diff --git a/static/llms-full.txt b/static/llms-full.txt index 49b25d4c..5b9f435a 100644 --- a/static/llms-full.txt +++ b/static/llms-full.txt @@ -223,6 +223,16 @@ Source: https://docs.mistral.ai/api/#tag/chat_classifications_v1_chat_classifica post /v1/chat/classifications +# Create Transcription +Source: https://docs.mistral.ai/api/#tag/audio_api_v1_transcriptions_post + +post /v1/audio/transcriptions + +# Create streaming transcription (SSE) +Source: https://docs.mistral.ai/api/#tag/audio_api_v1_transcriptions_post_stream + +post /v1/audio/transcriptions#stream + # List all libraries you have access to. Source: https://docs.mistral.ai/api/#tag/libraries_list_v1 @@ -5305,7 +5315,7 @@ console.log(transcriptionResponse); curl --location 'https://api.mistral.ai/v1/audio/transcriptions' \ --header "x-api-key: $MISTRAL_API_KEY" \ --form 'file=@"/path/to/file/audio.mp3"' \ - --form 'model="voxtral-mini-2507"' \ + --form 'model="voxtral-mini-2507"' ``` **With Language defined** @@ -5571,7 +5581,7 @@ client = Mistral(api_key=api_key) transcription_response = client.audio.transcriptions.complete( model=model, file_url="https://docs.mistral.ai/audio/obama.mp3", - timestamp_granularities="segment" + timestamp_granularities=["segment"] ) # Print the contents @@ -5593,7 +5603,7 @@ const client = new Mistral({ apiKey: apiKey }); const transcriptionResponse = await client.audio.transcriptions.complete({ model: "voxtral-mini-latest", fileUrl: "https://docs.mistral.ai/audio/obama.mp3", - timestamp_granularities: "segment" + timestamp_granularities: ["segment"] }); // Log the contents @@ -5607,7 +5617,7 @@ console.log(transcriptionResponse); curl --location 'https://api.mistral.ai/v1/audio/transcriptions' \ --header "x-api-key: $MISTRAL_API_KEY" \ --form 'file_url="https://docs.mistral.ai/audio/obama.mp3"' \ ---form 'model="voxtral-mini-latest"' +--form 'model="voxtral-mini-latest"' \ --form 'timestamp_granularities="segment"' ``` @@ -13088,7 +13098,7 @@ Source: https://docs.mistral.ai/docs/capabilities/vision Vision capabilities enable models to analyze images and provide insights based on visual content in addition to text. This multimodal approach opens up new possibilities for applications that require both textual and visual understanding. -For more specific use cases regarding document parsing and data extraction we recommend taking a look at our Document AI stack [here](../OCR/document_ai_overview). +For more specific use cases regarding document parsing and data extraction we recommend taking a look at our Document AI stack [here](../document_ai/document_ai_overview). ## Models with Vision Capabilities: - Pixtral 12B (`pixtral-12b-latest`) @@ -13739,7 +13749,10 @@ in two ways: This page focuses on the MaaS offering, where the following models are available: - Mistral Large (24.11, 24.07) -- Mistral Small (24.09) +- Mistral Medium (25.05) +- Mistral Small (25.03) +- Mistral Document AI (25.05) +- Mistral OCR (25.05) - Ministral 3B (24.10) - Mistral Nemo @@ -13843,9 +13856,11 @@ To run the examples below, set the following environment variables: ## Going further For more details and examples, refer to the following resources: +- [Release blog post for Mistral Document AI](https://techcommunity.microsoft.com/blog/aiplatformblog/deepening-our-partnership-with-mistral-ai-on-azure-ai-foundry/4434656) - [Release blog post for Mistral Large 2 and Mistral NeMo](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/ai-innovation-continues-introducing-mistral-large-2-and-mistral/ba-p/4200181). - [Azure documentation for MaaS deployment of Mistral models](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-mistral). - [Azure ML examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/sdk/python/foundation-models/mistral) with several Mistral-based samples. +- [Azure AI Foundry GitHub repository](https://github.com/azure-ai-foundry/foundry-samples/tree/main/samples/mistral) [IBM watsonx.ai] @@ -14089,7 +14104,7 @@ To run the examples below you will need to set the following environment variabl Codestral can be queried using an additional completion mode called fill-in-the-middle (FIM). For more information, see the -[code generation section](../../../capabilities/code_generation/#fill-in-the-middle-endpoint). +[code generation section](../../../capabilities/code_generation). @@ -14390,7 +14405,7 @@ for more details. Codestral can be queried using an additional completion mode called fill-in-the-middle (FIM). For more information, see the -[code generation section](../../../capabilities/code_generation/#fill-in-the-middle-endpoint). +[code generation section](../../../capabilities/code_generation). @@ -15693,7 +15708,7 @@ The [Mistral AI APIs](https://console.mistral.ai/) empower LLM applications via: - [Text generation](/capabilities/completion), enables streaming and provides the ability to display partial model results in real-time - [Vision](/capabilities/vision), enables the analysis of images and provides insights based on visual content