docs: merge starter projects and sample flows into templates (#8652)

* move-pages-and-add-redirects

* move-vector-rag-in-list

docs/docs/API-Reference/api-reference-api-examples.md

@@ -116,7 +116,7 @@ A dictionary of all Langflow components.
 Execute a specified flow by ID or name.
 The flow is executed as a batch, but LLM responses can be streamed.
 
-This example runs a [Basic Prompting](/starter-projects-basic-prompting) flow with a given `flow_id` and passes a JSON object as the input value.
+This example runs a [Basic Prompting](/basic-prompting) flow with a given `flow_id` and passes a JSON object as the input value.
 
 The parameters are passed in the request body. In this example, the values are the default values.
 

docs/docs/Components/components-agents.md

@@ -17,7 +17,7 @@ The agent then uses a connected LLM to reason through the problem to decide whic
 
 ## Use an agent in a flow
 
-The [simple agent starter project](/starter-projects-simple-agent) uses an [agent component](#agent-component) connected to URL and Calculator tools to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use. Tools are connected to agent components at the **Tools** port.
+The [simple agent starter project](/simple-agent) uses an [agent component](#agent-component) connected to URL and Calculator tools to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use. Tools are connected to agent components at the **Tools** port.
 
 ![Simple agent starter flow](/img/starter-flow-simple-agent.png)
 

docs/docs/Components/components-embedding-models.md

@@ -280,7 +280,7 @@ To run an embeddings inference locally, see the [HuggingFace documentation](http
 
 To connect the local Hugging Face model to the **Hugging Face embeddings inference** component and use it in a flow, follow these steps:
 
-1. Create a [Vector store RAG flow](/starter-projects-vector-store-rag).
+1. Create a [Vector store RAG flow](/vector-store-rag).
 There are two embeddings models in this flow that you can replace with **Hugging Face** embeddings inference components.
 2. Replace both **OpenAI** embeddings model components with **Hugging Face** model components.
 3. Connect both **Hugging Face** components to the **Embeddings** ports of the **Astra DB vector store** components.

docs/docs/Components/components-logic.md

@@ -237,7 +237,7 @@ The Run Flow component can also be used as a tool when connected to an [Agent](/
 When you select a flow, the component fetches the flow's graph structure and uses it to generate the inputs and outputs for the Run Flow component.
 
 To use the Run Flow component as a tool, do the following:
-1. Add the **Run Flow** component to the [Simple Agent](/starter-projects-simple-agent) flow.
+1. Add the **Run Flow** component to the [Simple Agent](/simple-agent) flow.
 2. In the **Flow Name** menu, select the sub-flow you want to run.
 The appearance of the **Run Flow** component changes to reflect the inputs and outputs of the selected flow.
 3. On the **Run Flow** component, enable **Tool Mode**.

docs/docs/Components/components-models.md

@@ -17,7 +17,7 @@ Model components receive inputs and prompts for generating text, and the generat
 
 The model output can also be sent to the **Language Model** port and on to a **Parse Data** component, where the output can be parsed into structured [Data](/concepts-objects) objects.
 
-This example has the OpenAI model in a chatbot flow. For more information, see the [Basic prompting flow](/starter-projects-basic-prompting).
+This example has the OpenAI model in a chatbot flow. For more information, see the [Basic prompting flow](/basic-prompting).
 
 ![](/img/starter-flow-basic-prompting.png)
 
@@ -227,7 +227,7 @@ For more information, see the [Google Generative AI documentation](https://cloud
 
 This component generates text using Groq's language models.
 
-1. To use this component in a flow, connect it as a **Model** in a flow like the [Basic prompting flow](/starter-projects-basic-prompting), or select it as the **Model Provider** if you're using an **Agent** component.
+1. To use this component in a flow, connect it as a **Model** in a flow like the [Basic prompting flow](/basic-prompting), or select it as the **Model Provider** if you're using an **Agent** component.
 
 ![Groq component in a basic prompting flow](/img/component-groq.png)
 
@@ -274,9 +274,9 @@ This component sends requests to the Hugging Face API to generate text using the
 
 The Hugging Face API is a hosted inference API for models hosted on Hugging Face, and requires a [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) to authenticate.
 
