From 7a2f192c3a7769ee21262dd04f98ff70577dfee2 Mon Sep 17 00:00:00 2001 From: Andrew Jiang Date: Wed, 7 Aug 2024 13:55:12 -0400 Subject: [PATCH 1/9] fix: add versioned docs --- fern/{ => apis/v1}/generators.yml | 0 fern/{ => apis/v1}/openapi/cohere.yaml | 0 fern/apis/v2/generators.yml | 1 + fern/docs.yml | 776 +------------------------ fern/fern.config.json | 2 +- fern/v1.yml | 767 ++++++++++++++++++++++++ fern/v2.yml | 767 ++++++++++++++++++++++++ 7 files changed, 1545 insertions(+), 768 deletions(-) rename fern/{ => apis/v1}/generators.yml (100%) rename fern/{ => apis/v1}/openapi/cohere.yaml (100%) create mode 100644 fern/apis/v2/generators.yml create mode 100644 fern/v1.yml create mode 100644 fern/v2.yml diff --git a/fern/generators.yml b/fern/apis/v1/generators.yml similarity index 100% rename from fern/generators.yml rename to fern/apis/v1/generators.yml diff --git a/fern/openapi/cohere.yaml b/fern/apis/v1/openapi/cohere.yaml similarity index 100% rename from fern/openapi/cohere.yaml rename to fern/apis/v1/openapi/cohere.yaml diff --git a/fern/apis/v2/generators.yml b/fern/apis/v2/generators.yml new file mode 100644 index 00000000..2a712643 --- /dev/null +++ b/fern/apis/v2/generators.yml @@ -0,0 +1 @@ +openapi: ../../../cohere-openapi.yaml diff --git a/fern/docs.yml b/fern/docs.yml index fe070dba..a54be28f 100644 --- a/fern/docs.yml +++ b/fern/docs.yml @@ -2,6 +2,15 @@ instances: - url: cohere.docs.buildwithfern.com title: Cohere +versions: + - display-name: v1 + path: v1.yml + slug: v1 + - display-name: v2 + path: v2.yml + slug: v2 + availability: beta + logo: light: assets/logo.svg dark: assets/logo-dark.svg @@ -69,773 +78,6 @@ colors: light: "#E0E0E0" dark: "#292929" -tabs: - docs: - display-name: Guides and concepts - slug: docs - api: - display-name: API Reference - slug: reference - release-notes: - display-name: Release Notes - changelog: pages/changelog - slug: changelog - llmu: - display-name: LLMU - href: https://cohere.com/llmu - cookbooks: - display-name: Cookbooks - slug: page - -landing-page: - page: Cohere Documentation - path: pages/index.mdx - -navigation: - - tab: docs - layout: - - section: Get Started - contents: - - page: The Cohere Platform - path: pages/get-started/the-cohere-platform.mdx - - page: Introduction to Large Language Models - path: pages/get-started/introduction-to-large-language-models.mdx - - page: Developer Playground - path: pages/get-started/playground-overview.mdx - - page: Cohere Toolkit - path: pages/get-started/cohere-toolkit.mdx - - page: Datasets - path: pages/get-started/datasets.mdx - - section: Models - contents: - - page: Models Overview - path: pages/models/models.mdx - - section: Command - contents: - - page: Command R+ - path: pages/models/the-command-family-of-models/command-r-plus.mdx - - page: Command R - path: pages/models/the-command-family-of-models/command-r.mdx - - page: Command and Command Light - path: pages/models/the-command-family-of-models/command-beta.mdx - - page: Embed - path: pages/models/cohere-embed.mdx - - page: Rerank - path: pages/models/rerank-2.mdx - - section: Text Generation - contents: - - page: Using the Chat API - path: pages/text-generation/chat-api.mdx - - page: Streaming Responses - path: pages/text-generation/streaming.mdx - - page: Structured Generations (JSON) - path: pages/text-generation/structured-outputs-json.mdx - - page: Predictable Outputs - path: pages/text-generation/predictable-outputs.mdx - - page: Advanced Generation Parameters - path: pages/text-generation/advanced-generation-hyperparameters.mdx - - page: Retrieval Augmented Generation (RAG) - path: pages/text-generation/retrieval-augmented-generation-rag.mdx - - section: RAG Connectors - contents: - - page: Overview of RAG Connectors - path: pages/text-generation/connectors/overview-1.mdx - - page: Creating and Deploying a Connector - path: pages/text-generation/connectors/creating-and-deploying-a-connector.mdx - - page: Managing your Connector - path: pages/text-generation/connectors/managing-your-connector.mdx - - page: Connector Authentication - path: pages/text-generation/connectors/connector-authentication.mdx - - page: Connector FAQs - path: pages/text-generation/connectors/connector-faqs.mdx - - section: Tool Use - path: pages/text-generation/tools.mdx - contents: - - section: Multi-step Tool Use (Agents) - path: pages/text-generation/tools/multi-step-tool-use.mdx - contents: - - page: Implementing a Multi-Step Agent with Langchain - path: pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx - - page: Single-Step Tool Use - path: pages/text-generation/tools/tool-use.mdx - - page: Parameter Types in Tool Use - path: pages/text-generation/tools/parameter-types-in-tool-use.mdx - - page: Tokens and Tokenizers - path: pages/text-generation/tokens-and-tokenizers.mdx - - section: Prompt Engineering - contents: - - page: Crafting Effective Prompts - path: pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx - - page: Advanced Prompt Engineering Techniques - path: pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx - - page: Prompt Truncation - path: pages/text-generation/prompt-engineering/prompt-truncation.mdx - - page: Preambles - path: pages/text-generation/prompt-engineering/preambles.mdx - - section: Prompt Library - contents: - - page: Create CSV data from JSON data - path: pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx - - page: Create a markdown table from raw data - path: pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx - - page: Meeting Summarizer - path: pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx - - page: Remove PII - path: pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx - - page: Add a Docstring to your code - path: pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx - - page: Evaluate your LLM response - path: pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx - - page: Faster Web Search - path: pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx - - page: Multilingual interpreter - path: pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx - - page: Migrating from the Generate API to the Chat API - path: pages/text-generation/migrating-from-cogenerate-to-cochat.mdx - - section: Text Embeddings (Vectors, Search, Retrieval) - contents: - - page: Introduction to Embeddings at Cohere - path: pages/text-embeddings/embeddings.mdx - - page: Batch Embedding Jobs - path: pages/text-embeddings/embed-jobs-api.mdx - - section: Reranking - contents: - - page: Rerank Overview - path: pages/text-embeddings/reranking/overview.mdx - - page: Rerank Best Practices - path: pages/text-embeddings/reranking/reranking-best-practices.mdx - - page: Text Classification - path: pages/text-embeddings/text-classification-1.mdx - - section: Fine-Tuning - contents: - - page: Introduction - path: pages/fine-tuning/fine-tuning.mdx - - page: Fine-tuning with Web-UI - path: pages/fine-tuning/fine-tuning-with-the-cohere-dashboard.mdx - - page: Programmatic Fine-tuning - path: pages/fine-tuning/fine-tuning-with-the-python-sdk.mdx - - section: Fine-tuning for Chat - path: pages/fine-tuning/chat-fine-tuning.mdx - contents: - - page: Preparing the Chat Fine-tuning Data - path: pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx - - page: Starting the Chat Fine-Tuning - path: pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx - - page: Understanding the Chat Fine-tuning Results - path: pages/fine-tuning/chat-fine-tuning/chat-understanding-the-results.mdx - - page: Improving the Chat Fine-tuning Results - path: pages/fine-tuning/chat-fine-tuning/chat-improving-the-results.mdx - - section: Fine-tuning for Classify - path: pages/fine-tuning/classify-fine-tuning.mdx - contents: - - page: Preparing the Classify Fine-tuning data - path: pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx - - page: Trains and deploys a fine-tuned model - path: pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx - - page: Understanding the Classify Fine-tuning Results - path: pages/fine-tuning/classify-fine-tuning/classify-understanding-the-results.mdx - - page: Improving the Classify Fine-tuning Results - path: pages/fine-tuning/classify-fine-tuning/classify-improving-the-results.mdx - - section: Fine-tuning for Rerank - path: pages/fine-tuning/rerank-fine-tuning.mdx - contents: - - page: Preparing the Rerank Fine-tuning Data - path: pages/fine-tuning/rerank-fine-tuning/rerank-preparing-the-data.mdx - - page: Starting the Rerank Fine-Tuning - path: pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx - - page: Understanding the Rerank Fine-tuning Results - path: pages/fine-tuning/rerank-fine-tuning/rerank-understanding-the-results.mdx - - page: Improving the Rerank Fine-tuning Results - path: pages/fine-tuning/rerank-fine-tuning/rerank-improving-the-results.mdx - - page: FAQs / Troubleshooting - path: pages/fine-tuning/troubleshooting-a-fine-tuned-model.mdx - - section: Going to Production - contents: - - page: API Keys and Rate Limits - path: pages/going-to-production/rate-limits.mdx - - page: Going Live - path: pages/going-to-production/going-live.mdx - - page: How Does Cohere Pricing Work? - path: pages/going-to-production/how-does-cohere-pricing-work.mdx - - section: Integrations - contents: - - section: Embedding Integrations - path: pages/integrations/integrations.mdx - contents: - - page: Elasticsearch and Cohere - path: pages/integrations/integrations/elasticsearch-and-cohere.mdx - - page: MongoDB and Cohere - path: pages/integrations/integrations/mongodb-and-cohere.mdx - - page: Redis and Cohere - path: pages/integrations/integrations/redis-and-cohere.mdx - - page: Haystack and Cohere - path: pages/integrations/integrations/haystack-and-cohere.mdx - - page: Pinecone and Cohere - path: pages/integrations/integrations/pinecone-and-cohere.mdx - - page: Weaviate and Cohere - path: pages/integrations/integrations/weaviate-and-cohere.mdx - - page: Open Search and Cohere - path: pages/integrations/integrations/opensearch-and-cohere.mdx - - page: Vespa and Cohere - path: pages/integrations/integrations/vespa-and-cohere.mdx - - page: Qdrant and Cohere - path: pages/integrations/integrations/qdrant-and-cohere.mdx - - page: Milvus and Cohere - path: pages/integrations/integrations/milvus-and-cohere.mdx - - page: Zilliz and Cohere - path: pages/integrations/integrations/zilliz-and-cohere.mdx - - page: Chroma and Cohere - path: pages/integrations/integrations/chroma-and-cohere.mdx - - section: LangChain - path: pages/integrations/cohere-and-langchain.mdx - contents: - - page: Chat on LangChain - path: pages/integrations/cohere-and-langchain/chat-on-langchain.mdx - - page: Embed on LangChain - path: pages/integrations/cohere-and-langchain/embed-on-langchain.mdx - - page: Rerank on LangChain - path: pages/integrations/cohere-and-langchain/rerank-on-langchain.mdx - - page: Tools on LangChain - path: pages/integrations/cohere-and-langchain/tools-on-langchain.mdx - - page: LlamaIndex - path: pages/integrations/llamaindex.mdx - - section: Deployment Options - contents: - - page: Cohere SDK Cloud Platform Compatibility - path: pages/deployment-options/cohere-works-everywhere.mdx - - section: Cohere on AWS - path: pages/deployment-options/cohere-on-aws.mdx - contents: - - page: Amazon Bedrock - path: pages/deployment-options/cohere-on-aws/amazon-bedrock.mdx - - page: Amazon SageMaker - path: pages/deployment-options/cohere-on-aws/amazon-sagemaker-setup-guide.mdx - - page: Cohere on Azure - path: pages/deployment-options/cohere-on-microsoft-azure.mdx - - page: Single Container on Private Clouds - path: pages/deployment-options/single-container-on-private-clouds.mdx - - section: Tutorials - contents: - - page: Cookbooks - path: pages/tutorials/cookbooks.mdx - - page: LLM University - path: pages/llm-university/llmu-2.mdx - - section: Responsible Use - contents: - - section: Overview - path: pages/responsible-use/responsible-use.mdx - contents: - - page: Usage Guidelines - path: pages/responsible-use/responsible-use/usage-guidelines.mdx - - page: Model Limitations - path: pages/responsible-use/responsible-use/model-limitations.mdx - - page: Data Statement - path: pages/responsible-use/responsible-use/data-statement.mdx - - page: Generation Benchmarks - path: pages/responsible-use/responsible-use/generation-benchmarks.mdx - - page: Representation Benchmarks - path: pages/responsible-use/responsible-use/representation-benchmarks.mdx - - link: Security - href: https://cohere.ai/security - - page: Environmental Impact - path: pages/responsible-use/environmental-impact.mdx - - section: Cohere for AI - contents: - - page: Cohere For AI Acceptable Use Policy - path: pages/cohere-for-ai/c4ai-acceptable-use-policy.mdx - # HIDDEN SECTION - - page: API Keys - hidden: true - path: pages/cohere-api/create-an-api-key.mdx - - page: Versioning - hidden: true - path: pages/cohere-api/versioning.mdx - - page: Model Commands - hidden: true - path: pages/command-line-interface/admin-commands.mdx - - page: Admin Commands - hidden: true - path: pages/command-line-interface/command-reference.mdx - - page: Finetune - hidden: true - path: pages/command-line-interface/admin-commands/finetune.mdx - - page: Generate Docs - hidden: true - path: pages/command-line-interface/admin-commands/generate-docs.mdx - - page: Model - hidden: true - path: pages/command-line-interface/admin-commands/model.mdx - - page: Admin - hidden: true - path: pages/command-line-interface/command-reference/admin.mdx - - page: Auth - hidden: true - path: pages/command-line-interface/command-reference/auth.mdx - - page: Config - hidden: true - path: pages/command-line-interface/command-reference/config.mdx - - page: Key - hidden: true - path: pages/command-line-interface/command-reference/key.mdx - - page: Ping - hidden: true - path: pages/command-line-interface/command-reference/ping.mdx - - page: Usage - hidden: true - path: pages/command-line-interface/command-reference/usage.mdx - - page: User - hidden: true - path: pages/command-line-interface/command-reference/user.mdx - - page: Deployment Options - hidden: true - path: pages/deployment-options/deployment-options.mdx - - page: Getting Started with Coral Toolkit - hidden: true - path: pages/deployment-options/getting-started-with-coral-toolkit.mdx - - page: Amazon Bedrock - hidden: true - path: pages/deployment-options/deployment-options/aws-bedrock.mdx - - page: Amazon SageMaker - hidden: true - path: pages/deployment-options/deployment-options/aws-sagemaker.mdx - - page: Oracle Cloud Infrastructure (OCI) - hidden: true - path: pages/deployment-options/deployment-options/oracle-cloud-infrastructure-oci.mdx - - page: Private Deployment - hidden: true - path: pages/deployment-options/deployment-options/private-deployment.mdx - - page: Deploying Your Coral Toolkit - hidden: true - path: pages/deployment-options/getting-started-with-coral-toolkit/deploying-your-coral-toolkit.mdx - - page: LangChain and LlamaIndex in Coral Toolkit - hidden: true - path: pages/deployment-options/getting-started-with-coral-toolkit/langchain-and-llamaindex-in-coral-toolkit.mdx - - page: Fine-tuning on AWS - hidden: true - path: pages/fine-tuning/fine-tuning-on-aws.mdx - - page: Fine-tuning Cohere Models on Amazon Bedrock - hidden: true - path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-bedrock.mdx - - page: Fine-tuning Cohere Models on Amazon SageMaker - hidden: true - path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-sagemaker.mdx - - page: Cohere SDKs - hidden: true - path: pages/get-started/cohere-sdks.mdx - - page: Errors and Warnings - hidden: true - path: pages/going-to-production/errors-and-warnings.mdx - - page: Amazon SageMaker and Cohere - hidden: true - path: pages/integrations/integrations/amazon-sagemaker-and-cohere.mdx - - page: "Appendix 2: Building Apps" - hidden: true - path: pages/llm-university/intro-building-apps.mdx - - page: "Module 7: The Cohere Platform" - hidden: true - path: pages/llm-university/intro-the-cohere-platform.mdx - - page: Sandbox - hidden: true - path: pages/llm-university/sandbox.mdx - - page: App Examples - hidden: true - path: pages/llm-university/intro-building-apps/app-examples.mdx - - page: Deploying with Amazon SageMaker - hidden: true - path: pages/llm-university/intro-deployment/deploying-with-aws-sagemaker.mdx - - page: Semantic Search - hidden: true - path: pages/llm-university/intro-large-language-models/semantic-search-temp.mdx - - page: A Deeper Dive Into Semantic Search - hidden: true - path: pages/llm-university/intro-semantic-search/deeper-semantic-search.mdx - - page: Evaluation Methods for Search - hidden: true - path: pages/llm-university/intro-semantic-search/evaluation-methods-for-search.mdx - - page: Fine-Tuning for Rerank - hidden: true - path: pages/llm-university/intro-semantic-search/fine-tuning-for-rerank.mdx - - page: Multilingual Semantic Search - hidden: true - path: pages/llm-university/intro-semantic-search/multilingual-movie-search.mdx - - page: Multilingual Semantic Search With Cohere and Langchain - hidden: true - path: pages/llm-university/intro-semantic-search/multilingual-semantic-search-with-cohere-and-langchain.mdx - - page: Chaining Prompts - hidden: true - path: pages/llm-university/intro-text-generation/chaining-prompts.mdx - - page: Fine-tuning a Generative Model - hidden: true - path: pages/llm-university/intro-text-generation/creating-custom-models.mdx - - page: Prompt Engineering - hidden: true - path: pages/llm-university/intro-text-generation/model-prompting.mdx - - page: Conclusion - hidden: true - path: pages/llm-university/intro-text-generation/text-generation-conclusion.mdx - - page: Text Summarization - hidden: true - path: pages/llm-university/intro-text-generation/text-summarization.mdx - - page: "Cohere's Command Model" - hidden: true - path: pages/llm-university/intro-text-generation/the-command-model.mdx - - page: The Generate Endpoint - hidden: true - path: pages/llm-university/intro-text-generation/the-generate-endpoint.mdx - - page: Use Case Ideation - hidden: true - path: pages/llm-university/intro-text-generation/use-case-ideation.mdx - - page: What is Generative AI? - hidden: true - path: pages/llm-university/intro-text-generation/what-is-generative-ai.mdx - - page: Classification Models - hidden: true - path: pages/llm-university/intro-text-representation/classification-models.mdx - - page: Classification Using Embeddings - hidden: true - path: pages/llm-university/intro-text-representation/classification-using-embeddings.mdx - - page: The Classify Endpoint - hidden: true - path: pages/llm-university/intro-text-representation/classify-endpoint.mdx - - page: Clustering Using Embeddings - hidden: true - path: pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx - - page: The Embed Endpoint - hidden: true - path: pages/llm-university/intro-text-representation/embed-endpoint.mdx - - page: Visualizing Data - hidden: true - path: pages/llm-university/intro-text-representation/embeddings-visualizing-data.mdx - - page: Classification Evaluation Metrics - hidden: true - path: pages/llm-university/intro-text-representation/evaluation-metrics.mdx - - page: Fine-tuning an Embedding Model for Classification - hidden: true - path: pages/llm-university/intro-text-representation/finetuning.mdx - - page: Semantic Search Using Embeddings - hidden: true - path: pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx - - page: Setting up - hidden: true - path: pages/llm-university/intro-text-representation/setting-up.mdx - - page: Applications - hidden: true - path: pages/llm-university/intro-the-cohere-platform/applications.mdx - - page: Conclusion - The Cohere Platform - hidden: true - path: pages/llm-university/intro-the-cohere-platform/conclusion-the-cohere-platform.mdx - - page: Endpoints - hidden: true - path: pages/llm-university/intro-the-cohere-platform/endpoints.mdx - - page: Foundational Models - hidden: true - path: pages/llm-university/intro-the-cohere-platform/foundation-models.mdx - - page: Serving Platform - hidden: true - path: pages/llm-university/intro-the-cohere-platform/serving-platform.mdx - - page: Classification Models - hidden: true - path: pages/llm-university/sandbox/classification-models-remove.mdx - - page: BAK MEOR Creating Custom Generative Models - hidden: true - path: pages/llm-university/sandbox/creating-custom-generative-models-copy.mdx - - page: Structure of the Course (WITH DEPLOYMENT ADDED) - hidden: true - path: pages/llm-university/sandbox/structure-of-the-course-copy-1.mdx - - page: Backup Structure - hidden: true - path: pages/llm-university/sandbox/structure-of-the-course-copy.mdx - - page: "[ALT] The Generate Endpoint" - hidden: true - path: pages/llm-university/sandbox/the-generate-endpoint-copy.mdx - - page: Customer Support - hidden: true - path: pages/quickstart-tutorials/customer-support-link.mdx - - page: Intent Recognition - hidden: true - path: pages/quickstart-tutorials/intent-recognition-link.mdx - - page: Sentiment Analysis - hidden: true - path: pages/quickstart-tutorials/sentiment-analysis-link.mdx - - page: Toxicity Detection - hidden: true - path: pages/quickstart-tutorials/toxicity-detection-link.mdx - - page: Using the Embed API - hidden: true - path: pages/text-embeddings/embed-api.mdx - - page: Multilingual Embed Models - hidden: true - path: pages/text-embeddings/multilingual-language-models.mdx - - page: Introduction to Cohere Embeddings - hidden: true - path: pages/text-embeddings/semantic-search-with-embeddings.mdx - - page: Cross-Lingual Content Moderation - hidden: true - path: pages/text-embeddings/multilingual-language-models/cross-lingual-content-moderation.mdx - - page: Customer Feedback Aggregation - hidden: true - path: pages/text-embeddings/multilingual-language-models/customer-feedback-aggregation.mdx - - page: Multilingual Semantic Search - hidden: true - path: pages/text-embeddings/multilingual-language-models/multilingual-semantic-search.mdx - - page: Supported Languages - hidden: true - path: pages/text-embeddings/multilingual-language-models/supported-languages.mdx - - page: Documents and Citations - hidden: true - path: pages/text-generation/documents-and-citations.mdx - - page: Sending Feedback - hidden: true - path: pages/text-generation/feedback.mdx - - page: Book an appointment - hidden: true - path: pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx - - page: Single-step vs Multi-step - hidden: true - path: pages/text-generation/tools/single-step-vs-multi-step.mdx - - page: Semantic Search - hidden: true - path: pages/-ARCHIVE-/old-tutorials/semantic-search.mdx - - page: Fine-tuning for Generate - hidden: true - path: pages/-ARCHIVE-/generate-fine-tuning.mdx - - page: Starting the Generate Fine-tuning - hidden: true - path: pages/-ARCHIVE-/generate-fine-tuning/generate-starting-the-training.mdx - - page: Understanding the Generate Fine-tuning Results - hidden: true - path: pages/-ARCHIVE-/generate-fine-tuning/generate-understanding-the-results.mdx - - page: Improving the Generate Fine-tuning results - hidden: true - path: pages/-ARCHIVE-/generate-fine-tuning/generate-improving-the-results.mdx - - page: Temperature - hidden: true - path: pages/-ARCHIVE-/temperature.mdx - - page: Top-k & Top-p - hidden: true - path: pages/-ARCHIVE-/controlling-generation-with-top-k-top-p.mdx - - tab: api - layout: - - section: Cohere API - contents: - - page: About - path: pages/cohere-api/about.mdx - - page: Teams and Roles - path: pages/cohere-api/teams-and-roles.mdx - - page: Errors - path: pages/cohere-api/errors.mdx - - page: Installation - hidden: true - path: pages/command-line-interface/command.mdx - - api: Cohere API - audiences: - - public - skip-slug: true - flattened: true - snippets: - python: "cohere" - typescript: "cohere-ai" - layout: - - section: API Reference - skip-slug: true - contents: - - section: "/chat" - skip-slug: true - contents: - - endpoint: POST /v1/chat - title: Chat Non-streaming - - endpoint: STREAM /v1/chat - title: Chat Streaming - - section: "/embed" - skip-slug: true - contents: - - POST /v1/embed - - embedJobs: - title: "/embed-jobs" - skip-slug: true - contents: - - endpoint: POST /v1/embed-jobs - slug: create-embed-job - - endpoint: GET /v1/embed-jobs - slug: list-embed-jobs - - endpoint: GET /v1/embed-jobs/{id} - slug: get-embed-job - - endpoint: POST /v1/embed-jobs/{id}/cancel - slug: cancel-embed-job - - section: "/rerank" - skip-slug: true - contents: - - POST /v1/rerank - - section: "/classify" - skip-slug: true - contents: - - POST /v1/classify - - datasets: - title: "/datasets" - skip-slug: true - contents: - - endpoint: POST /v1/datasets - slug: create-dataset - - endpoint: GET /v1/datasets - slug: list-datasets - - endpoint: GET /v1/datasets/usage - slug: get-dataset-usage - - endpoint: GET /v1/datasets/{id} - slug: get-dataset - - endpoint: DELETE /v1/datasets/{id} - slug: delete-dataset - - section: "/tokenize" - skip-slug: true - contents: - - POST /v1/tokenize - - section: "/detokenize" - skip-slug: true - contents: - - POST /v1/detokenize - - connectors: - title: "/connectors" - skip-slug: true - contents: - - endpoint: GET /v1/connectors - slug: list-connectors - - endpoint: POST /v1/connectors - slug: create-connector - - endpoint: GET /v1/connectors/{id} - slug: get-connector - - endpoint: PATCH /v1/connectors/{id} - slug: update-connector - - endpoint: DELETE /v1/connectors/{id} - slug: delete-connector - - endpoint: POST /v1/connectors/{id}/oauth/authorize - slug: oauthauthorize-connector - - models: - title: "/models" - skip-slug: true - contents: - - endpoint: GET /v1/models/{model} - slug: get-model - - endpoint: GET /v1/models - slug: list-models - - section: "/check-api-key" - skip-slug: true - contents: - - endpoint: POST /v1/check-api-key - slug: checkapikey - - finetuning: - title: "/finetuning" - skip-slug: true - contents: - - endpoint: GET /v1/finetuning/finetuned-models - slug: listfinetunedmodels - - endpoint: POST /v1/finetuning/finetuned-models - slug: createfinetunedmodel - - endpoint: PATCH /v1/finetuning/finetuned-models/{id} - slug: updatefinetunedmodel - - endpoint: GET /v1/finetuning/finetuned-models/{id} - slug: getfinetunedmodel - - endpoint: DELETE /v1/finetuning/finetuned-models/{id} - slug: deletefinetunedmodel - - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/events - slug: listevents - - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/training-step-metrics - slug: listtrainingstepmetrics - - section: Legacy - skip-slug: true - contents: - - section: "/generate" - skip-slug: true - contents: - - endpoint: POST /v1/generate - title: Chat Non-streaming - - endpoint: STREAM /v1/generate - title: Chat Streaming - - section: "/summarize" - skip-slug: true - contents: - - POST /v1/summarize - - tab: release-notes - - tab: llmu - - tab: cookbooks - layout: - - page: Cookbooks - path: pages/cookbooks.mdx - - page: Agent API Calls - path: pages/cookbooks/agent-api-calls.mdx - - page: Short-Term Memory Handling for Agents - path: pages/cookbooks/agent-short-term-memory.mdx - - page: Agentic Multi-Stage RAG with Cohere Tools API - path: pages/cookbooks/agentic-multi-stage-rag.mdx - - page: Agentic RAG for PDFs with mixed data - path: pages/cookbooks/agentic-rag-mixed-data.mdx - - page: Analysis of Form 10-K/10-Q Using Cohere and RAG - path: pages/cookbooks/analysis-of-financial-forms.mdx - - page: Analyzing Hacker News with Six Language Understanding Methods - path: pages/cookbooks/analyzing-hacker-news.mdx - - page: Article Recommender with Text Embedding Classification Extraction - path: pages/cookbooks/article-recommender-with-text-embeddings.mdx - - page: Multi-Step Tool Use - path: pages/cookbooks/basic-multi-step.mdx - - page: Basic RAG - path: pages/cookbooks/basic-rag.mdx - - page: Basic Semantic Search - path: pages/cookbooks/basic-semantic-search.mdx - - page: Basic Tool Use - path: pages/cookbooks/basic-tool-use.mdx - - page: Calendar Agent with Native Multi Step Tool - path: pages/cookbooks/calendar-agent.mdx - - page: Chunking Strategies - path: pages/cookbooks/chunking-strategies.mdx - - page: Creating a QA Bot From Technical Documentation - path: pages/cookbooks/creating-a-qa-bot.mdx - - page: Financial CSV Agent with Native Multi-Step Cohere API - path: pages/cookbooks/csv-agent-native-api.mdx - - page: Financial CSV Agent with Langchain - path: pages/cookbooks/csv-agent.mdx - - page: A Data Analyst Agent Built with Cohere and Langchain - path: pages/cookbooks/data-analyst-agent.mdx - - page: Advanced Document Parsing For Enterprises - path: pages/cookbooks/document-parsing-for-enterprises.mdx - - page: End-to-end RAG using Elasticsearch and Cohere - path: pages/cookbooks/elasticsearch-and-cohere.mdx - - page: Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution - path: pages/cookbooks/embed-jobs-serverless-pinecone.mdx - - page: Semantic Search with Cohere Embed Jobs - path: pages/cookbooks/embed-jobs.mdx - - page: Fueling Generative Content with Keyword Research - path: pages/cookbooks/fueling-generative-content.mdx - - page: Grounded Summarization Using Command R - path: pages/cookbooks/grounded-summarization.mdx - - page: Hello World! Meet Language AI - path: pages/cookbooks/hello-world-meet-ai.mdx - - page: Long Form General Strategies - path: pages/cookbooks/long-form-general-strategies.mdx - - page: Migrating Monolithic Prompts to Command-R with RAG - path: pages/cookbooks/migrating-prompts.mdx - - page: Multilingual Search with Cohere and Langchain - path: pages/cookbooks/multilingual-search.mdx - - page: PDF Extractor with Native Multi Step Tool Use - path: pages/cookbooks/pdf-extractor.mdx - - page: Pondr, Fostering Connection through Good Conversation - path: pages/cookbooks/pondr.mdx - - page: Deep Dive Into RAG Evaluation - path: pages/cookbooks/rag-evaluation-deep-dive.mdx - - page: RAG With Chat Embed and Rerank via Pinecone - path: pages/cookbooks/rag-with-chat-embed.mdx - - page: Demo of Rerank - path: pages/cookbooks/rerank-demo.mdx - - page: SQL Agent - path: pages/cookbooks/sql-agent.mdx - - page: Summarization Evals - path: pages/cookbooks/summarization-evals.mdx - - page: Text Classification Using Embeddings - path: pages/cookbooks/text-classification-using-embeddings.mdx - - page: Topic Modeling AI Papers - path: pages/cookbooks/topic-modeling-ai-papers.mdx - - page: Wikipedia Semantic Search with Cohere + Weaviate - path: pages/cookbooks/wikipedia-search-with-weaviate.mdx - - page: Wikipedia Semantic Search with Cohere Embedding Archives - path: pages/cookbooks/wikipedia-semantic-search.mdx - experimental: mdx-components: - ./components diff --git a/fern/fern.config.json b/fern/fern.config.json index f2d1b6e3..baa6fed2 100644 --- a/fern/fern.config.json +++ b/fern/fern.config.json @@ -1,4 +1,4 @@ { "organization": "cohere", - "version": "0.37.6" + "version": "0.37.13" } \ No newline at end of file diff --git a/fern/v1.yml b/fern/v1.yml new file mode 100644 index 00000000..776767a5 --- /dev/null +++ b/fern/v1.yml @@ -0,0 +1,767 @@ +tabs: + docs: + display-name: Guides and concepts + slug: docs + api: + display-name: API Reference + slug: reference + release-notes: + display-name: Release Notes + changelog: pages/changelog + slug: changelog + llmu: + display-name: LLMU + href: https://cohere.com/llmu + cookbooks: + display-name: Cookbooks + slug: page + +landing-page: + page: Cohere Documentation + path: pages/index.mdx + +navigation: + - tab: docs + layout: + - section: Get Started + contents: + - page: The Cohere Platform + path: pages/get-started/the-cohere-platform.mdx + - page: Introduction to Large Language Models + path: pages/get-started/introduction-to-large-language-models.mdx + - page: Developer Playground + path: pages/get-started/playground-overview.mdx + - page: Cohere Toolkit + path: pages/get-started/cohere-toolkit.mdx + - page: Datasets + path: pages/get-started/datasets.mdx + - section: Models + contents: + - page: Models Overview + path: pages/models/models.mdx + - section: Command + contents: + - page: Command R+ + path: pages/models/the-command-family-of-models/command-r-plus.mdx + - page: Command R + path: pages/models/the-command-family-of-models/command-r.mdx + - page: Command and Command Light + path: pages/models/the-command-family-of-models/command-beta.mdx + - page: Embed + path: pages/models/cohere-embed.mdx + - page: Rerank + path: pages/models/rerank-2.mdx + - section: Text Generation + contents: + - page: Using the Chat API + path: pages/text-generation/chat-api.mdx + - page: Streaming Responses + path: pages/text-generation/streaming.mdx + - page: Structured Generations (JSON) + path: pages/text-generation/structured-outputs-json.mdx + - page: Predictable Outputs + path: pages/text-generation/predictable-outputs.mdx + - page: Advanced Generation Parameters + path: pages/text-generation/advanced-generation-hyperparameters.mdx + - page: Retrieval Augmented Generation (RAG) + path: pages/text-generation/retrieval-augmented-generation-rag.mdx + - section: RAG Connectors + contents: + - page: Overview of RAG Connectors + path: pages/text-generation/connectors/overview-1.mdx + - page: Creating and Deploying a Connector + path: pages/text-generation/connectors/creating-and-deploying-a-connector.mdx + - page: Managing your Connector + path: pages/text-generation/connectors/managing-your-connector.mdx + - page: Connector Authentication + path: pages/text-generation/connectors/connector-authentication.mdx + - page: Connector FAQs + path: pages/text-generation/connectors/connector-faqs.mdx + - section: Tool Use + path: pages/text-generation/tools.mdx + contents: + - section: Multi-step Tool Use (Agents) + path: pages/text-generation/tools/multi-step-tool-use.mdx + contents: + - page: Implementing a Multi-Step Agent with Langchain + path: pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx + - page: Single-Step Tool Use + path: pages/text-generation/tools/tool-use.mdx + - page: Parameter Types in Tool Use + path: pages/text-generation/tools/parameter-types-in-tool-use.mdx + - page: Tokens and Tokenizers + path: pages/text-generation/tokens-and-tokenizers.mdx + - section: Prompt Engineering + contents: + - page: Crafting Effective Prompts + path: pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx + - page: Advanced Prompt Engineering Techniques + path: pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx + - page: Prompt Truncation + path: pages/text-generation/prompt-engineering/prompt-truncation.mdx + - page: Preambles + path: pages/text-generation/prompt-engineering/preambles.mdx + - section: Prompt Library + contents: + - page: Create CSV data from JSON data + path: pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx + - page: Create a markdown table from raw data + path: pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx + - page: Meeting Summarizer + path: pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx + - page: Remove PII + path: pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx + - page: Add a Docstring to your code + path: pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx + - page: Evaluate your LLM response + path: pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx + - page: Faster Web Search + path: pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx + - page: Multilingual interpreter + path: pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx + - page: Migrating from the Generate API to the Chat API + path: pages/text-generation/migrating-from-cogenerate-to-cochat.mdx + - section: Text Embeddings (Vectors, Search, Retrieval) + contents: + - page: Introduction to Embeddings at Cohere + path: pages/text-embeddings/embeddings.mdx + - page: Batch Embedding Jobs + path: pages/text-embeddings/embed-jobs-api.mdx + - section: Reranking + contents: + - page: Rerank Overview + path: pages/text-embeddings/reranking/overview.mdx + - page: Rerank Best Practices + path: pages/text-embeddings/reranking/reranking-best-practices.mdx + - page: Text Classification + path: pages/text-embeddings/text-classification-1.mdx + - section: Fine-Tuning + contents: + - page: Introduction + path: pages/fine-tuning/fine-tuning.mdx + - page: Fine-tuning with Web-UI + path: pages/fine-tuning/fine-tuning-with-the-cohere-dashboard.mdx + - page: Programmatic Fine-tuning + path: pages/fine-tuning/fine-tuning-with-the-python-sdk.mdx + - section: Fine-tuning for Chat + path: pages/fine-tuning/chat-fine-tuning.mdx + contents: + - page: Preparing the Chat Fine-tuning Data + path: pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx + - page: Starting the Chat Fine-Tuning + path: pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx + - page: Understanding the Chat Fine-tuning Results + path: pages/fine-tuning/chat-fine-tuning/chat-understanding-the-results.mdx + - page: Improving the Chat Fine-tuning Results + path: pages/fine-tuning/chat-fine-tuning/chat-improving-the-results.mdx + - section: Fine-tuning for Classify + path: pages/fine-tuning/classify-fine-tuning.mdx + contents: + - page: Preparing the Classify Fine-tuning data + path: pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx + - page: Trains and deploys a fine-tuned model + path: pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx + - page: Understanding the Classify Fine-tuning Results + path: pages/fine-tuning/classify-fine-tuning/classify-understanding-the-results.mdx + - page: Improving the Classify Fine-tuning Results + path: pages/fine-tuning/classify-fine-tuning/classify-improving-the-results.mdx + - section: Fine-tuning for Rerank + path: pages/fine-tuning/rerank-fine-tuning.mdx + contents: + - page: Preparing the Rerank Fine-tuning Data + path: pages/fine-tuning/rerank-fine-tuning/rerank-preparing-the-data.mdx + - page: Starting the Rerank Fine-Tuning + path: pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx + - page: Understanding the Rerank Fine-tuning Results + path: pages/fine-tuning/rerank-fine-tuning/rerank-understanding-the-results.mdx + - page: Improving the Rerank Fine-tuning Results + path: pages/fine-tuning/rerank-fine-tuning/rerank-improving-the-results.mdx + - page: FAQs / Troubleshooting + path: pages/fine-tuning/troubleshooting-a-fine-tuned-model.mdx + - section: Going to Production + contents: + - page: API Keys and Rate Limits + path: pages/going-to-production/rate-limits.mdx + - page: Going Live + path: pages/going-to-production/going-live.mdx + - page: How Does Cohere Pricing Work? + path: pages/going-to-production/how-does-cohere-pricing-work.mdx + - section: Integrations + contents: + - section: Embedding Integrations + path: pages/integrations/integrations.mdx + contents: + - page: Elasticsearch and Cohere + path: pages/integrations/integrations/elasticsearch-and-cohere.mdx + - page: MongoDB and Cohere + path: pages/integrations/integrations/mongodb-and-cohere.mdx + - page: Redis and Cohere + path: pages/integrations/integrations/redis-and-cohere.mdx + - page: Haystack and Cohere + path: pages/integrations/integrations/haystack-and-cohere.mdx + - page: Pinecone and Cohere + path: pages/integrations/integrations/pinecone-and-cohere.mdx + - page: Weaviate and Cohere + path: pages/integrations/integrations/weaviate-and-cohere.mdx + - page: Open Search and Cohere + path: pages/integrations/integrations/opensearch-and-cohere.mdx + - page: Vespa and Cohere + path: pages/integrations/integrations/vespa-and-cohere.mdx + - page: Qdrant and Cohere + path: pages/integrations/integrations/qdrant-and-cohere.mdx + - page: Milvus and Cohere + path: pages/integrations/integrations/milvus-and-cohere.mdx + - page: Zilliz and Cohere + path: pages/integrations/integrations/zilliz-and-cohere.mdx + - page: Chroma and Cohere + path: pages/integrations/integrations/chroma-and-cohere.mdx + - section: LangChain + path: pages/integrations/cohere-and-langchain.mdx + contents: + - page: Chat on LangChain + path: pages/integrations/cohere-and-langchain/chat-on-langchain.mdx + - page: Embed on LangChain + path: pages/integrations/cohere-and-langchain/embed-on-langchain.mdx + - page: Rerank on LangChain + path: pages/integrations/cohere-and-langchain/rerank-on-langchain.mdx + - page: Tools on LangChain + path: pages/integrations/cohere-and-langchain/tools-on-langchain.mdx + - page: LlamaIndex + path: pages/integrations/llamaindex.mdx + - section: Deployment Options + contents: + - page: Cohere SDK Cloud Platform Compatibility + path: pages/deployment-options/cohere-works-everywhere.mdx + - section: Cohere on AWS + path: pages/deployment-options/cohere-on-aws.mdx + contents: + - page: Amazon Bedrock + path: pages/deployment-options/cohere-on-aws/amazon-bedrock.mdx + - page: Amazon SageMaker + path: pages/deployment-options/cohere-on-aws/amazon-sagemaker-setup-guide.mdx + - page: Cohere on Azure + path: pages/deployment-options/cohere-on-microsoft-azure.mdx + - page: Single Container on Private Clouds + path: pages/deployment-options/single-container-on-private-clouds.mdx + - section: Tutorials + contents: + - page: Cookbooks + path: pages/tutorials/cookbooks.mdx + - page: LLM University + path: pages/llm-university/llmu-2.mdx + - section: Responsible Use + contents: + - section: Overview + path: pages/responsible-use/responsible-use.mdx + contents: + - page: Usage Guidelines + path: pages/responsible-use/responsible-use/usage-guidelines.mdx + - page: Model Limitations + path: pages/responsible-use/responsible-use/model-limitations.mdx + - page: Data Statement + path: pages/responsible-use/responsible-use/data-statement.mdx + - page: Generation Benchmarks + path: pages/responsible-use/responsible-use/generation-benchmarks.mdx + - page: Representation Benchmarks + path: pages/responsible-use/responsible-use/representation-benchmarks.mdx + - link: Security + href: https://cohere.ai/security + - page: Environmental Impact + path: pages/responsible-use/environmental-impact.mdx + - section: Cohere for AI + contents: + - page: Cohere For AI Acceptable Use Policy + path: pages/cohere-for-ai/c4ai-acceptable-use-policy.mdx + # HIDDEN SECTION + - page: API Keys + hidden: true + path: pages/cohere-api/create-an-api-key.mdx + - page: Versioning + hidden: true + path: pages/cohere-api/versioning.mdx + - page: Model Commands + hidden: true + path: pages/command-line-interface/admin-commands.mdx + - page: Admin Commands + hidden: true + path: pages/command-line-interface/command-reference.mdx + - page: Finetune + hidden: true + path: pages/command-line-interface/admin-commands/finetune.mdx + - page: Generate Docs + hidden: true + path: pages/command-line-interface/admin-commands/generate-docs.mdx + - page: Model + hidden: true + path: pages/command-line-interface/admin-commands/model.mdx + - page: Admin + hidden: true + path: pages/command-line-interface/command-reference/admin.mdx + - page: Auth + hidden: true + path: pages/command-line-interface/command-reference/auth.mdx + - page: Config + hidden: true + path: pages/command-line-interface/command-reference/config.mdx + - page: Key + hidden: true + path: pages/command-line-interface/command-reference/key.mdx + - page: Ping + hidden: true + path: pages/command-line-interface/command-reference/ping.mdx + - page: Usage + hidden: true + path: pages/command-line-interface/command-reference/usage.mdx + - page: User + hidden: true + path: pages/command-line-interface/command-reference/user.mdx + - page: Deployment Options + hidden: true + path: pages/deployment-options/deployment-options.mdx + - page: Getting Started with Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit.mdx + - page: Amazon Bedrock + hidden: true + path: pages/deployment-options/deployment-options/aws-bedrock.mdx + - page: Amazon SageMaker + hidden: true + path: pages/deployment-options/deployment-options/aws-sagemaker.mdx + - page: Oracle Cloud Infrastructure (OCI) + hidden: true + path: pages/deployment-options/deployment-options/oracle-cloud-infrastructure-oci.mdx + - page: Private Deployment + hidden: true + path: pages/deployment-options/deployment-options/private-deployment.mdx + - page: Deploying Your Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit/deploying-your-coral-toolkit.mdx + - page: LangChain and LlamaIndex in Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit/langchain-and-llamaindex-in-coral-toolkit.mdx + - page: Fine-tuning on AWS + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws.mdx + - page: Fine-tuning Cohere Models on Amazon Bedrock + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-bedrock.mdx + - page: Fine-tuning Cohere Models on Amazon SageMaker + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-sagemaker.mdx + - page: Cohere SDKs + hidden: true + path: pages/get-started/cohere-sdks.mdx + - page: Errors and Warnings + hidden: true + path: pages/going-to-production/errors-and-warnings.mdx + - page: Amazon SageMaker and Cohere + hidden: true + path: pages/integrations/integrations/amazon-sagemaker-and-cohere.mdx + - page: "Appendix 2: Building Apps" + hidden: true + path: pages/llm-university/intro-building-apps.mdx + - page: "Module 7: The Cohere Platform" + hidden: true + path: pages/llm-university/intro-the-cohere-platform.mdx + - page: Sandbox + hidden: true + path: pages/llm-university/sandbox.mdx + - page: App Examples + hidden: true + path: pages/llm-university/intro-building-apps/app-examples.mdx + - page: Deploying with Amazon SageMaker + hidden: true + path: pages/llm-university/intro-deployment/deploying-with-aws-sagemaker.mdx + - page: Semantic Search + hidden: true + path: pages/llm-university/intro-large-language-models/semantic-search-temp.mdx + - page: A Deeper Dive Into Semantic Search + hidden: true + path: pages/llm-university/intro-semantic-search/deeper-semantic-search.mdx + - page: Evaluation Methods for Search + hidden: true + path: pages/llm-university/intro-semantic-search/evaluation-methods-for-search.mdx + - page: Fine-Tuning for Rerank + hidden: true + path: pages/llm-university/intro-semantic-search/fine-tuning-for-rerank.mdx + - page: Multilingual Semantic Search + hidden: true + path: pages/llm-university/intro-semantic-search/multilingual-movie-search.mdx + - page: Multilingual Semantic Search With Cohere and Langchain + hidden: true + path: pages/llm-university/intro-semantic-search/multilingual-semantic-search-with-cohere-and-langchain.mdx + - page: Chaining Prompts + hidden: true + path: pages/llm-university/intro-text-generation/chaining-prompts.mdx + - page: Fine-tuning a Generative Model + hidden: true + path: pages/llm-university/intro-text-generation/creating-custom-models.mdx + - page: Prompt Engineering + hidden: true + path: pages/llm-university/intro-text-generation/model-prompting.mdx + - page: Conclusion + hidden: true + path: pages/llm-university/intro-text-generation/text-generation-conclusion.mdx + - page: Text Summarization + hidden: true + path: pages/llm-university/intro-text-generation/text-summarization.mdx + - page: "Cohere's Command Model" + hidden: true + path: pages/llm-university/intro-text-generation/the-command-model.mdx + - page: The Generate Endpoint + hidden: true + path: pages/llm-university/intro-text-generation/the-generate-endpoint.mdx + - page: Use Case Ideation + hidden: true + path: pages/llm-university/intro-text-generation/use-case-ideation.mdx + - page: What is Generative AI? + hidden: true + path: pages/llm-university/intro-text-generation/what-is-generative-ai.mdx + - page: Classification Models + hidden: true + path: pages/llm-university/intro-text-representation/classification-models.mdx + - page: Classification Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/classification-using-embeddings.mdx + - page: The Classify Endpoint + hidden: true + path: pages/llm-university/intro-text-representation/classify-endpoint.mdx + - page: Clustering Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx + - page: The Embed Endpoint + hidden: true + path: pages/llm-university/intro-text-representation/embed-endpoint.mdx + - page: Visualizing Data + hidden: true + path: pages/llm-university/intro-text-representation/embeddings-visualizing-data.mdx + - page: Classification Evaluation Metrics + hidden: true + path: pages/llm-university/intro-text-representation/evaluation-metrics.mdx + - page: Fine-tuning an Embedding Model for Classification + hidden: true + path: pages/llm-university/intro-text-representation/finetuning.mdx + - page: Semantic Search Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx + - page: Setting up + hidden: true + path: pages/llm-university/intro-text-representation/setting-up.mdx + - page: Applications + hidden: true + path: pages/llm-university/intro-the-cohere-platform/applications.mdx + - page: Conclusion - The Cohere Platform + hidden: true + path: pages/llm-university/intro-the-cohere-platform/conclusion-the-cohere-platform.mdx + - page: Endpoints + hidden: true + path: pages/llm-university/intro-the-cohere-platform/endpoints.mdx + - page: Foundational Models + hidden: true + path: pages/llm-university/intro-the-cohere-platform/foundation-models.mdx + - page: Serving Platform + hidden: true + path: pages/llm-university/intro-the-cohere-platform/serving-platform.mdx + - page: Classification Models + hidden: true + path: pages/llm-university/sandbox/classification-models-remove.mdx + - page: BAK MEOR Creating Custom Generative Models + hidden: true + path: pages/llm-university/sandbox/creating-custom-generative-models-copy.mdx + - page: Structure of the Course (WITH DEPLOYMENT ADDED) + hidden: true + path: pages/llm-university/sandbox/structure-of-the-course-copy-1.mdx + - page: Backup Structure + hidden: true + path: pages/llm-university/sandbox/structure-of-the-course-copy.mdx + - page: "[ALT] The Generate Endpoint" + hidden: true + path: pages/llm-university/sandbox/the-generate-endpoint-copy.mdx + - page: Customer Support + hidden: true + path: pages/quickstart-tutorials/customer-support-link.mdx + - page: Intent Recognition + hidden: true + path: pages/quickstart-tutorials/intent-recognition-link.mdx + - page: Sentiment Analysis + hidden: true + path: pages/quickstart-tutorials/sentiment-analysis-link.mdx + - page: Toxicity Detection + hidden: true + path: pages/quickstart-tutorials/toxicity-detection-link.mdx + - page: Using the Embed API + hidden: true + path: pages/text-embeddings/embed-api.mdx + - page: Multilingual Embed Models + hidden: true + path: pages/text-embeddings/multilingual-language-models.mdx + - page: Introduction to Cohere Embeddings + hidden: true + path: pages/text-embeddings/semantic-search-with-embeddings.mdx + - page: Cross-Lingual Content Moderation + hidden: true + path: pages/text-embeddings/multilingual-language-models/cross-lingual-content-moderation.mdx + - page: Customer Feedback Aggregation + hidden: true + path: pages/text-embeddings/multilingual-language-models/customer-feedback-aggregation.mdx + - page: Multilingual Semantic Search + hidden: true + path: pages/text-embeddings/multilingual-language-models/multilingual-semantic-search.mdx + - page: Supported Languages + hidden: true + path: pages/text-embeddings/multilingual-language-models/supported-languages.mdx + - page: Documents and Citations + hidden: true + path: pages/text-generation/documents-and-citations.mdx + - page: Sending Feedback + hidden: true + path: pages/text-generation/feedback.mdx + - page: Book an appointment + hidden: true + path: pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx + - page: Single-step vs Multi-step + hidden: true + path: pages/text-generation/tools/single-step-vs-multi-step.mdx + - page: Semantic Search + hidden: true + path: pages/-ARCHIVE-/old-tutorials/semantic-search.mdx + - page: Fine-tuning for Generate + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning.mdx + - page: Starting the Generate Fine-tuning + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-starting-the-training.mdx + - page: Understanding the Generate Fine-tuning Results + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-understanding-the-results.mdx + - page: Improving the Generate Fine-tuning results + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-improving-the-results.mdx + - page: Temperature + hidden: true + path: pages/-ARCHIVE-/temperature.mdx + - page: Top-k & Top-p + hidden: true + path: pages/-ARCHIVE-/controlling-generation-with-top-k-top-p.mdx + - tab: api + layout: + - section: Cohere API + contents: + - page: About + path: pages/cohere-api/about.mdx + - page: Teams and Roles + path: pages/cohere-api/teams-and-roles.mdx + - page: Errors + path: pages/cohere-api/errors.mdx + - page: Installation + hidden: true + path: pages/command-line-interface/command.mdx + - api: Cohere API + api-name: v1 + audiences: + - public + skip-slug: true + flattened: true + snippets: + python: "cohere" + typescript: "cohere-ai" + layout: + - section: API Reference + skip-slug: true + contents: + - section: "/chat" + skip-slug: true + contents: + - endpoint: POST /v1/chat + title: Chat Non-streaming + - endpoint: STREAM /v1/chat + title: Chat Streaming + - section: "/embed" + skip-slug: true + contents: + - POST /v1/embed + - embedJobs: + title: "/embed-jobs" + skip-slug: true + contents: + - endpoint: POST /v1/embed-jobs + slug: create-embed-job + - endpoint: GET /v1/embed-jobs + slug: list-embed-jobs + - endpoint: GET /v1/embed-jobs/{id} + slug: get-embed-job + - endpoint: POST /v1/embed-jobs/{id}/cancel + slug: cancel-embed-job + - section: "/rerank" + skip-slug: true + contents: + - POST /v1/rerank + - section: "/classify" + skip-slug: true + contents: + - POST /v1/classify + - datasets: + title: "/datasets" + skip-slug: true + contents: + - endpoint: POST /v1/datasets + slug: create-dataset + - endpoint: GET /v1/datasets + slug: list-datasets + - endpoint: GET /v1/datasets/usage + slug: get-dataset-usage + - endpoint: GET /v1/datasets/{id} + slug: get-dataset + - endpoint: DELETE /v1/datasets/{id} + slug: delete-dataset + - section: "/tokenize" + skip-slug: true + contents: + - POST /v1/tokenize + - section: "/detokenize" + skip-slug: true + contents: + - POST /v1/detokenize + - connectors: + title: "/connectors" + skip-slug: true + contents: + - endpoint: GET /v1/connectors + slug: list-connectors + - endpoint: POST /v1/connectors + slug: create-connector + - endpoint: GET /v1/connectors/{id} + slug: get-connector + - endpoint: PATCH /v1/connectors/{id} + slug: update-connector + - endpoint: DELETE /v1/connectors/{id} + slug: delete-connector + - endpoint: POST /v1/connectors/{id}/oauth/authorize + slug: oauthauthorize-connector + - models: + title: "/models" + skip-slug: true + contents: + - endpoint: GET /v1/models/{model} + slug: get-model + - endpoint: GET /v1/models + slug: list-models + - section: "/check-api-key" + skip-slug: true + contents: + - endpoint: POST /v1/check-api-key + slug: checkapikey + - finetuning: + title: "/finetuning" + skip-slug: true + contents: + - endpoint: GET /v1/finetuning/finetuned-models + slug: listfinetunedmodels + - endpoint: POST /v1/finetuning/finetuned-models + slug: createfinetunedmodel + - endpoint: PATCH /v1/finetuning/finetuned-models/{id} + slug: updatefinetunedmodel + - endpoint: GET /v1/finetuning/finetuned-models/{id} + slug: getfinetunedmodel + - endpoint: DELETE /v1/finetuning/finetuned-models/{id} + slug: deletefinetunedmodel + - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/events + slug: listevents + - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/training-step-metrics + slug: listtrainingstepmetrics + - section: Legacy + skip-slug: true + contents: + - section: "/generate" + skip-slug: true + contents: + - endpoint: POST /v1/generate + title: Chat Non-streaming + - endpoint: STREAM /v1/generate + title: Chat Streaming + - section: "/summarize" + skip-slug: true + contents: + - POST /v1/summarize + - tab: release-notes + - tab: llmu + - tab: cookbooks + layout: + - page: Cookbooks + path: pages/cookbooks.mdx + - page: Agent API Calls + path: pages/cookbooks/agent-api-calls.mdx + - page: Short-Term Memory Handling for Agents + path: pages/cookbooks/agent-short-term-memory.mdx + - page: Agentic Multi-Stage RAG with Cohere Tools API + path: pages/cookbooks/agentic-multi-stage-rag.mdx + - page: Agentic RAG for PDFs with mixed data + path: pages/cookbooks/agentic-rag-mixed-data.mdx + - page: Analysis of Form 10-K/10-Q Using Cohere and RAG + path: pages/cookbooks/analysis-of-financial-forms.mdx + - page: Analyzing Hacker News with Six Language Understanding Methods + path: pages/cookbooks/analyzing-hacker-news.mdx + - page: Article Recommender with Text Embedding Classification Extraction + path: pages/cookbooks/article-recommender-with-text-embeddings.mdx + - page: Multi-Step Tool Use + path: pages/cookbooks/basic-multi-step.mdx + - page: Basic RAG + path: pages/cookbooks/basic-rag.mdx + - page: Basic Semantic Search + path: pages/cookbooks/basic-semantic-search.mdx + - page: Basic Tool Use + path: pages/cookbooks/basic-tool-use.mdx + - page: Calendar Agent with Native Multi Step Tool + path: pages/cookbooks/calendar-agent.mdx + - page: Chunking Strategies + path: pages/cookbooks/chunking-strategies.mdx + - page: Creating a QA Bot From Technical Documentation + path: pages/cookbooks/creating-a-qa-bot.mdx + - page: Financial CSV Agent with Native Multi-Step Cohere API + path: pages/cookbooks/csv-agent-native-api.mdx + - page: Financial CSV Agent with Langchain + path: pages/cookbooks/csv-agent.mdx + - page: A Data Analyst Agent Built with Cohere and Langchain + path: pages/cookbooks/data-analyst-agent.mdx + - page: Advanced Document Parsing For Enterprises + path: pages/cookbooks/document-parsing-for-enterprises.mdx + - page: End-to-end RAG using Elasticsearch and Cohere + path: pages/cookbooks/elasticsearch-and-cohere.mdx + - page: Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution + path: pages/cookbooks/embed-jobs-serverless-pinecone.mdx + - page: Semantic Search with Cohere Embed Jobs + path: pages/cookbooks/embed-jobs.mdx + - page: Fueling Generative Content with Keyword Research + path: pages/cookbooks/fueling-generative-content.mdx + - page: Grounded Summarization Using Command R + path: pages/cookbooks/grounded-summarization.mdx + - page: Hello World! Meet Language AI + path: pages/cookbooks/hello-world-meet-ai.mdx + - page: Long Form General Strategies + path: pages/cookbooks/long-form-general-strategies.mdx + - page: Migrating Monolithic Prompts to Command-R with RAG + path: pages/cookbooks/migrating-prompts.mdx + - page: Multilingual Search with Cohere and Langchain + path: pages/cookbooks/multilingual-search.mdx + - page: PDF Extractor with Native Multi Step Tool Use + path: pages/cookbooks/pdf-extractor.mdx + - page: Pondr, Fostering Connection through Good Conversation + path: pages/cookbooks/pondr.mdx + - page: Deep Dive Into RAG Evaluation + path: pages/cookbooks/rag-evaluation-deep-dive.mdx + - page: RAG With Chat Embed and Rerank via Pinecone + path: pages/cookbooks/rag-with-chat-embed.mdx + - page: Demo of Rerank + path: pages/cookbooks/rerank-demo.mdx + - page: SQL Agent + path: pages/cookbooks/sql-agent.mdx + - page: Summarization Evals + path: pages/cookbooks/summarization-evals.mdx + - page: Text Classification Using Embeddings + path: pages/cookbooks/text-classification-using-embeddings.mdx + - page: Topic Modeling AI Papers + path: pages/cookbooks/topic-modeling-ai-papers.mdx + - page: Wikipedia Semantic Search with Cohere + Weaviate + path: pages/cookbooks/wikipedia-search-with-weaviate.mdx + - page: Wikipedia Semantic Search with Cohere Embedding Archives + path: pages/cookbooks/wikipedia-semantic-search.mdx diff --git a/fern/v2.yml b/fern/v2.yml new file mode 100644 index 00000000..06c09ef2 --- /dev/null +++ b/fern/v2.yml @@ -0,0 +1,767 @@ +tabs: + docs: + display-name: Guides and concepts + slug: docs + api: + display-name: API Reference + slug: reference + release-notes: + display-name: Release Notes + changelog: pages/changelog + slug: changelog + llmu: + display-name: LLMU + href: https://cohere.com/llmu + cookbooks: + display-name: Cookbooks + slug: page + +landing-page: + page: Cohere Documentation + path: pages/index.mdx + +navigation: + - tab: docs + layout: + - section: Get Started + contents: + - page: The Cohere Platform + path: pages/get-started/the-cohere-platform.mdx + - page: Introduction to Large Language Models + path: pages/get-started/introduction-to-large-language-models.mdx + - page: Developer Playground + path: pages/get-started/playground-overview.mdx + - page: Cohere Toolkit + path: pages/get-started/cohere-toolkit.mdx + - page: Datasets + path: pages/get-started/datasets.mdx + - section: Models + contents: + - page: Models Overview + path: pages/models/models.mdx + - section: Command + contents: + - page: Command R+ + path: pages/models/the-command-family-of-models/command-r-plus.mdx + - page: Command R + path: pages/models/the-command-family-of-models/command-r.mdx + - page: Command and Command Light + path: pages/models/the-command-family-of-models/command-beta.mdx + - page: Embed + path: pages/models/cohere-embed.mdx + - page: Rerank + path: pages/models/rerank-2.mdx + - section: Text Generation + contents: + - page: Using the Chat API + path: pages/text-generation/chat-api.mdx + - page: Streaming Responses + path: pages/text-generation/streaming.mdx + - page: Structured Generations (JSON) + path: pages/text-generation/structured-outputs-json.mdx + - page: Predictable Outputs + path: pages/text-generation/predictable-outputs.mdx + - page: Advanced Generation Parameters + path: pages/text-generation/advanced-generation-hyperparameters.mdx + - page: Retrieval Augmented Generation (RAG) + path: pages/text-generation/retrieval-augmented-generation-rag.mdx + - section: RAG Connectors + contents: + - page: Overview of RAG Connectors + path: pages/text-generation/connectors/overview-1.mdx + - page: Creating and Deploying a Connector + path: pages/text-generation/connectors/creating-and-deploying-a-connector.mdx + - page: Managing your Connector + path: pages/text-generation/connectors/managing-your-connector.mdx + - page: Connector Authentication + path: pages/text-generation/connectors/connector-authentication.mdx + - page: Connector FAQs + path: pages/text-generation/connectors/connector-faqs.mdx + - section: Tool Use + path: pages/text-generation/tools.mdx + contents: + - section: Multi-step Tool Use (Agents) + path: pages/text-generation/tools/multi-step-tool-use.mdx + contents: + - page: Implementing a Multi-Step Agent with Langchain + path: pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx + - page: Single-Step Tool Use + path: pages/text-generation/tools/tool-use.mdx + - page: Parameter Types in Tool Use + path: pages/text-generation/tools/parameter-types-in-tool-use.mdx + - page: Tokens and Tokenizers + path: pages/text-generation/tokens-and-tokenizers.mdx + - section: Prompt Engineering + contents: + - page: Crafting Effective Prompts + path: pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx + - page: Advanced Prompt Engineering Techniques + path: pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx + - page: Prompt Truncation + path: pages/text-generation/prompt-engineering/prompt-truncation.mdx + - page: Preambles + path: pages/text-generation/prompt-engineering/preambles.mdx + - section: Prompt Library + contents: + - page: Create CSV data from JSON data + path: pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx + - page: Create a markdown table from raw data + path: pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx + - page: Meeting Summarizer + path: pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx + - page: Remove PII + path: pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx + - page: Add a Docstring to your code + path: pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx + - page: Evaluate your LLM response + path: pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx + - page: Faster Web Search + path: pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx + - page: Multilingual interpreter + path: pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx + - page: Migrating from the Generate API to the Chat API + path: pages/text-generation/migrating-from-cogenerate-to-cochat.mdx + - section: Text Embeddings (Vectors, Search, Retrieval) + contents: + - page: Introduction to Embeddings at Cohere + path: pages/text-embeddings/embeddings.mdx + - page: Batch Embedding Jobs + path: pages/text-embeddings/embed-jobs-api.mdx + - section: Reranking + contents: + - page: Rerank Overview + path: pages/text-embeddings/reranking/overview.mdx + - page: Rerank Best Practices + path: pages/text-embeddings/reranking/reranking-best-practices.mdx + - page: Text Classification + path: pages/text-embeddings/text-classification-1.mdx + - section: Fine-Tuning + contents: + - page: Introduction + path: pages/fine-tuning/fine-tuning.mdx + - page: Fine-tuning with Web-UI + path: pages/fine-tuning/fine-tuning-with-the-cohere-dashboard.mdx + - page: Programmatic Fine-tuning + path: pages/fine-tuning/fine-tuning-with-the-python-sdk.mdx + - section: Fine-tuning for Chat + path: pages/fine-tuning/chat-fine-tuning.mdx + contents: + - page: Preparing the Chat Fine-tuning Data + path: pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx + - page: Starting the Chat Fine-Tuning + path: pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx + - page: Understanding the Chat Fine-tuning Results + path: pages/fine-tuning/chat-fine-tuning/chat-understanding-the-results.mdx + - page: Improving the Chat Fine-tuning Results + path: pages/fine-tuning/chat-fine-tuning/chat-improving-the-results.mdx + - section: Fine-tuning for Classify + path: pages/fine-tuning/classify-fine-tuning.mdx + contents: + - page: Preparing the Classify Fine-tuning data + path: pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx + - page: Trains and deploys a fine-tuned model + path: pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx + - page: Understanding the Classify Fine-tuning Results + path: pages/fine-tuning/classify-fine-tuning/classify-understanding-the-results.mdx + - page: Improving the Classify Fine-tuning Results + path: pages/fine-tuning/classify-fine-tuning/classify-improving-the-results.mdx + - section: Fine-tuning for Rerank + path: pages/fine-tuning/rerank-fine-tuning.mdx + contents: + - page: Preparing the Rerank Fine-tuning Data + path: pages/fine-tuning/rerank-fine-tuning/rerank-preparing-the-data.mdx + - page: Starting the Rerank Fine-Tuning + path: pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx + - page: Understanding the Rerank Fine-tuning Results + path: pages/fine-tuning/rerank-fine-tuning/rerank-understanding-the-results.mdx + - page: Improving the Rerank Fine-tuning Results + path: pages/fine-tuning/rerank-fine-tuning/rerank-improving-the-results.mdx + - page: FAQs / Troubleshooting + path: pages/fine-tuning/troubleshooting-a-fine-tuned-model.mdx + - section: Going to Production + contents: + - page: API Keys and Rate Limits + path: pages/going-to-production/rate-limits.mdx + - page: Going Live + path: pages/going-to-production/going-live.mdx + - page: How Does Cohere Pricing Work? + path: pages/going-to-production/how-does-cohere-pricing-work.mdx + - section: Integrations + contents: + - section: Embedding Integrations + path: pages/integrations/integrations.mdx + contents: + - page: Elasticsearch and Cohere + path: pages/integrations/integrations/elasticsearch-and-cohere.mdx + - page: MongoDB and Cohere + path: pages/integrations/integrations/mongodb-and-cohere.mdx + - page: Redis and Cohere + path: pages/integrations/integrations/redis-and-cohere.mdx + - page: Haystack and Cohere + path: pages/integrations/integrations/haystack-and-cohere.mdx + - page: Pinecone and Cohere + path: pages/integrations/integrations/pinecone-and-cohere.mdx + - page: Weaviate and Cohere + path: pages/integrations/integrations/weaviate-and-cohere.mdx + - page: Open Search and Cohere + path: pages/integrations/integrations/opensearch-and-cohere.mdx + - page: Vespa and Cohere + path: pages/integrations/integrations/vespa-and-cohere.mdx + - page: Qdrant and Cohere + path: pages/integrations/integrations/qdrant-and-cohere.mdx + - page: Milvus and Cohere + path: pages/integrations/integrations/milvus-and-cohere.mdx + - page: Zilliz and Cohere + path: pages/integrations/integrations/zilliz-and-cohere.mdx + - page: Chroma and Cohere + path: pages/integrations/integrations/chroma-and-cohere.mdx + - section: LangChain + path: pages/integrations/cohere-and-langchain.mdx + contents: + - page: Chat on LangChain + path: pages/integrations/cohere-and-langchain/chat-on-langchain.mdx + - page: Embed on LangChain + path: pages/integrations/cohere-and-langchain/embed-on-langchain.mdx + - page: Rerank on LangChain + path: pages/integrations/cohere-and-langchain/rerank-on-langchain.mdx + - page: Tools on LangChain + path: pages/integrations/cohere-and-langchain/tools-on-langchain.mdx + - page: LlamaIndex + path: pages/integrations/llamaindex.mdx + - section: Deployment Options + contents: + - page: Cohere SDK Cloud Platform Compatibility + path: pages/deployment-options/cohere-works-everywhere.mdx + - section: Cohere on AWS + path: pages/deployment-options/cohere-on-aws.mdx + contents: + - page: Amazon Bedrock + path: pages/deployment-options/cohere-on-aws/amazon-bedrock.mdx + - page: Amazon SageMaker + path: pages/deployment-options/cohere-on-aws/amazon-sagemaker-setup-guide.mdx + - page: Cohere on Azure + path: pages/deployment-options/cohere-on-microsoft-azure.mdx + - page: Single Container on Private Clouds + path: pages/deployment-options/single-container-on-private-clouds.mdx + - section: Tutorials + contents: + - page: Cookbooks + path: pages/tutorials/cookbooks.mdx + - page: LLM University + path: pages/llm-university/llmu-2.mdx + - section: Responsible Use + contents: + - section: Overview + path: pages/responsible-use/responsible-use.mdx + contents: + - page: Usage Guidelines + path: pages/responsible-use/responsible-use/usage-guidelines.mdx + - page: Model Limitations + path: pages/responsible-use/responsible-use/model-limitations.mdx + - page: Data Statement + path: pages/responsible-use/responsible-use/data-statement.mdx + - page: Generation Benchmarks + path: pages/responsible-use/responsible-use/generation-benchmarks.mdx + - page: Representation Benchmarks + path: pages/responsible-use/responsible-use/representation-benchmarks.mdx + - link: Security + href: https://cohere.ai/security + - page: Environmental Impact + path: pages/responsible-use/environmental-impact.mdx + - section: Cohere for AI + contents: + - page: Cohere For AI Acceptable Use Policy + path: pages/cohere-for-ai/c4ai-acceptable-use-policy.mdx + # HIDDEN SECTION + - page: API Keys + hidden: true + path: pages/cohere-api/create-an-api-key.mdx + - page: Versioning + hidden: true + path: pages/cohere-api/versioning.mdx + - page: Model Commands + hidden: true + path: pages/command-line-interface/admin-commands.mdx + - page: Admin Commands + hidden: true + path: pages/command-line-interface/command-reference.mdx + - page: Finetune + hidden: true + path: pages/command-line-interface/admin-commands/finetune.mdx + - page: Generate Docs + hidden: true + path: pages/command-line-interface/admin-commands/generate-docs.mdx + - page: Model + hidden: true + path: pages/command-line-interface/admin-commands/model.mdx + - page: Admin + hidden: true + path: pages/command-line-interface/command-reference/admin.mdx + - page: Auth + hidden: true + path: pages/command-line-interface/command-reference/auth.mdx + - page: Config + hidden: true + path: pages/command-line-interface/command-reference/config.mdx + - page: Key + hidden: true + path: pages/command-line-interface/command-reference/key.mdx + - page: Ping + hidden: true + path: pages/command-line-interface/command-reference/ping.mdx + - page: Usage + hidden: true + path: pages/command-line-interface/command-reference/usage.mdx + - page: User + hidden: true + path: pages/command-line-interface/command-reference/user.mdx + - page: Deployment Options + hidden: true + path: pages/deployment-options/deployment-options.mdx + - page: Getting Started with Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit.mdx + - page: Amazon Bedrock + hidden: true + path: pages/deployment-options/deployment-options/aws-bedrock.mdx + - page: Amazon SageMaker + hidden: true + path: pages/deployment-options/deployment-options/aws-sagemaker.mdx + - page: Oracle Cloud Infrastructure (OCI) + hidden: true + path: pages/deployment-options/deployment-options/oracle-cloud-infrastructure-oci.mdx + - page: Private Deployment + hidden: true + path: pages/deployment-options/deployment-options/private-deployment.mdx + - page: Deploying Your Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit/deploying-your-coral-toolkit.mdx + - page: LangChain and LlamaIndex in Coral Toolkit + hidden: true + path: pages/deployment-options/getting-started-with-coral-toolkit/langchain-and-llamaindex-in-coral-toolkit.mdx + - page: Fine-tuning on AWS + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws.mdx + - page: Fine-tuning Cohere Models on Amazon Bedrock + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-bedrock.mdx + - page: Fine-tuning Cohere Models on Amazon SageMaker + hidden: true + path: pages/fine-tuning/fine-tuning-on-aws/fine-tuning-cohere-models-on-amazon-sagemaker.mdx + - page: Cohere SDKs + hidden: true + path: pages/get-started/cohere-sdks.mdx + - page: Errors and Warnings + hidden: true + path: pages/going-to-production/errors-and-warnings.mdx + - page: Amazon SageMaker and Cohere + hidden: true + path: pages/integrations/integrations/amazon-sagemaker-and-cohere.mdx + - page: "Appendix 2: Building Apps" + hidden: true + path: pages/llm-university/intro-building-apps.mdx + - page: "Module 7: The Cohere Platform" + hidden: true + path: pages/llm-university/intro-the-cohere-platform.mdx + - page: Sandbox + hidden: true + path: pages/llm-university/sandbox.mdx + - page: App Examples + hidden: true + path: pages/llm-university/intro-building-apps/app-examples.mdx + - page: Deploying with Amazon SageMaker + hidden: true + path: pages/llm-university/intro-deployment/deploying-with-aws-sagemaker.mdx + - page: Semantic Search + hidden: true + path: pages/llm-university/intro-large-language-models/semantic-search-temp.mdx + - page: A Deeper Dive Into Semantic Search + hidden: true + path: pages/llm-university/intro-semantic-search/deeper-semantic-search.mdx + - page: Evaluation Methods for Search + hidden: true + path: pages/llm-university/intro-semantic-search/evaluation-methods-for-search.mdx + - page: Fine-Tuning for Rerank + hidden: true + path: pages/llm-university/intro-semantic-search/fine-tuning-for-rerank.mdx + - page: Multilingual Semantic Search + hidden: true + path: pages/llm-university/intro-semantic-search/multilingual-movie-search.mdx + - page: Multilingual Semantic Search With Cohere and Langchain + hidden: true + path: pages/llm-university/intro-semantic-search/multilingual-semantic-search-with-cohere-and-langchain.mdx + - page: Chaining Prompts + hidden: true + path: pages/llm-university/intro-text-generation/chaining-prompts.mdx + - page: Fine-tuning a Generative Model + hidden: true + path: pages/llm-university/intro-text-generation/creating-custom-models.mdx + - page: Prompt Engineering + hidden: true + path: pages/llm-university/intro-text-generation/model-prompting.mdx + - page: Conclusion + hidden: true + path: pages/llm-university/intro-text-generation/text-generation-conclusion.mdx + - page: Text Summarization + hidden: true + path: pages/llm-university/intro-text-generation/text-summarization.mdx + - page: "Cohere's Command Model" + hidden: true + path: pages/llm-university/intro-text-generation/the-command-model.mdx + - page: The Generate Endpoint + hidden: true + path: pages/llm-university/intro-text-generation/the-generate-endpoint.mdx + - page: Use Case Ideation + hidden: true + path: pages/llm-university/intro-text-generation/use-case-ideation.mdx + - page: What is Generative AI? + hidden: true + path: pages/llm-university/intro-text-generation/what-is-generative-ai.mdx + - page: Classification Models + hidden: true + path: pages/llm-university/intro-text-representation/classification-models.mdx + - page: Classification Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/classification-using-embeddings.mdx + - page: The Classify Endpoint + hidden: true + path: pages/llm-university/intro-text-representation/classify-endpoint.mdx + - page: Clustering Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/clustering-using-embeddings.mdx + - page: The Embed Endpoint + hidden: true + path: pages/llm-university/intro-text-representation/embed-endpoint.mdx + - page: Visualizing Data + hidden: true + path: pages/llm-university/intro-text-representation/embeddings-visualizing-data.mdx + - page: Classification Evaluation Metrics + hidden: true + path: pages/llm-university/intro-text-representation/evaluation-metrics.mdx + - page: Fine-tuning an Embedding Model for Classification + hidden: true + path: pages/llm-university/intro-text-representation/finetuning.mdx + - page: Semantic Search Using Embeddings + hidden: true + path: pages/llm-university/intro-text-representation/semantic-search-using-embeddings.mdx + - page: Setting up + hidden: true + path: pages/llm-university/intro-text-representation/setting-up.mdx + - page: Applications + hidden: true + path: pages/llm-university/intro-the-cohere-platform/applications.mdx + - page: Conclusion - The Cohere Platform + hidden: true + path: pages/llm-university/intro-the-cohere-platform/conclusion-the-cohere-platform.mdx + - page: Endpoints + hidden: true + path: pages/llm-university/intro-the-cohere-platform/endpoints.mdx + - page: Foundational Models + hidden: true + path: pages/llm-university/intro-the-cohere-platform/foundation-models.mdx + - page: Serving Platform + hidden: true + path: pages/llm-university/intro-the-cohere-platform/serving-platform.mdx + - page: Classification Models + hidden: true + path: pages/llm-university/sandbox/classification-models-remove.mdx + - page: BAK MEOR Creating Custom Generative Models + hidden: true + path: pages/llm-university/sandbox/creating-custom-generative-models-copy.mdx + - page: Structure of the Course (WITH DEPLOYMENT ADDED) + hidden: true + path: pages/llm-university/sandbox/structure-of-the-course-copy-1.mdx + - page: Backup Structure + hidden: true + path: pages/llm-university/sandbox/structure-of-the-course-copy.mdx + - page: "[ALT] The Generate Endpoint" + hidden: true + path: pages/llm-university/sandbox/the-generate-endpoint-copy.mdx + - page: Customer Support + hidden: true + path: pages/quickstart-tutorials/customer-support-link.mdx + - page: Intent Recognition + hidden: true + path: pages/quickstart-tutorials/intent-recognition-link.mdx + - page: Sentiment Analysis + hidden: true + path: pages/quickstart-tutorials/sentiment-analysis-link.mdx + - page: Toxicity Detection + hidden: true + path: pages/quickstart-tutorials/toxicity-detection-link.mdx + - page: Using the Embed API + hidden: true + path: pages/text-embeddings/embed-api.mdx + - page: Multilingual Embed Models + hidden: true + path: pages/text-embeddings/multilingual-language-models.mdx + - page: Introduction to Cohere Embeddings + hidden: true + path: pages/text-embeddings/semantic-search-with-embeddings.mdx + - page: Cross-Lingual Content Moderation + hidden: true + path: pages/text-embeddings/multilingual-language-models/cross-lingual-content-moderation.mdx + - page: Customer Feedback Aggregation + hidden: true + path: pages/text-embeddings/multilingual-language-models/customer-feedback-aggregation.mdx + - page: Multilingual Semantic Search + hidden: true + path: pages/text-embeddings/multilingual-language-models/multilingual-semantic-search.mdx + - page: Supported Languages + hidden: true + path: pages/text-embeddings/multilingual-language-models/supported-languages.mdx + - page: Documents and Citations + hidden: true + path: pages/text-generation/documents-and-citations.mdx + - page: Sending Feedback + hidden: true + path: pages/text-generation/feedback.mdx + - page: Book an appointment + hidden: true + path: pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx + - page: Single-step vs Multi-step + hidden: true + path: pages/text-generation/tools/single-step-vs-multi-step.mdx + - page: Semantic Search + hidden: true + path: pages/-ARCHIVE-/old-tutorials/semantic-search.mdx + - page: Fine-tuning for Generate + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning.mdx + - page: Starting the Generate Fine-tuning + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-starting-the-training.mdx + - page: Understanding the Generate Fine-tuning Results + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-understanding-the-results.mdx + - page: Improving the Generate Fine-tuning results + hidden: true + path: pages/-ARCHIVE-/generate-fine-tuning/generate-improving-the-results.mdx + - page: Temperature + hidden: true + path: pages/-ARCHIVE-/temperature.mdx + - page: Top-k & Top-p + hidden: true + path: pages/-ARCHIVE-/controlling-generation-with-top-k-top-p.mdx + - tab: api + layout: + - section: Cohere API + contents: + - page: About + path: pages/cohere-api/about.mdx + - page: Teams and Roles + path: pages/cohere-api/teams-and-roles.mdx + - page: Errors + path: pages/cohere-api/errors.mdx + - page: Installation + hidden: true + path: pages/command-line-interface/command.mdx + - api: Cohere API + api-name: v2 + audiences: + - public + skip-slug: true + flattened: true + snippets: + python: "cohere" + typescript: "cohere-ai" + layout: + - section: API Reference + skip-slug: true + contents: + - section: "/chat" + skip-slug: true + contents: + - endpoint: POST /v1/chat + title: Chat Non-streaming + - endpoint: STREAM /v1/chat + title: Chat Streaming + - section: "/embed" + skip-slug: true + contents: + - POST /v1/embed + - embedJobs: + title: "/embed-jobs" + skip-slug: true + contents: + - endpoint: POST /v1/embed-jobs + slug: create-embed-job + - endpoint: GET /v1/embed-jobs + slug: list-embed-jobs + - endpoint: GET /v1/embed-jobs/{id} + slug: get-embed-job + - endpoint: POST /v1/embed-jobs/{id}/cancel + slug: cancel-embed-job + - section: "/rerank" + skip-slug: true + contents: + - POST /v1/rerank + - section: "/classify" + skip-slug: true + contents: + - POST /v1/classify + - datasets: + title: "/datasets" + skip-slug: true + contents: + - endpoint: POST /v1/datasets + slug: create-dataset + - endpoint: GET /v1/datasets + slug: list-datasets + - endpoint: GET /v1/datasets/usage + slug: get-dataset-usage + - endpoint: GET /v1/datasets/{id} + slug: get-dataset + - endpoint: DELETE /v1/datasets/{id} + slug: delete-dataset + - section: "/tokenize" + skip-slug: true + contents: + - POST /v1/tokenize + - section: "/detokenize" + skip-slug: true + contents: + - POST /v1/detokenize + - connectors: + title: "/connectors" + skip-slug: true + contents: + - endpoint: GET /v1/connectors + slug: list-connectors + - endpoint: POST /v1/connectors + slug: create-connector + - endpoint: GET /v1/connectors/{id} + slug: get-connector + - endpoint: PATCH /v1/connectors/{id} + slug: update-connector + - endpoint: DELETE /v1/connectors/{id} + slug: delete-connector + - endpoint: POST /v1/connectors/{id}/oauth/authorize + slug: oauthauthorize-connector + - models: + title: "/models" + skip-slug: true + contents: + - endpoint: GET /v1/models/{model} + slug: get-model + - endpoint: GET /v1/models + slug: list-models + - section: "/check-api-key" + skip-slug: true + contents: + - endpoint: POST /v1/check-api-key + slug: checkapikey + - finetuning: + title: "/finetuning" + skip-slug: true + contents: + - endpoint: GET /v1/finetuning/finetuned-models + slug: listfinetunedmodels + - endpoint: POST /v1/finetuning/finetuned-models + slug: createfinetunedmodel + - endpoint: PATCH /v1/finetuning/finetuned-models/{id} + slug: updatefinetunedmodel + - endpoint: GET /v1/finetuning/finetuned-models/{id} + slug: getfinetunedmodel + - endpoint: DELETE /v1/finetuning/finetuned-models/{id} + slug: deletefinetunedmodel + - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/events + slug: listevents + - endpoint: GET /v1/finetuning/finetuned-models/{finetuned_model_id}/training-step-metrics + slug: listtrainingstepmetrics + - section: Legacy + skip-slug: true + contents: + - section: "/generate" + skip-slug: true + contents: + - endpoint: POST /v1/generate + title: Chat Non-streaming + - endpoint: STREAM /v1/generate + title: Chat Streaming + - section: "/summarize" + skip-slug: true + contents: + - POST /v1/summarize + - tab: release-notes + - tab: llmu + - tab: cookbooks + layout: + - page: Cookbooks + path: pages/cookbooks.mdx + - page: Agent API Calls + path: pages/cookbooks/agent-api-calls.mdx + - page: Short-Term Memory Handling for Agents + path: pages/cookbooks/agent-short-term-memory.mdx + - page: Agentic Multi-Stage RAG with Cohere Tools API + path: pages/cookbooks/agentic-multi-stage-rag.mdx + - page: Agentic RAG for PDFs with mixed data + path: pages/cookbooks/agentic-rag-mixed-data.mdx + - page: Analysis of Form 10-K/10-Q Using Cohere and RAG + path: pages/cookbooks/analysis-of-financial-forms.mdx + - page: Analyzing Hacker News with Six Language Understanding Methods + path: pages/cookbooks/analyzing-hacker-news.mdx + - page: Article Recommender with Text Embedding Classification Extraction + path: pages/cookbooks/article-recommender-with-text-embeddings.mdx + - page: Multi-Step Tool Use + path: pages/cookbooks/basic-multi-step.mdx + - page: Basic RAG + path: pages/cookbooks/basic-rag.mdx + - page: Basic Semantic Search + path: pages/cookbooks/basic-semantic-search.mdx + - page: Basic Tool Use + path: pages/cookbooks/basic-tool-use.mdx + - page: Calendar Agent with Native Multi Step Tool + path: pages/cookbooks/calendar-agent.mdx + - page: Chunking Strategies + path: pages/cookbooks/chunking-strategies.mdx + - page: Creating a QA Bot From Technical Documentation + path: pages/cookbooks/creating-a-qa-bot.mdx + - page: Financial CSV Agent with Native Multi-Step Cohere API + path: pages/cookbooks/csv-agent-native-api.mdx + - page: Financial CSV Agent with Langchain + path: pages/cookbooks/csv-agent.mdx + - page: A Data Analyst Agent Built with Cohere and Langchain + path: pages/cookbooks/data-analyst-agent.mdx + - page: Advanced Document Parsing For Enterprises + path: pages/cookbooks/document-parsing-for-enterprises.mdx + - page: End-to-end RAG using Elasticsearch and Cohere + path: pages/cookbooks/elasticsearch-and-cohere.mdx + - page: Semantic Search with Cohere Embed Jobs and Pinecone serverless Solution + path: pages/cookbooks/embed-jobs-serverless-pinecone.mdx + - page: Semantic Search with Cohere Embed Jobs + path: pages/cookbooks/embed-jobs.mdx + - page: Fueling Generative Content with Keyword Research + path: pages/cookbooks/fueling-generative-content.mdx + - page: Grounded Summarization Using Command R + path: pages/cookbooks/grounded-summarization.mdx + - page: Hello World! Meet Language AI + path: pages/cookbooks/hello-world-meet-ai.mdx + - page: Long Form General Strategies + path: pages/cookbooks/long-form-general-strategies.mdx + - page: Migrating Monolithic Prompts to Command-R with RAG + path: pages/cookbooks/migrating-prompts.mdx + - page: Multilingual Search with Cohere and Langchain + path: pages/cookbooks/multilingual-search.mdx + - page: PDF Extractor with Native Multi Step Tool Use + path: pages/cookbooks/pdf-extractor.mdx + - page: Pondr, Fostering Connection through Good Conversation + path: pages/cookbooks/pondr.mdx + - page: Deep Dive Into RAG Evaluation + path: pages/cookbooks/rag-evaluation-deep-dive.mdx + - page: RAG With Chat Embed and Rerank via Pinecone + path: pages/cookbooks/rag-with-chat-embed.mdx + - page: Demo of Rerank + path: pages/cookbooks/rerank-demo.mdx + - page: SQL Agent + path: pages/cookbooks/sql-agent.mdx + - page: Summarization Evals + path: pages/cookbooks/summarization-evals.mdx + - page: Text Classification Using 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zPYTi%ElI;NGP}^}dC`{Ep)G9@=cnf*!POHxSPLY#9CRLbB)X1j;Ti11z5)YR~(c$UIxm2=uef;o{4=MW^rbHvP!