in addition to text. -- [OCR](/capabilities/OCR/basic_ocr), allows the extraction of interleaved text and images from documents. +- [OCR](/capabilities/document_ai/basic_ocr), allows the extraction of interleaved text and images from documents. - [Code generation](/capabilities/code_generation), enpowers code generation tasks, including fill-in-the-middle and code completion. - [Embeddings](/capabilities/embeddings/overview), useful for RAG where it represents the meaning of text as a list of numbers. - [Function calling](/capabilities/function_calling), enables Mistral models to connect to external tools. @@ -16198,7 +16213,7 @@ Mistral provides two types of models: open models and premier models. | Model | Weight availability|Available via API| Description | Max Tokens| API Endpoints|Version| |--------------------|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:| -| Mistral Medium 3 | | :heavy_check_mark: | Our frontier-class multimodal model released May 2025. Learn more in our [blog post](https://mistral.ai/news/mistral-medium-3/) | 128k | `mistral-medium-2505` | 25.05| +| Mistral Medium 3.1 | | :heavy_check_mark: | Our frontier-class multimodal model released August 2025. Improving tone and performance. Read more about Medium 3 in our [blog post](https://mistral.ai/news/mistral-medium-3/) | 128k | `mistral-medium-2508` | 25.08| | Magistral Medium 1.1 | | :heavy_check_mark: | Our frontier-class reasoning model released July 2025. | 40k | `magistral-medium-2507` | 25.07| | Codestral 2508 | | :heavy_check_mark: | Our cutting-edge language model for coding released end of July 2025, Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Learn more in our [blog post](https://mistral.ai/news/codestral-25-08/) | 256k | `codestral-2508` | 25.08| | Voxtral Mini Transcribe | | :heavy_check_mark: | An efficient audio input model, fine-tuned and optimized for transcription purposes only. | | `voxtral-mini-2507` via `audio/transcriptions` | 25.07| @@ -16207,6 +16222,7 @@ Mistral provides two types of models: open models and premier models. | Magistral Medium 1 | | :heavy_check_mark: | Our first frontier-class reasoning model released June 2025. Learn more in our [blog post](https://mistral.ai/news/magistral/) | 40k | `magistral-medium-2506` | 25.06| | Ministral 3B | | :heavy_check_mark: | World’s best edge model. Learn more in our [blog post](https://mistral.ai/news/ministraux/) | 128k | `ministral-3b-2410` | 24.10| | Ministral 8B | :heavy_check_mark:
[Mistral Research License](https://mistral.ai/licenses/MRL-0.1.md)| :heavy_check_mark: |Powerful edge model with extremely high performance/price ratio. Learn more in our [blog post](https://mistral.ai/news/ministraux/) | 128k | `ministral-8b-2410` | 24.10| +| Mistral Medium 3 | | :heavy_check_mark: | Our frontier-class multimodal model released May 2025. Learn more in our [blog post](https://mistral.ai/news/mistral-medium-3/) | 128k | `mistral-medium-2505` | 25.05| | Codestral 2501 | | :heavy_check_mark: | Our cutting-edge language model for coding with the second version released January 2025, Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Learn more in our [blog post](https://mistral.ai/news/codestral-2501/) | 256k | `codestral-2501` | 25.01| | Mistral Large 2.1 |:heavy_check_mark:
[Mistral Research License](https://mistral.ai/licenses/MRL-0.1.md)| :heavy_check_mark: | Our top-tier large model for high-complexity tasks with the lastest version released November 2024. Learn more in our [blog post](https://mistral.ai/news/pixtral-large/) | 128k | `mistral-large-2411` | 24.11| | Pixtral Large |:heavy_check_mark:
[Mistral Research License](https://mistral.ai/licenses/MRL-0.1.md)| :heavy_check_mark: | Our first frontier-class multimodal model released November 2024. Learn more in our [blog post](https://mistral.ai/news/pixtral-large/) | 128k | `pixtral-large-2411` | 24.11| @@ -16241,8 +16257,8 @@ Additionally, be prepared for the deprecation of certain endpoints in the coming Here are the details of the available versions: - `magistral-medium-latest`: currently points to `magistral-medium-2507`. - `magistral-small-latest`: currently points to `magistral-small-2507`. -- `mistral-medium-latest`: currently points to `mistral-medium-2505`. -- `mistral-large-latest`: currently points to `mistral-large-2411`. +- `mistral-medium-latest`: currently points to `mistral-medium-2508`. +- `mistral-large-latest`: currently points to `mistral-medium-2508`, previously `mistral-large-2411`. - `pixtral-large-latest`: currently points to `pixtral-large-2411`. - `mistral-moderation-latest`: currently points to `mistral-moderation-2411`. - `ministral-3b-latest`: currently points to `ministral-3b-2410`. @@ -18984,6 +19000,24 @@ Here is an [example notebook](https://github.com/mistralai/cookbook/blob/main/th drawing +### Integration with Maxim + +Maxim AI provides comprehensive observability for your Mistral based AI applications. With Maxim's one-line integration, you can easily trace and analyse