-In this example based on the [Basic prompting flow](/starter-projects-basic-prompting), the **Hugging Face API** model component replaces the **Open AI** model. By selecting different hosted models, you can see how different models return different results.
+In this example based on the [Basic prompting flow](/basic-prompting), the **Hugging Face API** model component replaces the **Open AI** model. By selecting different hosted models, you can see how different models return different results.
 
-1. Create a [Basic prompting flow](/starter-projects-basic-prompting).
+1. Create a [Basic prompting flow](/basic-prompting).
 
 2. Replace the **OpenAI** model component with a **Hugging Face API** model component.
 

docs/docs/Components/components-tools.md

@@ -19,7 +19,7 @@ The agent then uses a connected LLM to reason through the problem to decide whic
 
 Tools are typically connected to agent components at the **Tools** port.
 
-The [simple agent starter project](/starter-projects-simple-agent) uses URL and Calculator tools connected to an [agent component](/components-agents#agent-component) to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use.
+The [simple agent starter project](/simple-agent) uses URL and Calculator tools connected to an [agent component](/components-agents#agent-component) to answer a user's questions. The OpenAI LLM acts as a brain for the agent to decide which tool to use.
 
 ![Simple agent starter flow](/img/starter-flow-simple-agent.png)
 

docs/docs/Components/components-vector-stores.md

@@ -81,7 +81,7 @@ The **Astra DB Vector Store** component offers two methods for generating embedd
 The embedding model selection is made when creating a new collection and cannot be changed later.
 :::
 
-For an example of using the **Astra DB Vector Store** component with an embedding model, see the [Vector Store RAG starter project](/starter-projects-vector-store-rag).
+For an example of using the **Astra DB Vector Store** component with an embedding model, see the [Vector Store RAG starter project](/vector-store-rag).
 
 For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).
 

docs/docs/Concepts/concepts-voice-mode.md

@@ -19,7 +19,7 @@ Your flow must have a [Chat input](/components-io#chat-input) component to inter
 
 Chat with an agent in the **Playground**, and get more recent results by asking the agent to use tools.
 
-1. Create a [Simple agent starter project](/starter-projects-simple-agent).
+1. Create a [Simple agent starter project](/simple-agent).
 2. Add your **OpenAI API key** credentials to the **Agent** component.
 3. To start a chat session, click **Playground**.
 4. To enable voice mode, click the <Icon name="Mic" aria-label="Microphone"/> icon.