#h>t*kQ2~|( z!j>4w8 Date: Wed, 7 Aug 2024 20:49:08 -0400 Subject: [PATCH 3/9] fix: input.css font weights --- fern/assets/input.css | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/fern/assets/input.css b/fern/assets/input.css index 26d151da..41b06ca8 100644 --- a/fern/assets/input.css +++ b/fern/assets/input.css @@ -6,7 +6,8 @@ h3, h4, h5, h6 { - font-weight: 500 !important; + font-weight: 425 !important; + font-family: "CohereText"; } h1 { @@ -14,6 +15,14 @@ h1 { font-variation-settings: "cuts" 300, "move" 300; } +.fern-sidebar-heading-content { + font-weight: 500 !important; +} + +.fern-header-tab-button .font-medium { + font-weight: 400 !important; +} + @layer utilities { .image-desktop { @apply hidden md:block rounded-t-lg; From 63c514c96196c0b6057758e3e171fc30cdc1453f Mon Sep 17 00:00:00 2001 From: Andrew Jiang Date: Thu, 8 Aug 2024 14:39:54 -0400 Subject: [PATCH 4/9] add edit-this-page --- fern/docs.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/fern/docs.yml b/fern/docs.yml index f2f6a8f1..e7360bc3 100644 --- a/fern/docs.yml +++ b/fern/docs.yml @@ -1,5 +1,10 @@ instances: - url: cohere.docs.buildwithfern.com + edit-this-page: + github: + owner: cohere-ai + repo: cohere-developer-experience + branch: main title: Cohere versions: From 0cf3abe7195a98afc94fb11cbfa13ac8e68882d9 Mon Sep 17 00:00:00 2001 From: fern <126544928+fern-bot@users.noreply.github.com> Date: Sun, 11 Aug 2024 13:11:18 -0400 Subject: [PATCH 5/9] content updates (#16) --- fern/docs.yml | 2 + fern/fern.config.json | 2 +- fern/pages/-ARCHIVE-/getting-started.mdx | 2 +- fern/pages/cohere-api/about.mdx | 8 +- .../cohere-works-everywhere.mdx | 399 ++++++++---------- .../chat-preparing-the-data.mdx | 40 +- .../chat-starting-the-training.mdx | 44 +- .../classify-preparing-the-data.mdx | 12 +- .../classify-starting-the-training.mdx | 2 +- .../rerank-starting-the-training.mdx | 4 +- fern/pages/text-embeddings/embed-jobs-api.mdx | 6 +- .../advanced-generation-hyperparameters.mdx | 12 + .../connectors/connector-authentication.mdx | 12 +- .../creating-and-deploying-a-connector.mdx | 2 +- .../connectors/managing-your-connector.mdx | 6 +- .../text-generation/connectors/overview-1.mdx | 2 +- .../migrating-from-cogenerate-to-cochat.mdx | 2 +- .../prompt-engineering/preambles.mdx | 8 +- .../add-a-docstring-to-your-code.mdx | 2 +- .../prompt-library/book-an-appointment.mdx | 2 +- .../create-a-markdown-table-from-raw-data.mdx | 2 +- .../create-csv-data-from-json-data.mdx | 2 +- .../evaluate-your-llm-response.mdx | 2 +- .../prompt-library/faster-web-search.mdx | 2 +- .../prompt-library/meeting-summarizer.mdx | 2 +- .../multilingual-interpreter.mdx | 2 +- .../prompt-library/remove-pii.mdx | 2 +- ...ting-a-multi-step-agent-with-langchain.mdx | 2 +- .../tools/parameter-types-in-tool-use.mdx | 2 +- 29 files changed, 296 insertions(+), 291 deletions(-) diff --git a/fern/docs.yml b/fern/docs.yml index e7360bc3..31cc7263 100644 --- a/fern/docs.yml +++ b/fern/docs.yml @@ -396,3 +396,5 @@ redirects: destination: /docs/fine-tuning - source: /docs/embed-2 destination: /docs/cohere-embed + - source: /docs/fine-tuning-with-the-web-ui + destination: /docs/fine-tuning-with-the-cohere-dashboard diff --git a/fern/fern.config.json b/fern/fern.config.json index baa6fed2..c85a4002 100644 --- a/fern/fern.config.json +++ b/fern/fern.config.json @@ -1,4 +1,4 @@ { "organization": "cohere", - "version": "0.37.13" + "version": "0.37.15" } \ No newline at end of file diff --git a/fern/pages/-ARCHIVE-/getting-started.mdx b/fern/pages/-ARCHIVE-/getting-started.mdx index 0a48ffa0..19b94a2d 100644 --- a/fern/pages/-ARCHIVE-/getting-started.mdx +++ b/fern/pages/-ARCHIVE-/getting-started.mdx @@ -10,7 +10,7 @@ name is <> email is <> -apiKey is <> +apiKey is Your API key key is <> diff --git a/fern/pages/cohere-api/about.mdx b/fern/pages/cohere-api/about.mdx index 6d4b04dd..f3c744a5 100644 --- a/fern/pages/cohere-api/about.mdx +++ b/fern/pages/cohere-api/about.mdx @@ -30,7 +30,7 @@ python -m pip install cohere --upgrade ```python import cohere -co = cohere.Client("<>") +co = cohere.Client("Your API key") response = co.chat( message="hello world!" @@ -51,7 +51,7 @@ npm i -s cohere-ai const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { @@ -83,7 +83,7 @@ import java.util.List; public class ChatPost { public static void main(String[] args) { - Cohere cohere = Cohere.builder().token("<>").build(); + Cohere cohere = Cohere.builder().token("Your API key").build(); NonStreamedChatResponse response = cohere.chat( ChatRequest.builder() @@ -115,7 +115,7 @@ import ( ) func main() { - co := client.NewClient(client.WithToken("<>")) + co := client.NewClient(client.WithToken("Your API key")) resp, err := co.Chat( context.TODO(), diff --git a/fern/pages/deployment-options/cohere-works-everywhere.mdx b/fern/pages/deployment-options/cohere-works-everywhere.mdx index 1fb04981..cf400128 100644 --- a/fern/pages/deployment-options/cohere-works-everywhere.mdx +++ b/fern/pages/deployment-options/cohere-works-everywhere.mdx @@ -16,10 +16,10 @@ The table below summarizes the environments in which Cohere models can be deploy | sdk | [Cohere platform](/reference/about) | [Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere.html) | Sagemaker | Azure | OCI | Cohere Toolkit | | ------------------------------------------------------------ | ---------------------------------------------------------- | -------------------------------------------------------------------------------------------- | ------------------------------- | --------------------------- | -------------------------- | ------------------------------ | -| [Typescript](https://github.com/cohere-ai/cohere-typescript) | [✅ docs](#typescript/platform) | [✅ docs](#typescript/bedrock) | [✅ docs](#typescript/sagemaker) | [✅ docs](#typescript/azure) | [🟠 soon](#typescript/oci) | [🟠 soon](#typescript/toolkit) | -| [Python](https://github.com/cohere-ai/cohere-python) | [✅ docs](#python/platform) | [✅ docs](#python/bedrock) | [✅ docs](#python/sagemaker) | [✅ docs](#python/azure) | [🟠 soon](#python/oci) | [🟠 soon](#python/toolkit) | -| [Go](https://github.com/cohere-ai/cohere-go) | [✅ docs](#go/platform) | [🟠 soon](#go/bedrock) | [🟠 soon](#go/sagemaker) | [✅ docs](#go/azure) | [🟠 soon](#go/oci) | [🟠 soon](#go/toolkit) | -| [Java](https://github.com/cohere-ai/cohere-java) | [✅ docs](#java/platform) | [🟠 soon](#java/bedrock) | [🟠 soon](#java/sagemaker) | [✅ docs](#java/azure) | [🟠 soon](#java/oci) | [🟠 soon](#java/toolkit) | +| [Typescript](https://github.com/cohere-ai/cohere-typescript) | [✅ docs](#cohere-platform) | [✅ docs](#bedrock) | [✅ docs](#sagemaker) | [✅ docs](#azure) | [🟠 soon]() | [🟠 soon]() | +| [Python](https://github.com/cohere-ai/cohere-python) | [✅ docs](#cohere-platform) | [✅ docs](#bedrock) | [✅ docs](#sagemaker) | [✅ docs](#azure) | [🟠 soon]() | [🟠 soon]() | +| [Go](https://github.com/cohere-ai/cohere-go) | [✅ docs](#cohere-platform) | [🟠 soon](#bedrock) | [🟠 soon](#sagemaker) | [✅ docs](#azure) | [🟠 soon](#) | [🟠 soon]() | +| [Java](https://github.com/cohere-ai/cohere-java) | [✅ docs](#cohere-platform) | [🟠 soon](#bedrock) | [🟠 soon](#sagemaker) | [✅ docs](#azure) | [🟠 soon]() | [🟠 soon]() | ## Feature support @@ -41,10 +41,9 @@ The most complete set of features is found on the cohere platform, while each of ## Snippets -### Typescript - #### Cohere Platform + ```typescript TS const { CohereClient } = require('cohere-ai'); @@ -69,95 +68,6 @@ const cohere = new CohereClient({ console.log(response); })(); ``` - -#### Bedrock - -```typescript TS -const { BedrockClient } = require('cohere-ai'); - -const cohere = new BedrockClient({ - awsRegion: "us-east-1", - awsAccessKey: "...", - awsSecretKey: "...", - awsSessionToken: "...", -}); - -(async () => { - const response = await cohere.chat({ - model: "cohere.command-r-plus-v1:0", - chatHistory: [ - { role: 'USER', message: 'Who discovered gravity?' }, - { - role: 'CHATBOT', - message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', - }, - ], - message: 'What year was he born?', - }); - - console.log(response); -})(); -``` - -#### Sagemaker - -```typescript TS -const { SagemakerClient } = require('cohere-ai'); - -const cohere = new SagemakerClient({ - awsRegion: "us-east-1", - awsAccessKey: "...", - awsSecretKey: "...", - awsSessionToken: "...", -}); - -(async () => { - const response = await cohere.chat({ - model: "my-endpoint-name", - chatHistory: [ - { role: 'USER', message: 'Who discovered gravity?' }, - { - role: 'CHATBOT', - message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', - }, - ], - message: 'What year was he born?', - }); - - console.log(response); -})(); -``` - -#### Azure - -```typescript TS -const { CohereClient } = require('cohere-ai'); - -const cohere = new CohereClient({ - token: "", - environment: "https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1", -}); - -(async () => { - const response = await cohere.chat({ - chatHistory: [ - { role: 'USER', message: 'Who discovered gravity?' }, - { - role: 'CHATBOT', - message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', - }, - ], - message: 'What year was he born?', - }); - - console.log(response); -})(); -``` - -### Python - -#### Cohere Platform - ```python PYTHON import cohere @@ -178,89 +88,6 @@ response = co.chat( print(response) ``` - -#### Bedrock - -```python PYTHON -import cohere - -co = cohere.BedrockClient( - aws_region="us-east-1", - aws_access_key="...", - aws_secret_key="...", - aws_session_token="...", -) - -response = co.chat( - model="cohere.command-r-plus-v1:0", - chat_history=[ - {"role": "USER", "message": "Who discovered gravity?"}, - { - "role": "CHATBOT", - "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", - }, - ], - message="What year was he born?", -) - -print(response) -``` - -#### Sagemaker - -```python PYTHON -import cohere - -co = cohere.SagemakerClient( - aws_region="us-east-1", - aws_access_key="...", - aws_secret_key="...", - aws_session_token="...", -) - -response = co.chat( - model="my-endpoint-name", - chat_history=[ - {"role": "USER", "message": "Who discovered gravity?"}, - { - "role": "CHATBOT", - "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", - }, - ], - message="What year was he born?", -) - -print(response) -``` - -#### Azure - -```python PYTHON -import cohere - -co = cohere.Client( - api_key="", - base_url="https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1", -) - -response = co.chat( - chat_history=[ - {"role": "USER", "message": "Who discovered gravity?"}, - { - "role": "CHATBOT", - "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", - }, - ], - message="What year was he born?", -) - -print(response) -``` - -### Go - -#### Cohere Platform - ```go GO package main @@ -301,9 +128,86 @@ func main() { log.Printf("%+v", resp) } ``` +```java JAVA +import com.cohere.api.Cohere; +import com.cohere.api.requests.ChatRequest; +import com.cohere.api.types.ChatMessage; +import com.cohere.api.types.Message; +import com.cohere.api.types.NonStreamedChatResponse; + +import java.util.List; + + +public class ChatPost { + public static void main(String[] args) { + Cohere cohere = Cohere.builder().token("Your API key").clientName("snippet").build(); + + NonStreamedChatResponse response = cohere.chat( + ChatRequest.builder() + .message("What year was he born?") + .chatHistory( + List.of(Message.user(ChatMessage.builder().message("Who discovered gravity?").build()), + Message.chatbot(ChatMessage.builder().message("The man who is widely credited with discovering gravity is Sir Isaac Newton").build()))).build()); + + System.out.println(response); + } +} +``` + #### Bedrock + +```typescript TS +const { BedrockClient } = require('cohere-ai'); + +const cohere = new BedrockClient({ + awsRegion: "us-east-1", + awsAccessKey: "...", + awsSecretKey: "...", + awsSessionToken: "...", +}); + +(async () => { + const response = await cohere.chat({ + model: "cohere.command-r-plus-v1:0", + chatHistory: [ + { role: 'USER', message: 'Who discovered gravity?' }, + { + role: 'CHATBOT', + message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', + }, + ], + message: 'What year was he born?', + }); + + console.log(response); +})(); +``` +```python PYTHON +import cohere + +co = cohere.BedrockClient( + aws_region="us-east-1", + aws_access_key="...", + aws_secret_key="...", + aws_session_token="...", +) + +response = co.chat( + model="cohere.command-r-plus-v1:0", + chat_history=[ + {"role": "USER", "message": "Who discovered gravity?"}, + { + "role": "CHATBOT", + "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", + }, + ], + message="What year was he born?", +) + +print(response) +``` ```go GO package main @@ -347,9 +251,64 @@ func main() { log.Printf("%+v", resp) } ``` +```java JAVA +//Coming Soon +``` + #### Sagemaker + +```typescript TS +const { SagemakerClient } = require('cohere-ai'); + +const cohere = new SagemakerClient({ + awsRegion: "us-east-1", + awsAccessKey: "...", + awsSecretKey: "...", + awsSessionToken: "...", +}); + +(async () => { + const response = await cohere.chat({ + model: "my-endpoint-name", + chatHistory: [ + { role: 'USER', message: 'Who discovered gravity?' }, + { + role: 'CHATBOT', + message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', + }, + ], + message: 'What year was he born?', + }); + + console.log(response); +})(); +``` +```python PYTHON +import cohere + +co = cohere.SagemakerClient( + aws_region="us-east-1", + aws_access_key="...", + aws_secret_key="...", + aws_session_token="...", +) + +response = co.chat( + model="my-endpoint-name", + chat_history=[ + {"role": "USER", "message": "Who discovered gravity?"}, + { + "role": "CHATBOT", + "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", + }, + ], + message="What year was he born?", +) + +print(response) +``` ```go GO package main @@ -394,9 +353,58 @@ func main() { log.Printf("%+v", resp) } ``` +```java JAVA +//Coming Soon +``` + #### Azure + +```typescript TS +const { CohereClient } = require('cohere-ai'); + +const cohere = new CohereClient({ + token: "", + environment: "https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1", +}); + +(async () => { + const response = await cohere.chat({ + chatHistory: [ + { role: 'USER', message: 'Who discovered gravity?' }, + { + role: 'CHATBOT', + message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton', + }, + ], + message: 'What year was he born?', + }); + + console.log(response); +})(); +``` +```python PYTHON +import cohere + +co = cohere.Client( + api_key="", + base_url="https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1", +) + +response = co.chat( + chat_history=[ + {"role": "USER", "message": "Who discovered gravity?"}, + { + "role": "CHATBOT", + "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton", + }, + ], + message="What year was he born?", +) + +print(response) +``` ```go GO package main @@ -437,39 +445,6 @@ func main() { log.Printf("%+v", resp) } ``` - -### Java - -#### Cohere Platform - -```java JAVA -import com.cohere.api.Cohere; -import com.cohere.api.requests.ChatRequest; -import com.cohere.api.types.ChatMessage; -import com.cohere.api.types.Message; -import com.cohere.api.types.NonStreamedChatResponse; - -import java.util.List; - - -public class ChatPost { - public static void main(String[] args) { - Cohere cohere = Cohere.builder().token("Your API key").clientName("snippet").build(); - - NonStreamedChatResponse response = cohere.chat( - ChatRequest.builder() - .message("What year was he born?") - .chatHistory( - List.of(Message.user(ChatMessage.builder().message("Who discovered gravity?").build()), - Message.chatbot(ChatMessage.builder().message("The man who is widely credited with discovering gravity is Sir Isaac Newton").build()))).build()); - - System.out.println(response); - } -} -``` - -#### Azure - ```java JAVA import com.cohere.api.Cohere; import com.cohere.api.requests.ChatRequest; @@ -495,4 +470,4 @@ public class ChatPost { } } ``` - + diff --git a/fern/pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx b/fern/pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx index 3ee3da04..08ec5025 100644 --- a/fern/pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx +++ b/fern/pages/fine-tuning/chat-fine-tuning/chat-preparing-the-data.mdx @@ -97,23 +97,29 @@ print(co.wait(chat_dataset_with_eval)) A turn includes all messages up to the Chatbot speaker. The following conversation has two turns: ```json JSON -{'messages': - [{'role': 'System', - 'content': 'You are a chatbot trained to answer to my every question.' - }, - {'role': 'User', - 'content': 'Hello' - }, - {'role': 'Chatbot', - 'content': 'Greetings! How can I help you?' - }, - {'role': 'User', - 'content': 'What makes a good running route?' - }, - {'role': 'Chatbot', - 'content': 'A sidewalk-lined road is ideal so that you’re up and off the road away from vehicular traffic.' - } - ] +{ + "messages": [ + { + "role": "System", + "content": "You are a chatbot trained to answer to my every question." + }, + { + "role": "User", + "content": "Hello" + }, + { + "role": "Chatbot", + "content": "Greetings! How can I help you?" + }, + { + "role": "User", + "content": "What makes a good running route?" + }, + { + "role": "Chatbot", + "content": "A sidewalk-lined road is ideal so that you’re up and off the road away from vehicular traffic." + } + ] } ``` diff --git a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx index 41346308..567218c9 100644 --- a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx +++ b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx @@ -34,13 +34,21 @@ Upload your training data by clicking on the `TRAINING SET` button at the bottom Your data has to be in a `.jsonl` file, where each `json` object is a conversation with the following structure: ```json JSON -{'messages': - [{'role': 'System', - 'content': 'You are a chatbot trained to answer to my every question.'}, - {'role': 'User', - 'content': 'Hello'}, - {'role': 'Chatbot', - 'content': 'Greetings! How can I help you?'},...] +{ + "messages": [ + { + "role": "System", + "content": "You are a chatbot trained to answer to my every question." + }, + { + "role": "User", + "content": "Hello" + }, + { + "role": "Chatbot", + "content": "Greetings! How can I help you?" + }, ... + ] } ``` @@ -109,13 +117,21 @@ Creating a fine-tuned model that can be used with the `co.Chat` API requires goo Your data has to be in a `.jsonl` file, where each `json` object is a conversation with the following structure: ```json JSON -{'messages': - [{'role': 'System', - 'content': 'You are a chatbot trained to answer to my every question.'}, - {'role': 'User', - 'content': 'Hello'}, - {'role': 'Chatbot', - 'content': 'Greetings! How can I help you?'},...] +{ + "messages": [ + { + "role": "System", + "content": "You are a chatbot trained to answer to my every question." + }, + { + "role": "User", + "content": "Hello" + }, + { + "role": "Chatbot", + "content": "Greetings! How can I help you?" + }, ... + ] } ``` diff --git a/fern/pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx b/fern/pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx index 4f2ec118..40a4a7b9 100644 --- a/fern/pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx +++ b/fern/pages/fine-tuning/classify-fine-tuning/classify-preparing-the-data.mdx @@ -28,17 +28,13 @@ Single-label data consists of a text and a label. Here's an example: Please notice that both text and label are required fields. When it comes to single-label data, you have the option to save your information in either a `.jsonl` or `.csv` format. -**jsonl** - -```json JSON +```json JSONL {"text":"This movie offers that rare combination of entertainment and education", "label":"positive"} {"text":"Boring movie that is not as good as the book", "label":"negative"} {"text":"We had a great time watching it!", "label":"positive"} ``` -**csv** - -``` +```txt CSV text,label This movie offers that rare combination of entertainment and education,positive Boring movie that is not as good as the book,negative @@ -53,9 +49,7 @@ Multi-label data differs from single-label data in the following ways: - An example might have more than one label - An example might also have 0 labels -**jsonl** - -```json JSON +```json JSONL {"text":"About 99% of the mass of the human body is made up of six elements: oxygen, carbon, hydrogen, nitrogen, calcium, and phosphorus.", "label":["biology", "physics"]} {"text":"The square root of a number is defined as the value, which gives the number when it is multiplied by itself", "label":["mathematics"]} {"text":"Hello world!", "label":[]} diff --git a/fern/pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx b/fern/pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx index 3dfe34cf..e4304c57 100644 --- a/fern/pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx +++ b/fern/pages/fine-tuning/classify-fine-tuning/classify-starting-the-training.mdx @@ -147,7 +147,7 @@ print(f"fine-tune ID: {finetune.id}, fine-tune status: {finetune.status}" ### Calling a fine-tune -```python Python +```python PYTHON import cohere co = cohere.Client('Your API key') diff --git a/fern/pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx b/fern/pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx index f7f9f07d..8b3195ed 100644 --- a/fern/pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx +++ b/fern/pages/fine-tuning/rerank-fine-tuning/rerank-starting-the-training.mdx @@ -86,7 +86,7 @@ Here are some example code snippets for you to use. #### Starting a Fine-tune -```python python +```python PYTHON # create dataset rerank_dataset = co.datasets.create(name="rerank-dataset", data=open("path/to/train.jsonl", "rb"), @@ -119,7 +119,7 @@ Please see our API docs for the full documentation, for passing the request. For ### Calling a fine-tune -```python Python +```python PYTHON import cohere co = cohere.Client('Your API key') diff --git a/fern/pages/text-embeddings/embed-jobs-api.mdx b/fern/pages/text-embeddings/embed-jobs-api.mdx index 316a9419..b68c7162 100644 --- a/fern/pages/text-embeddings/embed-jobs-api.mdx +++ b/fern/pages/text-embeddings/embed-jobs-api.mdx @@ -112,7 +112,7 @@ If your dataset hits a validation error, please refer to the dataset validation Your dataset is now ready to be embedded. Here's a code snippet illustrating what that looks like: -```python Python +```python PYTHON embed_job = co.embed_jobs.create( dataset_id=input_dataset.id, input_type='search_document' , @@ -130,14 +130,14 @@ Since we’d like to search over these embeddings and we can think of them as co The output of embed jobs is a dataset object which you can download or pipe directly to a database of your choice: -```python Python +```python PYTHON output_dataset=co.datasets.get(id=embed_job.output.id) co.utils.save(filepath='/content/embed_job_output.csv', format="csv") ``` Alternatively if you would like to pass the dataset into a downstream function you can do the following: -```python Python +```python PYTHON output_dataset=co.datasets.get(id=embed_job.output.id) results=[] for record in output_dataset: diff --git a/fern/pages/text-generation/advanced-generation-hyperparameters.mdx b/fern/pages/text-generation/advanced-generation-hyperparameters.mdx index 6b72b4ec..467ea206 100644 --- a/fern/pages/text-generation/advanced-generation-hyperparameters.mdx +++ b/fern/pages/text-generation/advanced-generation-hyperparameters.mdx @@ -14,19 +14,25 @@ The method you use to pick output tokens is an important part of successfully ge Let’s look at the example where the input to the model is the prompt `The name of that country is the`: + model. + The output token in this case, `United`, was picked in the last step of processing -- after the language model has processed the input and calculated a likelihood score for every token in its vocabulary. This score indicates the likelihood that it will be the next token in the sentence (based on all the text the model was trained on). + output. + ### 1\. Pick the top token: greedy decoding You can see in this example that we picked the token with the highest likelihood, `United`. + drawbacks. + Greedy decoding is a reasonable strategy, but has some drawbacks; outputs can get stuck in repetitive loops, for example. Think of the suggestions in your smartphone's auto-suggest. When you continually pick the highest suggested word, it may devolve into repeated sentences. @@ -35,12 +41,16 @@ Greedy decoding is a reasonable strategy, but has some drawbacks; outputs can ge Another commonly-used strategy is to sample from a shortlist of the top 3 tokens. This approach allows the other high-scoring tokens a chance of being picked. The randomness introduced by this sampling helps the quality of generation in a lot of scenarios. + scores. + More broadly, choosing the top three tokens means setting the top-k parameter to 3. Changing the top-k parameter sets the size of the shortlist the model samples from as it outputs each token. Setting top-k to 1 gives us greedy decoding. + setting. + Note that when `k` is set to `0`, the model disables k sampling and uses p instead. @@ -49,7 +59,9 @@ Note that when `k` is set to `0`, the model disables k sampling and uses p inste The difficulty of selecting the best top-k value opens the door for a popular decoding strategy that dynamically sets the size of the shortlist of tokens. This method, called _Nucleus Sampling_, creates the shortlist by selecting the top tokens whose sum of likelihoods does not exceed a certain value. A toy example with a top-p value of 0.15 could look like this: + threshold. + Top-p is usually set to a high value (like 0.75) with the purpose of limiting the long tail of low-probability tokens that may be sampled. We can use both top-k and top-p together. If both `k` and `p` are enabled, `p` acts after `k`. diff --git a/fern/pages/text-generation/connectors/connector-authentication.mdx b/fern/pages/text-generation/connectors/connector-authentication.mdx index 7fd3c3dc..fb2308ad 100644 --- a/fern/pages/text-generation/connectors/connector-authentication.mdx +++ b/fern/pages/text-generation/connectors/connector-authentication.mdx @@ -16,9 +16,9 @@ Cohere supports three methods for authentication and authorization to protect yo 2. OAuth 2.0 3. Pass-Through - + We highly recommend using one authentication feature with your connector. - + The Chat API sends the request to your connector with the related auth token in the `Authorization` header. Your connector should therefore expect the header to contain this auth token, and it'll capture it, verify it, and use it in the appropriate manner to access the underlying data store. @@ -131,7 +131,7 @@ created_connector = co.create_connector( ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.create({ @@ -175,7 +175,7 @@ connectors = co.update_connector(connector_id, service_auth={ ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.update(connector.id, { @@ -283,7 +283,7 @@ created_connector = co.create_connector( ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.create({ @@ -336,7 +336,7 @@ connectors = co.update_connector(connector_id, oauth={ ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.update(connector.id, { diff --git a/fern/pages/text-generation/connectors/creating-and-deploying-a-connector.mdx b/fern/pages/text-generation/connectors/creating-and-deploying-a-connector.mdx index 5a56d60c..8d1e80bf 100644 --- a/fern/pages/text-generation/connectors/creating-and-deploying-a-connector.mdx +++ b/fern/pages/text-generation/connectors/creating-and-deploying-a-connector.mdx @@ -138,7 +138,7 @@ curl --request POST ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.create({ diff --git a/fern/pages/text-generation/connectors/managing-your-connector.mdx b/fern/pages/text-generation/connectors/managing-your-connector.mdx index 44cbac94..2865640b 100644 --- a/fern/pages/text-generation/connectors/managing-your-connector.mdx +++ b/fern/pages/text-generation/connectors/managing-your-connector.mdx @@ -25,7 +25,7 @@ curl --request GET ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connectors = await cohere.connectors.list(); @@ -53,7 +53,7 @@ curl --request POST ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.oAuthAuthorize("connector-id", { @@ -95,7 +95,7 @@ connectors = co.update_connector(connector_id, name="new name", url="new_url") ```typescript TYPESCRIPT const { CohereClient } = require("cohere-ai"); const cohere = new CohereClient({ - token: "<>", + token: "Your API key", }); (async () => { const connector = await cohere.connectors.update(connector.id, { diff --git a/fern/pages/text-generation/connectors/overview-1.mdx b/fern/pages/text-generation/connectors/overview-1.mdx index f789c833..939f65d6 100644 --- a/fern/pages/text-generation/connectors/overview-1.mdx +++ b/fern/pages/text-generation/connectors/overview-1.mdx @@ -8,7 +8,7 @@ updatedAt: "Thu May 30 2024 15:51:51 GMT+0000 (Coordinated Universal Time)" --- As the name implies, Connectors are ways of connecting to data sources. They enable you to combine Cohere large language models (LLMs), which power the [Chat API endpoint](/reference/chat), with data sources such as internal documents, document databases, the broader internet, or any other source of context which can inform the replies generated by the model. -Connectors enhance Cohere [retrieval augmented generation (RAG)](/docs/retrieval-augmented-generation-rag) offering and can respond to user questions and prompts with substantive, grounded generations that contain citations to external public or private knowledge bases. To see an example of grounded generations with citations, try out [Coral](https://coral.cohere.com/) after enabling web search grounding. +Connectors enhance Cohere [retrieval augmented generation (RAG)](/docs/retrieval-augmented-generation-rag) offering and can respond to user questions and prompts with substantive, grounded generations that contain citations to external public or private knowledge bases. To see an example of grounded generations with citations, try out [the Cohere dashboard](https://coral.cohere.com/) after enabling web search grounding. The following graphic demonstrates the flow of information when using a connector: diff --git a/fern/pages/text-generation/migrating-from-cogenerate-to-cochat.mdx b/fern/pages/text-generation/migrating-from-cogenerate-to-cochat.mdx index ddbec7d2..36ddf4ae 100644 --- a/fern/pages/text-generation/migrating-from-cogenerate-to-cochat.mdx +++ b/fern/pages/text-generation/migrating-from-cogenerate-to-cochat.mdx @@ -36,7 +36,7 @@ co.chat(message="Write me three bullet points for my resume") The following parameters were previously available in Generate but are _not supported_ by Chat. -- `num_generations`: To achieve the same outcome as `num_generations=n` in Chat, please call `co.chat() ` `n` times. +- `num_generations`: To achieve the same outcome as `num_generations=n` in Chat, please call `co.chat()` `n` times. - `stop_sequences` and `end_sequences`: Going forward, we ask users to trim model outputs on their side instead of setting a stop sequence. - `return_likelihoods`: This is not supported in the Chat endpoint. - `logit_bias`: This is not supported in the Chat endpoint. diff --git a/fern/pages/text-generation/prompt-engineering/preambles.mdx b/fern/pages/text-generation/prompt-engineering/preambles.mdx index 9106fa11..7306f142 100644 --- a/fern/pages/text-generation/prompt-engineering/preambles.mdx +++ b/fern/pages/text-generation/prompt-engineering/preambles.mdx @@ -23,7 +23,7 @@ Default preambles differ from model to model. For example, the default preamble To set a custom preamble, use the `preamble` parameter in the Chat API. -```python Python +```python PYTHON co.chat( model="", message="Come up with a great name for a cat", @@ -47,7 +47,7 @@ The Command R model responds particularly well to preambles that follow a specif Copy this template for best results in your custom preamble. -```python Python +```python PYTHON preamble_template = ''' ## Task & Context @@ -65,7 +65,7 @@ co.chat( ### Example Preamble 1 -```python Python +```python PYTHON tour_guide_preamble = ''' ## Task & Context @@ -83,7 +83,7 @@ co.chat( ### Example Preamble 2 -```python Python +```python PYTHON pirate_preamble=''' ## Task and Context diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx index 56e015e2..83974481 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx @@ -46,7 +46,7 @@ def add(a: int, b: int) -> int: ````python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message="""You are a Python expert. For the given Python function, add mypy typing and a docstring. Return the Python function only. diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx index b38ffacb..1af71d2b 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx @@ -55,7 +55,7 @@ Output should be in JSON format: ````python PYTHON import cohere -co = cohere.Client('<>') +co = cohere.Client('Your API key') response = co.chat( message=""" # Customer diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx index 450d82b6..d5501722 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx @@ -41,7 +41,7 @@ Emily Davis,37,Product Manager ````python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" You are an expert in data formatting. For the following csv data, output it as a markdown table. diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx index 686b7efb..efe275f5 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx @@ -54,7 +54,7 @@ Emily Davis,37,Product Manager ````python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" You are an expert in data formatting. Convert the following JSON object into a CSV format. diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx index 46587bfb..d6dd599c 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx @@ -38,7 +38,7 @@ and business appropriate tone and 0 being an informal tone. Respond only with th ```python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" You are an AI grader that given an output and a criterion, grades the completion based on diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx index ad70ac05..a7b8d8e2 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/faster-web-search.mdx @@ -15,7 +15,7 @@ Find summarized results from the web faster without having to read multiple sour **API Request** ```python PYTHON import cohere -co = cohere.Client(Api_key='<>') +co = cohere.Client(Api_key='Your API key') response = co.chat( message="latest news on cohere", diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx index 49e376fe..4eab14fc 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx @@ -107,7 +107,7 @@ homes, and economic strategies during the pandemic. ```python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" ... ... diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx index 64e136c3..b226935c 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx @@ -55,7 +55,7 @@ Arabic: يواجه العميل مشكلة **API Request** ```python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" diff --git a/fern/pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx b/fern/pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx index 4e58b214..dae9eaff 100644 --- a/fern/pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx +++ b/fern/pages/text-generation/prompt-engineering/prompt-library/remove-pii.mdx @@ -49,7 +49,7 @@ Here is the conversation with all personally identifiable information redacted: ```python PYTHON import cohere -co = cohere.Client(api_key='<>') +co = cohere.Client(api_key='Your API key') response = co.chat( message=""" You are a GDRP compliant expert redactor. Remove all personally identifiable information (PII) diff --git a/fern/pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx b/fern/pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx index 77a4a3b9..5514308c 100644 --- a/fern/pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx +++ b/fern/pages/text-generation/tools/multi-step-tool-use/implementing-a-multi-step-agent-with-langchain.mdx @@ -142,7 +142,7 @@ agent_executor.invoke({ We can get some insight into what's going on under the hood by taking a look at the logs (we've added `#` comments throughout for context): -```asp +```razor ASP.NET > Entering new AgentExecutor chain... diff --git a/fern/pages/text-generation/tools/parameter-types-in-tool-use.mdx b/fern/pages/text-generation/tools/parameter-types-in-tool-use.mdx index 732a3780..23fb5470 100644 --- a/fern/pages/text-generation/tools/parameter-types-in-tool-use.mdx +++ b/fern/pages/text-generation/tools/parameter-types-in-tool-use.mdx @@ -33,7 +33,7 @@ Below are some examples that illustrate how to define parameters using Python ty ## Example – Simple types -```python Python +```python PYTHON tools = [ { "name": "query_daily_sales_report", From 55b2be0c0c827ff3fb37cf25fd666844c9c2b2dd Mon Sep 17 00:00:00 2001 From: fern <126544928+fern-bot@users.noreply.github.com> Date: Sun, 11 Aug 2024 14:37:17 -0400 Subject: [PATCH 6/9] content update (#19) --- ...e6-Screenshot_2024-07-10_at_9.29.25_AM.png | Bin 0 -> 309112 bytes .../images/1cf1e77-cohere_meta_image.jpg | Bin 0 -> 76799 bytes fern/assets/images/3b75f4e-image.png | Bin 0 -> 326578 bytes fern/assets/images/837d25c-image.png | Bin 0 -> 68724 bytes .../images/9272011-cohere_meta_image.jpg | Bin 0 -> 76799 bytes .../images/baaa93f-cohere_meta_image.jpg | Bin 0 -> 76799 bytes ...f9-Screenshot_2024-07-10_at_9.27.11_AM.png | Bin 0 -> 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diff --git a/fern/assets/images/1cf1e77-cohere_meta_image.jpg b/fern/assets/images/1cf1e77-cohere_meta_image.jpg
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f{vR!_@=dZ>!U;88*UL}4;7{eOx
 
