NLP Extract Features

Runs an ONNX feature extraction model against the message and replaces its content with the resulting embedding vector. This is the embedding step in a RAG pipeline: combine it with the RAG Chunker scanner and a vector store output.

Field	Type	Default	Description
Name	string	required	Identifier for this pipeline instance. Must be unique within the flow
Path	string	required	Local path where the model lives. See Path and download modes below
Enable Download	boolean	false	Fetch the model from HuggingFace into Path at startup
Repository	string	-	HuggingFace repository to download from. Required when Enable Download is on
ONNX File Path	string	model.onnx	Path of the .onnx file inside the HuggingFace repository (not on your disk). Change only when the repo publishes multiple variants like onnx/model_quantized.onnx
Normalization	boolean	false	L2-normalize the output vector. Required by most cosine-similarity vector stores

Path and download modes

The Path field changes meaning based on Enable Download:

• Download off - Path points to an existing ONNX model on disk. Either a .onnx file directly, or a directory that contains model.onnx plus tokenizer.json. Qaynaq does not fetch anything; if the files are missing the flow fails at startup.
• Download on - Path is the parent directory where qaynaq will place downloaded models. Qaynaq creates a sub-directory inside it per repository (e.g. ./models/sentence-transformers_all-MiniLM-L6-v2/). The parent directory is created if missing. The download runs once; subsequent restarts reuse what's already on disk.

Sharing models across flows

Different flows that use the same repository can safely share the same Path - they both land in the same per-repository sub-directory and reuse the files. Different repositories under the same Path get their own sub-directories. ./models is a fine default for most setups.

A worker also keeps a content-addressed cache in ~/.cache/huggingface/hub/, so even unrelated Paths never re-download the same model from HuggingFace.

Output

The message body is replaced with a float64 vector of the model's output dimension. For all-MiniLM-L6-v2 this is a 384-element array.

Metadata added to each message:

Field	Description
pipeline_name	The Name field above
output_type	Type of the model output tensor
output_shape	Shape of the output tensor

Suggested models

Model	Dimensions	Use case
sentence-transformers/all-MiniLM-L6-v2	384	Small, fast, general-purpose
sentence-transformers/all-mpnet-base-v2	768	Higher quality, slower
BAAI/bge-small-en-v1.5	384	Strong on retrieval benchmarks