LLM calls, metrics, and more. + +**Pros:** + +* Performance Analytics: Track latency, tokens consumed, and costs +* Advanced Visualisation: Understand agent trajectories through intuitive dashboards + +**Mistral integration Example:** + +* Learn how to integrate Maxim observability with the Mistral SDK in just one line of code - [Colab Notebook](https://github.com/mistralai/cookbook/blob/main/third_party/Maxim/cookbook_maxim_mistral_integration.ipynb) + +Maxim Documentation to use Mistral as an LLM Provider and Maxim as Logger - [Docs Link](https://www.getmaxim.ai/docs/sdk/python/integrations/mistral/mistral) + + +![Gif](https://raw.githubusercontent.com/akmadan/platform-docs-public/docs/observability-maxim-provider/static/img/guides/maxim_traces.gif) + [Other resources] Source: https://docs.mistral.ai/docs/guides/other-resources @@ -20736,18 +20770,3 @@ Mistral AI's LLM API endpoints charge based on the number of tokens in the input To help you estimate your costs, our tokenization API makes it easy to count the number of tokens in your text. Simply run `len(tokens)` as shown in the example above to get the total number of tokens in the text, which you can then use to estimate your cost based on our pricing information. - -[Mistral AI Crawlers] -Source: https://docs.mistral.ai/docs/robots - -## Mistral AI Crawlers - -Mistral AI employs web crawlers ("robots") and user agents to execute tasks for its products, either automatically or upon user request. To facilitate webmasters in managing how their sites and content interact with AI, Mistral AI utilizes specific robots.txt tags. - -### MistralAI-User - -MistralAI-User is for user actions in LeChat. When users ask LeChat a question, it may visit a web page to help answer and include a link to the source in its response. MistralAI-User governs which sites these user requests can be made to. It is not used for crawling the web in any automatic fashion, nor to crawl content for generative AI training. - -Full user-agent string: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; MistralAI-User/1.0; +https://docs.mistral.ai/robots) - -Published IP addresses: https://mistral.ai/mistralai-user-ips.json \ No newline at end of file diff --git a/static/llms.txt b/static/llms.txt index 51eb70bf..2ebd1702 100644 --- a/static/llms.txt +++ b/static/llms.txt @@ -2,78 +2,78 @@ ## Docs -[Agents & Conversations](https://docs.mistral.ai/docs/agents/agents_and_conversations.md): Agents & Conversations API: Create, manage agents with tools, and handle interactive conversations with persistent history -[Agents Function Calling](https://docs.mistral.ai/docs/agents/agents_function_calling.md): Agents use tools and function calling to perform tasks, with built-in and customizable options -[Agents Introduction](https://docs.mistral.ai/docs/agents/agents_introduction.md): AI agents autonomously execute tasks using LLMs, with tools, state persistence, and multi-agent collaboration via the Agents API -[Code Interpreter](https://docs.mistral.ai/docs/agents/connectors/code_interpreter.md): Code Interpreter enables safe, on-demand code execution for data analysis, graphing, and more in isolated containers -[Connectors Overview](https://docs.mistral.ai/docs/agents/connectors/connectors_overview.md): Connectors enable Agents and users to access tools like websearch, code interpreter, image generation, and document library on demand -[Document Library](https://docs.mistral.ai/docs/agents/connectors/document_library.md): Document Library enhances agents with uploaded documents via Mistral Cloud's built-in RAG tool -[Image Generation](https://docs.mistral.ai/docs/agents/connectors/image_generation.md): Built-in tool for agents to generate images on demand with detailed output handling and download options -[Websearch](https://docs.mistral.ai/docs/agents/connectors/websearch.md): Websearch enables models to browse the web for real-time, up-to-date information and access specific websites -[Agents Handoffs](https://docs.mistral.ai/docs/agents/handoffs.md): Agents Handoffs enable seamless task delegation and workflow automation between multiple agents with diverse tools and capabilities -[MCP](https://docs.mistral.ai/docs/agents/mcp.md): MCP is an open standard protocol for seamless AI model integration with data sources and tools -[Audio & Transcription](https://docs.mistral.ai/docs/capabilities/audio_and_transcription.md): Audio & Transcription: Voxtral models enable chat and transcription via audio input with various file-passing methods -[Batch Inference](https://docs.mistral.ai/docs/capabilities/batch_inference.md): Process multiple API requests in batches with customizable models, endpoints, and metadata -[Citations and References](https://docs.mistral.ai/docs/capabilities/citations_and_references.md): Citations and references enable models to ground responses with sources, ideal for RAG and agentic applications -[Coding](https://docs.mistral.ai/docs/capabilities/coding.md): Mistral AI offers Codestral