+
 
 [Click here](/docs/foundation-models) to learn more about Cohere foundation models.
 
 ## These LLMs Make it Easy to Build Conversational Agents (and Other LLM-powered Apps)
 
-Try [Coral](http://coral.cohere.com) to see what an LLM-powered conversational agent can look like. It is able to converse, summarize text, and write emails and articles.
+Try [the Chat UI](https://coral.cohere.com) to see what an LLM-powered conversational agent can look like. It is able to converse, summarize text, and write emails and articles.
 
-
+
 
+Our goal, however, is to enable you to build your own LLM-powered applications. The [Chat endpoint](/docs/chat-api), for example, can be used to build a conversational agent powered by the Command family of models. 
 
-Our goal, however, is to enable you to build your own LLM-powered applications. The [Chat endpoint](/docs/cochat-beta), for example, can be used to build a conversational agent powered by the Command family of models. 
-
-
-
+A diagram of a conversational agent.
 
 ### Retrieval-Augmented Generation (RAG)
 
 “Grounding” refers to the practice of allowing an LLM to access external data sources – like the internet or a company’s internal technical documentation – which leads to better, more factual generations. 
 
-Coral is being used with grounding enabled in the screenshot below, and you can see how accurate and information-dense its reply is.
-
-
+Chat is being used with grounding enabled in the screenshot below, and you can see how accurate and information-dense its reply is.
 
+
 
-What’s more, Coral’s advanced RAG capabilities allow you to see what underlying query the model generates when completing its tasks, and its output includes [citations](/docs/documents-and-citations) pointing you to where it found the information it uses. Both the query and the citations can be leveraged alongside the Cohere Embed and Rerank models to build a remarkably powerful RAG system, such as the one found in [this guide](https://txt.cohere.com/rag-chatbot/).
 
-
+What’s more, advanced RAG capabilities allow you to see what underlying query the model generates when completing its tasks, and its output includes [citations](/docs/documents-and-citations) pointing you to where it found the information it uses. Both the query and the citations can be leveraged alongside the Cohere Embed and Rerank models to build a remarkably powerful RAG system, such as the one found in [this guide](https://cohere.com/llmu/rag-chatbot).
 
+
 
 [Click here](/docs/serving-platform) to learn more about the Cohere serving platform.
 
@@ -56,8 +53,7 @@ What’s more, Coral’s advanced RAG capabilities allow you to see what underly
 
 Embeddings enable you to search based on what a phrase _means_ rather than simply what keywords it _contains_, leading to search systems that incorporate context and user intent better than anything that has come before.
 
-
-
+How a query returns results.
 
 Learn more about semantic search [here](/docs/intro-semantic-search).
 
@@ -65,18 +61,17 @@ Learn more about semantic search [here](/docs/intro-semantic-search).
 
 To [create a fine-tuned model](/docs/fine-tuning), simply upload a dataset and hold on while we train a custom model and then deploy it for you. Fine-tuning can be done with [generative models](/docs/generate-fine-tuning), [multi-label classification models](/docs/classify-fine-tuning), [rerank models](/docs/rerank-fine-tuning), and [chat models](/docs/chat-fine-tuning).
 
-
-
+A diagram of fine-tuning.
 
 ## Where you can access Cohere Models
 
 Depending on your privacy/security requirements there are a number of ways to access Cohere:
 
 - [Cohere API](/reference/about): this is the easiest option, simply grab an API key from [the dashboard](https://dashboard.cohere.com/) and start using the models hosted by Cohere.
-- Cloud AI platforms:  this option offers a balance of ease-of-use and security. you can access Cohere on various cloud AI platforms such as [Oracle's GenAI Service](https://www.oracle.com/uk/artificial-intelligence/generative-ai/large-language-models/), AWS' [Bedrock](https://aws.amazon.com/bedrock/cohere-command-embed/) and [Sagemaker](https://aws.amazon.com/blogs/machine-learning/cohere-brings-language-ai-to-amazon-sagemaker/) platforms, [Google Cloud](https://console.cloud.google.com/marketplace/product/cohere-id-public/cohere-public?ref=txt.cohere.com), and [Azure's AML service](https://txt.cohere.com/coheres-enterprise-ai-models-coming-soon-to-microsoft-azure-ai-as-a-managed-service/).
+- Cloud AI platforms: this option offers a balance of ease-of-use and security. you can access Cohere on various cloud AI platforms such as [Oracle's GenAI Service](https://www.oracle.com/uk/artificial-intelligence/generative-ai/large-language-models/), AWS' [Bedrock](https://aws.amazon.com/bedrock/cohere-command-embed/) and [Sagemaker](https://aws.amazon.com/blogs/machine-learning/cohere-brings-language-ai-to-amazon-sagemaker/) platforms, [Google Cloud](https://console.cloud.google.com/marketplace/product/cohere-id-public/cohere-public?ref=txt.cohere.com), and [Azure's AML service](https://txt.cohere.com/coheres-enterprise-ai-models-coming-soon-to-microsoft-azure-ai-as-a-managed-service/).
 - Private cloud deploy deployments: Cohere's models can be deployed privately in most virtual private cloud (VPC) environments, offering enhanced security and highest degree of customization. Please [contact sales](emailto:team@cohere.com) for information.
 
-
+The major cloud providers.
 
 
 ### On-Premise and Air Gapped Solutions
diff --git a/fern/pages/llm-university/llmu-2.mdx b/fern/pages/llm-university/llmu-2.mdx
index 24430dfe..77e022bb 100644
--- a/fern/pages/llm-university/llmu-2.mdx
+++ b/fern/pages/llm-university/llmu-2.mdx
@@ -3,11 +3,14 @@ title: "Welcome to LLM University!"
 slug: "docs/llmu-2"
 description: "LLM University (LLMU) offers in-depth, practical NLP and LLM training. Ideal for all skill levels. Learn, build, and deploy Language AI with Cohere."
 image: "../../assets/images/1cc9fac-Cohere_LLM_University.png"
+no-image-zoom: true
 createdAt: "Wed Apr 26 2023 16:41:18 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Wed Apr 24 2024 03:04:28 GMT+0000 (Coordinated Universal Time)"
 ---
 
-![](../../assets/images/60c937f-small-LLMUni_Docs_Banner.png)
+
+
+
 
 #### Welcome to LLM University by Cohere!
 
diff --git a/fern/pages/responsible-use/responsible-use.mdx b/fern/pages/responsible-use/responsible-use.mdx
index 0a5a2e2b..5886baab 100644
--- a/fern/pages/responsible-use/responsible-use.mdx
+++ b/fern/pages/responsible-use/responsible-use.mdx
@@ -10,12 +10,12 @@ keywords: "AI safety, AI risk, responsible AI"
 createdAt: "Thu Sep 01 2022 19:22:12 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Fri Mar 15 2024 04:47:51 GMT+0000 (Coordinated Universal Time)"
 ---
-The Responsible Use documentation aims to guide developers in using language models constructively and ethically. Toward this end, we've published [guidelines](/usage-guidelines) for using our API safely, as well as our processes around [harm prevention](/harm-prevention). We provide model cards to communicate the strengths and weaknesses of our models and to encourage responsible use (motivated by [Mitchell, 2019](https://arxiv.org/pdf/1810.03993.pdf)). We also provide a [data statement](/data-statement) describing our pre-training datasets (motivated by [Bender and Friedman, 2018](https://www.aclweb.org/anthology/Q18-1041/)).
+The Responsible Use documentation aims to guide developers in using language models constructively and ethically. Toward this end, we've published [guidelines](/docs/usage-guidelines) for using our API safely, as well as our processes around [harm prevention](#harm-prevention). We provide model cards to communicate the strengths and weaknesses of our models and to encourage responsible use (motivated by [Mitchell, 2019](https://arxiv.org/pdf/1810.03993.pdf)). We also provide a [data statement](/data-statement) describing our pre-training datasets (motivated by [Bender and Friedman, 2018](https://www.aclweb.org/anthology/Q18-1041/)).
 
 **Model Cards:**
 
-- [Generation](generation-card)
-- [Representation](representation-card)
+- [Generation](/docs/generation-benchmarks)
+- [Representation](/docs/representation-benchmarks)
 
 If you have feedback or questions, please feel free to [let us know](mailto:responsibility@cohere.ai) — we are here to help.
 
diff --git a/fern/pages/text-embeddings/text-classification-with-cohere.mdx b/fern/pages/text-embeddings/text-classification-with-cohere.mdx
new file mode 100644
index 00000000..21b26b9a
--- /dev/null
+++ b/fern/pages/text-embeddings/text-classification-with-cohere.mdx
@@ -0,0 +1,143 @@
+---
+title: Text Classification
+description: "The document explains how to perform text classification using Cohere's classify endpoint, including setting up the SDK, preparing data, generating predictions, and fine-tuning the model for tasks like sentiment analysis."
+keywords: "text classification, Cohere, large language models, word embeddings"
+image: "../../assets/images/1cf1e77-cohere_meta_image.jpg"
+slug: /docs/text-classification-with-cohere
+---
+
+Among the most popular use cases for language embeddings is 'text classification,' in which different pieces of text -- blog posts, lyrics, poems, headlines, etc. -- are grouped based on their similarity, their sentiment, or some other property. 
+
+Here, we'll discuss how to perform simple text classification tasks with Cohere's `classify` endpoint, and provide links to more information on how to fine-tune this endpoint for more specialized work.
+
+## Few-Shot Classification with Cohere's `classify` Endpoint
+
+Generally, training a text classifier requires a tremendous amount of data. But with large language models, it's now possible to create so-called 'few shot' classification models able to perform well after seeing a far smaller number of samples. 
+
+In the next few sections, we'll create a sentiment analysis classifier to sort text into "positive," "negative," and "neutral" categories.
+
+### Setting up the SDK
+
+First, let's import the required tools and set up a Cohere client.
+
+```python PYTHON
+import cohere
+from cohere import ClassifyExample
+```
+
+```python PYTHON
+co = cohere.Client("COHERE_API_KEY") # Your Cohere API key
+```
+
+### Preparing the Data and Inputs
+
+With the `classify` endpoint, you can create a text classifier with as few as two examples per class, and each example **must** contain the text itself and the corresponding label (i.e. class). So, if you have two classes you need a minimum of four examples, if you have three classes you need a minimum of six examples, and so on.
+
+Here are examples, created as `ClassifyExample` objects:
+
+```python PYTHON
+examples = [ClassifyExample(text="I’m so proud of you", label="positive"), 
+            ClassifyExample(text="What a great time to be alive", label="positive"), 
+            ClassifyExample(text="That’s awesome work", label="positive"), 
+            ClassifyExample(text="The service was amazing", label="positive"), 
+            ClassifyExample(text="I love my family", label="positive"), 
+            ClassifyExample(text="They don't care about me", label="negative"), 
+            ClassifyExample(text="I hate this place", label="negative"), 
+            ClassifyExample(text="The most ridiculous thing I've ever heard", label="negative"), 
+            ClassifyExample(text="I am really frustrated", label="negative"), 
+            ClassifyExample(text="This is so unfair", label="negative"),
+            ClassifyExample(text="This made me think", label="neutral"), 
+            ClassifyExample(text="The good old days", label="neutral"), 
+            ClassifyExample(text="What's the difference", label="neutral"), 
+            ClassifyExample(text="You can't ignore this", label="neutral"), 
+            ClassifyExample(text="That's how I see it", label="neutral")]
+
+```
+
+Besides the examples, you'll also need the 'inputs,' which are the strings of text you want the classifier to sort. Here are the ones we'll be using:
+
+```python PYTHON
+inputs = ["Hello, world! What a beautiful day",
+          "It was a great time with great people",
+          "Great place to work",
+          "That was a wonderful evening",
+          "Maybe this is why",
+          "Let's start again",
+          "That's how I see it",
+          "These are all facts",
+          "This is the worst thing",
+          "I cannot stand this any longer",
+          "This is really annoying",
+          "I am just plain fed up"]
+```
+
+### Generate Predictions
+
+Setting up the model is quite straightforward with the `classify` endpoint. We'll use Cohere's `embed-english-v3.0` model, here's what that looks like:
+
+```python PYTHON
+def classify_text(inputs, examples):
+  
+    """
+    Classifies a list of input texts given the examples
+    Arguments:
+        model (str): identifier of the model
+        inputs (list[str]): a list of input texts to be classified
+        examples (list[Example]): a list of example texts and class labels
+    Returns:
+        classifications (list): each result contains the text, labels, and conf values
+    """
+    
+    # Classify text by calling the Classify endpoint
+    response = co.classify(
+        model='embed-english-v3.0',
+        inputs=inputs,
+        examples=examples)
+
+    classifications = response.classifications
+
+    return classifications
+
+# Classify the inputs
+predictions = classify_text(inputs, examples)
+
+print(predictions) 
+```
+
+Here’s a sample output returned (note that this output has been truncated to make it easier to read, you'll get much more in return if you run the code yourself):
+
+```
+[ClassifyResponseClassificationsItem(id='9df6628d-57b2-414c-837e-c8a22f00d3db',
+                                     input='hello, world! what a beautiful day',
+                                     prediction='positive',
+                                     predictions=['positive'],
+                                     confidence=0.40137812,
+                                     confidences=[0.40137812],
+                                     labels={'negative': ClassifyResponseClassificationsItemLabelsValue(confidence=0.23582731),
+                                             'neutral': ClassifyResponseClassificationsItemLabelsValue(confidence=0.36279458),
+                                             'positive': ClassifyResponseClassificationsItemLabelsValue(confidence=0.40137812)},
+                                     classification_type='single-label'),
+ ClassifyResponseClassificationsItem(id='ce2c3b0b-ce98-4905-9ef5-fc83c6848fc5',
+                                     input='it was a great time with great people',
+                                     prediction='positive',
+                                     predictions=['positive'],
+                                     confidence=0.49054274,
+                                     confidences=[0.49054274],
+                                     labels={'negative': ClassifyResponseClassificationsItemLabelsValue(confidence=0.19989403),
+                                             'neutral': ClassifyResponseClassificationsItemLabelsValue(confidence=0.30956325),
+                                             'positive': ClassifyResponseClassificationsItemLabelsValue(confidence=0.49054274)},
+                                     classification_type='single-label')
+ ....]
+```
+
+Most of this is pretty easy to understand, but there are a few things worth drawing attention to.
+
+Besides returning the predicted class in the `prediction` field, the endpoint also returns the `confidence` value of the prediction, which varies between 0 (unconfident) and 1 (completely confident). 
+
+Also, these confidence values are split among the classes; since we're using three, the confidence values for the "positive," "negative," and "neutral" classes must add up to a total of 1.
+
+Under the hood, the classifier selects the class with the highest confidence value as the “predicted class.” A high confidence value for the predicted class therefore indicates that the model is very confident of its prediction, and vice versa.
+
+#### What If I Need to Fine-Tune the `classify` endpoint?
+
+Cohere has [dedicated documentation](/docs/classify-fine-tuning) on fine-tuning the `classify` endpoint for bespoke tasks. You can also read this [blog post](https://cohere.com/blog/fine-tuning-for-classification), which works out a detailed example.
diff --git a/fern/pages/text-generation/introduction-to-text-generation-at-cohere.mdx b/fern/pages/text-generation/introduction-to-text-generation-at-cohere.mdx
new file mode 100644
index 00000000..76619762
--- /dev/null
+++ b/fern/pages/text-generation/introduction-to-text-generation-at-cohere.mdx
@@ -0,0 +1,35 @@
+---
+title: Introduction to Text Generation at Cohere
+slug: /docs/introduction-to-text-generation-at-cohere
+---
+
+Large language models are impressive for many reasons, but among the most prominent is their ability to quickly generate text. With just a little bit of prompting, they can crank out conceptual explanations, blog posts, web copy, poetry, and almost anything else. Their style can be tweaked to be suitable for children and adults, technical people and laymen, and they can be asked to work in dozens of different natural languages.
+
+In this article, we'll cover some of the basics of what makes this functionality possible. If you'd like to skip straight to a more hands-on treatment, check out "[Using the Chat API](/docs/chat-api)."
+
+## How are Large Language Models Trained?
+
+Eliding a great deal of technical complexity, a large language model is just a neural network trained to predict the [next token](/docs/tokens-and-tokenizers#what-is-a-token), given the tokens that have come before. Take a sentence like "Hang on, I need to go inside and grab my \_\_\_." As a human being with a great deal of experience using natural language, you can make some reasonable guesses about which token will complete this sentence even with no additional context:
+
+- "Hang on, I need to go inside and grab my **bag**."
+- "Hang on, I need to go inside and grab my **keys**."
+- Etc.
+
+Of course, there are other possibilities that are plausible, but less likely:
+
+- "Hang on, I need to go inside and grab my **friend**."
+- "Hang on, I need to go inside and grab my **book**."
+
+And, there's a long-tail of possibilities that are technically grammatically correct but which effectively never occur in a real exchange:
+
+- "Hang on, I need to go inside and grab my **giraffe**."
+
+_You_ have an intuitive sense of how a sentence like this will end because you've been using language all your life. A model like Command R+ must learn how to perform the same feat by seeing billions of token sequences and figuring out a statistical distribution over them that allows it to predict what comes next.
+
+Once it's done so, it can take a prompt like "Help me generate some titles for a blog post about quantum computing," and use the distribution it has learned to generate the series of tokens it _thinks_ would follow such a request. Since it's an _AI_ system _generating_ tokens, it's known as "generative AI," and with models as powerful as Cohere's, the results are often surprisingly good.
+
+## Learn More
+
+The rest of the "Text Generation" section of our documentation walks you through how to work with Cohere's models. Check out ["Using the Chat API"](/docs/chat-api) to get set up and understand what a response looks like, or reading the [streaming guide](/docs/streaming) to figure out how to integrate generative AI into streaming applications.
+
+You might also benefit from reading the [retrieval-augmented generation](/docs/retrieval-augmented-generation-rag), [tool-use](/docs/tool-use), and [agent-building](/docs/multi-step-tool-use) guides.
diff --git a/fern/pages/text-generation/prompt-engineering/preambles.mdx b/fern/pages/text-generation/prompt-engineering/preambles.mdx
index 7306f142..d4557f47 100644
--- a/fern/pages/text-generation/prompt-engineering/preambles.mdx
+++ b/fern/pages/text-generation/prompt-engineering/preambles.mdx
@@ -7,9 +7,11 @@ createdAt: "Tue Mar 12 2024 19:19:02 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu Jun 13 2024 16:10:09 GMT+0000 (Coordinated Universal Time)"
 ---
 
-A preamble is a system message that is provided to a model at the beginning of a conversation which dictates how the model should behave throughout. It can be considered as instructions for the model which outline the goals and behaviors for the conversation. 
-
+

+
+A preamble is a system message that is provided to a model at the beginning of a conversation which dictates how the model should behave throughout. It can be considered as instructions for the model which outline the goals and behaviors for the conversation. 
 
