diff --git a/awsbedrock/gsi/.env.sample b/awsbedrock/query_based/.env.sample similarity index 100% rename from awsbedrock/gsi/.env.sample rename to awsbedrock/query_based/.env.sample diff --git a/awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb b/awsbedrock/query_based/RAG_with_Couchbase_and_Bedrock.ipynb similarity index 100% rename from awsbedrock/gsi/RAG_with_Couchbase_and_Bedrock.ipynb rename to awsbedrock/query_based/RAG_with_Couchbase_and_Bedrock.ipynb diff --git a/awsbedrock/gsi/frontmatter.md b/awsbedrock/query_based/frontmatter.md similarity index 53% rename from awsbedrock/gsi/frontmatter.md rename to awsbedrock/query_based/frontmatter.md index fe59e398..208d8ae9 100644 --- a/awsbedrock/gsi/frontmatter.md +++ b/awsbedrock/query_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-aws-bedrock-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock using GSI index -short_title: RAG with Couchbase and Amazon Bedrock using GSI index +path: "/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using GSI. + - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock with Couchbase Hyperscale and Composite Vector Index. - 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 @@ -12,11 +12,13 @@ filter: sdk technology: - vector search tags: - - GSI + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - Amazon Bedrock sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-aws-bedrock-couchbase-rag-with-hyperscale-vector-index", "/tutorial-aws-bedrock-couchbase-rag-with-composite-vector-index"] --- diff --git a/awsbedrock/fts/.env.sample b/awsbedrock/search_based/.env.sample similarity index 100% rename from awsbedrock/fts/.env.sample rename to awsbedrock/search_based/.env.sample diff --git a/awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb b/awsbedrock/search_based/RAG_with_Couchbase_and_Bedrock.ipynb similarity index 100% rename from awsbedrock/fts/RAG_with_Couchbase_and_Bedrock.ipynb rename to awsbedrock/search_based/RAG_with_Couchbase_and_Bedrock.ipynb diff --git a/awsbedrock/fts/frontmatter.md b/awsbedrock/search_based/frontmatter.md similarity index 70% rename from awsbedrock/fts/frontmatter.md rename to awsbedrock/search_based/frontmatter.md index 1241fbc7..16026884 100644 --- a/awsbedrock/fts/frontmatter.md +++ b/awsbedrock/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -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 +path: "/tutorial-aws-bedrock-couchbase-rag-with-search-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Amazon Bedrock with Search Vector Index +short_title: RAG with Couchbase and Amazon Bedrock with Search Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using FTS service. + - Learn how to build a semantic search engine using Couchbase and Amazon Bedrock using Search Vector Index. - 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 @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - Amazon Bedrock diff --git a/azure/gsi/.env.sample b/azure/query_based/.env.sample similarity index 100% rename from azure/gsi/.env.sample rename to azure/query_based/.env.sample diff --git a/azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb b/azure/query_based/RAG_with_Couchbase_and_AzureOpenAI.ipynb similarity index 100% rename from azure/gsi/RAG_with_Couchbase_and_AzureOpenAI.ipynb rename to azure/query_based/RAG_with_Couchbase_and_AzureOpenAI.ipynb diff --git a/azure/gsi/frontmatter.md b/azure/query_based/frontmatter.md similarity index 51% rename from azure/gsi/frontmatter.md rename to azure/query_based/frontmatter.md index 7ee74fe0..acec1ed9 100644 --- a/azure/gsi/frontmatter.md +++ b/azure/query_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-azure-openai-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI using GSI index -short_title: RAG with Couchbase and Azure OpenAI using GSI index +path: "/tutorial-azure-openai-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using GSI. + - Learn how to build a semantic search engine using Couchbase and Azure OpenAI with Couchbase Hyperscale and Composite Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Azure OpenAI embeddings. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,11 +12,13 @@ filter: sdk technology: - vector search tags: - - GSI + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - OpenAI sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-azure-openai-couchbase-rag-with-hyperscale-vector-index", "/tutorial-azure-openai-couchbase-rag-with-composite-vector-index"] --- diff --git a/azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb b/azure/search_based/RAG_with_Couchbase_and_AzureOpenAI.ipynb similarity index 100% rename from azure/fts/RAG_with_Couchbase_and_AzureOpenAI.ipynb rename to azure/search_based/RAG_with_Couchbase_and_AzureOpenAI.ipynb diff --git a/azure/fts/azure_index.json b/azure/search_based/azure_index.json similarity index 100% rename from azure/fts/azure_index.json rename to azure/search_based/azure_index.json diff --git a/azure/fts/frontmatter.md b/azure/search_based/frontmatter.md similarity index 68% rename from azure/fts/frontmatter.md rename to azure/search_based/frontmatter.md index 7ac845de..f34d8957 100644 --- a/azure/fts/frontmatter.md +++ b/azure/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-azure-openai-couchbase-rag-with-fts" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI using FTS service -short_title: RAG with Couchbase and Azure OpenAI using FTS service +path: "/tutorial-azure-openai-couchbase-rag-with-search-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Azure OpenAI with Search Vector Index +short_title: RAG with Couchbase and Azure OpenAI with Search Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using FTS service. + - Learn how to build a semantic search engine using Couchbase and Azure OpenAI using Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Azure OpenAI embeddings. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - OpenAI diff --git a/claudeai/gsi/.env.sample b/claudeai/query_based/.env.sample similarity index 100% rename from claudeai/gsi/.env.sample rename to claudeai/query_based/.env.sample diff --git a/claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb b/claudeai/query_based/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb similarity index 100% rename from claudeai/gsi/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb rename to claudeai/query_based/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb diff --git a/claudeai/gsi/frontmatter.md b/claudeai/query_based/frontmatter.md similarity index 51% rename from claudeai/gsi/frontmatter.md rename to claudeai/query_based/frontmatter.md index f65983db..f66ea2f4 100644 --- a/claudeai/gsi/frontmatter.md +++ b/claudeai/query_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-openai-claude-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude using GSI index -short_title: RAG with Couchbase, OpenAI, and Claude using GSI index +path: "/tutorial-openai-claude-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase, OpenAI, and Claude with Couchbase Hyperscale and Composite Vector Index description: - - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using GSI. + - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude with Couchbase Hyperscale and Composite Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenAI embeddings and use Claude as the language model. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,11 +12,13 @@ filter: sdk technology: - vector search tags: - - GSI + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - OpenAI sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-openai-claude-couchbase-rag-with-hyperscale-vector-index", "/tutorial-openai-claude-couchbase-rag-with-composite-vector-index"] --- diff --git a/claudeai/fts/.env.sample b/claudeai/search_based/.env.sample similarity index 100% rename from claudeai/fts/.env.sample rename to claudeai/search_based/.env.sample diff --git a/claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb b/claudeai/search_based/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb similarity index 100% rename from claudeai/fts/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb rename to claudeai/search_based/RAG_with_Couchbase_and_Claude(by_Anthropic).ipynb diff --git a/claudeai/fts/claude_index.json b/claudeai/search_based/claude_index.json similarity index 100% rename from claudeai/fts/claude_index.json rename to claudeai/search_based/claude_index.json diff --git a/claudeai/fts/frontmatter.md b/claudeai/search_based/frontmatter.md similarity index 67% rename from claudeai/fts/frontmatter.md rename to claudeai/search_based/frontmatter.md index de4106aa..36aff403 100644 --- a/claudeai/fts/frontmatter.md +++ b/claudeai/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-openai-claude-couchbase-rag-with-fts" -title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude using FTS service -short_title: RAG with Couchbase, OpenAI, and Claude using FTS service +path: "/tutorial-openai-claude-couchbase-rag-with-search-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase, OpenAI, and Claude with Search Vector Index +short_title: RAG with Couchbase, OpenAI, and Claude with Search Vector Index description: - - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using FTS service. + - Learn how to build a semantic search engine using Couchbase, OpenAI embeddings, and Anthropic's Claude using Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenAI embeddings and use Claude as the language model. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - OpenAI diff --git a/cohere/gsi/.env.sample b/cohere/query_based/.env.sample similarity index 100% rename from cohere/gsi/.env.sample rename to cohere/query_based/.env.sample diff --git a/cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb b/cohere/query_based/RAG_with_Couchbase_and_Cohere.ipynb similarity index 100% rename from cohere/gsi/RAG_with_Couchbase_and_Cohere.ipynb rename to cohere/query_based/RAG_with_Couchbase_and_Cohere.ipynb diff --git a/cohere/fts/frontmatter.md b/cohere/query_based/frontmatter.md similarity index 53% rename from cohere/fts/frontmatter.md rename to cohere/query_based/frontmatter.md index 90e370f0..72a9d15d 100644 --- a/cohere/fts/frontmatter.md +++ b/cohere/query_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-cohere-couchbase-rag-with-fts" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere using FTS service -short_title: RAG with Couchbase and Cohere using FTS service +path: "/tutorial-cohere-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Cohere using FTS service. + - Learn how to build a semantic search engine using Couchbase and Cohere with Couchbase Hyperscale and Composite Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Cohere embeddings and language models. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,11 +12,13 @@ filter: sdk technology: - vector search tags: - - FTS + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - Cohere sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-cohere-couchbase-rag-with-hyperscale-vector-index", "/tutorial-cohere-couchbase-rag-with-composite-vector-index"] --- diff --git a/cohere/fts/.env.sample b/cohere/search_based/.env.sample similarity index 100% rename from cohere/fts/.env.sample rename to cohere/search_based/.env.sample diff --git a/cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb b/cohere/search_based/RAG_with_Couchbase_and_Cohere.ipynb similarity index 100% rename from cohere/fts/RAG_with_Couchbase_and_Cohere.ipynb rename to cohere/search_based/RAG_with_Couchbase_and_Cohere.ipynb diff --git a/cohere/fts/cohere_index.json b/cohere/search_based/cohere_index.json similarity index 100% rename from cohere/fts/cohere_index.json rename to cohere/search_based/cohere_index.json diff --git a/cohere/gsi/frontmatter.md b/cohere/search_based/frontmatter.md similarity index 71% rename from cohere/gsi/frontmatter.md rename to cohere/search_based/frontmatter.md index e254f714..6fd2b333 100644 --- a/cohere/gsi/frontmatter.md +++ b/cohere/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-cohere-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with GSI -short_title: RAG with Couchbase and Cohere with GSI +path: "/tutorial-cohere-couchbase-rag-with-search-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Cohere with Search Vector Index +short_title: RAG with Couchbase and Cohere with Search Vector Index description: - - Learn how to build a semantic search engine using Couchbase and Cohere using GSI. + - Learn how to build a semantic search engine using Couchbase and Cohere using Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Cohere embeddings and language models. