Update references to main Generative AI article

kblaszczak-intel · Jan 13, 2025 · 4aae0f4 · 4aae0f4
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diff --git a/README.md b/README.md
@@ -100,7 +100,7 @@ OpenVINO supports the CPU, GPU, and NPU [devices](https://docs.openvino.ai/2024/
 
 ## Generative AI with OpenVINO
 
-Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-guide.html) to explore the capabilities of Generative AI using OpenVINO.
+Get started with the OpenVINO GenAI [installation](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html) and refer to the [detailed guide](https://docs.openvino.ai/2024/openvino-workflow-generative/genai-guide.html) to explore the capabilities of Generative AI using OpenVINO.
 
 Learn how to run LLMs and GenAI with [Samples](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples) in the [OpenVINO™ GenAI repo](https://github.com/openvinotoolkit/openvino.genai). See GenAI in action with Jupyter notebooks: [LLM-powered Chatbot](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/README.md) and [LLM Instruction-following pipeline](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-question-answering/README.md).
 

diff --git a/docs/articles_en/about-openvino/key-features.rst b/docs/articles_en/about-openvino/key-features.rst
@@ -13,7 +13,7 @@ Easy Integration
       :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
       OpenVINO optimizations to your PyTorch models directly with a single line of code.
 
-| :doc:`GenAI Out Of The Box <../learn-openvino/llm_inference_guide/genai-guide>`
+| :doc:`GenAI Out Of The Box <../openvino-workflow-generative/genai-guide>`
 |     With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
       Check out the GenAI guide for instructions on how to do it.
 

diff --git a/docs/articles_en/documentation/openvino-ecosystem.rst b/docs/articles_en/documentation/openvino-ecosystem.rst
@@ -24,7 +24,7 @@ you an overview of a whole ecosystem of tools and solutions under the OpenVINO u
 
 | **GenAI**
 | :bdg-link-dark:`Github <https://github.com/openvinotoolkit/openvino.genai>`
-  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/genai-guide.html>`
+  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/openvino-workflow-generative/genai-guide.html>`
 
 OpenVINO™ GenAI Library aims to simplify running inference of generative AI
 models. Check the LLM-powered Chatbot Jupyter notebook to see how GenAI works.
@@ -113,7 +113,7 @@ generative AI and vision models directly on your computer or edge device using O
 
 | **Tokenizers**
 | :bdg-link-dark:`Github <https://github.com/openvinotoolkit/openvino_tokenizers>`
-  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/ov-tokenizers.html>`
+  :bdg-link-success:`User Guide <https://docs.openvino.ai/2024/openvino-workflow-generative/ov-tokenizers.html>`
 
 OpenVINO Tokenizers add text processing operations to OpenVINO.
 

diff --git a/docs/articles_en/get-started/configurations/genai-dependencies.rst b/docs/articles_en/get-started/configurations/genai-dependencies.rst
@@ -27,5 +27,5 @@ Additional Resources
 * :doc:`OpenVINO GenAI Installation Guide <../install-openvino/install-openvino-genai>`
 * `OpenVINO GenAI repository <https://github.com/openvinotoolkit/openvino.genai>`__
 * :doc:`OpenVINO Installation Guide <../install-openvino>`
-* :doc:`OpenVINO Tokenizers <../../learn-openvino/llm_inference_guide/ov-tokenizers>`
+* :doc:`OpenVINO Tokenizers <../../openvino-workflow-generative/ov-tokenizers>`
 
diff --git a/docs/articles_en/get-started/install-openvino.rst b/docs/articles_en/get-started/install-openvino.rst
@@ -35,8 +35,8 @@ All currently supported versions are:
    A new OpenVINO GenAI Flavor streamlines application development by providing
    LLM-specific interfaces for easy integration of language models, handling tokenization and
    text generation. For installation and usage instructions, proceed to
-   :doc:`Install OpenVINO GenAI Flavor <../learn-openvino/llm_inference_guide/genai-guide>` and
-   :doc:`Run LLMs with OpenVINO GenAI Flavor <../learn-openvino/llm_inference_guide/genai-guide>`.
+   :doc:`Install OpenVINO GenAI Flavor <../openvino-workflow-generative/genai-guide>` and
+   :doc:`Run LLMs with OpenVINO GenAI Flavor <../openvino-workflow-generative/genai-guide>`.
 