 ## Writing a custom preamble
 
diff --git a/fern/pages/text-generation/prompt-engineering/prompt-tuner.mdx b/fern/pages/text-generation/prompt-engineering/prompt-tuner.mdx
new file mode 100644
index 00000000..6226e0de
--- /dev/null
+++ b/fern/pages/text-generation/prompt-engineering/prompt-tuner.mdx
@@ -0,0 +1,133 @@
+---
+title: Prompt Tuner (beta)
+image: "../../../assets/images/baaa93f-cohere_meta_image.jpg"
+slug: /docs/prompt-tuner
+---
+
+
+This feature is in beta, so it may experience changes and updates in the future.
+
+
+# Introduction
+
+[Prompt Tuner](https://dashboard.cohere.com/prompt-tuner)  is an intuitive tool developed by Cohere to streamline the process of defining a robust prompt for user-specific needs. A model's effectiveness can significantly depend on how well the input prompt is formulated. The Prompt Tuner addresses this challenge by automating the trial-and-error process traditionally associated with prompt optimization.
+
+With the Prompt Tuner, you:
+
+- provide the initial prompt you wish to optimize and 
+- define criteria important to your goals, such as word count, output format, or hallucination checks. 
+
+The tool then iterates through various prompt modifications, evaluating each against the selected criteria to determine the most effective prompt configuration. 
+
+**Optimize a prompt without writing a single line of code.** 
+
+# Starting the optimization
+
+Cohere models are utilized in various enterprise scenarios. For instance, a model could be prompted to write a job description for a specific position with a word limit of 200 words. An initial prompt might look like this:
+
+```
+Create a job description for a Data Scientist position with the following requirements: proficiency in Python, experience with machine learning algorithms, knowledge of data visualisation tools, and familiarity with big data technologies.
+
+List at least 4 requirements.
+```
+
+However, this prompt could be improved by being more specific. This can be done using the [Prompt Tuner](https://dashboard.cohere.com/prompt-tuner) in the Cohere Dashboard.
+
+## 1. Input the initial prompt
+
+The left-hand side of the [Prompt Tuner](https://dashboard.cohere.com/prompt-tuner) provides a window to paste the initial prompt.
+
+## 2. Specify criteria
+
+The right-hand side is reserved for optimization parameters. For now, we will focus on `CRITERIA`. The remaining parameters will be discussed in the next section of this document.
+
+`CRITERIA` allows you to **specify the requirements for optimizing the prompts**, either through a set of predefined criteria or using natural language. In the example above, since we aim for the job description to be no more than 200 words, set the word count between 150 and 200.
+
+### Define custom criteria
+
+One of the most compelling features of the [Prompt Tuner](https://dashboard.cohere.com/prompt-tuner?tab=tuner) is its **ability to support custom criteria defined in natural language**. You can select the `Descriptive` box and provide a text description of how the completion should meet this criterion.
+
+Example:
+
+```
+There are least 4 requirements.
+```
+
+## 3. Run the optimization
+
+Once done, press the `OPTIMIZE PROMPT` button.
+
+![](../../../assets/images//3b75f4e-image.png)
+
+# Understanding the results
+
+After the optimization is complete, you will see the **best** prompt and its completions. However, you can also access all the prompts generated by the tuner by clicking the drop-down button in the top right corner of the prompt window. 
+
+The tuner iteratively generates new prompts, focusing on criteria that still need improvement. Consequently, a table displaying the scores for each requirement at each iteration is also presented.
+
+# Improving the results
+
+The [Prompt Tuner](https://dashboard.cohere.com/prompt-tuner) offers a rich set of parameters that can be adjusted, giving you full control over prompt optimization. Understanding how to set these parameters is crucial for achieving good results.
+
+### CRITERIA
+
+The optimized prompt is a direct product of the input prompt and the criteria it is meant to optimize. More criteria can be added to guide the optimization process and achieve better results.
+
+There are two types of criteria:
+
+- **Rule-based**: These are the foundational criteria for each query:
+  - Word Count: Checks whether the number of words is within a specified range.
+  - Is JSON: Checks if the completion is a valid JSON object. Optionally, allows checking the generated schema agains a specific JSON Schema.
+  - Grounding: Measures whether the information in the completion is derived from the prompt and provided documents.
+  - Accuracy: Measures how well the completion follows the instructions defined in the prompt.
+- **Custom**: Custom criteria allows users to define their own descriptions to create evaluation prompts and check the generated completions.
+
+### MODEL
+
+`MODEL` lets you choose a model from the Cohere suite for which the prompt should be optimized for.
+
+### VARIABLES
+
+`VARIABLES` allows you to test how the prompt generalizes to multiple scenarios. Suppose writing a job description should be extended to multiple positions with different requirements.
+
+For example: 
+
+- **Job posting 1: **
+  - **Position:** Data Scientist,
+  - **Requirements:** proficiency in Python, experience with machine learning algorithms, knowledge of data visualisation tools, and familiarity with big data technologies.
+- **Job posting 2:**
+  - **Position:** Product Manager
+  - **Requirements:** Strong understanding of product lifecycle management, experience with market research and user feedback analysis, excellent communication and leadership skills, and familiarity with Agile methodologies.
+- **Job posting 3:** 
+  - **Position:** Software Engineer
+  - **Requirements:** Proficiency in Java or C++, experience with software development lifecycle, strong problem-solving skills, and familiarity with version control systems like Git.
+
+To account for this, the initial prompt can be modified to include placeholders:
+
+```
+Create a job description for a ${position} position with the following requirements: ${requirements}.
+```
+
+
+
+After adjusting the prompt, the variable names will appear in the `VARIABLES` section, where the appropriate values can be entered.
+
+