for code generation & FIM, and Devstral for agentic tool use in software development, with integrations for IDEs and frameworks -[Annotations](https://docs.mistral.ai/docs/capabilities/document_ai/annotations.md): Mistral Document AI API extracts structured data from documents using custom JSON annotations for bboxes and full documents -[Basic OCR](https://docs.mistral.ai/docs/capabilities/document_ai/basic_ocr.md): Extract text and structured content from PDFs and images with Mistral's Document AI OCR processor -[Document AI](https://docs.mistral.ai/docs/capabilities/document_ai/document_ai_overview.md): Mistral Document AI offers enterprise-grade OCR, structured data extraction, and multilingual support for fast, accurate document processing -[Document QnA](https://docs.mistral.ai/docs/capabilities/document_ai/document_qna.md): Document QnA combines OCR and AI to enable natural language queries on document content for insights and extraction -[Code Embeddings](https://docs.mistral.ai/docs/capabilities/embeddings/code_embeddings.md): Code embeddings enable retrieval, clustering, and analytics for code databases and coding assistants using Mistral AI's API -[Embeddings Overview](https://docs.mistral.ai/docs/capabilities/embeddings/embeddings_overview.md): Mistral AI's Embeddings API provides advanced vector representations for text and code, enabling NLP tasks like retrieval, clustering, and classification -[Text Embeddings](https://docs.mistral.ai/docs/capabilities/embeddings/text_embeddings.md): Generate and use text embeddings with Mistral AI's API for NLP tasks like similarity, classification, and retrieval -[Classifier Factory](https://docs.mistral.ai/docs/capabilities/finetuning/classifier-factory.md): Create and fine-tune custom classification models for intent detection, moderation, sentiment analysis, and more using Mistral's Classifier Factory -[Fine-tuning Overview](https://docs.mistral.ai/docs/capabilities/finetuning/finetuning_overview.md): Learn about fine-tuning AI models, its benefits, use cases, and available services for customization." (99 characters) -[Text & Vision Fine-tuning](https://docs.mistral.ai/docs/capabilities/finetuning/text-vision-finetuning.md): Fine-tune Mistral's text and vision models with custom datasets in JSONL format for domain-specific or conversational improvements -[Function calling](https://docs.mistral.ai/docs/capabilities/function-calling.md): Mistral models enable function calling to integrate external tools for dynamic, data-driven responses -[Moderation](https://docs.mistral.ai/docs/capabilities/moderation.md): Mistral's moderation API detects harmful content across multiple categories using AI-powered classification for text and conversations -[Predicted outputs](https://docs.mistral.ai/docs/capabilities/predicted-outputs.md): Optimize response time by predefining predictable content for faster, efficient AI outputs." (99 characters) -[Reasoning](https://docs.mistral.ai/docs/capabilities/reasoning.md): Reasoning models generate logical chains of thought to solve problems, improving accuracy with extra compute time." (99 characters) -[Custom Structured Output](https://docs.mistral.ai/docs/capabilities/structured-output/custom.md): Define and enforce JSON output formats using Pydantic or Zod schemas with Mistral AI +[Agents & Conversations](https://docs.mistral.ai/docs/agents/agents_and_conversations.md): Agents, Conversations, and Entries enhance API interactions with tools, history, and flexible event representation +[Agents Function Calling](https://docs.mistral.ai/docs/agents/agents_function_calling.md): Agents use function calling to execute tools and workflows, with built-in connectors and custom JSON schema support +[Agents Introduction](https://docs.mistral.ai/docs/agents/agents_introduction.md): AI agents are autonomous systems powered by LLMs that plan, use tools, and execute tasks to achieve goals, with APIs for multimodal models, persistent state, and collaboration +[Code Interpreter](https://docs.mistral.ai/docs/agents/connectors/code_interpreter.md): Code Interpreter enables secure, on-demand code execution in isolated containers for data analysis, graphing, and more +[Connectors Overview](https://docs.mistral.ai/docs/agents/connectors/connectors_overview.md): Connectors enable Agents and users to access tools like websearch, code interpreter, and more for on-demand answers +[Document Library](https://docs.mistral.ai/docs/agents/connectors/document_library.md): Document Library is a built-in RAG tool for agents to access and manage uploaded documents in Mistral Cloud +[Image Generation](https://docs.mistral.ai/docs/agents/connectors/image_generation.md): Image Generation tool enables agents to create images on demand." (99 characters) +[Websearch](https://docs.mistral.ai/docs/agents/connectors/websearch.md): Websearch enables models to browse the web for real-time info, bypassing training data limitations with search and URL access +[Agents