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - GSI + - Search Vector Index - Artificial Intelligence - LangChain - Cohere diff --git a/crewai-short-term-memory/gsi/frontmatter.md b/crewai-short-term-memory/gsi/frontmatter.md deleted file mode 100644 index 23cabbb7..00000000 --- a/crewai-short-term-memory/gsi/frontmatter.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -# frontmatter -path: "/tutorial-crewai-short-term-memory-couchbase-with-global-secondary-index" -title: Implementing Short-Term Memory for CrewAI Agents with Couchbase with GSI -short_title: CrewAI Short-Term Memory with Couchbase with GSI -description: - - Learn how to implement short-term memory for CrewAI agents using Couchbase's vector search capabilities with GSI. - - 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: - - GSI - - Artificial Intelligence - - LangChain - - CrewAI -sdk_language: - - python -length: 45 Mins ---- diff --git a/crewai-short-term-memory/fts/.env.sample b/crewai-short-term-memory/query_based/.env.sample similarity index 100% rename from crewai-short-term-memory/fts/.env.sample rename to crewai-short-term-memory/query_based/.env.sample diff --git a/crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb b/crewai-short-term-memory/query_based/CouchbaseStorage_Demo.ipynb similarity index 100% rename from crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb rename to crewai-short-term-memory/query_based/CouchbaseStorage_Demo.ipynb diff --git a/crewai-short-term-memory/query_based/frontmatter.md b/crewai-short-term-memory/query_based/frontmatter.md new file mode 100644 index 00000000..3b310f9f --- /dev/null +++ b/crewai-short-term-memory/query_based/frontmatter.md @@ -0,0 +1,24 @@ +--- +# frontmatter +path: "/tutorial-crewai-short-term-memory-couchbase-with-hyperscale-or-composite-vector-index" +title: Implementing Short-Term Memory for CrewAI Agents with Couchbase with Couchbase Hyperscale and Composite Vector Index +short_title: CrewAI Short-Term Memory with Couchbase with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to implement short-term memory for CrewAI agents using Couchbase's vector search capabilities with Couchbase Hyperscale and Composite 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: + - Hyperscale Vector Index + - Composite Vector Index + - Artificial Intelligence + - LangChain + - CrewAI +sdk_language: + - python +length: 45 Mins +alt_paths: ["/tutorial-crewai-short-term-memory-couchbase-with-hyperscale-vector-index", "/tutorial-crewai-short-term-memory-couchbase-with-composite-vector-index"] +--- diff --git a/crewai-short-term-memory/gsi/.env.sample b/crewai-short-term-memory/search_based/.env.sample similarity index 100% rename from crewai-short-term-memory/gsi/.env.sample rename to crewai-short-term-memory/search_based/.env.sample diff --git a/crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb b/crewai-short-term-memory/search_based/CouchbaseStorage_Demo.ipynb similarity index 100% rename from crewai-short-term-memory/fts/CouchbaseStorage_Demo.ipynb rename to crewai-short-term-memory/search_based/CouchbaseStorage_Demo.ipynb diff --git a/crewai-short-term-memory/fts/crew_index.json b/crewai-short-term-memory/search_based/crew_index.json similarity index 100% rename from crewai-short-term-memory/fts/crew_index.json rename to crewai-short-term-memory/search_based/crew_index.json diff --git a/crewai-short-term-memory/fts/frontmatter.md b/crewai-short-term-memory/search_based/frontmatter.md similarity index 86% rename from crewai-short-term-memory/fts/frontmatter.md rename to crewai-short-term-memory/search_based/frontmatter.md index 861c9c6e..5671fa51 100644 --- a/crewai-short-term-memory/fts/frontmatter.md +++ b/crewai-short-term-memory/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-crewai-short-term-memory-couchbase-with-fts" +path: "/tutorial-crewai-short-term-memory-couchbase-with-search-vector-index" title: Implementing Short-Term Memory for CrewAI Agents with Couchbase using FTS Service short_title: CrewAI Short-Term Memory with Couchbase using FTS description: @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - CrewAI diff --git a/crewai/gsi/frontmatter.md b/crewai/gsi/frontmatter.md deleted file mode 100644 index 450d7f41..00000000 --- a/crewai/gsi/frontmatter.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -# frontmatter -path: "/tutorial-crewai-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI with GSI -short_title: RAG with Couchbase and CrewAI with GSI -description: - - Learn how to build a semantic search engine using Couchbase and CrewAI. - - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with CrewAI's agent-based approach. - - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain, CrewAI, and Couchbase with GSI. -content_type: tutorial -filter: sdk -technology: - - vector search -tags: - - GSI - - Artificial Intelligence - - LangChain - - CrewAI -sdk_language: - - python -length: 60 Mins ---- diff --git a/crewai/gsi/.env.sample b/crewai/query_based/.env.sample similarity index 100% rename from crewai/gsi/.env.sample rename to crewai/query_based/.env.sample diff --git a/crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb b/crewai/query_based/RAG_with_Couchbase_and_CrewAI.ipynb similarity index 100% rename from crewai/gsi/RAG_with_Couchbase_and_CrewAI.ipynb rename to crewai/query_based/RAG_with_Couchbase_and_CrewAI.ipynb diff --git a/crewai/query_based/frontmatter.md b/crewai/query_based/frontmatter.md new file mode 100644 index 00000000..c428f03b --- /dev/null +++ b/crewai/query_based/frontmatter.md @@ -0,0 +1,24 @@ +--- +# frontmatter +path: "/tutorial-crewai-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and CrewAI with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to build a semantic search engine using Couchbase and CrewAI. + - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with CrewAI's agent-based approach. + - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain, CrewAI, and Couchbase with Couchbase Hyperscale and Composite