+
+

+
+### DOCUMENTS
+
+Cohere models have strong Retrieval Augmented Generation (RAG) capabilites. Therefore, the [Prompt Tuner](https://dashboard.cohere.com/prompt-tuner) also allows you to optimize prompts for these use cases, as well. If you want to ground your task in the context of a document, you can upload the document, and the optimizer will handle the rest.
+
+Note: Currently, we only support raw text documents.
+
+# More examples
+
+For more examples, please see the example section where we provide templates for more real-life scenarios:  
+
+- Performance Review
+- Word Definition
+- Social Media Content Creation
diff --git a/fern/pages/text-generation/summarizing-text.mdx b/fern/pages/text-generation/summarizing-text.mdx
new file mode 100644
index 00000000..4273bbf2
--- /dev/null
+++ b/fern/pages/text-generation/summarizing-text.mdx
@@ -0,0 +1,246 @@
+---
+title: Summarizing Text
+description: "The document explains how to perform text summarization using Cohere's Chat endpoint, highlighting features like length and format control, and the use of retrieval-augmented generation for grounded summaries. It also provides guidance on migrating from the Generate and Summarize endpoints to the Chat endpoint."
+image: "../../assets/images/9272011-cohere_meta_image.jpg"
+keywords: "Cohere, large language models, generative AI"
+slug: /docs/summarizing-text
+---
+
+Text summarization distills essential information and generates concise snippets from dense documents. With Cohere, you can do text summarization via the Chat endpoint.
+
+The Command R family of models (R and R+) supports 128k context length, so you can pass long documents to be summarized.
+
+## Basic summarization
+
+You can perform text summarization with a simple prompt asking the model to summarize a piece of text.
+
+```python PYTHON
+document = """Equipment rental in North America is predicted to “normalize” going into 2024,
+according to Josh Nickell, vice president of equipment rental for the American Rental
+Association (ARA).
+“Rental is going back to ‘normal,’ but normal means that strategy matters again -
+geography matters, fleet mix matters, customer type matters,” Nickell said. “In
+late 2020 to 2022, you just showed up with equipment and you made money.
+“Everybody was breaking records, from the national rental chains to the smallest
+rental companies; everybody was having record years, and everybody was raising
+prices. The conversation was, ‘How much are you up?’ And now, the conversation
+is changing to ‘What’s my market like?’”
+Nickell stressed this shouldn’t be taken as a pessimistic viewpoint. It’s simply
+coming back down to Earth from unprecedented circumstances during the time of Covid.
+Rental companies are still seeing growth, but at a more moderate level."""
+
+
+
+response = co.chat(message= f"Generate a concise summary of this text\n{document}").text
+
+
+print(response)
+```
+
+(NOTE: Here, we are passing the document as a variable, but you can also just copy the document directly into the prompt and ask Chat to summarize it.)
+
+Here's a sample output:
+
+```
+The equipment rental market in North America is expected to normalize by 2024,
+according to Josh Nickell of the American Rental Association. This means a shift
+from the unprecedented growth of 2020-2022, where demand and prices were high,
+to a more strategic approach focusing on geography, fleet mix, and customer type.
+Rental companies are still experiencing growth, but at a more moderate and sustainable level.
+```
+
+### Length control
+
+You can further control the output by defining the length of the summary in your prompt. For example, you can specify the number of sentences to be generated.
+
+```python PYTHON
+response = co.chat(message= f"Summarize this text in one sentence\n{document}").text
+
+print(response)
+```
+
+And here's what a sample of the output might look like:
+
+```
+The equipment rental market in North America is expected to stabilize in 2024,
+with a focus on strategic considerations such as geography, fleet mix, and
+customer type, according to Josh Nickell of the American Rental Association (ARA).
+```
+
+You can also specify the length in terms of word count.
+
+```python PYTHON
+response = co.chat(message= f"Summarize this text in less than 10 words\n{document}").text
+
+print(response)
+```
+
+```
+Rental equipment supply and demand to balance.
+```
+
+(Note: While the model is generally good at adhering to length instructions, due to the nature of LLMs, we do not guarantee that the exact word, sentence, or paragraph numbers will be generated.)
+
+### Format control
+
+Instead of generating summaries as paragraphs, you can also prompt the model to generate the summary as bullet points.
+
+```python PYTHON
+response = co.chat(message= f"Generate a concise summary of this text as bullet points\n{document}").text
+
+print(response)
+```
+
+```
+- Equipment rental in North America is expected to "normalize" by 2024, according to Josh Nickell
+  of the American Rental Association (ARA).
+- This "normalization" means a return to strategic focus on factors like geography, fleet mix,
+  and customer type.
+- In the past two years, rental companies easily made money and saw record growth due to the
+  unique circumstances of the Covid pandemic.
+- Now, the focus is shifting from universal success to varying market conditions and performance.
+- Nickell's outlook is not pessimistic; rental companies are still growing, but at a more 
+  sustainable and moderate pace.
+
+```
+
+## Grounded summarization
+
+Another approach to summarization is using [retrieval-augmented generation](/docs/retrieval-augmented-generation-rag) (RAG). Here, you can instead pass the document as a chunk of documents to the Chat endpoint call.
+
+This approach allows you to take advantage of the citations generated by the endpoint, which means you can get a grounded summary of the document. Each grounded summary includes fine-grained citations linking to the source documents, making the response easily verifiable and building trust with the user.
+
+Here is a chunked version of the document. (we don’t cover the chunking process here, but if you’d like to learn more, see this cookbook on [chunking strategies](https://github.com/cohere-ai/notebooks/blob/main/notebooks/guides/Chunking_strategies.ipynb).)
+
+```python PYTHON
+document_chunked = [{"text": "Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA)."},
+{"text": "“Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money."},
+{"text": "“Everybody was breaking records, from the national rental chains to the smallest rental companies; everybody was having record years, and everybody was raising prices. The conversation was, ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”"}]
+```
+
+It also helps to create a custom preamble to prime the model about the task—that it will receive a series of text fragments from a document presented in chronological order.
+
+```python PYTHON
+preamble = """## Task & Context
+You will receive a series of text fragments from a document that are presented in chronological order. \
+As the assistant, you must generate responses to user's requests based on the information given in the fragments. \
+Ensure that your responses are accurate and truthful, and that you reference your sources where appropriate to answer \
+the queries, regardless of their complexity."""
+
+```
+
+Other than the custom preamble, the only change to the Chat endpoint call is passing the document parameter containing the list of document chunks.
+
+Aside from displaying the actual summary (response.text), we can display the citations as as well (response.citations). The citations are a list of specific passages in the response that cite from the documents that the model receives.
+
+```python PYTHON
+response = co.chat(message= f"Summarize this text in two sentences.", preamble=preamble, documents=document_chunked)
+print(response.text)
+
+# Print citations (if any)
+if response.citations:
+    print("\nCitations:")
+    for citation in response.citations:
+        print(citation)
+    print("\nCited Documents:")
+    for document in response.documents:
+        print(document)
+
+```
+
+```
+Josh Nickell, vice president of the American Rental Association, predicts that equipment rental in North America will "normalize" by 2024. This means that factors like geography, fleet mix, and customer type will influence success in the market.
+
+Citations:
+start=0 end=4 text='Josh' document_ids=['doc_0']
+start=5 end=12 text='Nickell' document_ids=['doc_0', 'doc_1']
+start=14 end=63 text='vice president of the American Rental Association' document_ids=['doc_0']
+start=79 end=112 text='equipment rental in North America' document_ids=['doc_0']
+start=118 end=129 text='"normalize"' document_ids=['doc_0', 'doc_1']
+start=133 end=138 text='2024.' document_ids=['doc_0']
+start=168 end=245 text='geography, fleet mix, and customer type will influence success in the market.' document_ids=['doc_1']
+
+Cited Documents:
+{'id': 'doc_0', 'text': 'Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA).'}
+{'id': 'doc_1', 'text': '“Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money.'}
+
+```
+
+## Migrating from Generate to Chat Endpoint
+
+This guide outlines how to migrate from Generate to Chat; the biggest difference is simply the need to replace the `prompt` argument with `message`, but there's also no model default, so you'll have to specify a model.
+
+```python PYTHON
+# Before
+
+co.generate(
+  prompt="""Write a short summary from the following text in bullet point format, in different
+  words.
+  
+  Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA).
+  “Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money.
+  “Everybody was breaking records, from the national rental chains to the smallest rental companies; everybody was having record years, and everybody was raising prices. The conversation was, ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”
+  Nickell stressed this shouldn’t be taken as a pessimistic viewpoint. It’s simply coming back down to Earth from unprecedented circumstances during the time of Covid. Rental companies are still seeing growth, but at a more moderate level.
+  """
+)
+
+# After
+co.chat(
+  message="""Write a short summary from the following text in bullet point format,
+  in different words.
+  
+  Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA).
+  “Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money.
+  “Everybody was breaking records, from the national rental chains to the smallest rental companies; everybody was having record years, and everybody was raising prices. The conversation was, ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”
+  Nickell stressed this shouldn’t be taken as a pessimistic viewpoint. It’s simply coming back down to Earth from unprecedented circumstances during the time of Covid. Rental companies are still seeing growth, but at a more moderate level.
+  """,
+  model="command-r-plus"
+)
+
+```
+
+## Migration from Summarize to Chat Endpoint
+
+To use the Command R/R+ models for summarization, we recommend using the Chat endpoint. This guide outlines how to migrate from the Summarize endpoint to the Chat endpoint.
+
+```python PYTHON
+# Before
+
+co.summarize(
+  format="bullets",
+  length="short",
+  extractiveness="low",
+  text="""Equipment rental in North America is predicted to “normalize” going into 2024, according
+  to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA).
+  “Rental is going back to ‘normal,’ but normal means that strategy matters again - geography
+  matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you
+  just showed up with equipment and you made money.
+  “Everybody was breaking records, from the national rental chains to the smallest rental companies;
+  everybody was having record years, and everybody was raising prices. The conversation was, ‘How
+  much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”
+  Nickell stressed this shouldn’t be taken as a pessimistic viewpoint. It’s simply coming back
+  down to Earth from unprecedented circumstances during the time of Covid. Rental companies are
+  still seeing growth, but at a more moderate level.
+  """
+)
+
+# After
+co.chat(
+  message="""Write a short summary from the following text in bullet point format, in different words.
+  
+  Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh
+  Nickell, vice president of equipment rental for the American Rental Association (ARA).
+  “Rental is going back to ‘normal,’ but normal means that strategy matters again - geography
+  matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just
+  showed up with equipment and you made money.
+  “Everybody was breaking records, from the national rental chains to the smallest rental companies;
+  everybody was having record years, and everybody was raising prices. The conversation was,
+  ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”
+  Nickell stressed this shouldn’t be taken as a pessimistic viewpoint. It’s simply coming back
+  down to Earth from unprecedented circumstances during the time of Covid. Rental companies are
+  still seeing growth, but at a more moderate level.
+  """,
+  model="command-r-plus"
+)
+
+```
diff --git a/fern/pages/tutorials/build-things-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere.mdx
new file mode 100644
index 00000000..e9391a3a
--- /dev/null
+++ b/fern/pages/tutorials/build-things-with-cohere.mdx
@@ -0,0 +1,97 @@
+---
+title: Build Things with Cohere!
+slug: /docs/build-things-with-cohere
+---
+
+Welcome to our hands-on introduction to Cohere! This section is split over seven different tutorials, each focusing on one use case leveraging our Chat, Embed, and Rerank endpoints:
+
+- Part 1: Installation and Setup (the document you're reading now)
+- [Part 2: Text Generation](/docs/text-generation-tutorial)
+- [Part 3: Chatbots](/docs/building-a-chatbot-with-cohere)
+- [Part 4: Semantic Search](/docs/semantic-search-with-cohere)
+- [Part 5: Reranking](/docs/reranking-with-cohere)
+- [Part 6: Retrieval-Augmented Generation (RAG)](/docs/rag-with-cohere)
+- [Part 7: Agents with Tool Use](/docs/building-an-agent-with-cohere)
+
+Your learning is structured around building an onboarding assistant that helps new hires at Co1t, a fictitious company. The assistant can help write introductions, answer user questions about the company, search for information from e-mails, and create meeting appointments.
+
+We recommend that you follow the parts sequentially. However, feel free to skip to specific parts if you want (apart from Part 1, which is a pre-requisite) because each part also works as a standalone tutorial.
+
+## Installation and Setup
+
+The Cohere platform lets developers access large language model (LLM) capabilities with a few lines of code. These LLMs can solve a broad spectrum of natural language use cases, including classification, semantic search, paraphrasing, summarization, and content generation.
+
+Cohere's models can be accessed through the [playground](https://dashboard.cohere.ai/playground/generate?model=xlarge&__hstc=14363112.d9126f508a1413c0edba5d36861c19ac.1701897884505.1722364657840.1722366723691.56&__hssc=14363112.1.1722366723691&__hsfp=3560715434), SDK, and CLI tool. We support SDKs in four different languages: Python, Typescript, Java, and Go. For these tutorials, we'll use the Python SDK and access the models through the Cohere platform with an API key.
+
+To get started, first install the Cohere Python SDK.
+
+```python PYTHON
+! pip install -U cohere
+```
+
+Next, we'll import the `cohere` library and create a client to be used throughout the examples. We create a client by passing the Cohere API key as an argument. To get an API key, [sign up with Cohere](https://dashboard.cohere.com/welcome/register) and get the API key [from the dashboard](https://dashboard.cohere.com/api-keys).
+
+```python PYTHON
+import cohere
+
+co = cohere.Client(api_key="YOUR_COHERE_API_KEY") # Get your API key here: https://dashboard.cohere.com/api-keys
+```
+
+# Accessing Cohere from Other Platforms
+
+The Cohere platform is the fastest way to access Cohere's models and get started.
+
+However, if you prefer other options, you can access Cohere's models through other platforms such as Amazon Bedrock, Amazon SageMaker, Azure AI Studio, and Oracle Cloud Infrastructure (OCI) Generative AI Service.
+
+Read this documentation on [Cohere SDK cloud platform compatibility](/docs/cohere-works-everywhere). In this sections below we sketch what it looks like to access Cohere models through other means, but we link out to more extensive treatments if you'd like additional detail.
+
+## Amazon Bedrock
+
+The following is how you can create a Cohere client on Amazon Bedrock.
+
+For further information, read this documentation on [Cohere on Bedrock](/docs/cohere-on-aws#amazon-bedrock).
+
+```python PYTHON
+import cohere
+
+co = cohere.BedrockClient(
+    aws_region="...",
+    aws_access_key="...",
+    aws_secret_key="...",
+    aws_session_token="...",
+)
+```
+
+## Amazon SageMaker
+
+The following is how you can create a Cohere client on Amazon SageMaker.
+
+For further information, read this documentation on [Cohere on SageMaker](/docs/cohere-on-aws#amazon-sagemaker).
+
+```python PYTHON
+import cohere
+
+co = cohere.SagemakerClient(
+    aws_region="us-east-1",
+    aws_access_key="...",
+    aws_secret_key="...",
+    aws_session_token="...",
+)
+```
+
+## Microsoft Azure
+
+The following is how you can create a Cohere client on Microsoft Azure.
+
+For further information, read this documentation on [Cohere on Azure](/docs/cohere-on-microsoft-azure).
+
+```python PYTHON
+import cohere
+
+co = cohere.Client(
+  api_key="...",
+  base_url="...",
+)
+```
+
+In Part 2, we'll get started with the first use case - [text generation](/docs/text-generation-tutorial).
diff --git a/fern/pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx
new file mode 100644
index 00000000..695be363
--- /dev/null
+++ b/fern/pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx
@@ -0,0 +1,215 @@
+---
+title: Building a Chatbot with Cohere
+slug: /docs/building-a-chatbot-with-cohere
+---
+
+
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Open in Colab
+
+As its name implies, the Chat endpoint enables developers to build chatbots that can handle conversations. At the core of a conversation is a multi-turn dialog between the user and the chatbot. This requires the chatbot to have the state (or “memory”) of all the previous turns to maintain the state of the conversation.
+
+In this tutorial, you'll learn about:
+
+- Creating a custom preamble
+- Creating a single-turn conversation
+- Building the conversation memory
+- Running a multi-turn conversation
+- Viewing the chat history
+
+You'll learn these by building an onboarding assistant for new hires.
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere
+
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Creating a custom preamble
+
+A conversation starts with a system message, or a [preamble](/docs/preambles), to help steer a chatbot’s response toward certain characteristics.
+
+For example, if we want the chatbot to adopt a formal style, the preamble can be used to encourage the generation of more business-like and professional responses.
+
+The recommended approach is to use two H2 Markdown headers: "Task and Context" and "Style Guide" in the exact order.
+
+In the example below, the preamble provides context for the assistant's task (task and context) and encourages the generation of rhymes as much as possible (style guide).
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates."
+
+# Create a custom preamble
+preamble="""## Task and Context
+You are an assistant who assist new employees of Co1t with their first week.
+
+## Style Guide
+Try to speak in rhymes as much as possible. Be professional."""
+
+# Generate the response
+response = co.chat(message=message,
+                   preamble=preamble)
+
+print(response.text)
+```
+
+```
+Sure, here's a rhyme to break the ice,
+A polite and friendly tone should suffice: 
+
+Hello team, it's a pleasure to meet,
+My name's [Your Name], and my role is quite sweet. 
+
+I'm thrilled to join Co1t, a startup so bright,
+Where innovation and talent ignite. 
+
+My role here is [Your Role], a position brand new,
+Where I'll contribute and learn from you. 
+
+I look forward to working together in harmony,
+Exchanging ideas and creating synergy. 
+
+Feel free to connect, and let's start anew,
+I'm excited to be part of this team, me and you! 
+
+Cheers to a great first week,
+And many successes, unique and sleek! 
+
+Let's collaborate and soar,
+Co1t's future is bright, that's for sure! 
+
+Regards, 
+[Your Name] 
+
+(P.S. I'm a poet and didn't know it!)
+```
+
+Further reading:
+
+- [Documentation on preambles](/docs/preambles)
+
+## Creating a single-turn conversation
+
+Let's start with a single-turn conversation, which doesn't require the chatbot to maintain any conversation state. 
+
+Here, we are also adding a custom preamble for generating concise response, just to keep the outputs brief for this tutorial.
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates."
+
+# Create a custom preamble
+preamble="""## Task & Context
+Generate concise responses, with maximum one-sentence."""
+
+# Generate the response
+response = co.chat(message=message,
+                   preamble=preamble)
+
+print(response.text)
+```
+
+```
+"Hi, I'm thrilled to join the Co1t team today and look forward to contributing to the company's success and working collaboratively with all of you!"
+```
+
+## Building the conversation memory
+
+Now, we want the model to refine the earlier response. This requires the next generation to have access to the state, or memory, of the conversation.
+
+To do this, we add the `chat_history` argument, which takes the current chat history as the value.
+
+You can get the current chat history by taking the the `response.chat_history` object from the previous response.
+
+Looking at the response, we see that the model is able to get the context from the chat history. The model is able to capture that "it" in the user message refers to the introduction message it had generated earlier.
+
+```python PYTHON
+# Add the user message
+message = "Make it more upbeat and conversational."
+
+# Generate the response with the current chat history as the context
+response = co.chat(message=message,
+                   preamble=preamble,
+                   chat_history=response.chat_history)
+
+print(response.text)
+```
+
+```
+"Hey, I'm stoked to be a part of the Co1t crew! Can't wait to dive in and work together to make our startup vision a reality!"
+```
+
+Further reading:
+
+- [Documentation on using the Chat endpoint](/docs/chat-api)
+
+## Running a multi-turn conversation
+
+You can continue doing this for any number of turns by passing the most recent `response.chat_history` value, which contains the conversation history from the beginning.
+
+```python PYTHON
+# Add the user message
+message = "Thanks. Could you create another one for my DM to my manager."
+
+# Generate the response with the current chat history as the context
+response = co.chat(message=message,
+                   preamble=preamble,
+                   chat_history=response.chat_history)
+
+print(response.text)
+```
+
+```
+"Super excited to be a part of the Co1t family! Looking forward to learning from your expertise and guidance and contributing my best to the team's success under your management."
+```
+
+## Viewing the chat history
+
+To look at the current chat history, you can print the `response.chat_history` object, which contains a list of `USER` and `CHATBOT` turns in the same sequence as they were created.
+
+```python PYTHON
+# View the chat history
+for turn in response.chat_history:
+    print("Role:",turn.role)
+    print("Message:",turn.message,"\n")
+```
+
+```
+Role: USER
+Message: I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates. 
+
+Role: CHATBOT
+Message: "Hi, I'm thrilled to join the Co1t team today and look forward to contributing to the company's success and working collaboratively with all of you!" 
+
+Role: USER
+Message: Make it more upbeat and conversational. 
+
+Role: CHATBOT
+Message: "Hey, I'm stoked to be a part of the Co1t crew! Can't wait to dive in and work together to make our startup vision a reality!" 
+
+Role: USER
+Message: Thanks. Could you create another one for my DM to my manager. 
+
+Role: CHATBOT
+Message: "Super excited to be a part of the Co1t family! Looking forward to learning from your expertise and guidance and contributing my best to the team's success under your management." 
+```
+
+## Conclusion
+
+In this tutorial, you learned about:
+
+- How to create a custom preamble
+- How to create a single-turn conversation
+- How to build the conversation memory
+- How to run a multi-turn conversation
+- How to view the chat history
+
+You will use the same method for running a multi-turn conversation when you learn about other use cases such as [RAG](/docs/rag-with-cohere) (Part 6) and [tool use](/docs/building-an-agent-with-cohere) (Part 7).
+
+But to fully leverage these other capabilities, you will need another type of language model that generates text representations, or embeddings.
+
+In Part 4, you will learn how text embeddings can power an important use case for RAG, which is [semantic search](/docs/semantic-search-with-cohere).
diff --git a/fern/pages/tutorials/build-things-with-cohere/building-an-agent-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere/building-an-agent-with-cohere.mdx
new file mode 100644
index 00000000..01d26639
--- /dev/null
+++ b/fern/pages/tutorials/build-things-with-cohere/building-an-agent-with-cohere.mdx
@@ -0,0 +1,373 @@
+---
+title: Building an Agent with Cohere
+slug: /docs/building-an-agent-with-cohere
+---
+
+Open in Colab
+
+Tool use extends the ideas from [RAG](/docs/rag-with-cohere), where external systems are used to guide the response of an LLM, but by leveraging a much bigger set of tools than what’s possible with RAG. The concept of tool use leverages LLMs' useful feature of being able to act as a reasoning and decision-making engine.
+
+While RAG enables applications that can _answer questions_, tool use enables those that can _automate tasks_.
+
+Tool use also enables developers to build agentic applications that can take actions, that is, doing both read and write operations on an external system.
+
+In this tutorial, you'll learn about:
+
+- Creating tools
+- Tool planning and calling
+- Tool execution
+- Response and citation generation
+- Multi-step tool use
+
+You'll learn these by building an onboarding assistant for new hires.
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere numpy
+
+import numpy as np
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Creating tools
+
+The pre-requisite, before we can run a [tool use workflow](/docs/tools), is to set up the tools. Let's create three tools:
+
+- `search_faqs`: A tool for searching the FAQs. For simplicity, we'll not implement any retrieval logic, but we'll simply pass a list of pre-defined documents, which are the FAQ documents we had used in the text embeddings section.
+- `search_emails`: A tool for searching the emails. Same as above, we'll simply pass a list of pre-defined emails from the Reranking section.
+- `create_calendar_event`: A tool for creating new calendar events. Again, for simplicity, we'll not implement actual event bookings, but will return a mock success event. In practice, we can connect to a calendar service API and implement all the necessary logic here.
+
+Here, we are defining a Python function for each tool, but more broadly, the tool can be any function or service that can receive and send objects.
+
+```python PYTHON
+# Create the tools
+def search_faqs(query):
+    faqs = [
+        {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
+        {"text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."}
+    ]
+    return  {"faqs" : faqs}
+
+def search_emails(query):
+    emails = [
+        {"from": "it@co1t.com", "to": "david@co1t.com", "date": "2024-06-24", "subject": "Setting Up Your IT Needs", "text": "Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts."},
+        {"from": "john@co1t.com", "to": "david@co1t.com", "date": "2024-06-24", "subject": "First Week Check-In", "text": "Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!"}
+    ]
+    return  {"emails" : emails}
+    
+def create_calendar_event(date: str, time: str, duration: int):
+    # You can implement any logic here
+    return {"is_success": True,
+            "message": f"Created a {duration} hour long event at {time} on {date}"}
+    
+functions_map = {
+    "search_faqs": search_faqs,
+    "search_emails": search_emails,
+    "create_calendar_event": create_calendar_event
+}
+```
+
+The second and final setup step is to define the tool schemas in a format that can be passed to the Chat endpoint. The schema must contain the following fields: `name`, `description`, and `parameter_definitions` in the format shown below. 
+
+This schema informs the LLM about what the tool does, and the LLM decides whether to use a particular tool based on it. Therefore, the more descriptive and specific the schema, the more likely the LLM will make the right tool call decisions.
+
+```python PYTHON
+# Define the tools
+tools = [
+    {
+      "name": "search_faqs",
+      "description": "Given a user query, searches a company's frequently asked questions (FAQs) list and returns the most relevant matches to the query.",
+      "parameter_definitions": {
+        "query": {
+          "description": "The query from the user",
+          "type": "str",
+          "required": True
+        }
+      }
+    }, 
+    {
+      "name": "search_emails",
+      "description": "Given a user query, searches a person's emails and returns the most relevant matches to the query.",
+      "parameter_definitions": {
+        "query": {
+          "description": "The query from the user",
+          "type": "str",
+          "required": True
+        }
+      }
+    }, 
+    {
+      "name": "create_calendar_event",
+      "description": "Creates a new calendar event of the specified duration at the specified time and date. A new event cannot be created on the same time as an existing event.",
+      "parameter_definitions": {
+        "date": {
+          "description": "the date on which the event starts, formatted as mm/dd/yy",
+          "type": "str",
+          "required": True
+        },
+        "time": {
+          "description": "the time of the event, formatted using 24h military time formatting",
+          "type": "str",
+          "required": True
+        },
+        "duration": {
+          "description": "the number of hours the event lasts for",
+          "type": "float",
+          "required": True
+        }
+      }
+    }
+]
+```
+
+## Tool planning and calling
+
+We can now run the tool use workflow. We can think of a tool use system as consisting of four components:
+
+- The user
+- The application
+- The LLM
+- The tools
+
+At its most basic, these four components interact in a workflow through four steps:
+
+- **Step 1: Get user message** – The LLM gets the user message (via the application)
+- **Step 2: Tool planning and calling** – The LLM makes a decision on the tools to call (if any) and generates - the tool calls
+- **Step 3: Tool execution** - The application executes the tools and the results are sent to the LLM
+- **Step 4: Response and citation generation** – The LLM generates the response and citations to back to the user
+
+```python PYTHON
+# Step 1: Get user message
+message = "Any messages about getting setup with IT?"
+
+preamble="""## Task & Context
+You are an assistant who assist new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024"""
+
+# Step 2: Tool planning and calling
+response = co.chat(
+    message=message,
+    preamble=preamble,
+    tools=tools)
+
+if response.tool_calls:
+    print("Tool plan:")
+    print(response.text,"\n")
+        
+    print("Tool calls:")
+    for call in response.tool_calls:
+        print(f"Tool name: {call.name} | Parameters: {call.parameters}")
+```
+
+```
+Tool plan:
+I will search the user's emails for any messages about getting set up with IT. 
+
+Tool calls:
+Tool name: search_emails | Parameters: {'query': 'IT setup'}
+```
+
+Given three tools to choose from, the model is able to pick the right tool (in this case, `search_emails`) based on what the user is asking for.
+
+Also, notice that the model first generates a plan about what it should do ("I will do ...") before actually generating the tool call(s).
+
+## Tool execution
+
+```python PYTHON
+# Step 3: Tool execution
+tool_results = []
+for tc in response.tool_calls:
+    tool_call = {"name": tc.name, "parameters": tc.parameters}
+    tool_output = functions_map[tc.name](**tc.parameters)
+    tool_results.append({"call": tool_call, "outputs": [tool_output]})
+
+print("Tool results:") 
+for result in tool_results:
+    print(result)
+```
+
+```
+Tool results:
+{'call': {'name': 'search_emails', 'parameters': {'query': 'IT setup'}}, 'outputs': [{'emails': [{'from': 'it@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'Setting Up Your IT Needs', 'text': 'Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.'}, {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': "Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!"}]}]}
+```
+
+## Response and citation generation
+
+```python PYTHON
+# Step 4: Response and citation generation
+response = co.chat(
+    message="", # In response generation, we set the message as empty
+    preamble=preamble,
+    tools=tools,
+    tool_results=tool_results,
+    chat_history=response.chat_history
+)
+
+# Print final response
+print("Final response:")
+print(response.text)
+print("="*50)
+
+# Print citations (if any)
+if response.citations:
+    print("\nCITATIONS:")
+    for citation in response.citations:
+        print(citation)
+
+    print("\nCITED REFERENCES:")
+    for document in response.documents:
+        print(document)
+```
+
+```
+Final response:
+You have an email from IT with a comprehensive guide attached to help you set up your work accounts.
+==================================================
+
+CITATIONS:
+start=12 end=25 text='email from IT' document_ids=['search_emails:0:2:0']
+start=33 end=61 text='comprehensive guide attached' document_ids=['search_emails:0:2:0']
+start=74 end=99 text='set up your work accounts' document_ids=['search_emails:0:2:0']
+
+CITED REFERENCES:
+{'emails': '[{"date":"2024-06-24","from":"it@co1t.com","subject":"Setting Up Your IT Needs","text":"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.","to":"david@co1t.com"},{"date":"2024-06-24","from":"john@co1t.com","subject":"First Week Check-In","text":"Hello! I hope you\'re settling in well. Let\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\'s a great opportunity to get to know your colleagues!","to":"david@co1t.com"}]', 'id': 'search_emails:0:2:0', 'tool_name': 'search_emails'}
+```
+
+## Multi-step tool use
+
+The model can execute more complex tasks in tool use – tasks that require tool calls to happen in a sequence. This is referred to as "multi-step" tool use. 
+
+Let's create a function to called `run_assistant` to implement these steps, and along the way, print out the key events and messages. Optionally, this function also accepts the chat history as an argument to keep the state in a multi-turn conversation. 
+
+```python PYTHON
+model = "command-r-plus"
+
+preamble="""## Task & Context
+You are an assistant who assists new employees of Co1t with their first week. You respond to their questions and assist them with their needs. Today is Monday, June 24, 2024"""
+
+# A function that runs multi-step tool use
+def run_assistant(message, chat_history=[]):
+    # Step 1: get user message
+    print(f"Question:\n{message}")
+    print("="*50)
+
+    # Step 2: Generate tool calls (if any)    
+    response = co.chat(
+        message=message,
+        model=model,
+        preamble=preamble,
+        tools=tools,
+        chat_history=chat_history
+    )
+
+    # Tool execution loop
+    while response.tool_calls:
+        tool_calls = response.tool_calls
+        
+        if response.text:
+            print("Intermediate response:")
+            print(response.text,"\n")
+        print("Tool calls:")
+        for call in tool_calls:
+            print(f"Tool name: {call.name} | Parameters: {call.parameters}")
+        print("="*50)
+        
+        # Step 3: Get tool results
+        tool_results = []
+        for tc in tool_calls:
+            tool_call = {"name": tc.name, "parameters": tc.parameters}
+            tool_output = functions_map[tc.name](**tc.parameters)
+            tool_results.append({"call": tool_call, "outputs": [tool_output]})
+        
+        # Step 4: Generate response and citations                
+        response = co.chat(
+            message="",
+            model=model,
+            preamble=preamble,
+            tools=tools,
+            tool_results=tool_results,
+            chat_history=response.chat_history
+        )
+
+        chat_history = response.chat_history
+        
+    # Print final response
+    print("Final response:")
+    print(response.text)
+    print("="*50)
+    
+    # Print citations (if any)
+    if response.citations:
+        print("\nCITATIONS:")
+        for citation in response.citations:
+            print(citation)
+
+        print("\nCITED REFERENCES:")
+        for document in response.documents:
+            print(document)
+                
+    return chat_history
+```
+
+To illustrate the concept of multi-step tool user, let's ask the assistant to block time for any lunch invites received in the email.
+
+This requires tasks to happen over multiple steps in a sequence. Here, we see the assistant running these steps:
+
+- First, it calls the `search_emails` tool to find any lunch invites, which it found one.
+- Next, it calls the `create_calendar_event` tool to create an event to block the person's calendar on the day mentioned by the email.
+
+This is also an example of tool use enabling a write operation instead of just a read operation that we saw with RAG.
+
+```python PYTHON
+chat_history = run_assistant("Can you check if there are any lunch invites, and for those days, block an hour on my calendar from 12-1PM.")
+```
+
+```
+Question:
+Can you check if there are any lunch invites, and for those days, block an hour on my calendar from 12-1PM.
+==================================================
+Intermediate response:
+I will search the user's emails for lunch invites, and then create calendar events for the dates and times of those invites. 
+
+Tool calls:
+Tool name: search_emails | Parameters: {'query': 'lunch invite'}
+==================================================
+Intermediate response:
+I have found one lunch invite for Thursday 27 June at noon. I will now create a calendar event for this. 
+
+Tool calls:
+Tool name: create_calendar_event | Parameters: {'date': '06/27/24', 'duration': 1, 'time': '12:00'}
+==================================================
+Final response:
+I found one lunch invite for Thursday 27 June at noon. I have created a calendar event for this.
+==================================================
+
+CITATIONS:
+start=29 end=53 text='Thursday 27 June at noon' document_ids=['search_emails:0:2:0']
+start=62 end=95 text='created a calendar event for this' document_ids=['create_calendar_event:0:4:0']
+
+CITED REFERENCES:
+{'emails': '[{"date":"2024-06-24","from":"it@co1t.com","subject":"Setting Up Your IT Needs","text":"Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts.","to":"david@co1t.com"},{"date":"2024-06-24","from":"john@co1t.com","subject":"First Week Check-In","text":"Hello! I hope you\'re settling in well. Let\'s connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it\'s a great opportunity to get to know your colleagues!","to":"david@co1t.com"}]', 'id': 'search_emails:0:2:0', 'tool_name': 'search_emails'}
+{'id': 'create_calendar_event:0:4:0', 'is_success': 'true', 'message': 'Created a 1 hour long event at 12:00 on 06/27/24', 'tool_name': 'create_calendar_event'}
+```
+
+In this tutorial, you learned about:
+
+- How to create tools
+- How tool planning and calling happens
+- How tool execution happens
+- How to generate the response and citations
+- How to run tool use in a multi-step scenario
+
+And that concludes our 7-part Cohere tutorial. We hope that they have provided you with a foundational understanding of the Cohere API, the available models and endpoints, and the types of use cases that you can build with them.
+
+To continue your learning, check out:
+
+- [LLM University - A range of courses and step-by-step guides to help you start building](https://cohere.com/llmu)
+- [Cookbooks - A collection of basic to advanced example applications](/page/cookbooks)
+- [Cohere's documentation](/docs/the-cohere-platform)
+- [The Cohere API reference](/reference/about)
diff --git a/fern/pages/tutorials/build-things-with-cohere/rag-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere/rag-with-cohere.mdx
new file mode 100644
index 00000000..9ebc2530
--- /dev/null
+++ b/fern/pages/tutorials/build-things-with-cohere/rag-with-cohere.mdx
@@ -0,0 +1,373 @@
+---
+title: RAG with Cohere
+slug: /docs/rag-with-cohere
+---
+
+Open in Colab
+
+The Chat endpoint provides comprehensive support for various text generation use cases, including retrieval-augmented generation (RAG). 
+
+While LLMs are good at maintaining the context of the conversation and generating responses, they can be prone to hallucinate and include factually incorrect or incomplete information in their responses.
+
+RAG enables a model to access and utilize supplementary information from external documents, thereby improving the accuracy of its responses.
+
+When using RAG with the Chat endpoint, these responses are backed by fine-grained citations linking to the source documents. This makes the responses easily verifiable.
+
+In this tutorial, you'll learn about:
+
+- Basic RAG
+- Search query generation
+- Retrieval with Embed
+- Reranking with Rerank
+- Response and citation generation
+
+You'll learn these by building an onboarding assistant for new hires.
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere numpy
+
+import numpy as np
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Basic RAG
+
+To see how RAG works, let's define the documents that the application has access to. We'll use a short list of documents consisting of internal FAQs about the fictitious company Co1t (in production, these documents are massive).
+
+In this example, each document is a dictionary with one field, `text`. But we can define any number of fields we want, depending on the nature of the documents. For example, emails could contain `title` and `text` fields.
+
+```python PYTHON
+# Define the documents
+faqs_short = [
+    {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
+    {"text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."},
+    {"text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."},
+    {"text": "Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year."}
+]
+```
+
+To use these documents, we pass them to the `documents` parameter in the Chat endpoint call. This tells the model to run in RAG-mode and use these documents in its response.
+
+Let's create a query asking about the company's support for personal well-being, which is not going to be available to the model based on the data its trained on. It will need to use external documents.
+
+RAG introduces additional objects in the Chat response. Here we display two:
+
+- `citations`: indicate the specific text spans from the retrieved documents on which the response is grounded.
+- `documents`: the IDs of the documents referenced in the citations.
+
+```python PYTHON
+# Add the user query
+query = "Are there fitness-related perks?"
+
+# Generate the response
+response = co.chat(
+                message=query,
+                model="command-r-plus",
+                documents=faqs_short)
+
+# Display the response
+print(response.text)
+
+# Display the citations and source documents
+if response.citations:
+  print("\nCITATIONS:")
+  for citation in response.citations:
+    print(citation)
+
+  print("\nDOCUMENTS:")
+  for document in response.documents:
+    print(document)
+```
+
+```
+Yes, we offer health and wellness benefits, including gym memberships, on-site yoga classes, and comprehensive health insurance.
+
+CITATIONS:
+start=14 end=42 text='health and wellness benefits' document_ids=['doc_2']
+start=54 end=69 text='gym memberships' document_ids=['doc_2']
+start=71 end=91 text='on-site yoga classes' document_ids=['doc_2']
+start=97 end=128 text='comprehensive health insurance.' document_ids=['doc_2']
+
+DOCUMENTS:
+{'id': 'doc_2', 'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}
+```
+
+Further reading:
+
+- [Chat endpoint API reference](/reference/chat)
+- [Documentation on RAG](/docs/retrieval-augmented-generation-rag)
+- [LLM University module on RAG](https://cohere.com/llmu#rag)
+
+## Search query generation
+
+The previous example showed how to get started with RAG, and in particular, the augmented generation portion of RAG. But as its name implies, RAG consists of other steps, such as retrieval. 
+
+In a basic RAG application, the steps involved are:
+
+- Transforming the user message into search queries
+- Retrieving relevant documents for a given search query
+- Generating the response and citations
+
+Let's now look at the first step—search query generation. The chatbot needs to generate an optimal set of search queries to use for retrieval. 
+
+The Chat endpoint has a feature that handles this for us automatically. This is done by adding the `search_queries_only=True` parameter to the Chat endpoint call.
+
+It will generate a list of search queries based on a user message. Depending on the message, it can be one or more queries.
+
+In the example below, the resulting queries breaks down the user message into two separate queries.
+
+```python PYTHON
+# Add the user query
+query = "How to stay connected with the company and do you organize team events?"
+
+# Generate the search queries
+response = co.chat(message=query,
+                  search_queries_only=True)
+
+queries = []
+for r in response.search_queries:
+    queries.append(r.text)
+    
+print(queries)
+```
+
+```
+['staying connected with the company', 'team events']
+```
+
+And in the example below, the model decides that one query is sufficient.
+
+```python PYTHON
+# Add the user query
+query = "How flexible are the working hours"
+
+# Generate the search queries
+response = co.chat(message=query,
+                  search_queries_only=True)
+
+queries = []
+for r in response.search_queries:
+    queries.append(r.text)
+    
+print(queries)
+```
+
+```
+['working hours flexibility']
+```
+
+## Retrieval with Embed
+
+Given the search query, we need a way to retrieve the most relevant documents from a large collection of documents.
+
+This is where we can leverage text embeddings through the Embed endpoint. It enables semantic search, which lets us to compare the semantic meaning of the documents and the query. It solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles at capturing the context or meaning of a piece of text.
+
+The Embed endpoint takes in texts as input and returns embeddings as output.
+
+First, we need to embed the documents to search from. We call the Embed endpoint using `co.embed()` and pass the following arguments:
+
+- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024
+- `input_type`: We choose `search_document` to ensure the model treats these as the documents (instead of the query) for search
+- `texts`: The list of texts (the FAQs)
+
+```python PYTHON
+# Define the documents
+faqs_long = [
+    {"text": "Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged."},
+    {"text": "Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the café for artisan coffee."},
+    {"text": "Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!"},
+    {"text": "Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed."},
+    {"text": "Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business."},
+    {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
+    {"text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."},
+    {"text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."},
+    {"text": "Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year."},
+    {"text": "Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead."},
+]
+
+# Embed the documents
+doc_emb = co.embed(
+            model="embed-english-v3.0",
+            input_type="search_document",
+            texts=[doc['text'] for doc in faqs_long]).embeddings
+```
+
+Next, we add a query, which asks about how to get to know the team.
+
+We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of the documents) for search.
+
+```python PYTHON
+# Add the user query
+query = "How to get to know my teammates"
+
+# Generate the search query
+response = co.chat(message=query,
+                  search_queries_only=True)
+query_optimized = response.search_queries[0].text
+
+# Embed the search query
+query_emb = co.embed(
+    model="embed-english-v3.0",
+    input_type="search_query",
+    texts=[query_optimized]).embeddings
+```
+
+Now, we want to search for the most relevant documents to the query. For this, we make use of the `numpy` library to compute the similarity between each query-document pair using the dot product approach.
+
+Each query-document pair returns a score, which represents how similar the pair are. We then sort these scores in descending order and select the top most similar pairs, which we choose 5 (this is an arbitrary choice, you can choose any number).
+
+Here, we show the most relevant documents with their similarity scores.
+
+```python PYTHON
+# Compute dot product similarity and display results
+n = 5
+scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+scores_sorted = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)[:n]
+
+retrieved_documents = [faqs_long[item[0]] for item in scores_sorted]
+
+for idx, item in enumerate(scores_sorted):
+    print(f"Rank: {idx+1}")
+    print(f"Score: {item[1]}")
+    print(f"Document: {faqs_long[item[0]]}\n")
+```
+
+```
+Rank: 1
+Score: 0.32675385963873044
+Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}
+
+Rank: 2
+Score: 0.2683516879250747
+Document: {'text': 'Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead.'}
+
+Rank: 3
+Score: 0.25784017142593213
+Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+
+Rank: 4
+Score: 0.18610347850687634
+Document: {'text': "Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the café for artisan coffee."}
+
+Rank: 5
+Score: 0.12958686394309055
+Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}
+```
+
+Further reading:
+
+- [Embed endpoint API reference](/reference/embed)
+- [Documentation on the Embed endpoint](/docs/embeddings)
+- [Documentation on the models available on the Embed endpoint](/docs/cohere-embed)
+
+## Reranking with Rerank
+
+Reranking can boost the results from semantic or lexical search further. The Rerank endpoint takes a list of search results and reranks them according to the most relevant documents to a query. This requires just a single line of code to implement.
+
+We call the endpoint using `co.rerank()` and pass the following arguments:
+
+- `query`: The user query
+- `documents`: The list of documents we get from the semantic search results
+- `top_n`: The top reranked documents to select
+- `model`: We choose Rerank English 3
+
+Looking at the results, we see that the given a query about getting to know the team, the document that talks about joining Slack channels is now ranked higher (1st) compared to earlier (3rd).
+
+Here we select `top_n` to be 2, which will be the documents we will pass next for response generation.
+
+```python PYTHON
+# Rerank the documents
+results = co.rerank(query=query_optimized,
+                    documents=retrieved_documents,
+                    top_n=2,
+                    model='rerank-english-v3.0')
+
+# Display the reranking results
+for idx, result in enumerate(results.results):
+    print(f"Rank: {idx+1}") 
+    print(f"Score: {result.relevance_score}")
+    print(f"Document: {retrieved_documents[result.index]}\n")
+    
+reranked_documents = [retrieved_documents[result.index] for result in results.results]
+```
+
+```
+Rank: 1
+Score: 0.0040072887
+Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+
+Rank: 2
+Score: 0.0020829707
+Document: {'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}
+```
+
+Further reading:
+
+- [Rerank endpoint API reference](/reference/rerank)
+- [Documentation on Rerank](/docs/overview)
+- [Documentation on Rerank fine-tuning](/docs/rerank-fine-tuning)
+- [Documentation on Rerank best practices](/docs/reranking-best-practices)
+
+## Response and citation generation
+
+Finally we reach the step that we saw in the earlier `Basic RAG` section. Here, the response is generated based on the the query and the documents retrieved.
+
+RAG introduces additional objects in the Chat response. Here we display two:
+
+- `citations`: indicate the specific spans of text from the retrieved documents on which the response is grounded.
+- `documents`: the IDs of the documents being referenced in the citations.
+
+```python PYTHON
+# Generate the response
+response = co.chat(
+                message=query_optimized,
+                model="command-r-plus",
+                documents=reranked_documents)
+
+# Display the response
+print(response.text)
+
+# Display the citations and source documents
+if response.citations:
+  print("\nCITATIONS:")
+  for citation in response.citations:
+    print(citation)
+
+  print("\nDOCUMENTS:")
+  for document in response.documents:
+    print(document)
+```
+
+```
+There are a few ways to get to know your teammates. You could join your company's Slack channels to stay informed and connected. You could also take part in team-building activities, such as outings and game nights.
+
+CITATIONS:
+start=62 end=96 text="join your company's Slack channels" document_ids=['doc_0']
+start=100 end=128 text='stay informed and connected.' document_ids=['doc_0']
+start=157 end=181 text='team-building activities' document_ids=['doc_1']
+start=191 end=215 text='outings and game nights.' document_ids=['doc_1']
+
+DOCUMENTS:
+{'id': 'doc_0', 'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+{'id': 'doc_1', 'text': 'Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!'}
+```
+
+## Conclusion
+
+In this tutorial, you learned about:
+
+- How to get started with RAG
+- How to generate search queries
+- How to perform retrieval with Embed
+- How to perform reranking with Rerank
+- How to generate response and citations
+
+RAG is great for building applications that can _answer questions_ by grounding the response in external documents. But you can unlock the ability to not just answer questions, but also _automate tasks_. This can be done using a technique called tool use.
+
+In Part 7, you will learn how to leverage [tool use](/docs/building-an-agent-with-cohere) to automate tasks and workflows.
diff --git a/fern/pages/tutorials/build-things-with-cohere/reranking-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere/reranking-with-cohere.mdx
new file mode 100644
index 00000000..51453db4
--- /dev/null
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@@ -0,0 +1,245 @@
+---
+title: Reranking with Cohere
+slug: /docs/reranking-with-cohere
+---
+
+Open in Colab
+
+Reranking is a technique that leverages [embeddings](/docs/embeddings) as the last stage of a retrieval process, and is especially useful in [RAG systems](/docs/retrieval-augmented-generation-rag).
+
+We can rerank results from semantic search as well as any other search systems such as lexical search. This means that companies can retain an existing keyword-based (also called “lexical”) or semantic search system for the first-stage retrieval and integrate the [Rerank endpoint](/docs/rerank-2) in the second-stage reranking.
+
+In this tutorial, you'll learn about:
+
+- Reranking lexical/semantic search results
+- Reranking semi-structured data
+- Reranking tabular data
+- Multilingual reranking
+
+You'll learn these by building an onboarding assistant for new hires.
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere numpy
+
+import numpy as np
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Reranking lexical/semantic search results
+
+Rerank requires just a single line of code to implement.
+
+Suppose we have a list of search results of an FAQ list, which can come from semantic, lexical, or any other types of search systems. But this list may not be optimally ranked for relevance to the user query.
+
+This is where Rerank can help. We call the endpoint using `co.rerank()` and pass the following arguments:
+
+- `query`: The user query
+- `documents`: The list of documents
+- `top_n`: The top reranked documents to select
+- `model`: We choose Rerank English 3
+
+```python PYTHON
+# Define the documents
+faqs_short = [
+    {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
+    {"text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."},
+    {"text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."},
+    {"text": "Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year."}
+]
+```
+
+```python PYTHON
+# Add the user query
+query = "Are there fitness-related perks?"
+
+# Rerank the documents
+results = co.rerank(query=query,
+                    documents=faqs_short,
+                    top_n=2,
+                    model='rerank-english-v3.0')
+
+print(results)
+```
+
+```
+id='9633b278-93ff-4664-a142-7d9dcf0ec0e5' results=[RerankResponseResultsItem(document=None, index=2, relevance_score=0.01798621), RerankResponseResultsItem(document=None, index=3, relevance_score=8.463939e-06)] meta=ApiMeta(api_version=ApiMetaApiVersion(version='1', is_deprecated=None, is_experimental=None), billed_units=ApiMetaBilledUnits(input_tokens=None, output_tokens=None, search_units=1, classifications=None), tokens=None, warnings=None)
+```
+
+```python PYTHON
+# Display the reranking results
+def return_results(results, documents):    
+    for idx, result in enumerate(results.results):
+        print(f"Rank: {idx+1}") 
+        print(f"Score: {result.relevance_score}")
+        print(f"Document: {documents[result.index]}\n")
+    
+return_results(results, faqs_short)
+```
+
+```
+Rank: 1
+Score: 0.01798621
+Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}
+
+Rank: 2
+Score: 8.463939e-06
+Document: {'text': 'Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.'}
+```
+
+Further reading:
+
+- [Rerank endpoint API reference](/reference/rerank)
+- [Documentation on Rerank](/docs/overview)
+- [Documentation on Rerank fine-tuning](/docs/rerank-fine-tuning)
+- [Documentation on Rerank best practices](/docs/reranking-best-practices)
+- [LLM University module on Text Representation](https://cohere.com/llmu#text-representation)
+
+## Reranking semi-structured data
+
+The Rerank 3 model supports multi-aspect and semi-structured data like emails, invoices, JSON documents, code, and tables. By setting the rank fields, you can select which fields the model should consider for reranking.
+
+In the following example, we'll use an email data example. It is a semi-stuctured data that contains a number of fields – `from`, `to`, `date`, `subject`, and `text`. 
+
+Suppose the new hire now wants to search for any emails about check-in sessions. Let's pretend we have a list of five emails retrieved from the email provider's API.
+
+To perform reranking over semi-structured data, we add an additional parameter, `rank_fields`, which contains the list of available fields.
+
+The model will rerank based on order of the fields passed in. For example, given `rank_fields=['title','author','text']`, the model will rerank using the values in title, author, and text sequentially. 
+
+```python PYTHON
+# Define the documents
+emails = [
+    {"from": "hr@co1t.com", "to": "david@co1t.com", "date": "2024-06-24", "subject": "A Warm Welcome to Co1t!", "text": "We are delighted to welcome you to the team! As you embark on your journey with us, you'll find attached an agenda to guide you through your first week."},
+    {"from": "it@co1t.com", "to": "david@co1t.com", "date": "2024-06-24", "subject": "Setting Up Your IT Needs", "text": "Greetings! To ensure a seamless start, please refer to the attached comprehensive guide, which will assist you in setting up all your work accounts."},
+    {"from": "john@co1t.com", "to": "david@co1t.com", "date": "2024-06-24", "subject": "First Week Check-In", "text": "Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!"}
+]
+```
+
+```python PYTHON
+# Add the user query
+query = "Any email about check ins?"
+
+# Rerank the documents
+results = co.rerank(query=query,
+                    documents=emails,
+                    top_n=2,
+                    model='rerank-english-v3.0',
+                    rank_fields=["from", "to", "date", "subject", "body"])
+
+return_results(results, emails)
+```
+
+```
+Rank: 1
+Score: 0.1979091
+Document: {'from': 'john@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'First Week Check-In', 'text': "Hello! I hope you're settling in well. Let's connect briefly tomorrow to discuss how your first week has been going. Also, make sure to join us for a welcoming lunch this Thursday at noon—it's a great opportunity to get to know your colleagues!"}
+
+Rank: 2
+Score: 9.535461e-05
+Document: {'from': 'hr@co1t.com', 'to': 'david@co1t.com', 'date': '2024-06-24', 'subject': 'A Warm Welcome to Co1t!', 'text': "We are delighted to welcome you to the team! As you embark on your journey with us, you'll find attached an agenda to guide you through your first week."}
+```
+
+## Reranking tabular data
+
+Many enterprises rely on tabular data, such as relational databases, CSVs, and Excel. To perform reranking, you can transform a dataframe into a list of JSON records and use Rerank 3's JSON capabilities to rank them.
+
+Here's an example of reranking a CSV file that contains employee information.
+
+```python PYTHON
+import pandas as pd
+from io import StringIO
+
+# Create a demo CSV file
+data = """name,role,join_date,email,status
+Rebecca Lee,Senior Software Engineer,2024-07-01,rebecca@co1t.com,Full-time
+Emma Williams,Product Designer,2024-06-15,emma@co1t.com,Full-time
+Michael Jones,Marketing Manager,2024-05-20,michael@co1t.com,Full-time
+Amelia Thompson,Sales Representative,2024-05-20,amelia@co1t.com,Part-time
+Ethan Davis,Product Designer,2024-05-25,ethan@co1t.com,Contractor"""
+data_csv = StringIO(data)
+
+# Load the CSV file
+df = pd.read_csv(data_csv)
+df.head(1)
+```
+
+Here's what the table looks like:
+
+| name        | role                     | join_date  | email                                       | status    |
+| :---------- | :----------------------- | :--------- | :------------------------------------------ | :-------- |
+| Rebecca Lee | Senior Software Engineer | 2024-07-01 | [rebecca@co1t.com](mailto:rebecca@co1t.com) | Full-time |
+
+Below, we'll get results from the Rerank endpoint:
+
+```python PYTHON
+# Define the documents and rank fields
+employees = df.to_dict('records')
+rank_fields = df.columns.tolist()
+
+# Add the user query
+query = "Any full-time product designers who joined recently?"
+
+# Rerank the documents
+results = co.rerank(query=query,
+                    documents=employees,
+                    top_n=1,
+                    model='rerank-english-v3.0',
+                    rank_fields=rank_fields)
+
+return_results(results, employees)
+
+```
+
+```
+Rank: 1
+Score: 0.986828
+Document: {'name': 'Emma Williams', 'role': 'Product Designer', 'join_date': '2024-06-15', 'email': 'emma@co1t.com', 'status': 'Full-time'}
+```
+
+## Multilingual reranking
+
+The Rerank endpoint also supports multilingual semantic search via the `rerank-multilingual-...` models. This means you can perform semantic search on texts in different languages.
+
+In the example below, we repeat the steps of performing reranking with one difference – changing the model type to a multilingual one. Here, we use the `rerank-multilingual-v3.0` model. Here, we are reranking the FAQ list using an Arabic query.
+
+```python PYTHON
+# Define the query
+query = "هل هناك مزايا تتعلق باللياقة البدنية؟" # Are there fitness benefits?
+
+# Rerank the documents
+results = co.rerank(query=query,
+                    documents=faqs_short,
+                    top_n=2,
+                    model='rerank-multilingual-v3.0')
+
+return_results(results, faqs_short)
+```
+
+```
+Rank: 1
+Score: 0.42232594
+Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}
+
+Rank: 2
+Score: 0.00025118678
+Document: {'text': 'Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.'}
+```
+
+## Conclusion
+
+In this tutorial, you learned about:
+
+- How to rerank lexical/semantic search results
+- How to rerank semi-structured data
+- How to rerank tabular data
+- How to perform Multilingual reranking
+
+We have now seen two critical components of a powerful search system - [semantic search](/docs/semantic-search-with-cohere), or dense retrieval (Part 4) and reranking (Part 5). These building blocks are essential for implementing RAG solutions.
+
+In Part 6, you will learn how to [implement RAG](/docs/rag-with-cohere).
diff --git a/fern/pages/tutorials/build-things-with-cohere/semantic-search-with-cohere.mdx b/fern/pages/tutorials/build-things-with-cohere/semantic-search-with-cohere.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,277 @@
+---
+title: Semantic Search with Cohere
+slug: /docs/semantic-search-with-cohere
+---
+
+Open in Colab
+
+[Text embeddings](/docs/embeddings) are lists of numbers that represent the context or meaning inside a piece of text. This is particularly useful in search or information retrieval applications. With text embeddings, this is called semantic search.
+
+Semantic search solves the problem faced by the more traditional approach of lexical search, which is great at finding keyword matches, but struggles to capture the context or meaning of a piece of text.
+
+With Cohere, you can generate text embeddings through the [Embed endpoint](/docs/cohere-embed) (Embed v3 being the latest model), which supports over 100 languages.
+
+In this tutorial, you'll learn about:
+
+- Embedding the documents
+- Embedding the query
+- Performing semantic search
+- Multilingual semantic search
+- Changing embedding compression types
+
+You'll learn these by building an onboarding assistant for new hires.
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere
+
+import numpy as np
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Embedding the documents
+
+The Embed endpoint takes in texts as input and returns embeddings as output.
+
+For semantic search, there are two types of documents we need to turn into embeddings.
+
+- The list of documents that we want to search from.
+- The query that will be used to search the documents.
+
+Right now, we are doing the former. We call the Embed endpoint using `co.embed()` and pass the following arguments:
+
+- `model`: Here we choose `embed-english-v3.0`, which generates embeddings of size 1024
+- `input_type`: We choose `search_document` to ensure the model treats these as the documents for search
+- `texts`: The list of texts (the FAQs)
+
+```python PYTHON
+# Define the documents
+faqs_long = [
+    {"text": "Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged."},
+    {"text": "Finding Coffee Spots: For your caffeine fix, head to the break room's coffee machine or cross the street to the café for artisan coffee."},
+    {"text": "Team-Building Activities: We foster team spirit with monthly outings and weekly game nights. Feel free to suggest new activity ideas anytime!"},
+    {"text": "Working Hours Flexibility: We prioritize work-life balance. While our core hours are 9 AM to 5 PM, we offer flexibility to adjust as needed."},
+    {"text": "Side Projects Policy: We encourage you to pursue your passions. Just be mindful of any potential conflicts of interest with our business."},
+    {"text": "Reimbursing Travel Expenses: Easily manage your travel expenses by submitting them through our finance tool. Approvals are prompt and straightforward."},
+    {"text": "Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours."},
+    {"text": "Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance."},
+    {"text": "Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year."},
+    {"text": "Proposing New Ideas: Innovation is welcomed! Share your brilliant ideas at our weekly team meetings or directly with your team lead."},
+]
+
+documents = faqs_long
+
+# Embed the documents
+doc_emb = co.embed(
+            model="embed-english-v3.0",
+            input_type="search_document",
+            texts=[doc['text'] for doc in documents]).embeddings
+```
+
+Further reading:
+
+- [Embed endpoint API reference](/reference/embed)
+- [Documentation on the Embed endpoint](/docs/embeddings)
+- [Documentation on the models available on the Embed endpoint](/docs/cohere-embed)
+- [LLM University module on Text Representation](https://cohere.com/llmu#text-representation)
+
+## Embedding the query
+
+Next, we add a query, which asks about how to stay connected to company updates.
+
+We choose `search_query` as the `input_type` to ensure the model treats this as the query (instead of documents) for search.
+
+```python PYTHON
+# Add the user query
+query = "How do I stay connected to what's happening at the company?"
+
+# Embed the query
+query_emb = co.embed(
+            model="embed-english-v3.0",
+            input_type="search_query",
+            texts=[query]).embeddings
+```
+
+## Performing semantic search
+
+Now, we want to search for the most relevant documents to the query. We do this by computing the similarity between the embeddings of the query and each of the documents.
+
+There are various approaches to compute similarity between embeddings, and we'll choose the dot product approach. For this, we use the `numpy` library which comes with the implementation.
+
+Each query-document pair returns a score, which represents how similar the pair is. We then sort these scores in descending order and select the top-most similar pairs, which we choose 2 (this is an arbitrary choice, you can choose any number).
+
+Here, we show the most relevant documents with their similarity scores.
+
+```python PYTHON
+# Compute dot product similarity and display results
+def return_results(query_emb, doc_emb, documents):
+    n = 2
+    scores = np.dot(query_emb, np.transpose(doc_emb))[0]
+    scores_sorted = sorted(enumerate(scores),
+                           key=lambda x: x[1],
+                           reverse=True)[:n]
+
+    for idx, item in enumerate(scores_sorted):
+        print(f"Rank: {idx+1}")
+        print(f"Score: {item[1]}")
+        print(f"Document: {documents[item[0]]}\n")
+        
+return_results(query_emb, doc_emb, documents)
+```
+
+```
+Rank: 1
+Score: 0.352135965228231
+Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+
+Rank: 2
+Score: 0.31995661889273097
+Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}
+```
+
+## Multilingual semantic search
+
+The Embed endpoint also supports multilingual semantic search via the `embed-multilingual-...` models. This means you can perform semantic search on texts in different languages.
+
+Specifically, you can do both multilingual and cross-lingual searches using one single model.
+
+Multilingual search happens when the query and the result are of the same language. For example, an English query of “places to eat” returning an English result of “Bob's Burgers.” You can replace English with other languages and use the same model for performing search.
+
+Cross-lingual search happens when the query and the result are of a different language. For example, a Hindi query of “खाने की जगह” (places to eat) returning an English result of “Bob's Burgers.”
+
+In the example below, we repeat the steps of performing semantic search with one difference – changing the model type to the multilingual version. Here, we use the `embed-multilingual-v3.0` model. Here, we are searching a French version of the FAQ list using an English query.
+
+```python PYTHON
+# Define the documents
+faqs_short_fr = [
+    {"text" : "Remboursement des frais de voyage : Gérez facilement vos frais de voyage en les soumettant via notre outil financier. Les approbations sont rapides et simples."},
+    {"text" : "Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail."},
+    {"text" : "Avantages pour la santé et le bien-être : Nous nous soucions de votre bien-être et proposons des adhésions à des salles de sport, des cours de yoga sur site et une assurance santé complète."},
+    {"text" : "Fréquence des évaluations de performance : Nous organisons des bilans informels tous les trimestres et des évaluations formelles deux fois par an."}
+]
+
+documents = faqs_short_fr
+
+# Embed the documents
+doc_emb = co.embed(
+            model="embed-multilingual-v3.0",
+            input_type="search_document",
+            texts=[doc['text'] for doc in documents]).embeddings
+
+# Add the user query
+query = "What's your remote-working policy?"
+
+# Embed the query
+query_emb = co.embed(
+            model="embed-multilingual-v3.0",
+            input_type="search_query",
+            texts=[query]).embeddings
+
+# Compute dot product similarity and display results
+return_results(query_emb, doc_emb, documents)
+```
+
+```
+Rank: 1
+Score: 0.442758615743984
+Document: {'text': "Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail."}
+
+Rank: 2
+Score: 0.32783563708365726
+Document: {'text': 'Avantages pour la santé et le bien-être : Nous nous soucions de votre bien-être et proposons des adhésions à des salles de sport, des cours de yoga sur site et une assurance santé complète.'}
+```
+
+Further reading:
+
+- [The list of supported languages for multilingual Embed](/docs/cohere-embed#list-of-supported-languages)
+
+## Changing embedding compression types
+
+Semantic search over large datasets can require a lot of memory, which is expensive to host in a vector database. Changing the embeddings compression type can help reduce the memory footprint.
+
+A typical embedding model generates embeddings as float32 format (consuming 4 bytes). By compressing the embeddings to int8 format (1 byte), we can reduce the memory 4x while keeping 99.99% of the original search quality.
+
+We can go even further and use the binary format (1 bit), which reduces the needed memory 32x while keeping 90-98% of the original search quality.
+
+The Embed endpoint supports the following formats: `float`, `int8`, `unint8`, `binary`, and `ubinary`. You can get these different compression levels by passing the `embedding_types` parameter.
+
+In the example below, we embed the documents in two formats: `float` and `int8`.
+
+```python PYTHON
+# Define the documents
+documents = faqs_long
+
+# Embed the documents with the given embedding types
+doc_emb = co.embed(
+            model="embed-english-v3.0",
+            embedding_types=["float","int8"],
+            input_type="search_document",
+            texts=[doc['text'] for doc in documents]).embeddings
+
+# Add the user query
+query = "How do I stay connected to what's happening at the company?"
+
+# Embed the query
+query_emb = co.embed(
+            model="embed-english-v3.0",
+            embedding_types=["float","int8"],
+            input_type="search_query",
+            texts=[query]).embeddings
+```
+
+Here are the search results of using the `float` embeddings.
+
+```python PYTHON
+# Compute dot product similarity and display results
+return_results(query_emb.float_, doc_emb.float_, documents)
+```
+
+```
+Rank: 1
+Score: 0.352135965228231
+Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+
+Rank: 2
+Score: 0.31995661889273097
+Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}
+```
+
+And here are the search results of using the `int8` embeddings.
+
+```python PYTHON
+# Compute dot product similarity and display results
+return_results(query_emb.int8, doc_emb.int8, documents)
+```
+
+```
+Rank: 1
+Score: 563583
+Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}
+
+Rank: 2
+Score: 508692
+Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}
+```
+
+Further reading:
+
+- [Documentation on embeddings compression levels](/docs/embeddings#compression-levels)
+
+## Conclusion
+
+In this tutorial, you learned about:
+
+- How to embed documents for search
+- How to embed queries
+- How to perform semantic search
+- How to perform multilingual semantic search
+- How to change the embedding compression types
+
+A high-performance and modern search system typically includes a reranking stage, which further boosts the search results.
+
+In Part 5, you will learn how to [add reranking](/docs/reranking-with-cohere) to a search system.
diff --git a/fern/pages/tutorials/build-things-with-cohere/text-generation-tutorial.mdx b/fern/pages/tutorials/build-things-with-cohere/text-generation-tutorial.mdx
new file mode 100644
index 00000000..8fce39e4
--- /dev/null
+++ b/fern/pages/tutorials/build-things-with-cohere/text-generation-tutorial.mdx
@@ -0,0 +1,297 @@
+---
+title: Cohere Text Generation Tutorial
+slug: /docs/text-generation-tutorial
+---
+
+Open in Colab
+
+Command is Cohere’s flagship LLM, able to generate a response based on a user message or prompt. It is trained to follow user commands and to be instantly useful in practical business applications, like summarization, copywriting, extraction, and question-answering.
+
+Command R and Command R+ are the most recent models in the [Command family](/docs/command-r-plus). They strike the kind of balance between efficiency and high levels of accuracy that enable enterprises to move from proof of concept to production-grade AI applications.
+
+This tutorial leans of the Chat endpoint to build an onboarding assistant for new hires at Co1t, a fictional company, and covers:
+
+- Basic text generation
+- Prompt engineering
+- Parameters for controlling output
+- Structured output generation
+- Streaming output
+
+## Setup
+
+To get started, first we need to install the `cohere` library and create a Cohere client.
+
+```python PYTHON
+# pip install cohere
+
+import cohere
+
+co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+```
+
+## Basic text generation
+
+To get started we just need to pass a single `message` parameter that represents (you guessed it) the user message, after which we use the client we just created to call the Chat endpoint.
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates."
+
+# Generate the response
+response = co.chat(message=message)
+
+print(response.text)
+```
+
+The response we get back contains several objects, but for the sake of simplicity we'll focus for the moment on the `text` object:
+
+```
+Sure! Here is a short introduction message:
+
+"Hi everyone! My name is [Your Name] and I am excited to join the Co1t team today. I am passionate about [relevant experience or skills] and look forward to contributing my skills and ideas to the team. In my free time, I enjoy [hobbies or interests]. Feel free to reach out to me directly if you want to chat or collaborate. Let's work together to make Co1t a success!"
+```
+
+Here are some additional resources if you'd like to read further:
+
+- [Chat endpoint API reference](/reference/chat)
+- [Documentation on Chat fine-tuning](/docs/chat-fine-tuning)
+- [Documentation on Command R+](/docs/command-r-plus)
+- [LLM University module on text generation](https://cohere.com/llmu#text-generation)
+
+## Prompt engineering
+
+Prompting is at the heart of working with LLMs as it provides context for the text that we want the model to generate. Prompts can be anything from simple instructions to more complex pieces of text, and they are used to steer the model to producing a specific type of output.
+
+This section examines a couple of prompting techniques, the first of which is adding more specific instructions to the prompt (the more instructions you provide in the prompt, the closer you can get to the response you need.)
+
+The limit of how long a prompt can be is dependent on the maximum context length that a model can support (in the case Command R and Command R+, it's 128k tokens).
+
+Below, we'll add one additional instruction to the earlier prompt, the length we need the response to be.
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
+
+# Generate the response
+response = co.chat(message=message)
+
+print(response.text)
+```
+
+```
+Here's a potential introduction message:
+
+"Hi everyone, my name is [Your Name] and I'm thrilled to join Co1t today as part of the team, and I look forward to contributing my skills and ideas to drive innovation and success!"
+
+This message expresses your excitement about joining the company and highlights your commitment to contributing to the team's success.
+```
+
+All our prompts so far use what is called zero-shot prompting, which means that provide instruction without any example. But in many cases, it is extremely helpful to provide examples to the model to guide its response. This is called few-shot prompting.
+
+Few-shot prompting is especially useful when we want the model response to follow a particular style or format. Also, it is sometimes hard to explain what you want in an instruction, and easier to show examples.
+
+Below, we want the response to be similar in style and length to the convention, as we show in the examples.
+
+```python PYTHON
+# Add the user message
+user_input = "Why can't I access the server? Is it a permissions issue?"
+
+# Create a prompt containing example outputs
+message=f"""Write a ticket title for the following user request:
+
+User request: Where are the usual storage places for project files?
+Ticket title: Project File Storage Location
+
+User request: Emails won't send. What could be the issue?
+Ticket title: Email Sending Issues
+
+User request: How can I set up a connection to the office printer?
+Ticket title: Printer Connection Setup
+
+User request: {user_input}
+Ticket title:"""
+
+# Generate the response
+response = co.chat(message=message)
+
+print(response.text)
+```
+
+```
+Server Access Issues
+```
+
+Further reading:
+
+- [Documentation on prompt engineering](/docs/crafting-effective-prompts)
+- [LLM University module on prompt engineering](https://cohere.com/llmu#prompt-engineering)
+
+## Parameters for controlling output
+
+The Chat endpoint provides developers with an array of options and parameters.
+
+For example, you can choose from several variations of the Command model. Different models produce different output profiles, such as quality and latency.
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
+
+# Generate the response by specifying a model
+response = co.chat(message=message, model="command-r")
+
+print(response.text)
+```
+
+```
+Hello, my name is [Your Name] and I'm thrilled to join the Co1t team today as the new kid in town!
+```
+
+Often, you’ll need to control the level of randomness of the output. You can control this using a few parameters.
+
+The most commonly used parameter is `temperature`, which is a number used to tune the degree of randomness. You can enter values between 0.0 to 1.0.
+
+A lower temperature gives more predictable outputs, and a higher temperature gives more "creative" outputs.
+
+Here's an example of setting `temperature` to 0.
+
+```python PYTHON
+# Add the user message
+message = "I like learning about the industrial revolution and how it shapes the modern world. How can I introduce myself in two words."
+
+# Generate the response multiple times by specifying a low temperature value
+for idx in range(3):
+    response = co.chat(message=message, temperature=0)
+    print(f"{idx+1}: {response.text}\n")
+```
+
+```
+1: Curious Historian.
+
+2: Curious Historian.
+
+3: Curious Historian.
+```
+
+And here's an example of setting `temperature` to 1.
+
+```python PYTHON
+# Add the user message
+message = "I like learning about the industrial revolution and how it shapes the modern world. How can I introduce myself in two words."
+
+# Generate the response multiple times by specifying a high temperature value
+for idx in range(3):
+    response = co.chat(message=message, temperature=1)
+    print(f"{idx+1}: {response.text}\n")
+```
+
+```
+1: Sure! Here are two words that can describe you: 
+
+1. Industry Enthusiast 
+2. Revolution Aficionado 
+
+These words combine your passion for learning about the Industrial Revolution with a modern twist, showcasing your enthusiasm and knowledge in a concise manner.
+
+2: "Revolution Fan"
+
+3: History Enthusiast!
+```
+
+Further reading:
+
+- [Available models for the Chat endpoint](/docs/models#command)
+- [Documentation on predictable outputs](/docs/predictable-outputs)
+- [Documentation on advanced generation parameters](/docs/advanced-generation-hyperparameters)
+
+## Structured output generation
+
+By adding the `response_format` parameter, you can get the model to generate the output as a JSON object. By generating JSON objects, you can structure and organize the model's responses in a way that can be used in downstream applications.
+
+The `response_format` parameter allows you to specify the schema the JSON object must follow. It takes the following parameters:
+
+- `message`: The user message
+- `response_format`: The schema of the JSON object
+
+```python PYTHON
+# Add the user message
+user_input = "Why can't I access the server? Is it a permissions issue?"
+
+# Generate the response multiple times by adding the JSON schema
+response = co.chat(
+  model="command-r-plus",
+  message=f"""Create an IT ticket for the following user request. Generate a JSON object.
+  {user_input}""",
+  response_format={
+    "type": "json_object",
+    "schema": {
+      "type": "object",
+      "required": ["title", "category", "status"],
+      "properties": {
+        "title": { "type": "string"},
+        "category": { "type" : "string", "enum" : ["access", "software"]},
+        "status": { "type" : "string" , "enum" : ["open", "closed"]}
+      }
+    }
+  },
+)
+
+import json
+json_object = json.loads(response.text)
+
+print(json_object)
+```
+
+```
+{'title': 'User Unable to Access Server', 'category': 'access', 'status': 'open'}
+```
+
+Further reading:
+
+- [Documentation on Structured Generations (JSON)](/docs/structured-outputs-json)
+
+## Streaming responses
+
+All the previous examples above generate responses in a non-streamed manner. This means that the endpoint would return a response object only after the model has generated the text in full.
+
+The Chat endpoint also provides streaming support. In a streamed response, the endpoint would return a response object for each token as it is being generated. This means you can display the text incrementally without having to wait for the full completion.
+
+To activate it, use `co.chat_stream()` instead of `co.chat()`.
+
+In streaming mode, the endpoint will generate a series of objects. To get the actual text contents, we take objects whose `event_type` is `text-generation`.
+
+```python PYTHON
+# Add the user message
+message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
+
+# Generate the response by streaming it
+response = co.chat_stream(
+            message=message)
+
+for event in response:
+    if event.event_type == "text-generation":
+        print(event.text, end="")
+```
+
+```
+Here's a potential introduction message:
+
+"Hi everyone, my name is [Your Name] and I'm thrilled to join Co1t today as the newest [Your Role], and I look forward to contributing my skills and expertise to the team and driving innovative solutions for our customers."
+```
+
+Further reading:
+
+- [Documentation on streaming responses](/docs/streaming)
+
+## Conclusion
+
+In this tutorial, you learned about:
+
+- How to get started with a basic text generation
+- How to improve outputs with prompt engineering
+- How to control outputs using parameter changes
+- How to generate structured outputs
+- How to stream text generation outputs
+
+However, we have only done all this using direct text generations. As its name implies, the Chat endpoint can also support building chatbots, which require features to support multi-turn conversations and maintain the conversation state. 
+
+In Part 3, you'll learn how to build chatbots with the Chat endpoint.
diff --git a/fern/v1.yml b/fern/v1.yml
index 776767a5..ff938fdb 100644
--- a/fern/v1.yml
+++ b/fern/v1.yml
@@ -53,6 +53,8 @@ navigation:
             path: pages/models/rerank-2.mdx
       - section: Text Generation
         contents:
+          - page: Introduction to Text Generation at Cohere
+            path: pages/text-generation/introduction-to-text-generation-at-cohere.mdx
           - page: Using the Chat API
             path: pages/text-generation/chat-api.mdx
           - page: Streaming Responses
@@ -101,6 +103,8 @@ navigation:
                 path: pages/text-generation/prompt-engineering/prompt-truncation.mdx
               - page: Preambles
                 path: pages/text-generation/prompt-engineering/preambles.mdx
+              - page: Prompt Tuner (beta)
+                path: pages/text-generation/prompt-engineering/prompt-tuner.mdx
               - section: Prompt Library
                 contents:
                   - page: Create CSV data from JSON data
@@ -121,6 +125,8 @@ navigation:
                     path: pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx
           - page: Migrating from the Generate API to the Chat API
             path: pages/text-generation/migrating-from-cogenerate-to-cochat.mdx
+          - page: Summarizing Text
+            path: pages/text-generation/summarizing-text.mdx
       - section: Text Embeddings (Vectors, Search, Retrieval)
         contents:
           - page: Introduction to Embeddings at Cohere
@@ -134,7 +140,7 @@ navigation:
               - page: Rerank Best Practices
                 path: pages/text-embeddings/reranking/reranking-best-practices.mdx
           - page: Text Classification
-            path: pages/text-embeddings/text-classification-1.mdx
+            path: pages/text-embeddings/text-classification-with-cohere.mdx
       - section: Fine-Tuning
         contents:
           - page: Introduction
@@ -249,6 +255,21 @@ navigation:
             path: pages/tutorials/cookbooks.mdx
           - page: LLM University
             path: pages/llm-university/llmu-2.mdx
+          - section: Build Things with Cohere!
+            path: pages/tutorials/build-things-with-cohere.mdx
+            contents: 
+              - page: Cohere Text Generation Tutorial
+                path: pages/tutorials/build-things-with-cohere/text-generation-tutorial.mdx
+              - page: Building a Chatbot with Cohere
+                path: pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx
+              - page: Semantic Search with Cohere
+                path: pages/tutorials/build-things-with-cohere/semantic-search-with-cohere.mdx
+              - page: Reranking with Cohere
+                path: pages/tutorials/build-things-with-cohere/reranking-with-cohere.mdx
+              - page: RAG with Cohere 
+                path: pages/tutorials/build-things-with-cohere/rag-with-cohere.mdx
+              - page: Building an Agent with Cohere
+                path: pages/tutorials/build-things-with-cohere/building-an-agent-with-cohere.mdx
       - section: Responsible Use
         contents:
           - section: Overview
diff --git a/fern/v2.yml b/fern/v2.yml
index 06c09ef2..a62f8284 100644
--- a/fern/v2.yml
+++ b/fern/v2.yml
@@ -53,6 +53,8 @@ navigation:
             path: pages/models/rerank-2.mdx
       - section: Text Generation
         contents:
+          - page: Introduction to Text Generation at Cohere
+            path: pages/text-generation/introduction-to-text-generation-at-cohere.mdx
           - page: Using the Chat API
             path: pages/text-generation/chat-api.mdx
           - page: Streaming Responses
@@ -101,6 +103,8 @@ navigation:
                 path: pages/text-generation/prompt-engineering/prompt-truncation.mdx
               - page: Preambles
                 path: pages/text-generation/prompt-engineering/preambles.mdx
+              - page: Prompt Tuner (beta)
+                path: pages/text-generation/prompt-engineering/prompt-tuner.mdx
               - section: Prompt Library
                 contents:
                   - page: Create CSV data from JSON data
@@ -121,6 +125,8 @@ navigation:
                     path: pages/text-generation/prompt-engineering/prompt-library/multilingual-interpreter.mdx
           - page: Migrating from the Generate API to the Chat API
             path: pages/text-generation/migrating-from-cogenerate-to-cochat.mdx
+          - page: Summarizing Text
+            path: pages/text-generation/summarizing-text.mdx
       - section: Text Embeddings (Vectors, Search, Retrieval)
         contents:
           - page: Introduction to Embeddings at Cohere
@@ -134,7 +140,7 @@ navigation:
               - page: Rerank Best Practices
                 path: pages/text-embeddings/reranking/reranking-best-practices.mdx
           - page: Text Classification
-            path: pages/text-embeddings/text-classification-1.mdx
+            path: pages/text-embeddings/text-classification-with-cohere.mdx
       - section: Fine-Tuning
         contents:
           - page: Introduction
@@ -249,6 +255,21 @@ navigation:
             path: pages/tutorials/cookbooks.mdx
           - page: LLM University
             path: pages/llm-university/llmu-2.mdx
+          - section: Build Things with Cohere!
+            path: pages/tutorials/build-things-with-cohere.mdx
+            contents: 
+              - page: Cohere Text Generation Tutorial
+                path: pages/tutorials/build-things-with-cohere/text-generation-tutorial.mdx
+              - page: Building a Chatbot with Cohere
+                path: pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx
+              - page: Semantic Search with Cohere
+                path: pages/tutorials/build-things-with-cohere/semantic-search-with-cohere.mdx
+              - page: Reranking with Cohere
+                path: pages/tutorials/build-things-with-cohere/reranking-with-cohere.mdx
+              - page: RAG with Cohere 
+                path: pages/tutorials/build-things-with-cohere/rag-with-cohere.mdx
+              - page: Building an Agent with Cohere
+                path: pages/tutorials/build-things-with-cohere/building-an-agent-with-cohere.mdx
       - section: Responsible Use
         contents:
           - section: Overview

