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Update English documentation to match Chinese structure
Co-Authored-By: Junyan Qin <Chin> <[email protected]>
This commit is contained in:
Devin AI
co-authored by
Junyan Qin <Chin>
Junyan Qin <Chin>
parent
c50e58142c
commit
dbeb04eb38
+20
-155
@@ -1,163 +1,28 @@
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# Dialogue Commands
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# Command Details
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LangBot provides various commands that can be used during conversations to control the bot's behavior.
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> Parameters in `<>` are required, do not include `<>` when using
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> Parameters in `[]` are optional, do not include `[]` when using
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> Here we only list the commands briefly, for detailed usage please use the `!cmd` command
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## Command Prefix
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## Command List
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Commands start with a prefix, which by default is `!` or `！`. You can configure the command prefixes in the `data/config.yaml` file:
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```yaml
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trigger:
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command_prefixes:
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- "!"
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- "！"
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```
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## Available Commands
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### Help Command
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Available to any object
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```
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!help
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!help Display custom help information (can be changed in the configuration file)
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!cmd [command name] Display command list or detailed information for a specific command
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!list [page number] List the history sessions for this object
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!del <number> Delete the specified history record, you can view the number with !list
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!del all Delete all history records for this session object
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!last Switch to the previous session
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!next Switch to the next session
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!reset Reset the current session
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!prompt View all records of the current session
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!version Check the current version and check for updates
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!resend Rollback the previous request
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!plugin For usage, please see the `Management` section of the plugin introduction page
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```
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Displays a list of available commands.
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## Command Permission Control
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### Model Commands
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```
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!model list
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```
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Lists all available models.
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```
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!model use <model_name>
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```
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Switches to the specified model.
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```
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!model info
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```
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Displays information about the current model.
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### Context Commands
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```
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!context clear
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```
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Clears the current conversation context.
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```
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!context show
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```
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Displays the current conversation context.
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```
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!context length
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```
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Shows the length of the current context in tokens.
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### Pipeline Commands
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```
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!pipeline list
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```
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Lists all available pipelines.
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```
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!pipeline use <pipeline_id>
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```
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Switches to the specified pipeline.
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```
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!pipeline info
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```
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Displays information about the current pipeline.
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### System Commands
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```
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!system info
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```
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Displays system information, including version, uptime, and memory usage.
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```
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!system reload
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```
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Reloads the system configuration.
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### Plugin Commands
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```
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!plugin list
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```
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Lists all installed plugins.
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```
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!plugin enable <plugin_id>
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```
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Enables the specified plugin.
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```
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!plugin disable <plugin_id>
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```
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Disables the specified plugin.
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```
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!plugin info <plugin_id>
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```
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Displays information about the specified plugin.
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### Preset Commands
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```
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!preset list
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```
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Lists all available presets.
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```
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!preset use <preset_id>
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```
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Switches to the specified preset.
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```
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!preset info
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```
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Displays information about the current preset.
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## Command Permissions
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You can configure command permissions in the `data/config.yaml` file:
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```yaml
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command:
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permissions:
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- command: "system reload"
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roles:
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- admin
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- command: "plugin *"
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roles:
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- admin
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```
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## Custom Commands
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Plugins can register custom commands. Check the plugin documentation for available commands.
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You can [edit the system environment settings](/en/deploy/settings.md) to set permissions for command nodes. When a command is initiated, if the user's permission level (administrator is `2`, regular user is `1`) is greater than or equal to the command node's permission level, the command can be successfully executed.
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@@ -1,148 +1,48 @@
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# Modify Dialogue Pipeline
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# Modify Dialogue Pipeline Configuration (Pipelines)
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The dialogue pipeline is the core processing flow of LangBot, which handles user messages and generates responses.
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The pipeline controls the processing flow after receiving a message, as well as the information exchange with the large language model. Each bot can be bound to one pipeline, and the same pipeline can be bound to multiple bots.
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## Pipeline Structure
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LangBot automatically creates a default pipeline when first started. When creating a bot, it is automatically bound to the default pipeline. When creating the first model, it is automatically set as the model for the default pipeline.
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The pipeline consists of the following components:
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You can create multiple pipelines for different bots to adapt to different scenarios.
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1. **Trigger**: Determines whether to process a message
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2. **Moderation**: Filters and processes messages before sending to the model
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3. **AI**: Sends the message to the LLM and receives a response
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4. **Output**: Processes the model's response before sending it back to the user
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## Configuration Methods
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### Method 1: Configure via WebUI
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The current pipeline can be configured with the following features:
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After deploying LangBot, access the WebUI at `http://your-server-ip:5300` and configure the pipeline in the "Pipeline" section.
