Added two developer guide and removed from README ' builder docker image' and 'launch service from source' (#2590)

### What problem does this PR solve?


### Type of change

- [x] Documentation Update

docs/guides/_category_.json

@@ -1,8 +1,8 @@
 {
-  "label": "User Guides",
+  "label": "Guides",
   "position": 2,
   "link": {
     "type": "generated-index",
-    "description": "RAGFlow User Guides"
+    "description": "Guides for RAGFlow users and developers."
   }
 }

docs/guides/agentic_rag/_category_.json

@@ -3,6 +3,6 @@
   "position": 3,
   "link": {
     "type": "generated-index",
-    "description": "RAGFlow v0.8.0 introduces an agent mechanism, featuring a no-code workflow editor on the front end and a comprehensive graph-based task orchestration framework on the back end."
+    "description": "RAGFlow v0.8.0 introduces an agent mechanism, featuring a no-code workflow editor on the front end and a comprehensive graph-based task orchestration framework on the backend."
   }
 }

docs/guides/agentic_rag/agent_introduction.md

@@ -53,9 +53,9 @@ Please review the flowing description of the RAG-specific components before you
 | -------------- | ------------------------------------------------------------ |
 | **Retrieval**  | A component that retrieves information from specified knowledge bases and returns 'Empty response' if no information is found. Ensure the correct knowledge bases are selected. |
 | **Generate**   | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
-| **Answer**     | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
+| **Interact**   | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
 | **Categorize** | A component that uses the LLM to classify user inputs into predefined categories. Ensure you specify the name, description, and examples for each category, along with the corresponding next component. |
-| **Message**    | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Answer**, the interface component. |
+| **Message**    | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Interact**, the interface component. |
 | **Relevant**   | A component that uses the LLM to assess whether the upstream output is relevant to the user's latest query. Ensure you specify the next component for each judge result. |
 | **Rewrite**    | A component that refines a user query if it fails to retrieve relevant information from the knowledge base. It repeats this process until the predefined looping upper limit is reached. Ensure its upstream is **Relevant** and downstream is **Retrieval**. |
 | **Keyword**    | A component that retrieves top N search results from wikipedia.org. Ensure the TopN value is set properly before use. |
@@ -63,8 +63,8 @@ Please review the flowing description of the RAG-specific components before you
 :::caution NOTE
 
 - Ensure **Rewrite**'s upstream component is **Relevant** and downstream component is **Retrieval**.
-- Ensure the downstream component of **Message** is **Answer**.
-- The downstream component of **Begin** is always **Answer**.
+- Ensure the downstream component of **Message** is **Interact**.
+- The downstream component of **Begin** is always **Interact**.
 
 :::
 

docs/guides/agentic_rag/general_purpose_chatbot.md

@@ -26,7 +26,7 @@ To create a general-purpose chatbot agent using our template:
 3. On the **agent template** page, hover over the card on **General-purpose chatbot** and click **Use this template**.  
    *You are now directed to the **no-code workflow editor** page.*
 
-   ![workflow_editor](https://github.com/user-attachments/assets/9fc6891c-7784-43b8-ab4a-3b08a9e551c4)
+   ![workflow_editor](https://github.com/user-attachments/assets/52e7dc62-4bf5-4fbb-ab73-4a6e252065f0)
 
 :::tip NOTE
 RAGFlow's no-code editor spares you the trouble of coding, making agent development effortless.
@@ -40,10 +40,9 @@ Here’s a breakdown of each component and its role and requirements in the chat
   - Function: Sets the opening greeting for the user.
   - Purpose: Establishes a welcoming atmosphere and prepares the user for interaction.
 
-- **Answer**
+- **Interact**
   - Function: Serves as the interface between human and the bot.
   - Role: Acts as the downstream component of **Begin**.  
-  - Note: Though named "Answer", it does not engage with the LLM.
 
 - **Retrieval**
   - Function: Retrieves information from specified knowledge base(s).
@@ -78,7 +77,7 @@ Here’s a breakdown of each component and its role and requirements in the chat
 
 4. Click **Relevant** to review or change its settings:  
    *You may retain the current settings, but feel free to experiment with changes to understand how the agent operates.*
-   ![relevant_settings](https://github.com/user-attachments/assets/f582cc1c-0dd5-499c-813a-294dbfb941dd)
+   ![relevant_settings](https://github.com/user-attachments/assets/9ff7fdd8-7a69-4ee2-bfba-c7fb8029150f)
 