Vector Index. +content_type: tutorial +filter: sdk +technology: + - vector search +tags: + - Hyperscale Vector Index + - Composite Vector Index + - Artificial Intelligence + - LangChain + - CrewAI +sdk_language: + - python +length: 60 Mins +alt_paths: ["/tutorial-crewai-couchbase-rag-with-hyperscale-vector-index", "/tutorial-crewai-couchbase-rag-with-composite-vector-index"] +--- diff --git a/crewai/fts/.env.sample b/crewai/search_based/.env.sample similarity index 100% rename from crewai/fts/.env.sample rename to crewai/search_based/.env.sample diff --git a/crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb b/crewai/search_based/RAG_with_Couchbase_and_CrewAI.ipynb similarity index 100% rename from crewai/fts/RAG_with_Couchbase_and_CrewAI.ipynb rename to crewai/search_based/RAG_with_Couchbase_and_CrewAI.ipynb diff --git a/crewai/fts/crew_index.json b/crewai/search_based/crew_index.json similarity index 100% rename from crewai/fts/crew_index.json rename to crewai/search_based/crew_index.json diff --git a/crewai/fts/frontmatter.md b/crewai/search_based/frontmatter.md similarity index 88% rename from crewai/fts/frontmatter.md rename to crewai/search_based/frontmatter.md index 0f64bf06..c0ce096e 100644 --- a/crewai/fts/frontmatter.md +++ b/crewai/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-crewai-couchbase-rag-with-fts" +path: "/tutorial-crewai-couchbase-rag-with-search-vector-index" title: Retrieval-Augmented Generation (RAG) with Couchbase and CrewAI using FTS Service short_title: RAG with Couchbase and CrewAI using FTS description: @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - CrewAI diff --git a/haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/haystack/query_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb similarity index 100% rename from haystack/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb rename to haystack/query_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb diff --git a/haystack/gsi/frontmatter.md b/haystack/query_based/frontmatter.md similarity index 72% rename from haystack/gsi/frontmatter.md rename to haystack/query_based/frontmatter.md index 1da0623e..1d4a7d48 100644 --- a/haystack/gsi/frontmatter.md +++ b/haystack/query_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-openai-haystack-rag-with-gsi" +path: "/tutorial-openai-haystack-rag-with-hyperscale-or-composite-vector-index" title: "RAG with OpenAI, Haystack and Couchbase Hyperscale and Composite Vector Indexes" short_title: "RAG with OpenAI, Haystack and Couchbase CVI and HVI" description: @@ -15,8 +15,10 @@ tags: - OpenAI - Artificial Intelligence - Haystack - - GSI + - Hyperscale Vector Index + - Composite Vector Index sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-openai-haystack-rag-with-hyperscale-vector-index", "/tutorial-openai-haystack-rag-with-composite-vector-index"] --- diff --git a/haystack/fts/requirements.txt b/haystack/query_based/requirements.txt similarity index 100% rename from haystack/fts/requirements.txt rename to haystack/query_based/requirements.txt diff --git a/haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/haystack/search_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb similarity index 100% rename from haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb rename to haystack/search_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb diff --git a/haystack/fts/frontmatter.md b/haystack/search_based/frontmatter.md similarity index 89% rename from haystack/fts/frontmatter.md rename to haystack/search_based/frontmatter.md index d29ba8fe..a1b02376 100644 --- a/haystack/fts/frontmatter.md +++ b/haystack/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-openai-haystack-rag-with-fts" +path: "/tutorial-openai-haystack-rag-with-search-vector-index" title: "Retrieval-Augmented Generation (RAG) with OpenAI, Haystack and Couchbase Search Vector Index" short_title: "RAG with OpenAI, Haystack and Couchbase Search Vector Index" description: @@ -15,7 +15,7 @@ tags: - OpenAI - Artificial Intelligence - Haystack - - FTS + - Search Vector Index sdk_language: - python length: 60 Mins diff --git a/haystack/fts/fts_index.json b/haystack/search_based/fts_index.json similarity index 100% rename from haystack/fts/fts_index.json rename to haystack/search_based/fts_index.json diff --git a/haystack/gsi/requirements.txt b/haystack/search_based/requirements.txt similarity index 100% rename from haystack/gsi/requirements.txt rename to haystack/search_based/requirements.txt diff --git a/huggingface/gsi/frontmatter.md b/huggingface/gsi/frontmatter.md deleted file mode 100644 index 62d17118..00000000 --- a/huggingface/gsi/frontmatter.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -# frontmatter -path: "/tutorial-huggingface-couchbase-vector-search-with-global-secondary-index" -title: Using Hugging Face Embeddings with Couchbase Vector Search with GSI -short_title: Hugging Face with Couchbase Vector Search with GSI -description: - - Learn how to generate embeddings using Hugging Face and store them in Couchbase. - - This tutorial demonstrates how to use Couchbase's vector search capabilities with Hugging Face embeddings. - - You'll understand how to perform vector search to find relevant documents based on similarity with GSI. -content_type: tutorial -filter: sdk -technology: - - vector search -tags: - - GSI - - Artificial Intelligence - - Hugging Face -sdk_language: - - python -length: 30 Mins ---- diff --git a/huggingface/fts/.env.sample b/huggingface/query_based/.env.sample similarity index 100% rename from huggingface/fts/.env.sample rename to huggingface/query_based/.env.sample diff --git a/huggingface/query_based/frontmatter.md b/huggingface/query_based/frontmatter.md new file mode 100644 index 00000000..aa0c7b57 --- /dev/null +++ b/huggingface/query_based/frontmatter.md @@ -0,0 +1,23 @@ +--- +# frontmatter +path: "/tutorial-huggingface-couchbase-vector-search-with-hyperscale-or-composite-vector-index" +title: Using Hugging Face Embeddings with Couchbase Vector Search with Couchbase Hyperscale and Composite Vector Index +short_title: Hugging Face with Couchbase Vector Search with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to generate embeddings using Hugging Face and store them