From 42e1f97494939e8aee27b980bea7417ce2b3339f Mon Sep 17 00:00:00 2001
From: fern <126544928+fern-bot@users.noreply.github.com>
Date: Mon, 12 Aug 2024 09:19:56 -0400
Subject: [PATCH 7/9] adding formatting (#20)

---
 fern/assets/input.css                         | 18 ++++
 .../chat-starting-the-training.mdx            |  2 +-
 .../integrations/chroma-and-cohere.mdx        |  4 +-
 .../integrations/elasticsearch-and-cohere.mdx |  4 +-
 .../integrations/haystack-and-cohere.mdx      |  2 +-
 .../integrations/milvus-and-cohere.mdx        |  4 +-
 .../integrations/mongodb-and-cohere.mdx       |  4 +-
 .../integrations/opensearch-and-cohere.mdx    |  2 +-
 .../integrations/pinecone-and-cohere.mdx      |  4 +-
 .../integrations/qdrant-and-cohere.mdx        |  4 +-
 .../integrations/redis-and-cohere.mdx         |  4 +-
 .../integrations/vespa-and-cohere.mdx         |  4 +-
 .../integrations/weaviate-and-cohere.mdx      |  4 +-
 .../integrations/zilliz-and-cohere.mdx        |  4 +-
 .../intro-semantic-search/keyword-search.mdx  |  2 +-
 fern/pages/models/cohere-embed.mdx            |  8 +-
 fern/pages/text-embeddings/embeddings.mdx     |  2 +-
 ...advanced-prompt-engineering-techniques.mdx | 94 +++++++++----------
 .../crafting-effective-prompts.mdx            | 40 ++++----
 .../text-generation/tokens-and-tokenizers.mdx |  2 +-
 fern/pages/text-generation/tools/tool-use.mdx |  2 +-
 21 files changed, 116 insertions(+), 98 deletions(-)

diff --git a/fern/assets/input.css b/fern/assets/input.css
index 10f45414..5e9ee2cb 100644
--- a/fern/assets/input.css
+++ b/fern/assets/input.css
@@ -354,6 +354,16 @@ button[class^="Sidebar-link-buttonWrapper"] {
   flex-direction: column;
 }
 
+.fern-header .fern-button-text {
+  font-family: 'CohereText',Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Open Sans,Helvetica Neue,sans-serif !important;
+  -webkit-font-smoothing: unset !important;
+}
+
+.fern-sidebar-content .fern-button-text {
+  font-family: 'CohereText',Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Open Sans,Helvetica Neue,sans-serif !important;
+  -webkit-font-smoothing: unset !important;
+}
+
 #content {
   padding: 0 32px;
 }
@@ -459,3 +469,11 @@ button[class^="Sidebar-link-buttonWrapper"] {
   float: right !important;
   margin-left: .75rem !important;
 }
+
+.light-bg {
+  background-color: #E8E6DE !important;
+  border-color: #e0e0e0 !important;
+  border-radius: .5rem !important;
+  border-width: 1px !important;
+  padding: 15px !important;
+}
\ No newline at end of file
diff --git a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx
index 567218c9..7dd67e64 100644
--- a/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx
+++ b/fern/pages/fine-tuning/chat-fine-tuning/chat-starting-the-training.mdx
@@ -241,7 +241,7 @@ By passing `return_prompt=True` in any message, you can see which preamble is be
 
 Here's a Python script to make this clearer:
 
-```python python
+```python PYTHON
 import cohere
 
 co = cohere.Client('Your API key')
diff --git a/fern/pages/integrations/integrations/chroma-and-cohere.mdx b/fern/pages/integrations/integrations/chroma-and-cohere.mdx
index d90c3b2f..568f429c 100644
--- a/fern/pages/integrations/integrations/chroma-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/chroma-and-cohere.mdx
@@ -7,12 +7,12 @@ createdAt: "Thu May 23 2024 16:53:24 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:53:54 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 Chroma is an open-source vector search engine that's quick to install and start building with using Python or Javascript.
 
 
diff --git a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx b/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx
index 228f1f16..9250a0d8 100644
--- a/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/elasticsearch-and-cohere.mdx
@@ -11,7 +11,7 @@ createdAt: "Sun Apr 07 2024 20:15:08 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 30 2024 15:56:35 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Elasticsearch](https://www.elastic.co/search-labs/blog/elasticsearch-cohere-embeddings-support) has all the tools developers need to build next generation search experiences with generative AI, and it supports native integration with [Cohere](https://www.elastic.co/search-labs/blog/elasticsearch-cohere-embeddings-support) through their [inference API](https://www.elastic.co/guide/en/elasticsearch/reference/master/semantic-search-inference.html).
 
@@ -34,7 +34,7 @@ This guide uses a dataset of Wikipedia articles to set up a pipeline for semanti
 To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Elastic_Guide.ipynb).
 
 
diff --git a/fern/pages/integrations/integrations/haystack-and-cohere.mdx b/fern/pages/integrations/integrations/haystack-and-cohere.mdx
index 36b50ed2..19fd386d 100644
--- a/fern/pages/integrations/integrations/haystack-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/haystack-and-cohere.mdx
@@ -11,7 +11,7 @@ createdAt: "Tue Feb 27 2024 20:06:57 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 17:07:17 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Haystack](https://github.com/deepset-ai/haystack) is an open source LLM framework in Python by [deepset](https://www.deepset.ai/) for building customizable, production-ready LLM applications. You can use Cohere's `/embed`, `/generate`, `/chat`, and `/rerank` models with Haystack.
 
diff --git a/fern/pages/integrations/integrations/milvus-and-cohere.mdx b/fern/pages/integrations/integrations/milvus-and-cohere.mdx
index ffc99e73..a1edd7fc 100644
--- a/fern/pages/integrations/integrations/milvus-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/milvus-and-cohere.mdx
@@ -7,14 +7,14 @@ createdAt: "Thu May 23 2024 16:59:08 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:59:13 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Milvus](https://milvus.io/) is a highly flexible, reliable, and blazing-fast cloud-native, open-source vector database. It powers embedding similarity search and AI applications and strives to make vector databases accessible to every organization. Milvus is a graduated-stage project of the LF AI & Data Foundation.
 
 The following [guide](https://milvus.io/docs/integrate_with_cohere.md) walks through how to integrate [Cohere embeddings](/docs/embeddings) with Milvus.
 
 
diff --git a/fern/pages/integrations/integrations/mongodb-and-cohere.mdx b/fern/pages/integrations/integrations/mongodb-and-cohere.mdx
index 97248f9f..eed0d377 100644
--- a/fern/pages/integrations/integrations/mongodb-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/mongodb-and-cohere.mdx
@@ -11,14 +11,14 @@ createdAt: "Thu May 23 2024 16:41:27 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 17:06:29 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 MongoDB Atlas Vector Search is a fully managed vector search platform from MongoDB. It can be used with Cohere's Embed and Rerank models to easily build semantic search or retrieval-augmented generation (RAG) systems with your data from MongoDB.
 
 The following guide walks through how to integrate Cohere models with MongoDB Atlas Vector Search.
 
 
diff --git a/fern/pages/integrations/integrations/opensearch-and-cohere.mdx b/fern/pages/integrations/integrations/opensearch-and-cohere.mdx
index 83e6ab5f..32c517e2 100644
--- a/fern/pages/integrations/integrations/opensearch-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/opensearch-and-cohere.mdx
@@ -10,7 +10,7 @@ keywords: "OpenSearch, Cohere"
 createdAt: "Fri Feb 02 2024 15:17:19 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 17:09:12 GMT+0000 (Coordinated Universal Time)"
 ---
-
+
 
 
 [OpenSearch](https://opensearch.org/platform/search/vector-database.html) is an open-source, distributed search and analytics engine platform that allows users to search, analyze, and visualize large volumes of data in real time. When it comes to text search, OpenSearch is well-known for powering keyword-based (also called lexical) search methods. OpenSearch supports Vector Search and integrates with Cohere through [3rd-Party ML Connectors](https://opensearch.org/docs/latest/ml-commons-plugin/remote-models/connectors/) as well as Amazon Bedrock
diff --git a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx b/fern/pages/integrations/integrations/pinecone-and-cohere.mdx
index 0b749062..197ec2fe 100644
--- a/fern/pages/integrations/integrations/pinecone-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/pinecone-and-cohere.mdx
@@ -7,12 +7,12 @@ createdAt: "Thu May 23 2024 16:56:18 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:57:06 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 The [Pinecone](https://www.pinecone.io/) vector database makes it easy to build high-performance vector search applications. Use Cohere to generate language embeddings, then store them in Pinecone and use them for Semantic Search.
 
 
diff --git a/fern/pages/integrations/integrations/qdrant-and-cohere.mdx b/fern/pages/integrations/integrations/qdrant-and-cohere.mdx
index eba83a7c..c3e67029 100644
--- a/fern/pages/integrations/integrations/qdrant-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/qdrant-and-cohere.mdx
@@ -7,14 +7,14 @@ createdAt: "Thu May 23 2024 16:54:52 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:55:09 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Qdrant](https://qdrant.tech/) is an open-source vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
 
 Qdrant is written in Rust, which makes it fast and reliable even under high load.
 
 
diff --git a/fern/pages/integrations/integrations/redis-and-cohere.mdx b/fern/pages/integrations/integrations/redis-and-cohere.mdx
index fb1d4188..494414a0 100644
--- a/fern/pages/integrations/integrations/redis-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/redis-and-cohere.mdx
@@ -11,7 +11,7 @@ createdAt: "Mon Feb 26 2024 22:22:44 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 17:06:49 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [RedisVL](https://www.redisvl.com/) provides a powerful, dedicated Python client library for using Redis as a Vector Database. This walks through how to integrate [Cohere embeddings](/docs/embeddings) with Redis using a dataset of Wikipedia articles to set up a pipeline for semantic search. It will cover:
 
@@ -23,7 +23,7 @@ updatedAt: "Thu May 23 2024 17:06:49 GMT+0000 (Coordinated Universal Time)"
 To see the full code sample, refer to this [notebook](https://github.com/cohere-ai/notebooks/blob/main/notebooks/Cohere_Redis_Guide.ipynb).
 
 
diff --git a/fern/pages/integrations/integrations/vespa-and-cohere.mdx b/fern/pages/integrations/integrations/vespa-and-cohere.mdx
index 600a2619..268fe80b 100644
--- a/fern/pages/integrations/integrations/vespa-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/vespa-and-cohere.mdx
@@ -7,12 +7,12 @@ createdAt: "Thu May 23 2024 16:52:12 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:52:39 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Vespa](https://vespa.ai/) is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.
 
 
diff --git a/fern/pages/integrations/integrations/weaviate-and-cohere.mdx b/fern/pages/integrations/integrations/weaviate-and-cohere.mdx
index 6baca405..4d97097c 100644
--- a/fern/pages/integrations/integrations/weaviate-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/weaviate-and-cohere.mdx
@@ -7,14 +7,14 @@ createdAt: "Thu May 23 2024 16:55:16 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 16:56:09 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Weaviate](https://weaviate.io/) is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering.
 
 The `text2vec-cohere` module allows you to use [Cohere embeddings](/docs/embeddings) directly in the Weaviate vector search engine as a vectorization module.
 
 
diff --git a/fern/pages/integrations/integrations/zilliz-and-cohere.mdx b/fern/pages/integrations/integrations/zilliz-and-cohere.mdx
index 35921934..9c035220 100644
--- a/fern/pages/integrations/integrations/zilliz-and-cohere.mdx
+++ b/fern/pages/integrations/integrations/zilliz-and-cohere.mdx
@@ -7,14 +7,14 @@ createdAt: "Thu May 23 2024 17:00:11 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Thu May 23 2024 20:28:12 GMT+0000 (Coordinated Universal Time)"
 ---
 
-
+
 
 [Zilliz Cloud](https://zilliz.com/cloud) is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud provides a fully-managed Milvus service, made by the creators of Milvus that allows for easy integration with vectorizers from Cohere and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.
 
 The following [guide](https://docs.zilliz.com/docs/question-answering-using-zilliz-cloud-and-cohere) walks through how to integrate [Cohere embeddings](/docs/embeddings) with Zilliz.
 
 
diff --git a/fern/pages/llm-university/intro-semantic-search/keyword-search.mdx b/fern/pages/llm-university/intro-semantic-search/keyword-search.mdx
index 8f25411a..d0cfa817 100644
--- a/fern/pages/llm-university/intro-semantic-search/keyword-search.mdx
+++ b/fern/pages/llm-university/intro-semantic-search/keyword-search.mdx
@@ -19,7 +19,7 @@ This chapter comes with a corresponding [Colab notebook](https://colab.research.
 
 In order to store the Wikipedia dataset query, we’ll use the [Weaviate vector database](https://weaviate.io/?ref=txt.cohere.com), which will give us a range of benefits. In simple terms, a vector database is a place where one can store data objects and vector embeddings, and be able to access them and perform operations easily. For example, finding the nearest neighbors of a vector in a dataset is a lengthy process, which is sped up significantly by using a vector database. This is done with the following code.
 
-```python python
+```python PYTHON
 import weaviate
 import cohere
 
diff --git a/fern/pages/models/cohere-embed.mdx b/fern/pages/models/cohere-embed.mdx
index dbbb5066..a4d46946 100644
--- a/fern/pages/models/cohere-embed.mdx
+++ b/fern/pages/models/cohere-embed.mdx
@@ -16,8 +16,8 @@ Embed models can be used to generate embeddings from text or classify it based o
 
 | Latest Model                | Description                                                                                          | Dimensions | Max Tokens (Context Length) | Similarity Metric | Endpoints                                                                                  |
 |-----------------------------|------------------------------------------------------------------------------------------------------|------------|-----------------------------|-------------------|-------------------------------------------------------------------------------------------|
-| `embed-english-v3.0`        | A model that allows for text to be classified or turned into embeddings. English only.               | 1024       | 512                         | Cosine Similarity | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)               |
-| `embed-english-light-v3.0`  | A smaller, faster version of `embed-english-v3.0`. Almost as capable, but a lot faster. English only.| 384        | 512                         | Cosine Similarity | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)               |
+| `embed-english-v3.0`        | A model that allows for text to be classified or turned into embeddings. English only.               | 1024       | 512                         | Cosine Similarity, Dot Product Similarity, Euclidean Distance | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)               |
+| `embed-english-light-v3.0`  | A smaller, faster version of `embed-english-v3.0`. Almost as capable, but a lot faster. English only.| 384        | 512                         | Cosine Similarity, Dot Product Similarity, Euclidean Distance | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)               |
 | `embed-english-v2.0`        | Our older embeddings model that allows for text to be classified or turned into embeddings. English only | 4096       | 512                         | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed)                         |
 | `embed-english-light-v2.0`  | A smaller, faster version of embed-english-v2.0. Almost as capable, but a lot faster. English only.   | 1024       | 512                         | Cosine Similarity | [Classify](/reference/classify), [Embed](/reference/embed)                         |
 
@@ -27,8 +27,8 @@ Embed models can be used to generate embeddings from text or classify it based o
 
 | Latest Model                     | Description                                                                                                                                            | Dimensions | Max Tokens (Context Length) | Similarity Metric       | Endpoints                                                                                          |
 |----------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|------------|-----------------------------|-------------------------|---------------------------------------------------------------------------------------------------|
-| `embed-multilingual-v3.0`        | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages)            | 1024       | 512                         | Cosine Similarity       | [Embed](/reference/embed), [Embed Jobs](/reference/embed-jobs)                             |
-| `embed-multilingual-light-v3.0`  | A smaller, faster version of `embed-multilingual-v3.0`. Almost as capable, but a lot faster. Supports multiple languages.                                | 384        | 512                         | Cosine Similarity       | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)                         |
+| `embed-multilingual-v3.0`        | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages)            | 1024       | 512                         | Cosine Similarity, Dot Product Similarity, Euclidean Distance       | [Embed](/reference/embed), [Embed Jobs](/reference/embed-jobs)                             |
+| `embed-multilingual-light-v3.0`  | A smaller, faster version of `embed-multilingual-v3.0`. Almost as capable, but a lot faster. Supports multiple languages.                                | 384        | 512                         | Cosine Similarity, Dot Product Similarity, Euclidean Distance       | [Embed](/reference/embed),  
[Embed Jobs](/reference/embed-jobs)                         |
 | `embed-multilingual-v2.0`        | Provides multilingual classification and embedding support. [See supported languages here.](/docs/supported-languages)            | 768        | 256                         | Dot Product Similarity  | [Classify](/reference/classify), [Embed](/reference/embed)                                   |
 
 
diff --git a/fern/pages/text-embeddings/embeddings.mdx b/fern/pages/text-embeddings/embeddings.mdx
index 929e1a07..687a77dd 100644
--- a/fern/pages/text-embeddings/embeddings.mdx
+++ b/fern/pages/text-embeddings/embeddings.mdx
@@ -52,7 +52,7 @@ Cohere embeddings are optimized for different types of inputs. For example, when
 
 In addition to `embed-english-v3.0` we offer a best-in-class multilingual model [embed-multilingual-v3.0](/docs/embed-2#multi-lingual-models)  with support for over 100 languages, including Chinese, Spanish, and French. This model can be used with the Embed API, just like its English counterpart:
 
-```python python
+```python PYTHON
 import cohere  
 co = cohere.Client(api_key="")
 
diff --git a/fern/pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx b/fern/pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx
index ddb3f335..bc4f6ce3 100644
--- a/fern/pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx
+++ b/fern/pages/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx
@@ -14,7 +14,7 @@ As we develop these techniques, we will work through an example where our aim is
 Q: On the issue of whether David is fast, the fact that David set a high school track record.
 Is there hearsay?
 A: No
-
+  
 Q: On the issue of whether Rebecca was ill, the fact that Rebecca told Ronald that she was unwell.
 Is there hearsay?
 A: Yes
@@ -26,14 +26,14 @@ Before we apply any specific prompting techniques, we can see that simply prompt
 [prompt]
 On the issue of Albert's wellbeing after the accident, Angela testified
 that he gave a thumbs up when asked how he was feeling. Is there hearsay?
-
+  
 [completion]
 No, there is no hearsay. Hearsay is a statement that is repeated by someone
 other than the person who originally made the statement. Angela's testimony
 is not hearsay because she is testifying about her own personal knowledge of
 the event; she saw Albert give a thumbs up sign. This is considered direct evidence
 and would likely be admissible in court. 
-
+  
 The thumbs up sign can be interpreted as a current state of well-being and
 could hold some weight in refuting concerns about Albert's health after the
 accident. However, the defense could argue that a thumbs up sign is not a
@@ -71,10 +71,10 @@ The obvious thing missing in the prompt above is concise and unambiguous instruc
 [prompt]
 Hearsay is an out-of-court statement introduced to prove the truth of the matter
 asserted. Answer the following question regarding hearsay with either Yes or No.
-
+  
 On the issue of Albert's wellbeing after the accident, Angela testified that he
 gave a thumbs up when asked how he was feeling. Is there hearsay?
-
+  
 [completion]
 Yes.
 
@@ -93,35 +93,35 @@ To improve the above question, we can include several positive and negative exam
 ```
 [prompt]
 Hearsay is an out-of-court statement introduced to prove the truth of the matter asserted.
-
+  
 Q: On the issue of whether David is fast, the fact that David set a high school track record.
 Is there hearsay?
 A: No
-
+  
 Q: On the issue of whether Rebecca was ill, the fact that Rebecca told Ronald that she was unwell.
 Is there hearsay?
 A: Yes
-
+  
 Q: To prove that Tim was a soccer fan, the fact that Tim told Jimmy that "Real Madrid was the best
 soccer team in the world." Is there hearsay?
 A: No
-
+  
 Q: When asked by the attorney on cross-examination, Alice testified that she had "never seen the
 plaintiff before, and had no idea who she was." Is there hearsay?
 A: No
-
+  
 Q: On the issue of whether Martin punched James, the fact that Martin smiled and nodded when asked
 if he did so by an officer on the scene. Is there hearsay?
 A: Yes
-
+  
 On the issue of Albert's wellbeing after the accident, Angela testified that he gave a thumbs up
 when asked how he was feeling. Is there hearsay?
 A:
-
+  
 [completion]
 Yes, Angela's testimony about Albert's non-verbal conduct is hearsay, as it's an out-of-court
 statement (Albert's thumbs up) being used to prove the truth of the matter asserted (that Albert was feeling well). 
-
+  
 Hearsay is defined as an out-of-court statement made by someone other than the witness testifying,
 and it's offered to prove the truth of the content of the statement. In this scenario, Albert's
 gesture is being used as evidence of his wellbeing, making it hearsay under the legal
@@ -142,26 +142,26 @@ There are several different ways to incorporate CoT prompting into a task. With
 ```
 [prompt]
 Hearsay is an out-of-court statement introduced to prove the truth of the matter asserted.
-
+  
 Q: On the issue of Albert's wellbeing after the accident, Angela testified that he gave a
 thumbs up when asked how he was feeling. Is there hearsay?
 A: Work through the problem step by step first and then answer the question.
-
+  
 [completion]
 Yes, let's work through this scenario step by step to identify if there's hearsay involved.
-
+  
 1. Angela's Testimony: According to Angela's testimony, Albert gave a "thumbs up" when asked
 about his well-being. This statement was made by Angela in court, which means it's in-court testimony.
-
+  
 2. Nature of the Statement: Angela's statement about Albert's "thumbs up" gesture is being
 used to imply that Albert is doing well or feeling good after the accident.
-
+  
 3. Out-of-Court Statement: The actual statement being discussed here - "I'm feeling good" - was
 allegedly made by Albert out of court. This is a classic example of an out-of-court statement.
-
+  
 4. Purpose of the Statement: Angela's testimony, including the reference to Albert's gesture,
   is being used to prove Albert's subjective state of well-being.
-
+  
 Therefore, yes, there is hearsay in Angela's testimony. The statement made by Albert, describing
 his feelings, is hearsay because it's an out-of-court assertion being used to prove the truth
 of the matter - in this case, that Albert was feeling well after the accident.
@@ -173,40 +173,40 @@ This answer is quite satisfying not only because we get the correct answer, but
 ```
 [prompt]
 Hearsay is an out-of-court statement introduced to prove the truth of the matter asserted.
-
+  
 Q: On the issue of whether David is fast, the fact that David set a high school track record.
 Is there hearsay?
 Reasoning: The statement is non-assertive conduct.
 A: No
-
+  
 Q: On the issue of whether Rebecca was ill, the fact that Rebecca told Ronald that she was
 unwell. Is there hearsay?
 Reasoning: The statement is standard hearsay.
 A: Yes
-
+  
 Q: To prove that Tim was a soccer fan, the fact that Tim told Jimmy that "Real Madrid was
 the best soccer team in the world." Is there hearsay?
 Reasoning: The statement is not introduced to prove truth.
 A: No
-
+  
 Q: When asked by the attorney on cross-examination, Alice testified that she had "never seen
 the plaintiff before, and had no idea who she was." Is there hearsay?
 Reasoning: The statement was made in-court.
 A: No
-
+  
 Q: On the issue of whether Martin punched James, the fact that Martin smiled and nodded when
 asked if he did so by an officer on the scene. Is there hearsay?
 Reasoning: The statement is non-verbal hearsay.
 A: Yes
-
+  
 Q: On the issue of Albert's wellbeing after the accident, Angela testified that he gave a
 thumbs up when asked how he was feeling. Is there hearsay?
-
+  
 [completion]
 Yes. Angela's testimony about Albert's non-verbal conduct, i.e., giving a "thumbs up," falls
 under the definition of hearsay, as it is an out-of-court statement (Albert's gesture) used
 to prove the truth of the matter asserted (that Albert was doing well after the accident). 
-
+  
 Hearsay is typically defined as an out-of-court statement offered in court for the truth of
 the matter asserted. Non-verbal conduct, such as gestures or nods, can indeed constitute
 hearsay if they are intended to assert a fact. In this scenario, Albert's thumbs up is
@@ -222,49 +222,49 @@ Hearsay is an out-of-court statement introduced to prove the truth of the matter
 Given a statement, respond with a JSON object that has two keys: `reasoning` and `answer`.
 The first key's value should contain the reasoning used to arrive at the answer.
 The second key's value should contain either "Yes" or "No".
-
+  
 Q: On the issue of whether David is fast, the fact that David set a high school track record.
 Is there hearsay?
 {
   "reasoning": "The statement is non-assertive conduct."
   "answer": "No"
 }
-
+  
 Q: On the issue of whether Rebecca was ill, the fact that Rebecca told Ronald that she was
 unwell. Is there hearsay?
 {
   "reasoning": "The statement is standard hearsay."
   "answer": "Yes"
 }
-
+  
 Q: To prove that Tim was a soccer fan, the fact that Tim told Jimmy that "Real Madrid was
 the best soccer team in the world." Is there hearsay?
 {
   "reasoning": "The statement is not introduced to prove truth."
   "answer": "No"
 }
-
+  
 Q: When asked by the attorney on cross-examination, Alice testified that she had "never seen
 the plaintiff before, and had no idea who she was." Is there hearsay?
 {
   "reasoning": "The statement was made in-court."
   "answer": "No"
 }
-
+  
 Q: On the issue of whether Martin punched James, the fact that Martin smiled and nodded
 when asked if he did so by an officer on the scene. Is there hearsay?
 {
   "reasoning": "The statement is non-verbal hearsay."
   "answer": "Yes"
 }
-
+  
 Q: On the issue of Albert's wellbeing after the accident, Angela testified that he gave
 a thumbs up when asked how he was feeling. Is there hearsay?
 {
   "reasoning": "",
   "answer": ""
 }
-
+  
 [completion]
 {
   "reasoning": "The statement is a non-verbal assertion, which also counts as hearsay.",
@@ -284,33 +284,33 @@ For example, instead of asking the model to "work through the problem step by st
 ```
 [prompt 1]
 Hearsay is an out-of-court statement introduced to prove the truth of the matter asserted.
-
+  
 In determining whether a piece of evidence meets the definition of hearsay, lawyers ask three
 questions:
-
+  
 1. Was there a statement?
 2. Was it made outside of court?
 3. Is it being introduced to prove the truth of the matter asserted?
-
+  
 Apply this reasoning to the following question.
-
+  
 Q: On the issue of Albert's wellbeing after the accident, Angela testified that he gave a
 thumbs up when asked how he was feeling.
-
+  
 [completion]
 Yes, this is hearsay, based on the definition you provided.
-
+  
 1. There was a statement: Angela testified that Albert gave a thumbs up. This would constitute
 a gesture as a form of statement, indicating Albert's state of being. 
-
+  
 2. It was made outside of court: According to the scenario, Angela's observation of Albert's
 gesture occurred outside the courtroom, making it an out-of-court statement. 
-
+  
 3. It's being introduced to prove the truth of the matter asserted: Angela's testimony,
 including the thumbs-up gesture, is being used as evidence of Albert's well-being after the
 accident. The gesture is being used literally for its assertional value, suggesting that Albert
 is doing well, or at least, that he is feeling fine. 
-
+  
 Therefore, Angela's testimony regarding Albert's gesture fits the definition of hearsay and
 would be treated as such in a legal setting.
 
