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|[VLM Text Generation with continuous batching](continuous_batching/vlm/README.md)|Generate text with VLM models and continuous batching pipeline|
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|[OpenAI API text embeddings ](embeddings/README.md)|Get text embeddings via endpoint compatible with OpenAI API|
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|[Reranking with Cohere API](rerank/README.md)| Rerank documents via endpoint compatible with Cohere|
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|[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)| Example how to use RAG with model server endpoints|
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|[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/continuous_batching/rag/rag_demo.ipynb)| Example how to use RAG with model server endpoints|
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|[LLM on NPU](./llm_npu/README.md)| Generate text with LLM models and NPU acceleration|
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|[VLM on NPU](./vlm_npu/README.md)| Generate text with VLM models and NPU acceleration|
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|[Long context LLMs](./continuous_batching/long_context/README.md)| Recommendations for handling very long context in LLM models|
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| Demo | Description |
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|---|---|
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|[Image Classification](image_classification/python/README.md)|Run prediction on a JPEG image using image classification model via gRPC API.|
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|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/image_transformation). |
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|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/image_transformation). |
|[Age gender recognition](age_gender_recognition/python/README.md)| Run prediction on a JPEG image using age gender recognition model via gRPC API.|
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|[Face Detection](face_detection/python/README.md)|Run prediction on a JPEG image using face detection model via gRPC API.|
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## With DAG Pipelines
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| Demo | Description |
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|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md)| Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md)| Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md)| Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md)| Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
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|[Single Face Analysis Pipeline](single_face_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a simple pipeline of age-gender recognition and emotion recognition models via gRPC API to analyze image with a single face. This demo uses [pipeline](../docs/dag_scheduler.md)|
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|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md)|
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|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md)|
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|[Model Ensemble Pipeline](model_ensemble/python/README.md)|Combine multiple image classification models into one [pipeline](../docs/dag_scheduler.md) and aggregate results to improve classification accuracy. |
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|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/face_blur). |
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|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection). |
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|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/face_blur). |
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|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2025/4/src/custom_nodes/model_zoo_intel_object_detection). |
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```console
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pip3 install -r requirements.txt
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```
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Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
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Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
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This document demonstrates how to run inference requests for [BERT model](https://github.com/openvinotoolkit/open_model_zoo/tree/2022.1.0/models/intel/bert-small-uncased-whole-word-masking-squad-int8-0002) with OpenVINO Model Server. It provides questions answering functionality.
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In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
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In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
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NOTE: With `min_request_token_num` parameter you can specify the minimum size of the request. If the paragraph has too short, it is concatenated with the next one until it has required length. When there is no paragraphs left to concatenate request is created with the remaining content.
> **Note:** The users in China need to set environment variable HF_ENDPOINT="https://hf-mirror.com" before running the export script to connect to the HF Hub.
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> **Note:** This model requires ~150GB disk space and 60GB RAM for conversion. For deployment the model require ~16GB disk space and same amount of VRAM on the GPU.
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The service deployed above can be used in RAG chain using `langchain` library with OpenAI endpoint as the LLM engine.
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Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)
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Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/continuous_batching/rag/rag_demo.ipynb)
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## Scaling the Model Server
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Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/scaling/README.md).
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Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/continuous_batching/scaling/README.md).
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## Testing the model accuracy over serving API
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Check the [guide of using lm-evaluation-harness](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/accuracy/README.md)
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Check the [guide of using lm-evaluation-harness](https://github.com/openvinotoolkit/model_server/blob/releases/2025/4/demos/continuous_batching/accuracy/README.md)
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