in Couchbase. + - This tutorial demonstrates how to use Couchbase's vector search capabilities with Hugging Face embeddings. + - You'll understand how to perform vector search to find relevant documents based on similarity with Couchbase Hyperscale and Composite Vector Index. +content_type: tutorial +filter: sdk +technology: + - vector search +tags: + - Hyperscale Vector Index + - Composite Vector Index + - Artificial Intelligence + - Hugging Face +sdk_language: + - python +length: 30 Mins +alt_paths: ["/tutorial-huggingface-couchbase-vector-search-with-hyperscale-vector-index", "/tutorial-huggingface-couchbase-vector-search-with-composite-vector-index"] +--- diff --git a/huggingface/gsi/hugging_face.ipynb b/huggingface/query_based/hugging_face.ipynb similarity index 100% rename from huggingface/gsi/hugging_face.ipynb rename to huggingface/query_based/hugging_face.ipynb diff --git a/huggingface/gsi/.env.sample b/huggingface/search_based/.env.sample similarity index 100% rename from huggingface/gsi/.env.sample rename to huggingface/search_based/.env.sample diff --git a/huggingface/fts/frontmatter.md b/huggingface/search_based/frontmatter.md similarity index 86% rename from huggingface/fts/frontmatter.md rename to huggingface/search_based/frontmatter.md index 4367aef0..8ba36a1d 100644 --- a/huggingface/fts/frontmatter.md +++ b/huggingface/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-huggingface-couchbase-vector-search-with-fts" +path: "/tutorial-huggingface-couchbase-vector-search-with-search-vector-index" title: Using Hugging Face Embeddings with Couchbase Vector Search using FTS Service short_title: Hugging Face with Couchbase Vector Search using FTS Service description: @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - Hugging Face sdk_language: diff --git a/huggingface/fts/hugging_face.ipynb b/huggingface/search_based/hugging_face.ipynb similarity index 100% rename from huggingface/fts/hugging_face.ipynb rename to huggingface/search_based/hugging_face.ipynb diff --git a/huggingface/fts/huggingface_index.json b/huggingface/search_based/huggingface_index.json similarity index 100% rename from huggingface/fts/huggingface_index.json rename to huggingface/search_based/huggingface_index.json diff --git a/jinaai/gsi/.env.sample b/jinaai/query_based/.env.sample similarity index 100% rename from jinaai/gsi/.env.sample rename to jinaai/query_based/.env.sample diff --git a/jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb b/jinaai/query_based/RAG_with_Couchbase_and_Jina_AI.ipynb similarity index 100% rename from jinaai/gsi/RAG_with_Couchbase_and_Jina_AI.ipynb rename to jinaai/query_based/RAG_with_Couchbase_and_Jina_AI.ipynb diff --git a/jinaai/gsi/frontmatter.md b/jinaai/query_based/frontmatter.md similarity index 57% rename from jinaai/gsi/frontmatter.md rename to jinaai/query_based/frontmatter.md index 8f6e50c3..e85122e9 100644 --- a/jinaai/gsi/frontmatter.md +++ b/jinaai/query_based/frontmatter.md @@ -1,22 +1,24 @@ --- # frontmatter -path: "/tutorial-jina-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and Jina AI using GSI +path: "/tutorial-jina-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and Jina AI with Couchbase Hyperscale and Composite Vector Index short_title: RAG with Couchbase and Jina description: - Learn how to build a semantic search engine using Couchbase and Jina. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with Jina embeddings and language models. - - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase using GSI. + - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase with Couchbase Hyperscale and Composite Vector Index. content_type: tutorial filter: sdk technology: - vector search tags: - - GSI + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - Jina AI sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-jina-couchbase-rag-with-hyperscale-vector-index", "/tutorial-jina-couchbase-rag-with-composite-vector-index"] --- diff --git a/jinaai/fts/.env.sample b/jinaai/search_based/.env.sample similarity index 100% rename from jinaai/fts/.env.sample rename to jinaai/search_based/.env.sample diff --git a/jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb b/jinaai/search_based/RAG_with_Couchbase_and_Jina_AI.ipynb similarity index 100% rename from jinaai/fts/RAG_with_Couchbase_and_Jina_AI.ipynb rename to jinaai/search_based/RAG_with_Couchbase_and_Jina_AI.ipynb diff --git a/jinaai/fts/frontmatter.md b/jinaai/search_based/frontmatter.md similarity index 88% rename from jinaai/fts/frontmatter.md rename to jinaai/search_based/frontmatter.md index 5c28525d..fb6feba3 100644 --- a/jinaai/fts/frontmatter.md +++ b/jinaai/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-jina-couchbase-rag-with-fts" +path: "/tutorial-jina-couchbase-rag-with-search-vector-index" title: Retrieval-Augmented Generation (RAG) with Couchbase and Jina AI using FTS short_title: RAG with Couchbase and Jina description: @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - Jina AI diff --git a/jinaai/fts/jina_index.json b/jinaai/search_based/jina_index.json similarity index 100% rename from jinaai/fts/jina_index.json rename to jinaai/search_based/jina_index.json diff --git a/lamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/lamaindex/query_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb similarity index 100% rename from lamaindex/gsi/RAG_with_Couchbase_Capella_and_OpenAI.ipynb rename to lamaindex/query_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb diff --git a/lamaindex/gsi/frontmatter.md b/lamaindex/query_based/frontmatter.md similarity index 71% rename from lamaindex/gsi/frontmatter.md rename to lamaindex/query_based/frontmatter.md index 17e0aadf..63e97015 100644 --- a/lamaindex/gsi/frontmatter.md +++ b/lamaindex/query_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-openai-llamaindex-rag-with-gsi" +path: "/tutorial-openai-llamaindex-rag-with-hyperscale-or-composite-vector-index" title: "RAG with OpenAI, LlamaIndex and Couchbase Hyperscale and Composite Vector Indexes" short_title: "RAG with OpenAI, LlamaIndex and Couchbase CVI and HVI" description: @@ -15,8 +15,10 @@ tags: - OpenAI - Artificial Intelligence - LlamaIndex - - GSI + - Hyperscale Vector Index + - Composite Vector Index sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-openai-llamaindex-rag-with-hyperscale-vector-index", "/tutorial-openai-llamaindex-rag-with-composite-vector-index"] --- diff --git a/lamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb b/lamaindex/search_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb similarity index 100% rename from lamaindex/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb rename to lamaindex/search_based/RAG_with_Couchbase_Capella_and_OpenAI.ipynb diff --git a/lamaindex/fts/frontmatter.md b/lamaindex/search_based/frontmatter.md similarity index 89% rename from lamaindex/fts/frontmatter.md rename to lamaindex/search_based/frontmatter.md index a78893d3..cf16de49 100644 --- a/lamaindex/fts/frontmatter.md +++ b/lamaindex/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-openai-llamaindex-rag-with-fts" +path: "/tutorial-openai-llamaindex-rag-with-search-vector-index" title: "Retrieval-Augmented Generation (RAG) with OpenAI, LlamaIndex and Couchbase Search Vector Index" short_title: "RAG with OpenAI, LlamaIndex and Couchbase Search Vector Index" description: @@ -15,7 +15,7 @@ tags: - OpenAI - Artificial Intelligence - LlamaIndex - - FTS + - Search Vector Index sdk_language: - python length: 60 Mins diff --git a/lamaindex/fts/fts_index.json b/lamaindex/search_based/fts_index.json similarity index 100% rename from lamaindex/fts/fts_index.json rename to lamaindex/search_based/fts_index.json diff --git a/mistralai/gsi/frontmatter.md b/mistralai/gsi/frontmatter.md deleted file mode 100644 index fe55b4ac..00000000 --- a/mistralai/gsi/frontmatter.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -# frontmatter -path: "/tutorial-mistralai-couchbase-vector-search-with-global-secondary-index" -title: Using Mistral AI Embeddings using GSI Index -short_title: Mistral AI with Couchbase GSI Index -description: - - Learn how to generate embeddings using Mistral AI and store them in Couchbase using GSI. - - This tutorial demonstrates how to use Couchbase's GSI index capabilities with Mistral AI embeddings. - - You'll understand how to perform optimized vector search using Global Secondary Index for better performance. -content_type: tutorial -filter: sdk -technology: - - vector search -tags: - - Artificial Intelligence - - Mistral AI - - GSI -sdk_language: - - python -length: 30 Mins ---- \ No newline at end of file diff --git a/mistralai/fts/.env.sample b/mistralai/query_based/.env.sample similarity index 100% rename from mistralai/fts/.env.sample rename to mistralai/query_based/.env.sample diff --git a/mistralai/query_based/frontmatter.md b/mistralai/query_based/frontmatter.md new file mode 100644 index 00000000..da36b742 --- /dev/null +++ b/mistralai/query_based/frontmatter.md @@ -0,0 +1,22 @@ +--- +# frontmatter +path: "/tutorial-mistralai-couchbase-vector-search-with-hyperscale-or-composite-vector-index" +title: Using Mistral AI Embeddings with Couchbase Hyperscale and Composite Vector Index +short_title: Mistral AI with Couchbase with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to generate embeddings using Mistral AI and store them in Couchbase with Couchbase Hyperscale and Composite Vector Index. + - This tutorial demonstrates how to use Couchbase's Hyperscale and Composite Vector Index capabilities with Mistral AI embeddings. + - You'll understand how to perform optimized vector search using Hyperscale and Composite Vector Index for better performance. +content_type: tutorial +filter: sdk +technology: + - vector search +tags: + - Artificial Intelligence + - Mistral AI + - Hyperscale Vector Index + - Composite Vector Index +sdk_language: + - python +length: 30 Mins +--- diff --git a/mistralai/gsi/mistralai.ipynb b/mistralai/query_based/mistralai.ipynb similarity index 100% rename from mistralai/gsi/mistralai.ipynb rename to mistralai/query_based/mistralai.ipynb diff --git a/mistralai/gsi/.env.sample b/mistralai/search_based/.env.sample similarity index 100% rename from mistralai/gsi/.env.sample rename to mistralai/search_based/.env.sample diff --git a/mistralai/fts/frontmatter.md b/mistralai/search_based/frontmatter.md similarity index 67% rename from mistralai/fts/frontmatter.md rename to mistralai/search_based/frontmatter.md index 901cd4f6..4d36f06c 100644 --- a/mistralai/fts/frontmatter.md +++ b/mistralai/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-mistralai-couchbase-vector-search-with-fts" -title: Using Mistral AI Embeddings with Couchbase Vector Search using FTS service -short_title: Mistral AI with Couchbase Vector Search using FTS service +path: "/tutorial-mistralai-couchbase-vector-search-with-search-vector-index" +title: Using Mistral AI Embeddings with Couchbase Vector Search with Search Vector Index +short_title: Mistral AI with Couchbase Vector Search with Search Vector Index description: - - Learn how to generate embeddings using Mistral AI and store them in Couchbase using FTS service. + - Learn how to generate embeddings using Mistral AI and store them in Couchbase using Search Vector Index. - This tutorial demonstrates how to use Couchbase's vector search capabilities with Mistral AI embeddings. - You'll understand how to perform vector search to find relevant documents based on similarity. content_type: tutorial @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - Mistral AI sdk_language: diff --git a/mistralai/fts/mistralai.ipynb b/mistralai/search_based/mistralai.ipynb similarity index 100% rename from mistralai/fts/mistralai.ipynb rename to mistralai/search_based/mistralai.ipynb diff --git a/mistralai/fts/mistralai_index.json b/mistralai/search_based/mistralai_index.json similarity index 100% rename from mistralai/fts/mistralai_index.json rename to mistralai/search_based/mistralai_index.json diff --git a/openrouter-deepseek/gsi/.env.sample b/openrouter-deepseek/query_based/.env.sample similarity index 100% rename from openrouter-deepseek/gsi/.env.sample rename to openrouter-deepseek/query_based/.env.sample diff --git a/openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb b/openrouter-deepseek/query_based/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb similarity index 100% rename from openrouter-deepseek/gsi/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb rename to openrouter-deepseek/query_based/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb diff --git a/openrouter-deepseek/gsi/frontmatter.md b/openrouter-deepseek/query_based/frontmatter.md similarity index 53% rename from openrouter-deepseek/gsi/frontmatter.md rename to openrouter-deepseek/query_based/frontmatter.md index 2cd04153..21275b96 100644 --- a/openrouter-deepseek/gsi/frontmatter.md +++ b/openrouter-deepseek/query_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-openrouter-deepseek-with-global-secondary-index" -title: Retrieval-Augmented Generation with Couchbase and OpenRouter Deepseek using GSI index -short_title: RAG with Couchbase and OpenRouter Deepseek using GSI index +path: "/tutorial-openrouter-deepseek-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation with Couchbase and OpenRouter Deepseek with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and OpenRouter Deepseek with Couchbase Hyperscale and Composite Vector Index description: - - Learn how to build a semantic search engine using Couchbase and OpenRouter with Deepseek using GSI index. + - Learn how to build a semantic search engine using Couchbase and OpenRouter with Deepseek with Couchbase Hyperscale and Composite Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenRouter Deepseek as both embeddings and language model provider. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,7 +12,8 @@ filter: sdk technology: - vector search tags: - - GSI + - Hyperscale Vector Index + - Composite Vector Index - Artificial Intelligence - LangChain - Deepseek @@ -20,4 +21,5 @@ tags: sdk_language: - python length: 60 Mins +alt_paths: ["/tutorial-openrouter-deepseek-with-hyperscale-vector-index", "/tutorial-openrouter-deepseek-with-composite-vector-index"] --- diff --git a/openrouter-deepseek/fts/.env.sample b/openrouter-deepseek/search_based/.env.sample similarity index 100% rename from openrouter-deepseek/fts/.env.sample rename to openrouter-deepseek/search_based/.env.sample diff --git a/openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb b/openrouter-deepseek/search_based/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb similarity index 100% rename from openrouter-deepseek/fts/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb rename to openrouter-deepseek/search_based/RAG_with_Couchbase_and_Openrouter_Deepseek.ipynb diff --git a/openrouter-deepseek/fts/deepseek_index.json b/openrouter-deepseek/search_based/deepseek_index.json similarity index 100% rename from openrouter-deepseek/fts/deepseek_index.json rename to openrouter-deepseek/search_based/deepseek_index.json diff --git a/openrouter-deepseek/fts/frontmatter.md b/openrouter-deepseek/search_based/frontmatter.md similarity index 69% rename from openrouter-deepseek/fts/frontmatter.md rename to openrouter-deepseek/search_based/frontmatter.md index 3c33a3b9..0891e006 100644 --- a/openrouter-deepseek/fts/frontmatter.md +++ b/openrouter-deepseek/search_based/frontmatter.md @@ -1,10 +1,10 @@ --- # frontmatter -path: "/tutorial-openrouter-deepseek-with-fts" -title: Retrieval-Augmented Generation with Couchbase and OpenRouter Deepseek using FTS service -short_title: RAG with Couchbase and OpenRouter Deepseek using FTS service +path: "/tutorial-openrouter-deepseek-with-search-vector-index" +title: Retrieval-Augmented Generation with Couchbase and OpenRouter Deepseek with Search Vector Index +short_title: RAG with Couchbase and OpenRouter Deepseek with Search Vector Index description: - - Learn how to build a semantic search engine using Couchbase and OpenRouter with Deepseek using FTS service. + - Learn how to build a semantic search engine using Couchbase and OpenRouter with Deepseek using Search Vector Index. - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with OpenRouter Deepseek as both embeddings and language model provider. - You'll understand how to perform Retrieval-Augmented Generation (RAG) using LangChain and Couchbase. content_type: tutorial @@ -12,7 +12,7 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - Deepseek diff --git a/pydantic_ai/gsi/frontmatter.md b/pydantic_ai/gsi/frontmatter.md deleted file mode 100644 index 7609ee47..00000000 --- a/pydantic_ai/gsi/frontmatter.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -# frontmatter -path: "/tutorial-pydantic-ai-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and PydanticAI using GSI -short_title: RAG with Couchbase and PydanticAI using GSI -description: - - Learn how to build a semantic search engine using Couchbase and PydanticAI using GSI. - - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with PydanticAI using GSI indexes. - - You'll understand how to perform Retrieval-Augmented Generation (RAG) using PydanticAI and Couchbase with GSI optimization. -content_type: tutorial -filter: sdk -technology: - - vector search -tags: - - Artificial Intelligence - - LangChain - - OpenAI - - PydanticAI - - GSI -sdk_language: - - python -length: 30 Mins ---- \ No newline at end of file diff --git a/pydantic_ai/gsi/.env.sample b/pydantic_ai/query_based/.env.sample similarity index 100% rename from pydantic_ai/gsi/.env.sample rename to pydantic_ai/query_based/.env.sample diff --git a/pydantic_ai/gsi/RAG_with_Couchbase_and_PydanticAI.ipynb b/pydantic_ai/query_based/RAG_with_Couchbase_and_PydanticAI.ipynb similarity index 100% rename from pydantic_ai/gsi/RAG_with_Couchbase_and_PydanticAI.ipynb rename to pydantic_ai/query_based/RAG_with_Couchbase_and_PydanticAI.ipynb diff --git a/pydantic_ai/query_based/frontmatter.md b/pydantic_ai/query_based/frontmatter.md new file mode 100644 index 00000000..d8fa26f9 --- /dev/null +++ b/pydantic_ai/query_based/frontmatter.md @@ -0,0 +1,24 @@ +--- +# frontmatter +path: "/tutorial-pydantic-ai-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and PydanticAI with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and PydanticAI with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to build a semantic search engine using Couchbase and PydanticAI with Couchbase Hyperscale and Composite Vector Index. + - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with PydanticAI with Couchbase Hyperscale and Composite Vector Index.es. + - You'll understand how to perform Retrieval-Augmented