components llm_rag_crack_and_chunk_and_embed

LLM - Crack, Chunk and Embed Data

llm_rag_crack_and_chunk_and_embed

Overview

Creates chunks no larger than chunk_size from input_data, extracted document titles are prepended to each chunk

LLM models have token limits for the prompts passed to them, this is a limiting factor at embedding time and even more limiting at prompt completion time as only so much context can be passed along with instructions to the LLM and user queries. Chunking allows splitting source data of various formats into small but coherent snippets of information which can be 'packed' into LLM prompts when asking for answers to user query related to the source documents.

Supported formats: md, txt, html/htm, pdf, ppt(x), doc(x), xls(x), py

Also generates embeddings vectors for data chunks if configured.

If embeddings_container is supplied, input chunks are compared to existing chunks in the Embeddings Container and only changed/new chunks are embedded, existing chunks being reused.

Version: 0.0.48

Tags

Preview

View in Studio: https://ml.azure.com/registries/azureml/components/llm_rag_crack_and_chunk_and_embed/version/0.0.48

Inputs

Input AzureML Data

Name	Description	Type	Default	Optional	Enum
input_data	Uri Folder containing files to be chunked.	uri_folder

Files to handle from source

Name	Description	Type	Default	Optional	Enum
input_glob	Limit files opened from input_data, defaults to '**/*'.	string		True

Chunking options

Name	Description	Type	Default	Optional	Enum
chunk_size	Maximum number of tokens to put in each chunk.	integer	768
chunk_overlap	Number of tokens to overlap between chunks.	integer	0
doc_intel_connection_id	Connection id for Document Intelligence service. If provided, will be used to extract content from .pdf document.	string		True
citation_url	Base URL to join with file paths to create full source file URL for chunk metadata.	string		True
citation_replacement_regex	A JSON string with two fields, 'match_pattern' and 'replacement_pattern' to be used with re.sub on the source url. e.g. '{"match_pattern": "(.)/articles/(.)(\.[^.]+)$", "replacement_pattern": "\1/\2"}' would remove '/articles' from the middle of the url.	string		True
use_rcts	Whether to use RecursiveCharacterTextSplitter to split documents into chunks	string	True		['True', 'False']

If adding to previously generated Embeddings

Name	Description	Type	Default	Optional	Enum
embeddings_container	Folder containing previously generated embeddings. Should be parent folder of the 'embeddings' output path used for for this component. Will compare input data to existing embeddings and only embed changed/new data, reusing existing chunks.	uri_folder		True

Embeddings settings

Name	Description	Type	Default	Optional	Enum
embeddings_model	The model to use to embed data. E.g. 'hugging_face://model/sentence-transformers/all-mpnet-base-v2' or 'azure_open_ai://deployment/{deployment_name}/model/{model_name}'	string		True
embeddings_connection_id	The connection id of the Embeddings Model provider to use.	string		True
batch_size	Batch size to use when embedding data.	integer	100
num_workers	Number of workers to use when embedding data. -1 defaults to CPUs / 2.	integer	-1
verbosity	Verbosity level for embedding process, specific to document processing information. 0: Aggregate Source/Document Info, 1: Source Ids logged as processed, 2: Document Ids logged as processed.	integer	0

Outputs

Name	Description	Type
embeddings	Where to save data with embeddings. This should be a subfolder of previous embeddings if supplied, typically named using '${name}'. e.g. /my/prev/embeddings/${name}	uri_folder

Environment

azureml:llm-rag-embeddings@latest