 .. dropdown:: Building OpenVINO from Source
 

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst b/docs/articles_en/get-started/install-openvino/install-openvino-genai.rst
@@ -5,7 +5,7 @@ OpenVINO GenAI is a new flavor of OpenVINO, aiming to simplify running inference
 It hides the complexity of the generation process and minimizes the amount of code required.
 You can now provide a model and input context directly to OpenVINO, which performs tokenization of the
 input text, executes the generation loop on the selected device, and returns the generated text.
-For a quickstart guide, refer to the :doc:`GenAI API Guide <../../learn-openvino/llm_inference_guide/genai-guide>`.
+For a quickstart guide, refer to the :doc:`GenAI API Guide <../../openvino-workflow-generative/genai-guide>`.
 
 To see GenAI in action, check the Jupyter notebooks:
 `LLM-powered Chatbot <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/README.md>`__ and
@@ -28,7 +28,7 @@ but use the *openvino-genai* package instead of *openvino*:
 Archive Installation
 ###############################
 
-The OpenVINO GenAI archive package includes the OpenVINO™ Runtime and :doc:`Tokenizers <../../learn-openvino/llm_inference_guide/ov-tokenizers>`.
+The OpenVINO GenAI archive package includes the OpenVINO™ Runtime and :doc:`Tokenizers <../../openvino-workflow-generative/ov-tokenizers>`.
 To install the GenAI flavor of OpenVINO from an archive file, follow the standard installation steps for your system
 but instead of using the vanilla package file, download the one with OpenVINO GenAI:
 

diff --git a/docs/articles_en/learn-openvino.rst b/docs/articles_en/learn-openvino.rst
@@ -27,6 +27,3 @@ as well as an experienced user.
 
 | :doc:`OpenVINO Samples <learn-openvino/openvino-samples>`
 | The OpenVINO samples (Python and C++) are simple console applications that show how to use specific OpenVINO API features. They can assist you in executing tasks such as loading a model, running inference, querying particular device capabilities, etc.
-
-| :doc:`Generative AI workflow <learn-openvino/llm_inference_guide>`
-| Detailed information on how OpenVINO accelerates Generative AI use cases and what models it supports. This tutorial provides instructions for running Generative AI models using Hugging Face Optimum Intel and Native OpenVINO APIs.
diff --git a/docs/articles_en/openvino-workflow-generative.rst b/docs/articles_en/openvino-workflow-generative.rst
@@ -9,10 +9,10 @@ Generative AI workflow
    :maxdepth: 1
    :hidden:
 
-   Generative Model Preparation <llm_inference_guide/genai-model-preparation>
-   Inference with OpenVINO GenAI <llm_inference_guide/genai-guide>
-   Inference with Optimum Intel <llm_inference_guide/llm-inference-hf>
-   OpenVINO Tokenizers <llm_inference_guide/ov-tokenizers>
+   Generative Model Preparation <openvino-workflow-generative/genai-model-preparation>
+   Inference with OpenVINO GenAI <openvino-workflow-generative/genai-guide>
+   Inference with Optimum Intel <openvino-workflow-generative/llm-inference-hf>
+   OpenVINO Tokenizers <openvino-workflow-generative/ov-tokenizers>
 
 
 
@@ -58,7 +58,7 @@ options:
 Note that the base version of OpenVINO may also be used to run generative AI. Although it may
 offer a simpler environment, with fewer dependencies, it has significant limitations and a more
 demanding implementation process. For reference, see
-`the article on generative AI usage of OpenVINO 2024.6 <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-native-ov.html>`__.
+`the article on generative AI usage of OpenVINO 2024.6 <https://docs.openvino.ai/2024/openvino-workflow-generative/llm-inference-native-ov.html>`__.
 