Handoffs](https://docs.mistral.ai/docs/agents/handoffs.md): Agents Handoffs enable seamless task delegation and conversation transfers between multiple agents in automated workflows +[MCP](https://docs.mistral.ai/docs/agents/mcp.md): MCP standardizes AI model integration with data sources for seamless, secure, and efficient contextual access +[Audio & Transcription](https://docs.mistral.ai/docs/capabilities/audio_and_transcription.md): Audio & Transcription: Models for chat and transcription with audio input support +[Batch Inference](https://docs.mistral.ai/docs/capabilities/batch_inference.md): Prepare and upload batch requests, then create a job to process them with specified models and endpoints +[Citations and References](https://docs.mistral.ai/docs/capabilities/citations_and_references.md): Citations and references enable models to ground responses with sources, enhancing RAG and agentic applications +[Coding](https://docs.mistral.ai/docs/capabilities/coding.md): LLMs for coding: Codestral for code generation, Devstral for agentic tool use, with FIM and chat endpoints +[Annotations](https://docs.mistral.ai/docs/capabilities/document_ai/annotations.md): Mistral Document AI API adds structured JSON annotations for OCR, including bbox and document annotations for efficient data extraction +[Basic OCR](https://docs.mistral.ai/docs/capabilities/document_ai/basic_ocr.md): Extract text and structured content from PDFs with Mistral's OCR API, preserving formatting and supporting multiple formats +[Document AI](https://docs.mistral.ai/docs/capabilities/document_ai/document_ai_overview.md): Mistral Document AI offers enterprise-level OCR, structured data extraction, and multilingual support for fast, accurate document processing +[Document QnA](https://docs.mistral.ai/docs/capabilities/document_ai/document_qna.md): Document AI QnA enables natural language queries on documents using OCR and large language models for insights and answers +[Code Embeddings](https://docs.mistral.ai/docs/capabilities/embeddings/code_embeddings.md): Code embeddings power retrieval, clustering, and analytics for code databases and coding assistants +[Embeddings Overview](https://docs.mistral.ai/docs/capabilities/embeddings/embeddings_overview.md): Mistral AI's Embeddings API provides state-of-the-art vector representations for text and code, enabling NLP tasks like retrieval, clustering, and search +[Text Embeddings](https://docs.mistral.ai/docs/capabilities/embeddings/text_embeddings.md): Generate 1024-dimension text embeddings using Mistral AI's embeddings API for NLP applications +[Classifier Factory](https://docs.mistral.ai/docs/capabilities/finetuning/classifier-factory.md): Classifier Factory: Tools for moderation, intent detection, and sentiment analysis to enhance efficiency and user experience +[Fine-tuning Overview](https://docs.mistral.ai/docs/capabilities/finetuning/finetuning_overview.md): Learn about fine-tuning costs, storage fees, and when to choose it over prompt engineering for AI models +[Text & Vision Fine-tuning](https://docs.mistral.ai/docs/capabilities/finetuning/text-vision-finetuning.md): Fine-tune text and vision models for domain-specific tasks or conversational styles using JSONL datasets +[Function calling](https://docs.mistral.ai/docs/capabilities/function-calling.md): Mistral models enable function calling to integrate external tools for custom applications and problem-solving +[Moderation](https://docs.mistral.ai/docs/capabilities/moderation.md): New moderation API using Mistral model to detect harmful text in raw and conversational content +[Predicted outputs](https://docs.mistral.ai/docs/capabilities/predicted-outputs.md): Optimizes response time by predefining predictable content to improve efficiency in tasks like code editing +[Reasoning](https://docs.mistral.ai/docs/capabilities/reasoning.md): Reasoning enhances CoT by generating logical steps before conclusions, improving problem-solving with deeper exploration +[Custom Structured Output](https://docs.mistral.ai/docs/capabilities/structured-output/custom.md): Define and enforce JSON output structure using Pydantic models with Mistral AI [JSON mode](https://docs.mistral.ai/docs/capabilities/structured-output/json-mode.md): Enable JSON mode by setting `response_format` to `{\"type\": \"json_object\"}` in API requests -[Structured Output](https://docs.mistral.ai/docs/capabilities/structured-output/overview.md): Learn to generate structured outputs like JSON for LLM agents and pipelines, with custom and flexible formatting options -[Text and Chat Completions](https://docs.mistral.ai/docs/capabilities/text_and_chat_completions.md): Mistral models enable chat and text completions with customizable prompts, roles, and streaming options -[Vision](https://docs.mistral.ai/docs/capabilities/vision.md): Multimodal AI models analyze images and text for insights, supporting use cases like OCR, chart understanding, and receipt transcription -[AWS Bedrock](https://docs.mistral.ai/docs/deployment/cloud/aws.md): Deploy