66 lines
2.0 KiB
Markdown
66 lines
2.0 KiB
Markdown
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# Documentation
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- Class name: LLMComplete
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- Category: SALT/Language Toolkit/Querying
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- Output node: False
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LLMComplete节点旨在使用指定的语言模型基于给定的提示生成文本补全。它抽象了查询语言模型的复杂性，允许轻松集成到需要自然语言处理能力的大型系统中。
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# Input types
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## Required
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- llm_model
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- 指定用于生成文本补全的语言模型。这个参数至关重要，因为它决定了生成文本的语言和知识能力。
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- Comfy dtype: LLM_MODEL
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- Python dtype: Dict[str, Any]
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- prompt
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- 语言模型基于此输入文本提示生成补全。这个参数对于引导模型的输出朝向所需的上下文或问题至关重要。
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- Comfy dtype: STRING
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- Python dtype: str
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# Output types
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- completion
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- 基于输入提示和指定语言模型生成的文本补全。
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- Comfy dtype: STRING
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- Python dtype: str
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class LLMComplete:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"llm_model": ("LLM_MODEL", ),
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"prompt": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "The circumference of the Earth is"}),
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},
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"optional": {
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#"image_documents": ("DOCUMENT",)
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}
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}
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RETURN_TYPES = ("STRING", )
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RETURN_NAMES = ("completion", )
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FUNCTION = "complete"
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CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Querying"
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def complete(self, llm_model:Dict[str, Any], prompt:str, documents:List[Any] = None) -> str:
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if not documents:
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response = llm_model['llm'].complete(prompt)
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else:
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documents_images = [doc for doc in documents if isinstance(doc, ImageDocument)]
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response = llm_model['llm'].complete(prompt, image_documents=documents_images)
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pprint(response, indent=4)
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return (response.text, )
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```
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