Merge pull request #405 from mistralai/ocr-minor-refresh

Cookbooks Refresh

Author: pandora-s-git

README.md

@@ -13,7 +13,6 @@ For the full Mistral documentation, visit [docs.mistral.ai](https://docs.mistral
 | [basic_RAG.ipynb](mistral/rag/basic_RAG.ipynb)                                 | RAG                          | RAG from scratch with Mistral AI API                                             |
 | [embeddings.ipynb](mistral/embeddings/embeddings.ipynb)                        | embeddings                   | Use Mistral embeddings API for classification and clustering                     |
 | [function_calling.ipynb](mistral/function_calling/function_calling.ipynb)      | function calling             | Use Mistral API for function calling                                             |
-| [text_to_SQL.ipynb](mistral/function_calling/text_to_SQL.ipynb)      | function calling             | Use Mistral API for function calling on a multi tables text to SQL usecase                                             |
 | [evaluation.ipynb](mistral/evaluation/evaluation.ipynb)                        | evaluation                   | Evaluate models with Mistral API                                                 |
 | [mistral-search-engine.ipynb](mistral/rag/mistral-search-engine.ipynb)         | RAG, function calling        | Search engine built with Mistral API, function calling and RAG                   |
 | [rag_via_function_calling.ipynb](mistral/rag/RAG_via_function_calling.ipynb)         | RAG, function calling        | Use function calling as a router for a RAG based on multiple data sources                   |
@@ -80,9 +79,7 @@ The preview renders committed notebook content without executing notebook code.
 | [basic_RAG.ipynb](mistral/rag/basic_RAG.ipynb)                                 | RAG                          | RAG from scratch with Mistral AI API                                             |
 | [embeddings.ipynb](mistral/embeddings/embeddings.ipynb)                        | embeddings                   | Use Mistral embeddings API for classification and clustering                     |                                           |
 | [function_calling.ipynb](mistral/function_calling/function_calling.ipynb)      | function calling             | Use Mistral API for function calling                                             |
-| [text_to_SQL.ipynb](mistral/function_calling/text_to_SQL.ipynb)      | function calling             | Use Mistral API for function calling on a multi tables text to SQL usecase                                             |
 | [evaluation.ipynb](mistral/evaluation/evaluation.ipynb)                        | evaluation                   | Evaluate models with Mistral API                                                 |
-| [mistral_finetune_api.ipynb](mistral/fine_tune/mistral_finetune_api.ipynb)     | fine-tuning                  | Finetune a model with Mistral fine-tuning API                                    |
 | [mistral-search-engine.ipynb](mistral/rag/mistral-search-engine.ipynb)         | RAG, function calling        | Search engine built with Mistral API, function calling and RAG                   |
 | [rag_via_function_calling.ipynb](mistral/rag/RAG_via_function_calling.ipynb)         | RAG, function calling        | Use function calling as a router for a RAG based on multiple data sources                   |
 | [prefix_use_cases.ipynb](mistral/prompting/prefix_use_cases.ipynb)             | prefix, prompting            | Cool examples with Mistral's prefix feature                                      |
@@ -98,11 +95,6 @@ The preview renders committed notebook content without executing notebook code.
 | [batch_ocr.ipynb](mistral/ocr/batch_ocr.ipynb) | OCR, batch | Using OCR to extract text data from datasets. |
 | [structured_ocr.ipynb](mistral/ocr/structured_ocr.ipynb) | OCR, structured outputs | Extracting structured outputs from documents. |
 | [RAG_evaluation.ipynb](mistral/evaluation/RAG_evaluation.ipynb) | evaluation, structured outputs, LLM As a Judge | Evaluate RAG with LLM as a Judge and structured outputs |
-| [product_classification.ipynb](mistral/classifier_factory/product_classification.ipynb) | fine-tuning, classifier | Fine-tuning a classifier for food classification. |
-| [intent_classification.ipynb](mistral/classifier_factory/intent_classification.ipynb) | fine-tuning, classifier | Fine-tuning a classifier for intent classification. |
-| [moderation_classifier.ipynb](mistral/classifier_factory/moderation_classifier.ipynb) | fine-tuning, classifier | Fine-tuning a classifier for moderation. |
-| [pixtral_finetune_on_satellite_data.ipynb](mistral/fine_tune/pixtral_finetune_on_satellite_data.ipynb) | fine-tuning, image processing, batch | Fine-tuning a Pixtral-12B for satellite images classification. |
-| [sts_demo.py](mistral/tts/sts_demo.py) | chat, tts | STT -> LLM -> TTS Demo. |
 | [llm_judge_campaign_workflow.ipynb](mistral/observability/llm_judge_campaign_workflow.ipynb) | observability, evaluation | Run campaigns with LLM judges to classify agent behaviors at scale |
 | [manage_datasets.ipynb](mistral/observability/manage_datasets.ipynb) | observability, datasets | Create and manage datasets for evaluation or fine-tuning |
 | [Custom MCP Server](mistral/lechat_custom_mcp_server/custom-mcp-server.md) | MCP, connectors | Build a custom MCP server (tic-tac-toe) and connect it to Vibe |