Merge branch 'main' into semantic-kernel-tutorial

Author: azaddhirajkumar

test/test-markdown-frontmatter.js

@@ -6,7 +6,7 @@ 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', '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', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', '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', 'Semantic Kernel']
+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', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', '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', 'Semantic Kernel']
 
 const technologies = ['connectors', 'kv', 'query', 'capella', 'server', 'index', 'mobile', 'fts', 'sync gateway', 'eventing', 'analytics', 'udf', 'vector search', 'react', 'edge-server', 'app-services']
 

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

@@ -12,6 +12,7 @@ filter: sdk
 technology:
   - vector search
 tags:
+  - FTS
   - Artificial Intelligence
   - Autogen
   - Ag2

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

@@ -1879,31 +1879,8 @@ logger.info("--- Lambda Deployment Complete --- ")
     2025-06-09 13:40:23,605 - INFO - Running make command: make -f /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/lambda_functions/Makefile clean package (in /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/lambda_functions)
     2025-06-09 13:40:50,341 - INFO - Make command completed successfully.
     2025-06-09 13:40:50,343 - INFO - Moving and renaming /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/lambda_functions/lambda_package.zip to /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip
-    2025-06-09 13:40:50,351 - INFO - Zip file ready: /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip
-    2025-06-09 13:40:50,362 - INFO - Cleaning up temporary script: /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/lambda_functions/lambda_function.py
-    2025-06-09 13:40:50,362 - INFO - Lambda function packaged at: /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip
-    2025-06-09 13:40:50,363 - INFO - Creating/Updating Lambda function 'bedrock_agent_search_format_exp'...
-    2025-06-09 13:40:50,363 - INFO - Deploying Lambda function bedrock_agent_search_format_exp from /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip...
-    2025-06-09 13:40:50,371 - INFO - Found credentials in environment variables.
-    2025-06-09 13:40:50,516 - INFO - Zip file size: 50.91 MB
-    2025-06-09 13:40:50,516 - INFO - Package size (50.91 MB) requires S3 deployment.
-    2025-06-09 13:40:50,517 - INFO - Preparing to upload /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip to S3 in region us-east-1...
-    2025-06-09 13:40:51,552 - INFO - Generated unique S3 bucket name: lambda-deployment-598307997273-1749456651
-    2025-06-09 13:40:52,277 - INFO - Creating S3 bucket: lambda-deployment-598307997273-1749456651...
-    2025-06-09 13:40:52,666 - INFO - Created S3 bucket: lambda-deployment-598307997273-1749456651. Waiting for availability...
-    2025-06-09 13:40:52,929 - INFO - Bucket lambda-deployment-598307997273-1749456651 is available.
-    2025-06-09 13:40:52,929 - INFO - Uploading /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip to s3://lambda-deployment-598307997273-1749456651/lambda/bedrock_agent_search_and_format.zip-0b1fc73a...
-    2025-06-09 13:41:03,991 - INFO - Successfully uploaded to s3://lambda-deployment-598307997273-1749456651/lambda/bedrock_agent_search_and_format.zip-0b1fc73a
-    2025-06-09 13:41:03,993 - INFO - Creating function 'bedrock_agent_search_format_exp' (attempt 1/3)...
-    2025-06-09 13:41:06,690 - INFO - Successfully created function 'bedrock_agent_search_format_exp' with ARN: arn:aws:lambda:us-east-1:598307997273:function:bedrock_agent_search_format_exp
-    2025-06-09 13:41:11,693 - INFO - Adding basic invoke permission (AllowBedrockInvokeBasic-bedrock_agent_search_format_exp) to bedrock_agent_search_format_exp...
-    2025-06-09 13:41:12,007 - INFO - Successfully added basic invoke permission AllowBedrockInvokeBasic-bedrock_agent_search_format_exp.
-    2025-06-09 13:41:12,007 - INFO - Waiting for function 'bedrock_agent_search_format_exp' to become active...
-    2025-06-09 13:41:12,306 - INFO - Function 'bedrock_agent_search_format_exp' is active.
-    2025-06-09 13:41:12,308 - INFO - Search/Format Lambda Deployed: arn:aws:lambda:us-east-1:598307997273:function:bedrock_agent_search_format_exp
-    2025-06-09 13:41:12,308 - INFO - Cleaning up deployment zip file...
-    2025-06-09 13:41:12,310 - INFO - Removed zip file: /Users/kaustavghosh/Desktop/vector-search-cookbook/awsbedrock-agents/lambda-approach/bedrock_agent_search_and_format.zip
-    2025-06-09 13:41:12,311 - INFO - --- Lambda Deployment Complete --- 
+    
+    ... (output truncated for brevity)
 
 
 ### 4.6 Agent Setup

…book/agentchat_RetrieveChat_couchbase.md …/ag2-agentchat_RetrieveChat_couchbase.mdtutorial/markdown/generated/vector-search-cookbook/agentchat_RetrieveChat_couchbase.md renamed to tutorial/markdown/generated/vector-search-cookbook/ag2-agentchat_RetrieveChat_couchbase.md tutorial/markdown/generated/vector-search-cookbook/agentchat_RetrieveChat_couchbase.md renamed to tutorial/markdown/generated/vector-search-cookbook/ag2-agentchat_RetrieveChat_couchbase.md

@@ -1,17 +1,18 @@
 ---
 # frontmatter
-path: "/tutorial-aws-bedrock-couchbase-rag"
-title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock
-short_title: RAG with Couchbase and Amazon Bedrock
+path: "/tutorial-aws-bedrock-couchbase-rag-with-fts"
+title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using FTS service
+short_title: RAG with Couchbase and Amazon Bedrock using FTS service
 description:
-  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock.
+  - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using FTS service.
   - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Amazon Bedrock's Titan embeddings and Claude language model.
   - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase.
 content_type: tutorial
 filter: sdk
 technology:
   - vector search
 tags:
+  - FTS
   - Artificial Intelligence
   - LangChain
   - Amazon Bedrock
@@ -24,15 +25,15 @@ length: 60 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/awsbedrock/RAG_with_Couchbase_and_Bedrock.ipynb)
+[View Source](https://github.com/couchbase-examples/vector-search-cookbook/tree/main/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb)
 
 # Introduction
 
-In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database and [Amazon Bedrock](https://aws.amazon.com/bedrock/) as both the embedding and language model provider. 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 is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch.
+In this guide, we will walk you through building a powerful semantic search engine using Couchbase as the backend database and [Amazon Bedrock](https://aws.amazon.com/bedrock/) as both the embedding and language model provider. 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 is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system using the FTS service from scratch. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-aws-bedrock-couchbase-rag-with-global-secondary-index/)
 
 # 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/awsbedrock/RAG_with_Couchbase_and_Bedrock.ipynb).
+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/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb).
 
 You can either download the notebook file and run it on [Google Colab](https://colab.research.google.com/) or run it on your system by setting up the Python environment.