Retrieval Augmented Generation Sample

This example showcases inference of Text Embedding Models. The application limited configuration configuration options to encourage the reader to explore and modify the source code. For example, change the device for inference to GPU. The sample features openvino_genai.TextEmbeddingPipeline and uses text as an input source.

Download and Convert the Model and Tokenizers

The --upgrade-strategy eager option is needed to ensure optimum-intel is upgraded to the latest version.

Install ../../export-requirements.txt to convert a model.

pip install --upgrade-strategy eager -r ../../export-requirements.txt

Then, run the export with Optimum CLI:

optimum-cli export openvino --trust-remote-code --model BAAI/bge-small-en-v1.5 BAAI/bge-small-en-v1.5

Alternatively, do it in Python code:

from optimum.exporters.openvino.convert import export_tokenizer
from optimum.intel import OVModelForFeatureExtraction
from transformers import AutoTokenizer

output_dir = "embedding_model"

model = OVModelForFeatureExtraction.from_pretrained("BAAI/bge-small-en-v1.5", export=True, trust_remote_code=True)
model.save_pretrained(output_dir)

tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-small-en-v1.5")
export_tokenizer(tokenizer, output_dir)

Run

Install deployment-requirements.txt via pip install -r ../../deployment-requirements.txt and then, run a sample:

python text_embeddings.py BAAI/bge-small-en-v1.5 "Document 1" "Document 2"

See SUPPORTED_MODELS.md for the list of supported models.

Text Embedding Pipeline Usage

import openvino_genai

pipeline = openvino_genai.TextEmbeddingPipeline(model_dir, "CPU")

embeddings = pipeline.embed_documents(["document1", "document2"])