docs: update docs

README.md

@@ -23,7 +23,7 @@
 
 </div>
 
-This sample shows how to build a serverless AI chat experience with Retrieval-Augmented Generation using [LangChain.js](https://js.langchain.com/) and Azure. The application is hosted on [Azure Static Web Apps](https://learn.microsoft.com/azure/static-web-apps/overview) and [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript), with [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) as the vector database. You can use it as a starting point for building more complex AI applications.
+This sample shows how to build a serverless AI chat experience with Retrieval-Augmented Generation using [LangChain.js](https://js.langchain.com/) and Azure. The application is hosted on [Azure Static Web Apps](https://learn.microsoft.com/azure/static-web-apps/overview) and [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript), with [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-) as the vector database. You can use it as a starting point for building more complex AI applications.
 
 > [!TIP]
 > You can test this application locally without any cost using [Ollama](https://ollama.com/). Follow the instructions in the [Local Development](#local-development) section to get started.
@@ -44,7 +44,7 @@ This application is made from multiple components:
 
 - A serverless API built with [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript) and using [LangChain.js](https://js.langchain.com/) to ingest the documents and generate responses to the user chat queries. The code is located in the `packages/api` folder.
 
-- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search).
+- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
 
 - A file storage to store the source documents, using [Azure Blob Storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-introduction).
 
@@ -53,7 +53,7 @@ We use the [HTTP protocol for AI chat apps](https://aka.ms/chatprotocol) to comm
 ## Features
 
 - **Serverless Architecture**: Utilizes Azure Functions and Azure Static Web Apps for a fully serverless deployment.
-- **Retrieval-Augmented Generation (RAG)**: Combines the power of Azure AI Search and LangChain.js to provide relevant and accurate responses.
+- **Retrieval-Augmented Generation (RAG)**: Combines the power of Azure Cosmos DB and LangChain.js to provide relevant and accurate responses.
 - **Scalable and Cost-Effective**: Leverages Azure's serverless offerings to provide a scalable and cost-effective solution.
 - **Local Development**: Supports local development using Ollama for testing without any cloud costs.
 
@@ -212,7 +212,7 @@ Here are some resources to learn more about the technologies used in this sample
 - [LangChain.js documentation](https://js.langchain.com)
 - [Generative AI For Beginners](https://github.com/microsoft/generative-ai-for-beginners)
 - [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/overview)
-- [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)
+- [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/)
 - [Ask YouTube: LangChain.js + Azure Quickstart sample](https://github.com/Azure-Samples/langchainjs-quickstart-demo)
 - [Chat + Enterprise data with Azure OpenAI and Azure AI Search](https://github.com/Azure-Samples/azure-search-openai-javascript)
 - [Revolutionize your Enterprise Data with Chat: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog)

docs/cost.md

@@ -1,12 +1,12 @@
 ## Cost estimation
 
 Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage.
-However, you can use the [Azure pricing calculator](https://azure.com/e/c504007c9f024699a37f5d947dbb1e79) for the resources below to get an estimate.
+However, you can use the [Azure pricing calculator](https://azure.com/e/a586bf32fdfa4bb9b368a6b6543c4b50) for the resources below to get an estimate.
 
 - Azure Functions: Consumption plan, Free for the first 1M executions. Pricing per execution and memory used. [Pricing](https://azure.microsoft.com/pricing/details/functions/)
 - Azure Static Web Apps: Free tier, 100GB bandwidth. Pricing per GB served. [Pricing](https://azure.microsoft.com/pricing/details/app-service/static/)
 - Azure OpenAI: Standard tier, GPT and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/)
-- Azure AI Search: Basic tier, 1 replica. Pricing per hour. [Pricing](https://azure.microsoft.com/pricing/details/search/)
+- Azure Cosmos DB: Serverless tier. Pricing per request unit (RU). [Pricing](https://azure.microsoft.com/pricing/details/cosmos-db/autoscale-provisioned/)
 - Azure Blob Storage: Standard tier with LRS. Pricing per GB stored and data transfer. [Pricing](https://azure.microsoft.com/pricing/details/storage/blobs/)
 
 ⚠️ To avoid unnecessary costs, remember to take down your app if it's no longer in use,

docs/faq.md

@@ -7,7 +7,7 @@ Retrieval-Augmented Generation (RAG) is a method used in artificial intelligence
 
 At its core, RAG involves two main components:
 
-- **Retriever**: Think "_like a search engine_", finding relevant information from a knowledgebase, usually a vector database. In this sample, we're using Azure AI Search as our vector database.
+- **Retriever**: Think "_like a search engine_", finding relevant information from a knowledgebase, usually a vector database. In this sample, we're using Azure Cosmos DB for NoSQL as our vector database.
 
