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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 TextEmbeddingPipeline and TextRerankPipeline, which use 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 --task feature-extraction --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 --task text-classification --model cross-encoder/ms-marco-MiniLM-L6-v2 cross-encoder/ms-marco-MiniLM-L6-v2

Run​

Compile GenAI JavaScript bindings archive first using the instructions.

Run npm install and the example will be ready to run.

1. Text Embedding Sample (text_embeddings.js)​

  • 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: 
    node text_embeddings.js <MODEL_DIR> "Document 1" "Document 2"

Refer to the Supported Models for more details.

2. Text Rerank Sample (text_rerank.js)​

  • 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: 
    node text_rerank.js <MODEL_DIR> "<QUERY>" "<TEXT 1>" ["<TEXT 2>" ...]

Text Embedding Pipeline Usage

import { TextEmbeddingPipeline } from 'openvino-genai-node';

const pipeline = await TextEmbeddingPipeline(model_dir, "CPU");

const embeddings = await pipeline.embedDocuments(["document1", "document2"]);

Text Rerank Pipeline Usage

import { TextRerankPipeline } from 'openvino-genai-node';

const pipeline = await TextRerankPipeline(modelPath, { device: "CPU" });

const rerankResult = await pipeline.rerank(query, documents);