and query Mistral AI models on AWS Bedrock with fully managed, serverless endpoints -[Azure AI](https://docs.mistral.ai/docs/deployment/cloud/azure.md): Deploy and query Mistral AI models on Azure AI via serverless MaaS or GPU-based endpoints -[IBM watsonx.ai](https://docs.mistral.ai/docs/deployment/cloud/ibm-watsonx.md): Mistral AI's Large model on IBM watsonx.ai: SaaS & on-premise deployment with setup, API access, and usage guides -[Outscale](https://docs.mistral.ai/docs/deployment/cloud/outscale.md): Deploy and query Mistral AI models on Outscale via managed VMs and REST APIs +[Structured Output](https://docs.mistral.ai/docs/capabilities/structured-output/overview.md): Learn to generate structured JSON or custom outputs from LLMs for reliable agent workflows +[Text and Chat Completions](https://docs.mistral.ai/docs/capabilities/text_and_chat_completions.md): Mistral models enable chat and text completions via natural language prompts, with flexible API options for streaming and async responses +[Vision](https://docs.mistral.ai/docs/capabilities/vision.md): Vision models analyze images and text for multimodal insights, supporting applications like document parsing and data extraction +[AWS Bedrock](https://docs.mistral.ai/docs/deployment/cloud/aws.md): Deploy Mistral AI models on AWS Bedrock as fully managed, serverless endpoints +[Azure AI](https://docs.mistral.ai/docs/deployment/cloud/azure.md): Deploy Mistral AI models on Azure AI with pay-as-you-go or real-time GPU-based endpoints +[IBM watsonx.ai](https://docs.mistral.ai/docs/deployment/cloud/ibm-watsonx.md): Mistral AI's Large model on IBM watsonx.ai for managed & on-premise deployments with API access setup +[Outscale](https://docs.mistral.ai/docs/deployment/cloud/outscale.md): Deploy and query Mistral AI models on Outscale via managed VMs and GPUs [Cloud](https://docs.mistral.ai/docs/deployment/cloud/overview.md): Access Mistral AI models via Azure, AWS, Google Cloud, Snowflake, IBM, and Outscale using cloud credits -[Snowflake Cortex](https://docs.mistral.ai/docs/deployment/cloud/sfcortex.md): Access Mistral AI models on Snowflake Cortex as serverless, fully managed endpoints for SQL & Python -[Vertex AI](https://docs.mistral.ai/docs/deployment/cloud/vertex.md): Deploy and query Mistral AI models on Google Cloud Vertex AI as serverless endpoints -[Workspaces](https://docs.mistral.ai/docs/deployment/laplateforme/organization.md): La Plateforme workspaces enable team collaboration, access control, and shared fine-tuned models." (99 characters) -[La Plateforme](https://docs.mistral.ai/docs/deployment/laplateforme/overview.md): Mistral AI's La Plateforme offers pay-as-you-go API access to its latest models with flexible deployment options -[Pricing](https://docs.mistral.ai/docs/deployment/laplateforme/pricing.md): Check the pricing page for detailed API cost information -[Rate limit and usage tiers](https://docs.mistral.ai/docs/deployment/laplateforme/tier.md): Learn about Mistral's API rate limits, usage tiers, and how to upgrade for higher capacity." (99 characters) -[Deploy with Cerebrium](https://docs.mistral.ai/docs/deployment/self-deployment/cerebrium.md): Deploy AI apps effortlessly with Cerebrium's serverless GPU infrastructure and auto-scaling." (99 characters) -[Deploy with Cloudflare Workers AI](https://docs.mistral.ai/docs/deployment/self-deployment/cloudflare.md): Deploy AI models on Cloudflare's global network with Workers AI for serverless GPU-powered LLMs +[Snowflake Cortex](https://docs.mistral.ai/docs/deployment/cloud/sfcortex.md): Access Mistral AI models on Snowflake Cortex as serverless, fully managed endpoints +[Vertex AI](https://docs.mistral.ai/docs/deployment/cloud/vertex.md): Deploy Mistral AI models on Google Cloud Vertex AI as serverless endpoints +[Workspaces](https://docs.mistral.ai/docs/deployment/laplateforme/organization.md): La Plateforme workspaces enable team collaboration, access management, and shared fine-tuned models." (99 characters) +[La Plateforme](https://docs.mistral.ai/docs/deployment/laplateforme/overview.md): Mistral AI's pay-as-you-go API platform for accessing latest large language models +[Pricing](https://docs.mistral.ai/docs/deployment/laplateforme/pricing.md): Check the pricing page for detailed API cost information." (99 characters) +[Rate limit and usage tiers](https://docs.mistral.ai/docs/deployment/laplateforme/tier.md): Learn about Mistral's API rate limits, usage tiers, and how to check or upgrade your workspace limits +[Deploy with Cerebrium](https://docs.mistral.ai/docs/deployment/self-deployment/cerebrium.md): Deploy AI apps effortlessly with Cerebrium's serverless GPU infrastructure, auto-scaling and pay-per-use +[Deploy with Cloudflare Workers AI](https://docs.mistral.ai/docs/deployment/self-deployment/cloudflare.md): Deploy AI models on Cloudflare's global network with serverless GPUs via Workers AI [Self-deployment](https://docs.mistral.ai/docs/deployment/self-deployment/overview.md): Deploy