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79 lines
3.8 KiB
Plaintext
79 lines
3.8 KiB
Plaintext
---
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title: Tool Calling & Termination
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description: FastGPT Tool Calling module overview
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---
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### What is a Tool
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A tool can be a built-in module (e.g., AI Chat, Knowledge Base Search, HTTP) or a plugin.
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Tool calling lets the LLM dynamically decide the workflow path instead of following a fixed sequence. (The trade-off is higher token consumption.)
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### Tool Components
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1. **Tool description.** Typically the module or plugin description that tells the LLM what the tool does.
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2. **Tool parameters.** For built-in modules, parameters are fixed and require no extra configuration. For plugins, parameters are configurable.
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### How Tools Work
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To understand how tools run, you need to know the execution prerequisites:
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1. A tool description is required. It tells the LLM what the tool does, and the LLM uses contextual semantics to decide whether to invoke it.
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2. Tool parameters. Some tools require special parameters when called. Each parameter has two key properties: `parameter description` and `required`.
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Based on the tool description, parameter descriptions, and whether parameters are required, the LLM decides whether to call the tool. The scenarios are:
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1. **Tools without parameters:** The LLM decides based solely on the tool description. Example: get current time.
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2. **Tools with parameters:**
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1. No required parameters: The tool can still be called even without suitable context parameters, though the LLM may sometimes fabricate a value.
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2. Has required parameters: If no suitable parameters are available, the LLM may skip the tool. Use prompts to guide users into providing the needed parameters.
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#### Tool Calling Logic
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Models that support `function calling` can invoke multiple tools in a single turn. The calling logic:
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### How to Use
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In the advanced workflow editor, drag from the tool calling connection point. Eligible tools display a diamond icon at the top, which you can connect to the diamond at the bottom of the tool calling module.
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Connected tools automatically separate tool inputs from regular inputs. You can also edit the `description` to fine-tune when the tool gets called.
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Debugging tool calling is still more art than science, so start with a small number of tools, optimize them, then gradually add more.
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#### Use Cases
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By default, after the tool calling node invokes a tool, it returns the tool's output to the AI for summarization. If you don't need the AI to summarize, place this node at the end of the tool's workflow branch.
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In the example below, after the Knowledge Base Search runs, results are sent to an HTTP request. The search results are not returned to the tool calling node for AI summarization.
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### Additional Nodes
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When you use the tool calling node, a Tool Calling Termination node and a Custom Variable node also become available, further enhancing the tool calling experience.
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#### Tool Calling Termination
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Tool Calling Termination ends the current call cycle. Place it after a tool node. When the workflow reaches this node, it forcibly ends the current tool call -- no further tools are invoked, and the AI won't generate a summary based on tool results.
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### Custom Tool Variables
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Custom variables extend tool input capabilities. For nodes that aren't recognized as tool parameters or can't be directly tool-called, you can define custom tool variables with appropriate parameter descriptions. The tool calling node will then invoke this node and its downstream workflow accordingly.
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### Related Examples
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- [Google Search](https://doc.fastgpt.in/docs/use-cases/app-cases/google_search/)
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- [Send Lark Webhook](https://doc.fastgpt.in/docs/use-cases/app-cases/feishu_webhook/)
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