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## AI Capabilities
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### Method 2: Configure via Configuration File
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You can choose to use: regular large language model `Built-in Agent`, [`Dify`](https://dify.ai/), [`Alibaba Cloud Bailian`](https://www.aliyun.com/product/bailian?source=5176.29345612&userCode=ys4ad8gs)
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Edit the `data/config.yaml` file and modify the `pipelines` section:
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AI capabilities are mainly divided into two parts: selecting the runner and configuring runner parameters.
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```yaml
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pipelines:
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- id: default
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components:
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- id: trigger
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type: trigger
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config:
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# Trigger configuration
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- id: moderation
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type: moderation
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config:
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# Moderation configuration
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- id: ai
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type: ai
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config:
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# AI configuration
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- id: output
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type: output
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config:
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# Output configuration
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```
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The runner defines how to schedule the large model to process messages. The default is `Built-in Agent`, which is a multi-round Agent strategy implemented by LangBot. Only when this runner is selected will the models and tools configured internally in LangBot be used.
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## Component Configuration
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*<!--  -->*
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### Trigger Component
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<img width="400px" src="/assets/image/zh/deploy/pipelines/edit_pipeline_ai.png" alt="edit_pipeline" />
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The trigger component determines whether a message should be processed by the pipeline.
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You can also choose to use external LLMOps platforms such as `Dify`, [`Alibaba Cloud Bailian`](https://www.aliyun.com/product/bailian?source=5176.29345612&userCode=ys4ad8gs). In this case, the models, prompts, tools, and other resources used will be provided by the LLMOps platform.
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```yaml
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- id: trigger
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type: trigger
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config:
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# Whether to respond to messages that start with a command prefix
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command_prefix: true
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# Command prefixes (characters that trigger the bot)
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command_prefixes:
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- "!"
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- "！"
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# Whether to respond to messages that mention the bot
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at: true
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# Whether to respond to private messages
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private: true
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# Whether to respond to group messages without being triggered by command prefix or mention
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group_chat: false
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```
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<img width="400px" src="/assets/image/zh/deploy/pipelines/more_runner.png" alt="more_runner" />
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### Moderation Component
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- For detailed steps on connecting to Dify, you can [refer to the tutorial](/en/workshop/dify-service-api.html).
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The moderation component filters and processes messages before sending them to the model.
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### Conversation Variables
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```yaml
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- id: moderation
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type: moderation
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config:
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# Whether to enable sensitive word filtering
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sensitive_word_filter: true
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# Whether to enable rate limiting
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rate_limit: true
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# Rate limit configuration
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rate_limit_config:
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# Number of messages allowed per time period
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count: 10
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# Time period in seconds
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period: 60
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# Whether to enable context management
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context: true
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# Context configuration
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context_config:
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# Maximum number of messages to include in the context
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max_count: 10
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# Maximum context length in tokens
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max_tokens: 4000
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```
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When using Dify or Alibaba Cloud Bailian, LangBot explicitly passes the following parameters, which you can add to the start node of the Dify workflow:
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### AI Component
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- `user_message_text`: The plain text of the user message
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- `session_id`: User session ID, `person_<id>` for private chat, `group_<id>` for group chat
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- `conversation_id`: String, user session ID, generated by LangBot. It will be regenerated after the user resets the session
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- `msg_create_time`: Numeric type, timestamp (seconds) when this message was received
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The AI component sends the message to the LLM and receives a response.
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You can [customize any variable through plugins](/en/plugin/dev/api-ref.html#%E8%AE%BE%E7%BD%AE%E8%AF%B7%E6%B1%82%E5%8F%98%E9%87%8F).
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```yaml
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- id: ai
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type: ai
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config:
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# System prompt
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system_prompt: "You are LangBot, a helpful assistant."
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# Whether to enable function calling
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function_call: true
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# Whether to enable streaming responses
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stream: true
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```
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### Output Component
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## Other Configurations
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The output component processes the model's response before sending it back to the user.
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```yaml
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- id: output
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type: output
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config:
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# Whether to enable message splitting for long responses
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split: true
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# Maximum length of a single message
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max_length: 1000
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```
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## Multiple Pipelines
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You can configure multiple pipelines for different scenarios:
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```yaml
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pipelines:
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- id: default
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# Default pipeline configuration
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- id: image-generation
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# Pipeline for image generation
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- id: code-assistant
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# Pipeline for code assistance
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```
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To switch between pipelines, use the following command:
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```
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!pipeline use image-generation
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```
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Please refer to the descriptions in the configuration form.
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@@ -1,4 +1,4 @@
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# Configure Bots
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# Configure Bots (Bots)
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Bots are used to receive message events from messaging platforms and call pipelines to process messages.
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Reference in New Issue
Block a user