Mistral AI models on your infrastructure using vLLM, TensorRT-LLM, TGI, or tools like SkyPilot and Cerebrium [Deploy with SkyPilot](https://docs.mistral.ai/docs/deployment/self-deployment/skypilot.md): Deploy AI models on any cloud with SkyPilot for cost savings, high GPU availability, and managed execution -[Text Generation Inference](https://docs.mistral.ai/docs/deployment/self-deployment/tgi.md): TGI is a toolkit for deploying and serving LLMs with high-performance text generation features like quantization and OpenAI-like API support -[TensorRT](https://docs.mistral.ai/docs/deployment/self-deployment/trt.md): Guide to building and deploying TensorRT-LLM engines with Triton inference server +[Text Generation Inference](https://docs.mistral.ai/docs/deployment/self-deployment/tgi.md): TGI is a high-performance toolkit for deploying and serving open-access LLMs with features like quantization and streaming +[TensorRT](https://docs.mistral.ai/docs/deployment/self-deployment/trt.md): Guide to building and deploying TensorRT-LLM engines for Mistral-7B and Mixtral-8X7B models [vLLM](https://docs.mistral.ai/docs/deployment/self-deployment/vllm.md): vLLM is an open-source LLM inference engine optimized for deploying Mistral models on-premise -[SDK Clients](https://docs.mistral.ai/docs/getting-started/clients.md): Official Python & TypeScript SDKs and community clients for Mistral AI -[Bienvenue to Mistral AI Documentation](https://docs.mistral.ai/docs/getting-started/docs_introduction.md): Mistral AI offers open-source and commercial LLMs, APIs, and tools for developers and enterprises to build AI-powered applications -[Glossary](https://docs.mistral.ai/docs/getting-started/glossary.md): Glossary of key AI and LLM terms, including LLMs, text generation, tokens, MoE, RAG, fine-tuning, function calling, embeddings, and temperature -[Model customization](https://docs.mistral.ai/docs/getting-started/model_customization.md): Learn how to customize LLMs for your application with system prompts, fine-tuning, and moderation layers -[Models Benchmarks](https://docs.mistral.ai/docs/getting-started/models/benchmark.md): Mistral's benchmarked models excel in reasoning, multilingual tasks, coding, and multimodal capabilities, outperforming competitors in key benchmarks -[Model selection](https://docs.mistral.ai/docs/getting-started/models/model_selection.md): Guide to selecting Mistral models based on performance, cost, and use case complexity." (99 characters) -[Models Overview](https://docs.mistral.ai/docs/getting-started/models/overview.md): Mistral offers open and premier models for various tasks, including text, code, audio, and multimodal processing -[Model weights](https://docs.mistral.ai/docs/getting-started/models/weights.md): Open-source pre-trained and instruction-tuned models with various licenses, download links, and usage guidelines -[Quickstart](https://docs.mistral.ai/docs/getting-started/quickstart.md): Quickstart guide for setting up a Mistral AI account, configuring billing, and using the API for models and embeddings -[Basic RAG](https://docs.mistral.ai/docs/guides/basic-RAG.md): Learn how to build a basic RAG system by combining retrieval and generation for AI-powered knowledge-based responses -[Ambassador](https://docs.mistral.ai/docs/guides/contribute/ambassador.md): Join Mistral AI's Ambassador Program to advocate, create content, and gain exclusive benefits for AI enthusiasts -[Contribute](https://docs.mistral.ai/docs/guides/contribute/overview.md): Learn how to contribute to Mistral AI through docs, code, community, and the Ambassador Program -[Evaluation](https://docs.mistral.ai/docs/guides/evaluation.md): Guide to evaluating LLMs for specific tasks with metrics, human, and LLM-based methods -[Fine-tuning](https://docs.mistral.ai/docs/guides/finetuning.md): Fine-tuning models incurs a $2 monthly storage fee per model; see pricing for details -[ 01 Intro Basics](https://docs.mistral.ai/docs/guides/finetuning_sections/_01_intro_basics.md): Learn the basics of fine-tuning LLMs with Mistral AI's API and open-source tools for optimized performance -[ 02 Prepare Dataset](https://docs.mistral.ai/docs/guides/finetuning_sections/_02_prepare_dataset.md): Learn how to prepare datasets for fine-tuning models across various use cases, from tone to coding and RAG -[download the validation and reformat script](https://docs.mistral.ai/docs/guides/finetuning_sections/_03_e2e_examples.md): Download the reformat_data.py script to validate and reformat datasets for Mistral API fine-tuning -[get data from hugging face](https://docs.mistral.ai/docs/guides/finetuning_sections/_04_faq.md): FAQ on data validation, size limits, job creation, and fine-tuning details for Mistral API and mistral-finetune -[Observability](https://docs.mistral.ai/docs/guides/observability.md): Observability for LLMs ensures visibility, debugging, and performance optimization across prototyping, testing, and production +[SDK