Update README.md

README.md

@@ -79,34 +79,37 @@ The provided OpenVINO™ IR model is compatible with:
 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
 
 ```
-pip install "langchain-community>=0.2.15" optimum[openvino] huggingface_hub
+pip install optimum[openvino]
 ```
 
 2. Run model inference:
 
 ```
-from langchain_community.embeddings import OpenVINOBgeEmbeddings
+import torch
+from transformers import AutoTokenizer
 
+from optimum.intel.openvino import OVModelForFeatureExtraction
 
-embedding_model_name = 'OpenVINO/bge-base-en-v1.5-int8-ov'
-embedding_model_kwargs = {"device": "CPU", "compile": False}
-encode_kwargs = {
-    "mean_pooling": False,
-    "normalize_embeddings": True,
-    "batch_size": 4,
-}
 
-embedding = OpenVINOBgeEmbeddings(
-    model_name_or_path=embedding_model_name,
-    model_kwargs=embedding_model_kwargs,
-    encode_kwargs=encode_kwargs,
-)
+# Sentences we want sentence embeddings for
+sentences = ["Sample Data-1", "Sample Data-2"]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov')
+model = OVModelForFeatureExtraction.from_pretrained('OpenVINO/bge-base-en-v1.5-int8-ov')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+model_output = model(**encoded_input)
 
-embedding.ov_model.compile()
+# Perform pooling. In this case, cls pooling.
+sentence_embeddings = model_output[0][:, 0]
 
-text = "This is a test document."
-embedding_result = embedding.embed_query(text)
-embedding_result[:3]
+# normalize embeddings
+sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
+print("Sentence embeddings:", sentence_embeddings)
 ```
 
 For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).