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Retrieval Augmented Generation Sample

This example showcases inference of Text Embedding and Text Rerank Models. The application has limited 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 openvino_genai.TextRerankPipeline 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

To export text embedding model run Optimum CLI command:

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

To export text reranking model run Optimum CLI command:

optimum-cli export openvino --trust-remote-code --model cross-encoder/ms-marco-MiniLM-L6-v2 cross-encoder/ms-marco-MiniLM-L6-v2

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:

1. Text Embedding Sample (text_embeddings.py)​

  • Description: Demonstrates inference of text embedding models using OpenVINO GenAI. Converts input text into vector embeddings for downstream tasks such as retrieval or semantic search.
  • Run Command: 
    python text_embeddings.py <MODEL_DIR> "Document 1" "Document 2"

Refer to the Supported Models for more details.

2. Text Rerank Sample (text_rerank.py)​

  • Description: Demonstrates inference of text rerank models using OpenVINO GenAI. Reranks a list of candidate documents based on their relevance to a query using a cross-encoder or reranker model.
  • Run Command: 
    python text_rerank.py <MODEL_DIR> "<QUERY>" "<TEXT 1>" ["<TEXT 2>" ...]

Text Embedding Pipeline Usage

import openvino_genai

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

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

Text Rerank Pipeline Usage

import openvino_genai

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

rerank_result = pipeline.rerank(query, documents)