Clients](https://docs.mistral.ai/docs/getting-started/clients.md): Python & Typescript SDK clients for Mistral AI, with community third-party options +[Bienvenue to Mistral AI Documentation](https://docs.mistral.ai/docs/getting-started/docs_introduction.md): Mistral AI offers open-source and commercial LLMs for developers, with premier models like Mistral Medium and Codestral +[Glossary](https://docs.mistral.ai/docs/getting-started/glossary.md): Glossary of key terms related to large language models (LLMs) and text generation +[Model customization](https://docs.mistral.ai/docs/getting-started/model_customization.md): Guide to building applications with custom LLMs for iterative, user-driven AI development +[Models Benchmarks](https://docs.mistral.ai/docs/getting-started/models/benchmark.md): Standardized benchmarks evaluate LLM performance, comparing strengths in reasoning, multilingual tasks, math, and code generation +[Model selection](https://docs.mistral.ai/docs/getting-started/models/model_selection.md): Guide to selecting Mistral models based on performance, cost, and use-case complexity +[Models Overview](https://docs.mistral.ai/docs/getting-started/models/overview.md): Mistral offers open and premier models, including multimodal and reasoning options with API access and commercial licensing +[Model weights](https://docs.mistral.ai/docs/getting-started/models/weights.md): Open-source pre-trained and instruction-tuned models with varying licenses; commercial options available +[Quickstart](https://docs.mistral.ai/docs/getting-started/quickstart.md): Set up your Mistral account, configure billing, and generate API keys to start using Mistral AI +[Basic RAG](https://docs.mistral.ai/docs/guides/basic-RAG.md): RAG combines LLMs with retrieval systems to generate answers using external knowledge." (99 characters) +[Ambassador](https://docs.mistral.ai/docs/guides/contribute/ambassador.md): Join Mistral AI's Ambassador Program to advocate for AI, share expertise, and support the community. Apply by July 1, 2025 +[Contribute](https://docs.mistral.ai/docs/guides/contribute/overview.md): Learn how to contribute to Mistral AI through docs, code, or the Ambassador Program +[Evaluation](https://docs.mistral.ai/docs/guides/evaluation.md): Guide to evaluating LLMs for specific use cases with metrics, LLM, and human-based methods +[Fine-tuning](https://docs.mistral.ai/docs/guides/finetuning.md): Fine-tuning models incurs a $2 monthly storage fee; see pricing for details +[ 01 Intro Basics](https://docs.mistral.ai/docs/guides/finetuning_sections/_01_intro_basics.md): Learn the basics of fine-tuning LLMs to optimize performance for specific tasks using Mistral AI's tools +[ 02 Prepare Dataset](https://docs.mistral.ai/docs/guides/finetuning_sections/_02_prepare_dataset.md): Prepare training data for fine-tuning models with specific use cases and examples +[download the validation and reformat script](https://docs.mistral.ai/docs/guides/finetuning_sections/_03_e2e_examples.md): Download the reformat_data.py script to validate and reformat Mistral API fine-tuning datasets +[get data from hugging face](https://docs.mistral.ai/docs/guides/finetuning_sections/_04_faq.md): Learn how to fetch, validate, and format data from Hugging Face for Mistral models +[Observability](https://docs.mistral.ai/docs/guides/observability.md): Observability ensures visibility, debugging, and continuous improvement for LLM systems in production." (99 characters) [Other resources](https://docs.mistral.ai/docs/guides/other-resources.md): Explore Mistral AI Cookbook for code examples, community contributions, and third-party tool integrations -[Prefix](https://docs.mistral.ai/docs/guides/prefix.md): Prefixes enhance model responses by improving language adherence, saving tokens, enabling roleplay, and strengthening safeguards -[Prompting capabilities](https://docs.mistral.ai/docs/guides/prompting-capabilities.md): Learn effective prompting techniques for classification, summarization, personalization, and evaluation with Mistral models +[Prefix](https://docs.mistral.ai/docs/guides/prefix.md): Prefixes enhance instruction adherence and response control for models in various use cases +[Prompting capabilities](https://docs.mistral.ai/docs/guides/prompting-capabilities.md): Learn how to craft effective prompts for classification, summarization, personalization, and evaluation with Mistral models [Sampling](https://docs.mistral.ai/docs/guides/sampling.md): Learn how to adjust LLM sampling parameters like Temperature, Top P, and penalties for better output control -[Tokenization](https://docs.mistral.ai/docs/guides/tokenization.md): Learn about Mistral AI's tokenization process, including subword tokenization, control tokens, and Python implementation for LLMs \ No newline at end of file +[Tokenization](https://docs.mistral.ai/docs/guides/tokenization.md): Tokenization breaks text into subword units for LLM processing, with Mistral AI's open-source tools for Python \ No newline at end of file