From f460b2f175a254d95cd197a2d2cb58e4df0b17d5 Mon Sep 17 00:00:00 2001
From: Fangyin Cheng <staneyffer@gmail.com>
Date: Tue, 10 Sep 2024 19:07:14 +0800
Subject: [PATCH] docs: Add flow usage document (#1999)

---
 docs/docs/application/awel.md | 105 +++++++++++++++++++++++++++++++++-
 1 file changed, 104 insertions(+), 1 deletion(-)

diff --git a/docs/docs/application/awel.md b/docs/docs/application/awel.md
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 # Use Data App With AWEL
 
+## What Is AWEL?
+
+> Agentic Workflow Expression Language(AWEL) is a set of intelligent agent workflow expression language specially designed for large model application
+development.
+
+You can found more information about AWEL in [AWEL](../awel/awel.md) and 
+[AWEL Tutorial](../awel/tutorial/) if you want to know more about AWEL.
+
+In short, you can use AWEL to develop LLM applications with AWEL Python API.
+
+## What Is AWEL Flow?
+
+AWEL flow allows you to develop LLM applications without writing code. It is built on top of AWEL Python API.
+
+
+## Visit Your AWEL Flows in `AWEL Flow` Page
+
+In the `AWEL Flow` page, you can see all the AWEL flows you have created. You can also create a new AWEL flow by clicking the `Create Flow` button.
+
+
+<p align="left">
+  <img src={'/img/application/awel/awel_flow_page.png'} width="720px"/>
+</p>
+
+
+## Examples
+
+### Build Your RAG Application
+
+To build your RAG application, you need to create a knowledge space according to [Chat Knowledge Base](./apps/chat_knowledge.md) first.
+Then, click the `Create Flow` button to create a new flow. 
+
+In the flow editor, you can drag and drop the nodes to build your RAG application.
+
+1. You will see an empty flow editor like below:
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_empty_page_img.png'} width="720px"/>
+</p>
+
+2. Drag a `Streaming LLM Operator` node to the flow editor.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_llm_1.png'} width="720px"/>
+</p>
+
+3. Drag a `Knowledge Operator` node to the flow editor.
+
+You can click the "+" button in the `Streaming LLM Operator` node's second input(`"HOContext"`), 
+it will show a list of nodes that can be connected to current node of input, then you can select the `Knowledge Operator` node.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_llm_2_.png'} width="720px"/>
+</p>
+
+The options of nodes can be connected as follows:
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_llm_3.png'} width="720px"/>
+</p>
+
+Then, drag the `Knowledge Operator` node and connect it to the `Streaming LLM Operator` node.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_def_rag_ko_1.png'} width="720px"/>
+</p>
+
+Please select your knowledge space in the `Knowledge Operator` node's `Knowledge Space Name` option.
+
+4. Drag a `Common LLM Http Trigger` node to the flow editor.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_ko_2.png'} width="720px"/>
+</p>
+
+4. Drag a `Common Chat Prompt Template` **resource** node to the flow editor.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_prompt_1.png'} width="720px"/>
+</p>
+
+And you can type your prompt template in the `Common Chat Prompt Template` parameters.
+
+5. Drag a `OpenAI Streaming Output Operator` node to the flow editor.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_output_1.png'} width="720px"/>
+</p>
+
+6. Click the `Save` button in the top right corner to save your flow.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_save_1.png'} width="720px"/>
+</p>
+
+Lastly, you will see your RAG application in the `AWEL Flow` page.
+
+<p align="left">
+  <img src={'/img/application/awel/flow_dev_rag_show_1.png'} width="720px"/>
+</p>
+
+After that, you can use it to build your APP according to [App Manage](./apps/app_manage.md).
 
 ## Reference
+
 - [AWEL](../awel/awel.md)
 - [AWEL CookBook](../awel/cookbook/)
-- [AWEL Tutorial](../awel/awel_tutorial/)
+- [AWEL Tutorial](../awel/tutorial/)