docs/docs/Concepts/embedded-chat-widget.md

@@ -16,7 +16,7 @@ For a sandbox example, see the [Langflow embedded chat CodeSandbox](https://code
 The following example includes the minimum required inputs, called [props](https://react.dev/learn/passing-props-to-a-component) in React, for using the chat widget in your HTML code, which are `host_url` and `flow_id`.
 The `host_url` value must be `HTTPS`, and may not include a `/` after the URL.
 The `flow_id` value is found in your Langflow URL.
-For a Langflow server running the [Basic prompting flow](/starter-projects-basic-prompting) at `https://c822-73-64-93-151.ngrok-free.app/flow/dcbed533-859f-4b99-b1f5-16fce884f28f`, your chat widget code is similar to the following:
+For a Langflow server running the [Basic prompting flow](/basic-prompting) at `https://c822-73-64-93-151.ngrok-free.app/flow/dcbed533-859f-4b99-b1f5-16fce884f28f`, your chat widget code is similar to the following:
 ```html
 <html>
   <head>

docs/docs/Get-Started/welcome-to-langflow.md

@@ -24,7 +24,7 @@ For example:
 * [Craft intelligent chatbots](/memory-chatbot)
 * [Build document analysis systems](/document-qa)
 * [Generate compelling content](/blog-writer)
-* [Orchestrate multi-agent applications](/starter-projects-simple-agent)
+* [Orchestrate multi-agent applications](/simple-agent)
 * [Create agents with Langflow](/agents-overview)
 * [Use Langflow as an MCP server](/mcp-server)
 * [Use Langflow as an MCP client](/mcp-client)

docs/docs/Integrations/Arize/integrations-arize.md

@@ -67,7 +67,7 @@ For more information, see the [Arize documentation](https://docs.arize.com/phoen
 
 ## Run a flow and view metrics in Arize
 
-1. In Langflow, select the [Simple agent](/starter-projects-simple-agent) starter project.
+1. In Langflow, select the [Simple agent](/simple-agent) starter project.
 2. In the **Agent** component's **OpenAI API Key** field, paste your **OpenAI API key**.
 3. Click **Playground**.
 Ask your Agent some questions to generate traffic.

docs/docs/Integrations/mcp-component-astra.md

@@ -17,7 +17,7 @@ Use the [MCP connection component](/mcp-client) to connect Langflow to a [Datast
 
 4. Get your database's **Astra DB API endpoint** and an **Astra DB application token** with the Database Administrator role. For more information, see [Generate an application token for a database](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#database-token).
 
-5. Create a [Simple agent starter project](/starter-projects-simple-agent) if you want to follow along with this guide. Otherwise, you can use an existing flow or create a new, blank flow.
+5. Create a [Simple agent starter project](/simple-agent) if you want to follow along with this guide. Otherwise, you can use an existing flow or create a new, blank flow.
 
 6. Remove the **URL** tool, and then replace it with an [MCP connection component](/mcp-client).
 The flow should look like this:

docs/docs/Templates/basic-prompting.md

@@ -1,6 +1,6 @@
 ---
 title: Basic prompting
-slug: /starter-projects-basic-prompting
+slug: /basic-prompting
 ---
 
 import Icon from "@site/src/components/icon";

docs/docs/Templates/memory-chatbot.md

@@ -9,7 +9,7 @@ import Icon from "@site/src/components/icon";
 The **Chat memory** component is also known as the **Message history** component.
 :::
 
-This flow extends the [basic prompting flow](/starter-projects-basic-prompting) with a **Message history** component that stores up to 100 previous chat messages and uses them to provide context for the current conversation.
+This flow extends the [basic prompting flow](/basic-prompting) with a **Message history** component that stores up to 100 previous chat messages and uses them to provide context for the current conversation.
 
 ## Prerequisites
 

docs/docs/Templates/simple-agent.md

@@ -1,6 +1,6 @@
 ---
 title: Simple agent
-slug: /starter-projects-simple-agent
+slug: /simple-agent
 ---
 
 Build a **Simple Agent** flow for an agentic application using the [Tool-calling agent](/agents-tool-calling-agent-component) component.

docs/docs/Templates/vector-store-rag.md

@@ -1,6 +1,6 @@
 ---
 title: Vector store RAG
-slug: /starter-projects-vector-store-rag
+slug: /vector-store-rag
 ---
 
 import Icon from "@site/src/components/icon";

docs/docusaurus.config.js

@@ -200,9 +200,10 @@ const config = {
             from: ["/starter-projects-document-qa", "/tutorials-document-qa"],
           },
           {
-            to: "/starter-projects-simple-agent",
+            to: "/simple-agent",
             from: [
               "/math-agent",
+              "/starter-projects-simple-agent",
               "/starter-projects-math-agent",
               "/tutorials-math-agent",
             ],
@@ -268,6 +269,14 @@ const config = {
               "/deployment-kubernetes",
             ],
           },
+          {
+            to: "/basic-prompting",
+            from: "/starter-projects-basic-prompting",
+          },
+          {
+            to: "/vector-store-rag",
+            from: "/starter-projects-vector-store-rag",
+          },
           // add more redirects like this
           // {
           //   to: '/docs/anotherpage',

docs/sidebars.js

@@ -11,23 +11,17 @@ module.exports = {
     },
     {
       type: "category",
-      label: "Starter projects",
+      label: "Templates",
       items: [
-        'Starter-Projects/starter-projects-basic-prompting',
+        'Templates/basic-prompting',
-        'Starter-Projects/starter-projects-vector-store-rag',
+        'Templates/simple-agent',
-        'Starter-Projects/starter-projects-simple-agent',
+        'Templates/blog-writer',
-      ],
+        'Templates/document-qa',
-    },
+        'Templates/memory-chatbot',
-    {
+        'Templates/vector-store-rag',
-      type: "category",
+        'Templates/financial-report-parser',
-      label: "Sample flows",
+        'Templates/sequential-agent',
-      items: [
+        'Templates/travel-planning-agent',
-        'Sample-Flows/blog-writer',
-        'Sample-Flows/document-qa',
-        'Sample-Flows/memory-chatbot',
-        'Sample-Flows/financial-report-parser',
-        'Sample-Flows/sequential-agent',
-        'Sample-Flows/travel-planning-agent',
       ],
     },
     {