Generation (RAG) using PydanticAI and Couchbase with Couchbase Hyperscale and Composite Vector Index. optimization. +content_type: tutorial +filter: sdk +technology: + - vector search +tags: + - Artificial Intelligence + - LangChain + - OpenAI + - PydanticAI + - Hyperscale Vector Index + - Composite Vector Index +sdk_language: + - python +length: 30 Mins +--- diff --git a/pydantic_ai/fts/.env.sample b/pydantic_ai/search_based/.env.sample similarity index 100% rename from pydantic_ai/fts/.env.sample rename to pydantic_ai/search_based/.env.sample diff --git a/pydantic_ai/fts/RAG_with_Couchbase_and_PydanticAI.ipynb b/pydantic_ai/search_based/RAG_with_Couchbase_and_PydanticAI.ipynb similarity index 100% rename from pydantic_ai/fts/RAG_with_Couchbase_and_PydanticAI.ipynb rename to pydantic_ai/search_based/RAG_with_Couchbase_and_PydanticAI.ipynb diff --git a/pydantic_ai/fts/frontmatter.md b/pydantic_ai/search_based/frontmatter.md similarity index 87% rename from pydantic_ai/fts/frontmatter.md rename to pydantic_ai/search_based/frontmatter.md index ca846e6f..21f4f55d 100644 --- a/pydantic_ai/fts/frontmatter.md +++ b/pydantic_ai/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-pydantic-ai-couchbase-rag-with-fts" +path: "/tutorial-pydantic-ai-couchbase-rag-with-search-vector-index" title: Retrieval-Augmented Generation (RAG) with Couchbase and PydanticAI using FTS short_title: RAG with Couchbase and PydanticAI description: @@ -12,13 +12,12 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - OpenAI - PydanticAI - - FTS sdk_language: - python length: 30 Mins ---- \ No newline at end of file +--- diff --git a/pydantic_ai/fts/pydantic_ai_index.json b/pydantic_ai/search_based/pydantic_ai_index.json similarity index 100% rename from pydantic_ai/fts/pydantic_ai_index.json rename to pydantic_ai/search_based/pydantic_ai_index.json diff --git a/smolagents/gsi/frontmatter.md b/smolagents/gsi/frontmatter.md deleted file mode 100644 index 994979a5..00000000 --- a/smolagents/gsi/frontmatter.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -# frontmatter -path: "/tutorial-smolagents-couchbase-rag-with-global-secondary-index" -title: Retrieval-Augmented Generation (RAG) with Couchbase and smolagents using GSI -short_title: RAG with Couchbase and smolagents using GSI -description: - - Learn how to build a semantic search engine using Couchbase and Hugging Face smolagents using GSI. - - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with smolagents using GSI indexes. - - You'll understand how to perform Retrieval-Augmented Generation (RAG) using smolagents and Couchbase with GSI optimization. -content_type: tutorial -filter: sdk -technology: - - vector search -tags: - - Artificial Intelligence - - LangChain - - OpenAI - - smolagents - - GSI -sdk_language: - - python -length: 30 Mins ---- diff --git a/smolagents/gsi/.env.sample b/smolagents/query_based/.env.sample similarity index 100% rename from smolagents/gsi/.env.sample rename to smolagents/query_based/.env.sample diff --git a/smolagents/gsi/RAG_with_Couchbase_and_SmolAgents.ipynb b/smolagents/query_based/RAG_with_Couchbase_and_SmolAgents.ipynb similarity index 100% rename from smolagents/gsi/RAG_with_Couchbase_and_SmolAgents.ipynb rename to smolagents/query_based/RAG_with_Couchbase_and_SmolAgents.ipynb diff --git a/smolagents/query_based/frontmatter.md b/smolagents/query_based/frontmatter.md new file mode 100644 index 00000000..e25fafb7 --- /dev/null +++ b/smolagents/query_based/frontmatter.md @@ -0,0 +1,25 @@ +--- +# frontmatter +path: "/tutorial-smolagents-couchbase-rag-with-hyperscale-or-composite-vector-index" +title: Retrieval-Augmented Generation (RAG) with Couchbase and smolagents with Couchbase Hyperscale and Composite Vector Index +short_title: RAG with Couchbase and smolagents with Couchbase Hyperscale and Composite Vector Index +description: + - Learn how to build a semantic search engine using Couchbase and Hugging Face smolagents with Couchbase Hyperscale and Composite Vector Index. + - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with smolagents with Couchbase Hyperscale and Composite Vector Indexes. + - You'll understand how to perform Retrieval-Augmented Generation (RAG) using smolagents and Couchbase with Couchbase Hyperscale and Composite Vector Index optimization. +content_type: tutorial +filter: sdk +technology: + - vector search +tags: + - Artificial Intelligence + - LangChain + - OpenAI + - smolagents + - Hyperscale Vector Index + - Composite Vector Index +sdk_language: + - python +length: 30 Mins +alt_paths: ["/tutorial-smolagents-couchbase-rag-with-hyperscale-vector-index", "/tutorial-smolagents-couchbase-rag-with-composite-vector-index"] +--- diff --git a/smolagents/fts/.env.sample b/smolagents/search_based/.env.sample similarity index 100% rename from smolagents/fts/.env.sample rename to smolagents/search_based/.env.sample diff --git a/smolagents/fts/RAG_with_Couchbase_and_SmolAgents.ipynb b/smolagents/search_based/RAG_with_Couchbase_and_SmolAgents.ipynb similarity index 100% rename from smolagents/fts/RAG_with_Couchbase_and_SmolAgents.ipynb rename to smolagents/search_based/RAG_with_Couchbase_and_SmolAgents.ipynb diff --git a/smolagents/fts/frontmatter.md b/smolagents/search_based/frontmatter.md similarity index 87% rename from smolagents/fts/frontmatter.md rename to smolagents/search_based/frontmatter.md index 3d8d102c..84c2c9c8 100644 --- a/smolagents/fts/frontmatter.md +++ b/smolagents/search_based/frontmatter.md @@ -1,6 +1,6 @@ --- # frontmatter -path: "/tutorial-smolagents-couchbase-rag-with-fts" +path: "/tutorial-smolagents-couchbase-rag-with-search-vector-index" title: Retrieval-Augmented Generation (RAG) with Couchbase and smolagents short_title: RAG with Couchbase and smolagents description: @@ -12,13 +12,12 @@ filter: sdk technology: - vector search tags: - - FTS + - Search Vector Index - Artificial Intelligence - LangChain - OpenAI - smolagents - - FTS sdk_language: - python length: 30 Mins ---- \ No newline at end of file +--- diff --git a/smolagents/fts/smolagents_index.json b/smolagents/search_based/smolagents_index.json similarity index 100% rename from smolagents/fts/smolagents_index.json rename to smolagents/search_based/smolagents_index.json