-index)
+This notebook demonstrates how to implement a custom storage backend for CrewAI's memory system using Couchbase and Search Vector Index. Alternatively if you want to perform semantic search using Hyperscale or Composite Vector Index, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-hyperscale-or-composite-vector-index)
 
 Here's a breakdown of each section:
 
-How to run this tutorial
-----------------------
+### How to run this tutorial
+
 This tutorial is available as a Jupyter Notebook (.ipynb file) that you can run 
-interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb).
+interactively. You can access the original notebook [here](https://github.com/couchbase-examples/vector-search-cookbook/blob/main/crewai-short-term-memory/search_based/CouchbaseStorage_Demo.ipynb).
 
 You can either:
 - Download the notebook file and run it on [Google Colab](https://colab.research.google.com)
 - Run it on your system by setting up the Python environment
 
-Before you start
----------------
+### Before you start
 
 1. Create and Deploy Your Free Tier Operational cluster on [Capella](https://cloud.couchbase.com/sign-up)
    - To get started with [Couchbase Capella](https://cloud.couchbase.com), create an account and use it to deploy 
@@ -58,7 +58,7 @@ Before you start
      used in the application
    - Allow access to the Cluster from the IP on which the application is running by following the [Network Security documentation](https://docs.couchbase.com/cloud/security/security.html#public-access)
 
-# Memory in AI Agents
+## Memory in AI Agents
 
 Memory in AI agents is a crucial capability that allows them to retain and utilize information across interactions, making them more effective and contextually aware. Without memory, agents would be limited to processing only the immediate input, lacking the ability to build upon past experiences or maintain continuity in conversations.
 
@@ -119,7 +119,7 @@ This section installs the necessary Python packages:
 
 
 ```python
-%pip install --quiet crewai==0.186.1 langchain-couchbase==0.4.0 langchain-openai==0.3.33 python-dotenv==1.1.1
+%pip install --quiet crewai==0.186.1 langchain-couchbase==1.0.1 langchain-openai python-dotenv==1.1.1
 ```
 
     Note: you may need to restart the kernel to use updated packages.
@@ -175,6 +175,23 @@ The script uses environment variables to store sensitive information, enhancing
 >
 > You can set these variables in a `.env` file in the same directory as this notebook, or set them directly in your environment.
 
+### Google Colab Setup
+
+If running in Google Colab, use the Secrets feature:
+1. Click the 🔑 (key icon) in the left sidebar
+2. Add secrets with these names: `OPENAI_API_KEY`, `CB_HOST`, `CB_USERNAME`, `CB_PASSWORD`
+3. Toggle "Notebook access" for each secret
+4. Run this code before the environment verification cell:
+
+```python
+from google.colab import userdata
+import os
+os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')
+os.environ['CB_HOST'] = userdata.get('CB_HOST')
+os.environ['CB_USERNAME'] = userdata.get('CB_USERNAME')
+os.environ['CB_PASSWORD'] = userdata.get('CB_PASSWORD')
+```
+
 
 ```python
 load_dotenv("./.env")
@@ -188,12 +205,13 @@ for var in required_vars:
 
 ## Implement CouchbaseStorage
 
-This section demonstrates the implementation of a custom vector storage solution using Couchbase:
+This section demonstrates the implementation of a custom vector storage solution using Couchbase with Search Vector Index:
 
 > **Note on Implementation:** This example uses the LangChain Couchbase integration (`langchain_couchbase`) for simplicity and to demonstrate integration with the broader LangChain ecosystem. In production environments, you may want to use the Couchbase SDK directly for better performance and more control.
 
-> For more information on using the Couchbase SDK directly, refer to:
+> For more information on using the Couchbase SDK directly and Search Vector Indexes, refer to:
 > - [Couchbase Python SDK Documentation](https://docs.couchbase.com/python-sdk/current/howtos/full-text-searching-with-sdk.html#single-vector-query)
+> - [Couchbase Vector Index Documentation](https://docs.couchbase.com/cloud/vector-index/use-vector-indexes.html)
 
 
 ```python
@@ -374,13 +392,13 @@ class CouchbaseStorage(RAGStorage):
                             break
                     break
             if not search_available:
-                raise RuntimeError("Search/FTS service not found or not responding")
+                raise RuntimeError("Search service not found or not responding")
 
             # Set up storage configuration
             self.bucket_name = os.getenv('CB_BUCKET_NAME', 'vector-search-testing')
-            self.scope_name = os.getenv('SCOPE_NAME', 'shared')
-            self.collection_name = os.getenv('COLLECTION_NAME', 'crew')
-            self.index_name = os.getenv('INDEX_NAME', 'vector_search_crew')
+            self.scope_name = os.getenv('CB_SCOPE_NAME', 'shared')
+            self.collection_name = os.getenv('CB_COLLECTION_NAME', 'crew')
+            self.index_name = os.getenv('CB_INDEX_NAME', 'vector_search_crew')
 
             # Initialize vector store
             self.vector_store = CouchbaseSearchVectorStore(