 - **Generator**: Acts like a writer, taking the prompt and information retrieved to create a response. We're using here a Large Language Model (LLM) for this task.
 
@@ -114,7 +114,7 @@ The `azd up` command comes from the [Azure Developer CLI](https://learn.microsof
 
 The `azd up` command uses the `azure.yaml` file combined with the infrastructure-as-code `.bicep` files in the `infra/` folder. The `azure.yaml` file for this project declares several "hooks" for the prepackage step and postprovision steps. The `up` command first runs the `prepackage` hook which installs Node dependencies and builds the TypeScript files. It then packages all the code (both frontend and backend services) into a zip file which it will deploy later.
 
-Next, it provisions the resources based on `main.bicep` and `main.parameters.json`. At that point, since there is no default value for the OpenAI resource location, it asks you to pick a location from a short list of available regions. Then it will send requests to Azure to provision all the required resources. With everything provisioned, it runs the `postprovision` hook to process the local data and add it to an Azure AI Search index.
+Next, it provisions the resources based on `main.bicep` and `main.parameters.json`. At that point, since there is no default value for the OpenAI resource location, it asks you to pick a location from a short list of available regions. Then it will send requests to Azure to provision all the required resources. With everything provisioned, it runs the `postprovision` hook to process the local data and add it to an Azure Cosmos DB index.
 
 Finally, it looks at `azure.yaml` to determine the Azure host (Functions and Static Web Apps, in this case) and uploads the zip to Azure. The `azd up` command is now complete, but it may take some time for the app to be fully available and working after the initial deploy.
 

docs/readme.md

@@ -17,7 +17,7 @@ description: Build your own serverless AI Chat with Retrieval-Augmented-Generati
 
 <!-- Learn samples onboarding: https://review.learn.microsoft.com/en-us/help/contribute/samples/process/onboarding?branch=main -->
 <!-- prettier-ignore -->
-This sample shows how to build a serverless AI chat experience with Retrieval-Augmented Generation using [LangChain.js](https://js.langchain.com/) and Azure. The application is hosted on [Azure Static Web Apps](https://learn.microsoft.com/azure/static-web-apps/overview) and [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript), with [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) as the vector database. You can use it as a starting point for building more complex AI applications.
+This sample shows how to build a serverless AI chat experience with Retrieval-Augmented Generation using [LangChain.js](https://js.langchain.com/) and Azure. The application is hosted on [Azure Static Web Apps](https://learn.microsoft.com/azure/static-web-apps/overview) and [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript), with [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search) as the vector database. You can use it as a starting point for building more complex AI applications.
 
 ![Animation showing the chat app in action](./images/demo.gif)
 
@@ -35,7 +35,7 @@ This application is made from multiple components:
 
 - A serverless API built with [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript) and using [LangChain.js](https://js.langchain.com/) to ingest the documents and generate responses to the user chat queries. The code is located in the `packages/api` folder.
 
-- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search).
+- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
 
 - A file storage to store the source documents, using [Azure Blob Storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-introduction).
 
@@ -106,7 +106,7 @@ Here are some resources to learn more about the technologies used in this sample
 - [LangChain.js documentation](https://js.langchain.com)
 - [Generative AI For Beginners](https://github.com/microsoft/generative-ai-for-beginners)
 - [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/overview)
-- [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search)
+- [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/)
 - [Ask YouTube: LangChain.js + Azure Quickstart sample](https://github.com/Azure-Samples/langchainjs-quickstart-demo)
 - [Chat + Enterprise data with Azure OpenAI and Azure AI Search](https://github.com/Azure-Samples/azure-search-openai-javascript)
 - [Revolutionize your Enterprise Data with Chat: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog)

docs/tutorial/03-understanding-rag.md

@@ -73,7 +73,7 @@ RAG has significant implications in many fields:
 
 - **Educational Content:** Educators can update their materials with the latest research and studies to ensure relevance and accuracy.
 
-> **Note:** To learn more about the RAG architecture, please refer to the official documentation of the Azure AI Search Documentation service, which can be accessed [here](https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview).
+> **Note:** To learn more about the RAG architecture, please refer to the official documentation of the Azure Cosmos DB Documentation service, which can be accessed [here](https://learn.microsoft.com/azure/cosmos-db/gen-ai/rag).
 
 ## Next Steps