165 lines
6.4 KiB
Markdown
165 lines
6.4 KiB
Markdown
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# Documentation
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- Class name: LLMInputToDocuments
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- Category: SALT/Language Toolkit/Documents
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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LLMInputToDocuments节点旨在将输入数据转换为结构化的文档格式。它能够整合额外信息，并支持输入的连接，从而增强文档创建过程。这个节点在处理和组织文本数据方面发挥着关键作用，为后续的语言模型处理或文档分析奠定基础。
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# Input types
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## Required
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- input_data
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- 这是要转换成文档格式的主要数据，作为文档创建过程的核心内容。它可以是各种形式的文本数据，节点会将其结构化为适合进一步处理的文档格式。
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- Comfy dtype: *
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- Python dtype: str
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## Optional
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- extra_info
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- 这是一个可选的JSON格式字符串，用于在文档中包含元数据。通过这个参数，可以为文档添加额外的上下文或信息，丰富文档的内容和可用性。
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- Comfy dtype: STRING
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- Python dtype: str
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- concat_input
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- 这是一个布尔标志，用于指示是否将输入数据连接成单个文档，或将每个输入作为单独的文档处理。这个选项影响了最终生成的文档的结构和粒度。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- documents
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- 输出是转换后的结构化文档或文档集合。这些文档包含了输入数据的内容，可能还enriched了可选的元数据，并根据连接设置进行了调整。这个输出为后续的文本分析或处理提供了结构化的基础。
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- Comfy dtype: DOCUMENT
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- Python dtype: Tuple[List[Document]]
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class LLMInputToDocuments:
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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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"input_data": (WILDCARD,),
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},
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"optional": {
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"extra_info": ("STRING", {"default": "{}"}),
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"concat_input": ("BOOLEAN", {"default": False})
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}
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}
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RETURN_TYPES = ("DOCUMENT",)
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RETURN_NAMES = ("documents",)
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FUNCTION = "to_documents"
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CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Documents"
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def to_documents(self, input_data, extra_info="{}", concat_input=False):
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documents = []
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# Try to parse extra_info
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try:
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extra_info = json.loads(extra_info) if extra_info.strip() else {}
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except json.JSONDecodeError:
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print("Invalid JSON for `extra_info` supplied, defaulting to empty `extra_info` dict.")
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extra_info = {}
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if not isinstance(extra_info, dict):
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print("Failed to decode `extra_info`, defaulting to empty `extra_info` dict.")
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extra_info = {}
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# Dict to string doc
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if isinstance(input_data, dict) and concat_input:
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doc_text = "\n".join(f"{key}: {value}" for key, value in input_data.items())
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documents.append(Document(text=doc_text, metadata={"source_type": "dict", **extra_info}))
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elif isinstance(input_data, dict):
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for key, value in input_data.items():
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doc_text = f"{key}: {value}"
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documents.append(Document(text=doc_text, metadata={"source_type": "dict", **extra_info}))
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# List to string doc
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elif isinstance(input_data, list) and concat_input:
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doc_text = "\n".join(str(item) for item in input_data)
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documents.append(Document(text=doc_text, metadata={"source_type": "list", **extra_info}))
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elif isinstance(input_data, list):
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for item in input_data:
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doc_text = str(item)
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documents.append(Document(text=doc_text, metadata={"source_type": "list", **extra_info}))
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# Primitive to string doc
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elif isinstance(input_data, (str, int, float)):
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documents.append(Document(text=str(input_data), metadata={"source_type": type(input_data).__name__, **extra_info}))
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elif isinstance(input_data, torch.Tensor):
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temp = folder_paths.get_temp_directory()
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os.makedirs(temp, exist_ok=True)
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output_path = os.path.join(temp, str(uuid.uuid4()))
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os.makedirs(output_path, exist_ok=True)
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images = []
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image_paths = []
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for img in input_data:
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if img.shape[-1] == 3:
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images.append(self.tensor2pil(img))
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else:
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images.append(self.mask2pil(img))
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if not images:
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raise ValueError("Invalid image tensor input provided to convert to PIL!")
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try:
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for index, pil_image in enumerate(images):
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file_prefix = "llm_image_input_"
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file_ext = ".jpeg"
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filename = f"{file_prefix}_{index:04d}{file_ext}"
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image_path = os.path.join(output_path, filename)
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pil_image.save(image_path, quality=100)
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image_paths.append(image_path)
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if os.path.exists(image_path):
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print(f"[SALT] Saved LLM document image to `{image_path}`")
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else:
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print(f"[SALT] Unable to save LLM document image to `{image_path}`")
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except Exception as e:
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raise e
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reader = SimpleDirectoryReader(
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input_dir=output_path,
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exclude_hidden=True,
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recursive=False
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)
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documents = reader.load_data()
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if not documents:
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raise ValueError("No documents found in the specified directory.")
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else:
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raise ValueError(f"LLMInputToDocuments does not support type `{type(input_data).__name__}`. Please provide: dict, list, str, int, float.")
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pprint(documents, indent=4)
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return (documents,)
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def tensor2pil(self, x):
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return Image.fromarray(np.clip(255. * x.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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def mask2pil(self, x):
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x = 1. - x
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if x.ndim != 3:
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print(f"Expected a 3D tensor ([N, H, W]). Got {x.ndim} dimensions.")
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x = x.unsqueeze(0)
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x_np = x.cpu().numpy()
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if x_np.ndim != 3:
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x_np = np.expand_dims(x_np, axis=0)
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return Image.fromarray(np.clip(255. * x_np[0, :, :], 0, 255).astype(np.uint8), 'L')
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```
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