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80 lines
2.7 KiB
Plaintext
80 lines
2.7 KiB
Plaintext
---
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title: "Tool Response"
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description: ""
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icon: "arrow-turn-down-left"
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mode: "wide"
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---
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Tools return results using `ActionResult` or simple strings.
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## Return Types
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```python
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@tools.action('My tool')
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def my_tool() -> str:
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return "Task completed successfully"
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@tools.action('Advanced tool')
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def advanced_tool() -> ActionResult:
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return ActionResult(
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extracted_content="Main result",
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long_term_memory="Remember this info",
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error="Something went wrong",
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is_done=True,
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success=True,
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attachments=["file.pdf"],
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)
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```
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## ActionResult Properties
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- `extracted_content` (default: `None`) - Main result passed to LLM, this is equivalent to returning a string.
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- `include_extracted_content_only_once` (default: `False`) - Set to `True` for large content to include it only once in the LLM input.
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- `long_term_memory` (default: `None`) - This is always included in the LLM input for all future steps.
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- `error` (default: `None`) - Error message, we catch exceptions and set this automatically. This is always included in the LLM input.
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- `is_done` (default: `False`) - Tool completes entire task
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- `success` (default: `None`) - Task success (only valid with `is_done=True`)
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- `attachments` (default: `None`) - Files to show user
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- `metadata` (default: `None`) - Debug/observability data
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## Why `extracted_content` and `long_term_memory`?
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With this you control the context for the LLM.
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### 1. Include short content always in context
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```python
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def simple_tool() -> str:
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return "Hello, world!" # Keep in context for all future steps
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```
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### 2. Show long content once, remember subset in context
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```python
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return ActionResult(
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extracted_content="[500 lines of product data...]", # Shows to LLM once
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include_extracted_content_only_once=True, # Never show full output again
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long_term_memory="Found 50 products" # Only this in future steps
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)
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```
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We save the full `extracted_content` to files which the LLM can read in future steps.
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### 3. Dont show long content, remember subset in context
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```python
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return ActionResult(
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extracted_content="[500 lines of product data...]", # The LLM never sees this because `long_term_memory` overrides it and `include_extracted_content_only_once` is not used
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long_term_memory="Saved user's favorite products", # This is shown to the LLM in future steps
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)
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```
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## Terminating the Agent
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Set `is_done=True` to stop the agent completely. Use when your tool finishes the entire task:
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```python
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@tools.action(description='Complete the task')
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def finish_task() -> ActionResult:
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return ActionResult(
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extracted_content="Task completed!",
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is_done=True, # Stops the agent
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success=True # Task succeeded
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)
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```
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