 5. Click **Rewrite** to select a different model for query rewriting or update the maximum loop times for query rewriting:  
    ![choose_model](https://github.com/user-attachments/assets/2bac1d6c-c4f1-42ac-997b-102858c3f550)

docs/guides/develop/_category_.json

@@ -0,0 +1,8 @@
+{
+  "label": "Develop",
+  "position": 10,
+  "link": {
+    "type": "generated-index",
+    "description": "Guides for Hardcore Developers"
+  }
+}

docs/guides/develop/build_docker_image.md

@@ -0,0 +1,43 @@
+---
+sidebar_position: 1
+slug: /build_docker_image
+---
+
+# Build a RAGFlow Docker Image
+
+A guide explaining how to build a RAGFlow Docker image from its source code. By following this guide, you'll be able to create a local Docker image that can be used for development, debugging, or testing purposes.
+
+## Target Audience
+
+- Developers who have added new features or modified the existing code and require a Docker image to view and debug their changes.
+- Testers looking to explore the latest features of RAGFlow in a Docker image.
+
+## Prerequisites
+
+- CPU ≥ 4 cores
+- RAM ≥ 16 GB
+- Disk ≥ 50 GB
+- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
+
+:::tip NOTE
+If you have not installed Docker on your local machine (Windows, Mac, or Linux), see the [Install Docker Engine](https://docs.docker.com/engine/install/) guide.
+:::
+
+## Build a RAGFlow Docker Image
+
+To build a RAGFlow Docker image from source code:
+
+### Git Clone the Repository
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+```
+
+### Build the Docker Image
+
+Navigate to the `ragflow` directory where the Dockerfile and other necessary files are located. Now you can build the Docker image using the provided Dockerfile. The command below specifies which Dockerfile to use and tages the image with a name for reference purpose.
+
+```bash
+cd ragflow/
+docker build -f Dockerfile.scratch -t infiniflow/ragflow:dev .
+```

docs/guides/develop/launch_ragflow_from_source.md

@@ -0,0 +1,129 @@
+---
+sidebar_position: 2
+slug: /launch_ragflow_from_source
+---
+
+# Launch the RAGFlow Service from Source 
+
+A guide explaining how to set up a RAGFlow service from its source code. By following this guide, you'll be able to debug using the source code.
+
+## Target Audience
+
+Developers who have added new features or modified existing code and wish to debug using the source code, *provided that* their machine has the target deployment environment set up.
+
+## Prerequisites
+
+- CPU ≥ 4 cores
+- RAM ≥ 16 GB
+- Disk ≥ 50 GB
+- Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
+
+:::tip NOTE
+If you have not installed Docker on your local machine (Windows, Mac, or Linux), see the [Install Docker Engine](https://docs.docker.com/engine/install/) guide.
+:::
+
+## Launch the Service from Source
+
+To launch the RAGFlow service from source code:
+
+### Clone the RAGFlow Repository
+
+```bash
+git clone https://github.com/infiniflow/ragflow.git
+cd ragflow/
+```
+
+### Install Python dependencies
+
+1. Install Poetry:
+   
+   ```bash
+   curl -sSL https://install.python-poetry.org | python3 -
+   ```
+
+2. Configure Poetry:
+
+   ```bash
+   export POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true
+   ```
+
+3. Install Python dependencies:
+
+   ```bash
+   ~/.local/bin/poetry install --sync --no-root
+   ```
+   *A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*
+
+### Launch Third-party Services
+
+The following command launches the 'base' services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
+
+```bash
+docker compose -f docker/docker-compose-base.yml up -d
+```
+
+### Update `host` and `port` Settings for Third-party Services
+
+1. Add the following line to `/etc/hosts` to resolve all hosts specified in **docker/service_conf.yaml** to `127.0.0.1`:
+
+   ```
+   127.0.0.1       es01 mysql minio redis
+   ```
+
+2. In **docker/service_conf.yaml**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
+
+### Launch the RAGFlow Backend Service
+
+1. Comment out the `nginx` line in **docker/entrypoint.sh**.
+
+   ```
+   # /usr/sbin/nginx
+   ```
+
+2. Activate the Python virtual environment:
+
+   ```bash
+   source .venv/bin/activate
+   export PYTHONPATH=$(pwd)
+   ```
+
+3. **Optional:** If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
+ 
+   ```bash
+   export HF_ENDPOINT=https://hf-mirror.com
+   ```
+
+4. Run the **entrypoint.sh** script to launch the backend service:
+
+   ```
+   bash docker/entrypoint.sh
+   ```
+
+### Launch the RAGFlow frontend service
+
+1. Navigate to the `web` directory and install the frontend dependencies:
+
+   ```bash
+   cd web
+   npm install --force
+   ```
+
+2. Update `proxy.target` in **.umirc.ts** to `http://127.0.0.1:9380`:
+
+   ```bash
+   vim .umirc.ts
+   ```
+
+3. Start up the RAGFlow frontend service:
+
+   ```bash
+   npm run dev 
+   ```  
+
+   *The following message appears, showing the IP address and port number of your frontend service:*  
+
+   ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
+
+### Access the RAGFlow service
+
+In your web browser, enter `http://127.0.0.1:<PORT>/`, ensuring the port number matches that shown in the screenshot above.