102 lines
3.3 KiB
Markdown
102 lines
3.3 KiB
Markdown
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# Documentation
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- Class name: LLMVectorStoreIndexAdv
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- Category: SALT/Language Toolkit/Indexing
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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LLMVectorStoreIndexAdv节点旨在优化大型语言模型中的向量存储和索引操作。它采用先进技术管理向量数据，目标是以可扩展的方式提升检索和存储过程的效率。
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# Input types
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## Required
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- llm_model
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- 指定用于生成嵌入的大型语言模型，通过决定文本的向量表示，在索引过程中扮演关键角色。
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- Comfy dtype: LLM_MODEL
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- Python dtype: dict
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- document
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- 待索引的输入文档，每个文档的文本和可选元数据都会被处理并转换为向量嵌入。
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- Comfy dtype: DOCUMENT
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- Python dtype: Sequence[Document]
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## Optional
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- chunk_size
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- 定义文本处理的分块大小，影响文档的分割和索引方式。
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- Comfy dtype: COMBO[INT]
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- Python dtype: int
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- chunk_overlap
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- 指定连续文本块之间的重叠部分，影响索引过程的连续性和覆盖范围。
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- Comfy dtype: INT
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- Python dtype: int
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- optional_llm_context
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- 可选的上下文参数，允许根据大型语言模型的特定需求或配置来自定义索引过程。
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- Comfy dtype: LLM_CONTEXT
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- Python dtype: dict
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# Output types
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- llm_index
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- 输出是一个索引对象，它能够高效存储和检索向量嵌入，代表已处理的文档。
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- Comfy dtype: LLM_INDEX
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- Python dtype: VectorStoreIndex
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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 LLMVectorStoreIndexAdv:
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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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"document": ("DOCUMENT",),
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},
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"optional": {
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"chunk_size": ([128, 256, 512, 1024, 2048],),
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"chunk_overlap": ("INT", {"min": 0, "max": 512, "default": 0}),
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"optional_llm_context": ("LLM_CONTEXT",)
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},
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}
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RETURN_TYPES = ("LLM_INDEX",)
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RETURN_NAMES = ("llm_index",)
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FUNCTION = "index"
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CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Indexing"
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def index(self, llm_model, document, chunk_size=1024, chunk_overlap=0, optional_llm_context=None):
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embed_model = llm_model.get("embed_model", None)
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if not embed_model:
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raise ValueError("Unable to determine LLM Embedding Model")
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splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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tokenizer = MockTokenizer(max_tokens=chunk_size, char_per_token=1)
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documents = []
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for doc in document:
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print(doc)
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metadata = {}
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text = doc.text
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if doc.metadata:
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metadata = doc.metadata
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token_count = tokenizer.count(metadata)
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if token_count > 1024:
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metadata = tokenizer.truncate(metadata)
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documents.append(Document(text=text, extra_info=metadata))
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index = VectorStoreIndex.from_documents(
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documents,
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embed_model=embed_model,
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service_context=optional_llm_context,
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transformations=[splitter]
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)
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return (index,)
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```
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