 The advantages of using OpenVINO for generative model deployment:
 
@@ -90,8 +90,8 @@ The advantages of using OpenVINO for generative model deployment:
 
 Proceed to guides on:
 
-* :doc:`OpenVINO GenAI Flavor <./llm_inference_guide/genai-guide>`
-* :doc:`Hugging Face and Optimum Intel <./llm_inference_guide/llm-inference-hf>`
-* `Generative AI with Base OpenVINO <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-native-ov.html>`__
+* :doc:`OpenVINO GenAI Flavor <./openvino-workflow-generative/genai-guide>`
+* :doc:`Hugging Face and Optimum Intel <./openvino-workflow-generative/llm-inference-hf>`
+* `Generative AI with Base OpenVINO <https://docs.openvino.ai/2024/openvino-workflow-generative/llm-inference-native-ov.html>`__
 
 
diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
@@ -105,7 +105,7 @@ By default, weights are compressed asymmetrically to "INT8_ASYM" mode.
          print(results)
 
       For more details, refer to the article on how to
-      :doc:`infer LLMs using Optimum Intel <../../learn-openvino/llm_inference_guide/llm-inference-hf>`.
+      :doc:`infer LLMs using Optimum Intel <../../openvino-workflow-generative/llm-inference-hf>`.
 
    .. tab-item:: Compression with NNCF
       :sync: nncf
@@ -221,7 +221,7 @@ depending on the model.
 
 
       For more details, refer to the article on how to
-      :doc:`infer LLMs using Optimum Intel <../../../learn-openvino/llm_inference_guide/llm-inference-hf>`.
+      :doc:`infer LLMs using Optimum Intel <../../../openvino-workflow-generative/llm-inference-hf>`.
 
 The code snippet below shows how to do 4-bit quantization of the model weights represented
 in OpenVINO IR using NNCF:
@@ -344,7 +344,7 @@ load the compressed model later for faster time to first inference.
 .. tip::
 
    Models optimized with with NNCF or Optimum Intel can be used with
-   :doc:`OpenVINO GenAI <../../learn-openvino/llm_inference_guide/genai-guide>`.
+   :doc:`OpenVINO GenAI <../../openvino-workflow-generative/genai-guide>`.
 
 
 Auto-tuning of Weight Compression Parameters

diff --git a/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst b/docs/articles_en/openvino-workflow/running-inference/stateful-models.rst
@@ -66,7 +66,7 @@ from the application code to OpenVINO and all related internal work is hidden fr
 
 There are three methods of turning an OpenVINO model into a stateful one:
 
-* :doc:`Optimum-Intel <../../learn-openvino/llm_inference_guide/llm-inference-hf>` - the most user-friendly option. All necessary optimizations
+* :doc:`Optimum-Intel <../../openvino-workflow-generative/llm-inference-hf>` - the most user-friendly option. All necessary optimizations
   are recognized and applied automatically. The drawback is, the tool does not work with all
   models.
 

diff --git a/...orkflow/running-inference/stateful-models/obtaining-stateful-openvino-model.rst b/...orkflow/running-inference/stateful-models/obtaining-stateful-openvino-model.rst
@@ -10,7 +10,7 @@ and you have three ways to do it:
 
 * `Optimum-Intel <https://github.com/huggingface/optimum-intel>`__ - an automated solution
   applicable to a selection of models (not covered by this article, for a usage guide
-  refer to the :doc:`LLM Inference with Hugging Face and Optimum Intel <../../../learn-openvino/llm_inference_guide>` article).
+  refer to the :doc:`LLM Inference with Hugging Face and Optimum Intel <../../../openvino-workflow-generative>` article).
 * :ref:`MakeStateful transformation <ov_ug_make_stateful>` - to choose which pairs of
   Parameter and Result to replace.
 * :ref:`LowLatency2 transformation <ov_ug_low_latency>` - to detect and replace Parameter

diff --git a/docs/notebooks/llm-agent-functioncall-qwen-with-output.rst b/docs/notebooks/llm-agent-functioncall-qwen-with-output.rst
@@ -258,7 +258,7 @@ pipeline.
 
 You can get additional inference speed improvement with `Dynamic
 Quantization of activations and KV-cache quantization on
-CPU <https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide/llm-inference-hf.html#enabling-openvino-runtime-optimizations>`__.
+CPU <https://docs.openvino.ai/2024/openvino-workflow-generative/llm-inference-hf.html#enabling-openvino-runtime-optimizations>`__.
 These options can be enabled with ``ov_config`` as follows:
 
 .. code:: ipython3