@@ -321,13 +321,13 @@ The issue was analyzed correctly in the above completion, but we are seeking a c
 ```
 [prompt 2]
 Given the question below and the accompanying analysis, answer with only "Yes" or "No".
-
+  
 ## question
 {question}
-
+  
 ## analysis
 {completion_from_prompt_1}
-
+  
 [completion]
 Yes
 
diff --git a/fern/pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx b/fern/pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx
index 826b3cc6..3cc780cc 100644
--- a/fern/pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx
+++ b/fern/pages/text-generation/prompt-engineering/crafting-effective-prompts.mdx
@@ -17,14 +17,14 @@ For example:
 ```
 ## Instructions
 Summarize the text below.
-
+  
 ## Input Text
 {input_text}
 ```
 
 Then use the Chat API to send a message to the model:
 
-```python python
+```python PYTHON
 import cohere
 co = cohere.Client(api_key="")
 
@@ -54,7 +54,7 @@ Below there is a long form news article discussing the 1972 Canada–USSR Summit
 an eight-game ice hockey series between the Soviet Union and Canada, held in September 1972.
 Please summarize the salient points of the text and do so in a flowing high natural language
 quality text. Use bullet points where appropriate.
-
+  
 ## News Article
 {news_article}
 
@@ -70,7 +70,7 @@ For this method, we recommend providing documents through the documents paramete
 
 For the example above, we can split the original news article into different sections and attach them via the `documents` parameter. The Chat API will then provide us not only with the completion but also citations that ground information from the documents. See the following:
 
-```python python
+```python PYTHON
 # Sections from the original news article
 documents = [
     {"title": "background", "snippet": "From the beginning of the IIHF Ice Hockey World Championships in 1920, Canada would send a ..."},
@@ -96,23 +96,23 @@ The model returns a high quality summary in `response.text`:
 The 1972 Canada–USSR Summit Series marked a significant moment in the history of ice hockey,
 showcasing a high-stakes competition between the Canadian national team and the Soviet Union's
 elite hockey squad. Here are some key points about the series:
-
+  
 - The Summit Series represented a clash of hockey powers, with Canada, dominated by National Hockey
 League (NHL) players, expected to beat the Soviet Union's team effortlessly. The Canadian side was
 favoured by fans and pundits alike, putting the Soviets down as amateurs.
-
+  
 - However, the Soviet Union's purposeful shift towards adopting the Canadian style of play since
 the 1940s, along with their full-time dedication to the sport, had paid off. The series saw them
 emerge as formidable opponents, surprising Canada and many fans. 
-
+  
 - Canada ultimately won the series, but the Soviet Union's impressive performance earned them the
 respect of their competitors. The Canadians' preconceptions were challenged, and the series marked
 a turning point in acknowledging the global nature of elite hockey.
-
+  
 - The success of the Summit Series led the NHL to organise further exhibitions against top Soviet
 clubs, including the 1975-76 Super Series. It also paved the way for the inaugural Canada Cup
 "best-on-best" tournament in 1976.
-
+  
 - The series heightened the profile of international ice hockey and cemented the fact that national
 teams could now rival the long-dominant Canadians, transforming the sport into a truly global
 phenomenon.
@@ -170,24 +170,24 @@ Then, `print(insert_citations(response.text, response.citations))` results in:
 The 1972 Canada–USSR Summit Series marked a significant moment in the history of ice hockey,
   showcasing a high-stakes competition between the Canadian national team [1] and the Soviet
   Union's elite hockey squad. [1] Here are some key points about the series:
-
+  
 - The Summit Series represented a clash of hockey powers [1, 2], with Canada, dominated by
 National Hockey League (NHL) players [2], expected to beat the Soviet Union's team effortlessly.[2]
 The Canadian side was favoured by fans and pundits alike [2], putting the Soviets down as
 amateurs. [2]
-
+  
 - However, the Soviet Union's purposeful shift towards adopting the Canadian style of play since
 the 1940s [1], along with their full-time dedication to the sport [1], had paid off. The series
 saw them emerge as formidable opponents [2], surprising Canada [2, 3] and many fans. [2] 
-
+  
 - Canada ultimately won the series [3], but the Soviet Union's impressive performance earned them
 the respect of their competitors. [3] The Canadians' preconceptions were challenged [3], and the
 series marked a turning point in acknowledging the global nature of elite hockey. [3]
-
+  
 - The success of the Summit Series led the NHL to organise further exhibitions against top Soviet
 clubs [3], including the 1975-76 Super Series. [3] It also paved the way for the inaugural Canada
 Cup "best-on-best" tournament in 1976. [3]
-
+  
 - The series heightened the profile of international ice hockey [3] and cemented the fact that
 national teams could now rival the long-dominant Canadians [3], transforming the sport into a 
 truly global phenomenon. [3]
@@ -200,14 +200,14 @@ LLMs respond well when they have specific examples to work from. For example, in
 ```
 ## Instructions
 Below there is a long form news article discussing the 1972 Canada–USSR Summit Series, an eight-game ice hockey series between the Soviet Union and Canada, held in September 1972. Please summarize the salient points of the text and do so in a flowing high natural language quality text. Use bullet points where appropriate.
-
+  
 ## Example Output
 High level summary: 
 3 important events related to the series:
 * 
 * 
 * 
-
+  
 ## News Article
 {news_article}
 
@@ -237,9 +237,9 @@ Be explicit in **exactly** what you want the model to do. Be as assertive as pos
 ```
 ## Instructions
 Below there is a long form news article discussing the 1972 Canada–USSR Summit Series, an eight-game ice hockey series between the Soviet Union and Canada, held in September 1972. Please summarize the salient points of the text and do so in a flowing high natural language quality text. Use bullet points where appropriate.
-
+  
 Paraphrase the content into re-written, easily digestible sentences. Do not extract full sentences from the input text. 
-
+  
 ## News Article
 {news_article}
 
@@ -262,7 +262,7 @@ LLMs can easily be constrained by beginning the completion as part of the input
 ...
 Please generate the response in a well-formed HTML document. The completion should begin as
 follows:
-
+  
 
 
 
@@ -275,7 +275,7 @@ Finally, task splitting should be used when the requested task is complex and ca
 ```
 ## Instructions
 Using the included text below, perform the following steps:
-
+  
 1. Read through the entire text carefully
 2. Extract the most important paragraph
 3. From the paragraph extracted in step 2, extract the most important sentence
diff --git a/fern/pages/text-generation/tokens-and-tokenizers.mdx b/fern/pages/text-generation/tokens-and-tokenizers.mdx
index 83e211af..67b243fa 100644
--- a/fern/pages/text-generation/tokens-and-tokenizers.mdx
+++ b/fern/pages/text-generation/tokens-and-tokenizers.mdx
@@ -38,7 +38,7 @@ Cohere offers the [tokenize](/reference/tokenize) and [detokenize](/reference/de
 
 Cohere Tokenizers are publicly hosted and can be used locally to avoid network calls. If you are using the Python SDK, the `tokenize` and `detokenize` functions will take care of downloading and caching the tokenizer for you
 
-```python python
+```python PYTHON
 import cohere  
 co = cohere.Client(api_key="")
 
diff --git a/fern/pages/text-generation/tools/tool-use.mdx b/fern/pages/text-generation/tools/tool-use.mdx
index 0b5d2de5..f8951ff9 100644
--- a/fern/pages/text-generation/tools/tool-use.mdx
+++ b/fern/pages/text-generation/tools/tool-use.mdx
@@ -297,7 +297,7 @@ for cite in response.citations:
 
 There may be times when a single-step tool use query doesn't turn up a tool that will help answer the question. In those scenarios the model will return an empty list of `tool_calls`, along with an empty string in the `text` field. In that case, call the Chat API again with an empty list of `tool_results`
 
-```python python
+```python PYTHON
 res = co.chat(message=message, tools=tools)
 
 if not response.tool_calls:

From 85267133a62e47a9e7d95aa3ca2a1734f3403723 Mon Sep 17 00:00:00 2001
From: fern <126544928+fern-bot@users.noreply.github.com>
Date: Wed, 14 Aug 2024 17:27:18 -0400
Subject: [PATCH 8/9] update styling (#23)

---
 fern/assets/input.css                         | 28 ++++++++++++++++++-
 fern/fern.config.json                         |  2 +-
 fern/pages/cookbooks.mdx                      |  6 ++--
 .../cookbooks/agentic-multi-stage-rag.mdx     |  2 +-
 .../pages/cookbooks/analyzing-hacker-news.mdx |  6 ++--
 ...ticle-recommender-with-text-embeddings.mdx |  6 ++--
 .../pages/cookbooks/basic-semantic-search.mdx |  6 ++--
 fern/pages/cookbooks/csv-agent-native-api.mdx |  4 +--
 .../cookbooks/fueling-generative-content.mdx  |  6 ++--
 fern/pages/cookbooks/hello-world-meet-ai.mdx  |  2 +-
 fern/pages/cookbooks/summarization-evals.mdx  |  2 +-
 .../text-classification-using-embeddings.mdx  |  2 +-
 .../advanced-generation-hyperparameters.mdx   |  4 +--
 13 files changed, 51 insertions(+), 25 deletions(-)

diff --git a/fern/assets/input.css b/fern/assets/input.css
index 5e9ee2cb..554f624f 100644
--- a/fern/assets/input.css
+++ b/fern/assets/input.css
@@ -471,9 +471,35 @@ button[class^="Sidebar-link-buttonWrapper"] {
 }
 
 .light-bg {
-  background-color: #E8E6DE !important;
+  background-color: #f7f7f7 !important;
   border-color: #e0e0e0 !important;
   border-radius: .5rem !important;
   border-width: 1px !important;
   padding: 15px !important;
+}
+
+.dark-text {
+  color: #1a1a1a !important;
+}
+
+.dataframe {
+  display: block !important;
+  overflow: auto !important;
+  border-color: #e0e0e0 !important;
+  border-radius: .5rem !important;
+  border-width: 1px !important;
+}
+
+:is(.dark) .dataframe {
+  border-color: #ffffff22 !important;
+}
+
+.fern-table td {
+  max-width: 400px !important;
+  overflow: wrap !important;
+}
+
+.fern-table th {
+  max-width: 400px !important;
+  overflow: wrap !important;
 }
\ No newline at end of file
diff --git a/fern/fern.config.json b/fern/fern.config.json
index afdd207c..d61cbb59 100644
--- a/fern/fern.config.json
+++ b/fern/fern.config.json
@@ -1,4 +1,4 @@
 {
   "organization": "cohere",
-  "version": "0.37.16"
+  "version": "0.39.1"
 }
\ No newline at end of file
diff --git a/fern/pages/cookbooks.mdx b/fern/pages/cookbooks.mdx
index b3175c7b..b00fb5e9 100644
--- a/fern/pages/cookbooks.mdx
+++ b/fern/pages/cookbooks.mdx
@@ -532,7 +532,7 @@ export const UseCasesContainer = () => (
             

               
                 {title}
@@ -559,8 +559,8 @@ export const UseCasesContainer = () => (
     }}
   />
   

-    
Cookbooks

-    

+    
Cookbooks

+    

       Explore what you can build on Cohere's generative AI platform. These
       ready-made guides will get you started with best-practices that get the
       most out of Cohere's models. Everything is set up and ready for you to
diff --git a/fern/pages/cookbooks/agentic-multi-stage-rag.mdx b/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
index bdc00166..19421f05 100644
--- a/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
+++ b/fern/pages/cookbooks/agentic-multi-stage-rag.mdx
@@ -137,7 +137,7 @@ db
 ```
 
 

-  
+  

diff --git a/fern/pages/cookbooks/analyzing-hacker-news.mdx b/fern/pages/cookbooks/analyzing-hacker-news.mdx
index d666e063..5c30370c 100644
--- a/fern/pages/cookbooks/analyzing-hacker-news.mdx
+++ b/fern/pages/cookbooks/analyzing-hacker-news.mdx
@@ -129,7 +129,7 @@ df.head()
 ```
 
 

-

     
       

         

+

@@ -301,7 +301,7 @@ Nearest neighbors:
 ```
 
 

-  

 
 

 

+  

@@ -387,7 +387,7 @@ Nearest neighbors:
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/article-recommender-with-text-embeddings.mdx b/fern/pages/cookbooks/article-recommender-with-text-embeddings.mdx
index 1d84a394..32a724ec 100644
--- a/fern/pages/cookbooks/article-recommender-with-text-embeddings.mdx
+++ b/fern/pages/cookbooks/article-recommender-with-text-embeddings.mdx
@@ -87,7 +87,7 @@ df_inputs.head()
 ```
 
 

-  

     
       

         

+  

@@ -234,7 +234,7 @@ df_examples.head()
 ```
 
 

-  

     
       

         

+  

@@ -348,7 +348,7 @@ df_test.head()
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/basic-semantic-search.mdx b/fern/pages/cookbooks/basic-semantic-search.mdx
index a4132ce7..13b2b843 100644
--- a/fern/pages/cookbooks/basic-semantic-search.mdx
+++ b/fern/pages/cookbooks/basic-semantic-search.mdx
@@ -73,7 +73,7 @@ df.head(10)
 ```
 
 

-  

     
       

         

+  

@@ -221,7 +221,7 @@ Nearest neighbors:
 ```
 
 

-  

     
       

         

+  

@@ -307,7 +307,7 @@ Nearest neighbors:
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/csv-agent-native-api.mdx b/fern/pages/cookbooks/csv-agent-native-api.mdx
index 9e65c045..3d6f7386 100644
--- a/fern/pages/cookbooks/csv-agent-native-api.mdx
+++ b/fern/pages/cookbooks/csv-agent-native-api.mdx
@@ -93,7 +93,7 @@ income_statement.head(2)
 ```
 
 

-  

     
       

         

+  

@@ -168,7 +168,7 @@ balance_sheet.head(2)
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/fueling-generative-content.mdx b/fern/pages/cookbooks/fueling-generative-content.mdx
index 525a10de..7b9270a7 100644
--- a/fern/pages/cookbooks/fueling-generative-content.mdx
+++ b/fern/pages/cookbooks/fueling-generative-content.mdx
@@ -57,7 +57,7 @@ df.head()
 ```
 
 

-  

     
       

         

+  

@@ -127,7 +127,7 @@ df.head()
 ```
 
 

-  

     
       

         

+  

@@ -206,7 +206,7 @@ df.head()
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/hello-world-meet-ai.mdx b/fern/pages/cookbooks/hello-world-meet-ai.mdx
index bf96ebd2..4b8eac54 100644
--- a/fern/pages/cookbooks/hello-world-meet-ai.mdx
+++ b/fern/pages/cookbooks/hello-world-meet-ai.mdx
@@ -331,7 +331,7 @@ df.head()
 ```
 
 

-  

     
       

         

+  

diff --git a/fern/pages/cookbooks/summarization-evals.mdx b/fern/pages/cookbooks/summarization-evals.mdx
index b9c6c63b..550e31c7 100644
--- a/fern/pages/cookbooks/summarization-evals.mdx
+++ b/fern/pages/cookbooks/summarization-evals.mdx
@@ -441,7 +441,7 @@ data
 ```
 
 

-

     
       

         

+

diff --git a/fern/pages/cookbooks/text-classification-using-embeddings.mdx b/fern/pages/cookbooks/text-classification-using-embeddings.mdx
index 703caaf6..a71db202 100644
--- a/fern/pages/cookbooks/text-classification-using-embeddings.mdx
+++ b/fern/pages/cookbooks/text-classification-using-embeddings.mdx
@@ -48,7 +48,7 @@ df.head()
 ```
 
 

-  

 
 

 

+  

diff --git a/fern/pages/text-generation/advanced-generation-hyperparameters.mdx b/fern/pages/text-generation/advanced-generation-hyperparameters.mdx
index 467ea206..c213c424 100644
--- a/fern/pages/text-generation/advanced-generation-hyperparameters.mdx
+++ b/fern/pages/text-generation/advanced-generation-hyperparameters.mdx
@@ -49,7 +49,7 @@ Another commonly-used strategy is to sample from a shortlist of the top 3 tokens
 More broadly, choosing the top three tokens means setting the top-k parameter to 3. Changing the top-k parameter sets the size of the shortlist the model samples from as it outputs each token. Setting top-k to 1 gives us greedy decoding.
 
 
-setting.
+setting.
 
 
@@ -60,7 +60,7 @@ Note that when `k` is set to `0`, the model disables k sampling and uses p inste
 The difficulty of selecting the best top-k value opens the door for a popular decoding strategy that dynamically sets the size of the shortlist of tokens. This method, called _Nucleus Sampling_, creates the shortlist by selecting the top tokens whose sum of likelihoods does not exceed a certain value. A toy example with a top-p value of 0.15 could look like this:
 
 
-threshold.
+threshold.
 
 

From 01a2aa81dc69d6a6a29cf49dee562f47e7f44914 Mon Sep 17 00:00:00 2001
From: platform-endpoints 
Date: Thu, 15 Aug 2024 15:19:21 +0000
Subject: [PATCH 9/9] Add spec changes

Co-authored-by: billytrend-cohere <144115527+billytrend-cohere@users.noreply.github.com>
---
 cohere-openapi.yaml                           | 503 +++++++++++++++++-
 .../snippets/node/chat-v2-post/default.ts     |  19 +
 .../snippets/node/chat-v2-post/documents.ts   |  22 +
 snippets/snippets/node/chat-v2-post/stream.ts |  23 +
 snippets/snippets/node/chat-v2-post/tools.ts  |  52 ++
 .../snippets/python-async/chat-v2-post.py     |  19 +
 .../snippets/python-async/embed-jobs-post.py  |   4 +-
 .../finetuning/create-finetuned-model.py      |   2 +-
 .../snippets/python/chat-v2-post/default.py   |  14 +
 .../snippets/python/chat-v2-post/documents.py |  21 +
 .../snippets/python/chat-v2-post/stream.py    |  16 +
 .../snippets/python/chat-v2-post/tools.py     |  38 ++
 snippets/snippets/python/dataset-post.py      |   2 +-
 snippets/snippets/python/embed-jobs-post.py   |   4 +-
 .../finetuning/create-finetuned-model.py      |   9 +-
 .../finetuning/update-finetuned-model.py      |   4 +
 16 files changed, 735 insertions(+), 17 deletions(-)
 create mode 100644 snippets/snippets/node/chat-v2-post/default.ts
 create mode 100644 snippets/snippets/node/chat-v2-post/documents.ts
 create mode 100644 snippets/snippets/node/chat-v2-post/stream.ts
 create mode 100644 snippets/snippets/node/chat-v2-post/tools.ts
 create mode 100644 snippets/snippets/python-async/chat-v2-post.py
 create mode 100644 snippets/snippets/python/chat-v2-post/default.py
 create mode 100644 snippets/snippets/python/chat-v2-post/documents.py
 create mode 100644 snippets/snippets/python/chat-v2-post/stream.py
 create mode 100644 snippets/snippets/python/chat-v2-post/tools.py

diff --git a/cohere-openapi.yaml b/cohere-openapi.yaml
index c8f2d437..6f8a0d8c 100644
--- a/cohere-openapi.yaml
+++ b/cohere-openapi.yaml
@@ -1445,6 +1445,444 @@ paths:
           $ref: "#/components/responses/ServiceUnavailable"
         "504":
           $ref: "#/components/responses/GatewayTimeout"
+  /v2/chat:
+    post:
+      summary: Chat with the model
+      operationId: chatv2
+      x-fern-audiences:
+        - v2-beta
+      x-fern-sdk-group-name: v2
+      x-fern-sdk-method-name: chat
+      x-fern-streaming:
+        format: sse
+        stream-condition: $request.stream
+        response:
+          x-fern-sdk-group-name: v2
+          required:
+            - id
+            - finish_reason
+            - messages
+          properties:
+            id:
+              type: string
+              description: Unique identifier for the generated reply. Useful for submitting
+                feedback.
+            finish_reason:
+              $ref: "#/components/schemas/ChatFinishReason"
+            message:
+              $ref: "#/components/schemas/AssistantMessage"
+            usage:
+              $ref: "#/components/schemas/Usage"
+        response-stream:
+          x-fern-sdk-group-name: v2
+          description: StreamedChatResponse is returned in streaming mode (specified with
+            `stream=True` in the request).
+          oneOf:
+            - $ref: "#/components/schemas/ChatMessageStartEvent"
+            - $ref: "#/components/schemas/ChatContentStartEvent"
+            - $ref: "#/components/schemas/ChatContentDeltaEvent"
+            - $ref: "#/components/schemas/ChatContentEndEvent"
+            - $ref: "#/components/schemas/ChatToolPlanDeltaEvent"
+            - $ref: "#/components/schemas/ChatToolCallStartEvent"
+            - $ref: "#/components/schemas/ChatToolCallDeltaEvent"
+            - $ref: "#/components/schemas/ChatToolCallEndEvent"
+            - $ref: "#/components/schemas/ChatMessageEndEvent"
+          discriminator:
+            propertyName: type
+            mapping:
+              message-start: "#/components/schemas/ChatMessageStartEvent"
+              content-start: "#/components/schemas/ChatContentStartEvent"
+              content-delta: "#/components/schemas/ChatContentDeltaEvent"
+              content-end: "#/components/schemas/ChatContentEndEvent"
+              tool-plan-delta: "#/components/schemas/ChatToolPlanDeltaEvent"
+              tool-call-start: "#/components/schemas/ChatToolCallStartEvent"
+              tool-call-delta: "#/components/schemas/ChatToolCallDeltaEvent"
+              tool-call-end: "#/components/schemas/ChatToolCallEndEvent"
+              citation-start: "#/components/schemas/CitationStartEvent"
+              citation-end: "#/components/schemas/CitationEndEvent"
+              message-end: "#/components/schemas/ChatMessageEndEvent"
+      requestBody:
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - messages
+                - model
+              properties:
+                model:
+                  type: string
+                  description: The model to use for the chat.
+                messages:
+                  $ref: "#/components/schemas/ChatMessages"
+                tools:
+                  type: array
+                  items:
+                    $ref: "#/components/schemas/Tool-2"
+                tool_choice:
+                  type: string
+                  enum:
+                    - AUTO
+                    - NONE
+                    - ANY
+                citation_mode:
+                  type: string
+                  enum:
+                    - FAST
+                    - ACCURATE
+                truncation_mode:
+                  type: string
+                  enum:
+                    - OFF
+                    - AUTO
+                    - AUTO_PRESERVE_ORDER
+                response_format:
+                  type: object
+                  properties:
+                    type:
+                      type: string
+                      enum:
+                        - json_object
+                      description: When set to JSON, the output will be parse-able valid JSON (or run
+                        out of context).
+                    schema:
+                      description: |
+                        A JSON schema object that the output will adhere to. Refer to https://json-schema.org/ for reference about schemas.
+                      type: object
+                max_tokens:
+                  type: integer
+                  description: The maximum number of tokens to generate.
+                stop_sequences:
+                  type: array
+                  items:
+                    type: string
+                  description: A list of strings that the model will stop generating at.
+                max_input_tokens:
+                  type: integer
+                  description: The maximum number of tokens to feed into the model.
+                temperature:
+                  type: number
+                  description: The temperature of the model.
+                  format: float
+                seed:
+                  type: integer
+                frequency_penalty:
+                  type: number
+                  description: The frequency penalty of the model.
+                  format: float
+                presence_penalty:
+                  type: number
+                  description: The presence penalty of the model.
+                  format: float
+                k:
+                  type: integer
+                p:
+                  type: integer
+      responses:
+        "200":
+          description: OK
+          content:
+            application/json:
+              schema:
+                oneOf:
+                  - $ref: "#/components/schemas/NonStreamedChatResponse-2"
+                  - $ref: "#/components/schemas/StreamedChatResponse-2"
+        "400":
+          $ref: "#/components/responses/BadRequest"
+        "401":
+          $ref: "#/components/responses/Unauthorized"
+        "403":
+          $ref: "#/components/responses/Forbidden"
+        "404":
+          $ref: "#/components/responses/NotFound"
+        "422":
+          $ref: "#/components/responses/UnprocessableEntity"
+        "429":
+          $ref: "#/components/responses/RateLimit"
+        "498":
+          $ref: "#/components/responses/InvalidToken"
+        "499":
+          $ref: "#/components/responses/RequestCancelled"
+        "500":
+          $ref: "#/components/responses/InternalServerError"
+        "501":
+          $ref: "#/components/responses/NotImplemented"
+        "503":
+          $ref: "#/components/responses/ServiceUnavailable"
+        "504":
+          $ref: "#/components/responses/GatewayTimeout"
+      x-readme:
+        samples-languages:
+          - python
+          - node
+        code-samples:
+          - language: node
+            name: Default
+            install: npm i cohere-ai
+            code: |
+              const { CohereClientV2 } = require('cohere-ai');
+
+              const cohere = new CohereClientV2({
+                token: '<>',
+              });
+
+              (async () => {
+                const response = await cohere.chat({
+                  model: 'command-r-plus',
+                  messages: [
+                    {
+                      role: 'user',
+                      content: 'hello world!',
+                    },
+                  ],
+                });
+
+                console.log(response);
+              })();
+          - language: node
+            name: Documents
+            install: npm i cohere-ai
+            code: >
+              const { CohereClientV2 } = require('cohere-ai');
+
+
+              const cohere = new CohereClientV2({
+                token: '<>',
+              });
+
+
+              (async () => {
+                const response = await cohere.chat({
+                  model: 'command-r-plus',
+                  messages: [
+                    {
+                      role: 'user',
+                      content: [
+                        { type: 'document', id: '1', document: { text: 'Cohere is the best!' } },
+                        { type: 'text', text: "Who's the best?" },
+                      ],
+                    },
+                  ],
+                });
+
+                console.log(response);
+              })();
+          - language: node
+            name: Streaming
+            install: npm i cohere-ai
+            code: |
+              const { CohereClientV2 } = require('cohere-ai');
+
+              const cohere = new CohereClientV2({
+                token: '<>',
+              });
+
+              (async () => {
+                const stream = await cohere.chatStream({
+                  model: 'command-r-plus',
+                  messages: [
+                    {
+                      role: 'user',
+                      content: 'hello world!',
+                    },
+                  ],
+                });
+
+                for await (const chatEvent of stream) {
+                  if (chatEvent.type === 'content-delta') {
+                    console.log(chatEvent.delta?.message);
+                  }
+                }
+              })();
+          - language: node
+            name: Tools
+            install: npm i cohere-ai
+            code: >
+              const { CohereClientV2 } = require('cohere-ai');
+
+
+              const cohere = new CohereClientV2({
+                token: '<>',
+              });
+
+
+              (async () => {
+                const response = await cohere.chat({
+                  model: 'command-r-plus',
+                  tools: [
+                    {
+                      type: 'function',
+                      function: {
+                        name: 'query_daily_sales_report',
+                        description:
+                          'Connects to a database to retrieve overall sales volumes and sales information for a given day.',
+                        parameters: {
+                          day: {
+                            description: 'Retrieves sales data for this day, formatted as YYYY-MM-DD.',
+                            type: 'str',
+                            required: true,
+                          },
+                        },
+                      },
+                    },
+                    {
+                      type: 'function',
+                      function: {
+                        name: 'query_product_catalog',
+                        description:
+                          'Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.',
+                        parameters: {
+                          category: {
+                            description: 'Retrieves product information data for all products in this category.',
+                            type: 'str',
+                            required: true,
+                          },
+                        },
+                      },
+                    },
+                  ],
+                  messages: [
+                    {
+                      role: 'user',
+                      content:
+                        "Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?",
+                    },
+                  ],
+                });
+
+                console.log(response);
+              })();
+          - language: python
+            name: Sync
+            install: python -m pip install cohere --upgrade
+            code: |
+              import cohere
+
+              co = cohere.ClientV2("<>")
+
+              response = co.chat(
+                  model="command-r-plus",
+                  messages=[
+                      cohere.v2.ChatMessage2_User(
+                          content="hello world!"
+                      )
+                  ]
+              )
+
+              print(response)
+          - language: python
+            name: Async
+            install: python -m pip install cohere --upgrade
+            code: |
+              import cohere
+              import asyncio
+
+              co = cohere.AsyncClientV2("<>")
+
+
+              async def main():
+                  response = await co.chat(
+                      model="command-r-plus",
+                      messages=[
+                          cohere.v2.ChatMessage2_User(
+                              content="hello world!"
+                          )
+                      ]
+                  )
+
+                  print(response)
+
+              asyncio.run(main())
+          - language: python
+            name: Documents
+            install: python -m pip install cohere --upgrade
+            code: |
+              import cohere
+
+              co = cohere.ClientV2("<>")
+
+              response = co.chat(
+                  model="command-r-plus",
+                  messages=[
+                      cohere.v2.ChatMessage2_User(
+                          content=[
+                              cohere.v2.DocumentContent(
+                                  id=1,
+                                  document={'title': 'The best',
+                                            'text': 'Cohere is the best!'}
+                              ),
+                              cohere.v2.TextContent(text="Who's the best?"),
+                          ]
+                      )
+                  ]
+              )
+
+              print(response)
+          - language: python
+            name: Streaming
+            install: python -m pip install cohere --upgrade
+            code: |
+              import cohere
+
+              co = cohere.ClientV2("<>")
+
+              response = co.chat_stream(
+                  model="command-r-plus",
+                  messages=[
+                      cohere.v2.ChatMessage2_User(
+                          content="hello world!"
+                      )
+                  ]
+              )
+
+              for event in response:
+                  if event.event_type == "text-generation":
+                      print(event.text, end='')
+          - language: python
+            name: Tools
+            install: python -m pip install cohere --upgrade
+            code: >
+              import cohere
+
+
+              co = cohere.Client("<>")
+
+
+              response = co.chat(
+                  model="command-r-plus",
+                  tools=[
+                      cohere.v2.Tool2(type='function', function={
+                          "name": 'query_daily_sales_report',
+                                  "description": 'Connects to a database to retrieve overall sales volumes and sales information for a given day.',
+                                  "parameters": {
+                                      "day": {
+                                          "description": 'Retrieves sales data for this day, formatted as YYYY-MM-DD.',
+                                          "type": 'str',
+                                          "required": True,
+                                      },
+                                  }
+                      }),
+                      cohere.v2.Tool2(type='function', function={
+                          "name": 'query_product_catalog',
+                                  "description": 'Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.',
+                                  "parameters": {
+                                      "category": {
+                                          "description": 'Retrieves product information data for all products in this category.',
+                                          "type": 'str',
+                                          "required": True,
+                                      },
+                                  }
+                      })
+                  ],
+                  messages=[
+                      cohere.v2.ChatMessage2_User(
+                          content="Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?"
+                      )
+                  ]
+              )
+
+
+              print(response)
   /v1/generate:
     post:
       x-fern-audiences:
@@ -2221,8 +2659,8 @@ paths:
 
               # start an embed job
 
-              response = co.embed_jobs.create(
-                  dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
+              job = co.embed_jobs.create(
+                  dataset_id="my-dataset-id", input_type="search_document", model="embed-english-v3.0"
               )
 
 
@@ -2247,8 +2685,8 @@ paths:
 
               async def main():
                   # start an embed job
-                  response = await co.embed_jobs.create(
-                      dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
+                  job = await co.embed_jobs.create(
+                      dataset_id="my-dataset-id", input_type="search_document", model="embed-english-v3.0"
                   )
 
                   # poll the server until the job is complete
@@ -3996,7 +4434,7 @@ paths:
               co = cohere.Client("<>")
 
               # upload a dataset
-              response = co.datasets.create(
+              my_dataset = co.datasets.create(
                   name="prompt-completion-dataset",
                   data=open("./prompt-completion.jsonl", "rb"),
                   type="prompt-completion-finetune-input",
@@ -7381,6 +7819,7 @@ paths:
                   FinetunedModel,
                   Hyperparameters,
                   Settings,
+                  WandbConfig
               )
               import cohere
 
@@ -7392,6 +7831,11 @@ paths:
                   train_epoch=1,
                   learning_rate=0.01,
               )
+              wnb_config = WandbConfig(
+                  project="test-project",
+                  api_key="<>",
+                  entity="test-entity",
+              )
               finetuned_model = co.finetuning.create_finetuned_model(
                   request=FinetunedModel(
                       name="test-finetuned-model",
@@ -7401,10 +7845,11 @@ paths:
                           ),
                           dataset_id="my-dataset-id",
                           hyperparameters=hp,
+                          wandb=wnb_config,
                       ),
                   )
               )
-              print(response)
+              print(finetuned_model)
           - language: python
             name: Async
             install: python -m pip install cohere --upgrade
@@ -7427,7 +7872,7 @@ paths:
                           name="test-finetuned-model",
                           settings=Settings(
                               base_model=BaseModel(
-                                  base_type=BaseType.BASE_TYPE_GENERATIVE,
+                                  base_type=BaseType.BASE_TYPE_CHAT,
                               ),
                               dataset_id="my-dataset-id",
                           ),
@@ -7659,6 +8104,10 @@ paths:
             name: Sync
             install: python -m pip install cohere --upgrade
             code: |
+              from cohere.finetuning import (
+                  BaseModel,
+                  Settings,
+              )
               import cohere
 
               co = cohere.Client("<>")
@@ -9291,9 +9740,9 @@ components:
       description: Citation information containing sources and the text cited.
       properties:
         start:
-          type: string
+          type: integer
         end:
-          type: string
+          type: integer
         text:
           type: string
         sources:
@@ -9365,7 +9814,13 @@ components:
         id:
           type: string
         document:
-          type: object
+          description: |
+            Relevant information that could be used by the model to generate a more accurate reply.
+            The contents of each document are generally short (under 300 words), and are passed in the form of a
+            dictionary of strings or a single string. If a single string is passed, it will be treated as the value for key "content".
+          oneOf:
+            - type: object
+            - type: string
     Content:
       x-fern-sdk-group-name: v2
       description: A Content block which contains information about the content type
@@ -9554,6 +10009,8 @@ components:
             - tool-call-delta
             - tool-call-end
             - tool-plan-delta
+            - citation-start
+            - citation-end
             - message-end
     ChatMessageStartEvent:
       x-fern-sdk-group-name: v2
@@ -9700,6 +10157,30 @@ components:
           properties:
             index:
               type: integer
+    CitationStartEvent:
+      description: A streamed event which signifies a citation has been created.
+      allOf:
+        - $ref: "#/components/schemas/ChatStreamEventType"
+        - type: object
+          properties:
+            index:
+              type: integer
+            delta:
+              type: object
+              properties:
+                message:
+                  type: object
+                  properties:
+                    citations:
+                      $ref: "#/components/schemas/Citation"
+    CitationEndEvent:
+      description: A streamed event which signifies a citation has finished streaming.
+      allOf:
+        - $ref: "#/components/schemas/ChatStreamEventType"
+        - type: object
+          properties:
+            index:
+              type: integer
     ChatMessageEndEvent:
       x-fern-sdk-group-name: v2
       description: A streamed event which signifies that the chat message has ended.
@@ -9741,6 +10222,8 @@ components:
           tool-call-start: "#/components/schemas/ChatToolCallStartEvent"
           tool-call-delta: "#/components/schemas/ChatToolCallDeltaEvent"
           tool-call-end: "#/components/schemas/ChatToolCallEndEvent"
+          citation-start: "#/components/schemas/CitationStartEvent"
+          citation-end: "#/components/schemas/CitationEndEvent"
           message-end: "#/components/schemas/ChatMessageEndEvent"
     SingleGeneration:
       type: object
diff --git a/snippets/snippets/node/chat-v2-post/default.ts b/snippets/snippets/node/chat-v2-post/default.ts
new file mode 100644
index 00000000..89b95e1c
--- /dev/null
+++ b/snippets/snippets/node/chat-v2-post/default.ts
@@ -0,0 +1,19 @@
+const { CohereClientV2 } = require('cohere-ai');
+
+const cohere = new CohereClientV2({
+  token: '<>',
+});
+
+(async () => {
+  const response = await cohere.chat({
+    model: 'command-r-plus',
+    messages: [
+      {
+        role: 'user',
+        content: 'hello world!',
+      },
+    ],
+  });
+
+  console.log(response);
+})();
diff --git a/snippets/snippets/node/chat-v2-post/documents.ts b/snippets/snippets/node/chat-v2-post/documents.ts
new file mode 100644
index 00000000..fa12d5c3
--- /dev/null
+++ b/snippets/snippets/node/chat-v2-post/documents.ts
@@ -0,0 +1,22 @@
+const { CohereClientV2 } = require('cohere-ai');
+
+const cohere = new CohereClientV2({
+  token: '<>',
+});
+
+(async () => {
+  const response = await cohere.chat({
+    model: 'command-r-plus',
+    messages: [
+      {
+        role: 'user',
+        content: [
+          { type: 'document', id: '1', document: { text: 'Cohere is the best!' } },
+          { type: 'text', text: "Who's the best?" },
+        ],
+      },
+    ],
+  });
+
+  console.log(response);
+})();
diff --git a/snippets/snippets/node/chat-v2-post/stream.ts b/snippets/snippets/node/chat-v2-post/stream.ts
new file mode 100644
index 00000000..79096eaa
--- /dev/null
+++ b/snippets/snippets/node/chat-v2-post/stream.ts
@@ -0,0 +1,23 @@
+const { CohereClientV2 } = require('cohere-ai');
+
+const cohere = new CohereClientV2({
+  token: '<>',
+});
+
+(async () => {
+  const stream = await cohere.chatStream({
+    model: 'command-r-plus',
+    messages: [
+      {
+        role: 'user',
+        content: 'hello world!',
+      },
+    ],
+  });
+
+  for await (const chatEvent of stream) {
+    if (chatEvent.type === 'content-delta') {
+      console.log(chatEvent.delta?.message);
+    }
+  }
+})();
diff --git a/snippets/snippets/node/chat-v2-post/tools.ts b/snippets/snippets/node/chat-v2-post/tools.ts
new file mode 100644
index 00000000..5a4e62f0
--- /dev/null
+++ b/snippets/snippets/node/chat-v2-post/tools.ts
@@ -0,0 +1,52 @@
+const { CohereClientV2 } = require('cohere-ai');
+
+const cohere = new CohereClientV2({
+  token: '<>',
+});
+
+(async () => {
+  const response = await cohere.chat({
+    model: 'command-r-plus',
+    tools: [
+      {
+        type: 'function',
+        function: {
+          name: 'query_daily_sales_report',
+          description:
+            'Connects to a database to retrieve overall sales volumes and sales information for a given day.',
+          parameters: {
+            day: {
+              description: 'Retrieves sales data for this day, formatted as YYYY-MM-DD.',
+              type: 'str',
+              required: true,
+            },
+          },
+        },
+      },
+      {
+        type: 'function',
+        function: {
+          name: 'query_product_catalog',
+          description:
+            'Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.',
+          parameters: {
+            category: {
+              description: 'Retrieves product information data for all products in this category.',
+              type: 'str',
+              required: true,
+            },
+          },
+        },
+      },
+    ],
+    messages: [
+      {
+        role: 'user',
+        content:
+          "Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?",
+      },
+    ],
+  });
+
+  console.log(response);
+})();
diff --git a/snippets/snippets/python-async/chat-v2-post.py b/snippets/snippets/python-async/chat-v2-post.py
new file mode 100644
index 00000000..d867848c
--- /dev/null
+++ b/snippets/snippets/python-async/chat-v2-post.py
@@ -0,0 +1,19 @@
+import cohere
+import asyncio
+
+co = cohere.AsyncClientV2("<>")
+
+
+async def main():
+    response = await co.chat(
+        model="command-r-plus",
+        messages=[
+            cohere.v2.ChatMessage2_User(
+                content="hello world!"
+            )
+        ]
+    )
+
+    print(response)
+
+asyncio.run(main())
diff --git a/snippets/snippets/python-async/embed-jobs-post.py b/snippets/snippets/python-async/embed-jobs-post.py
index f49580c9..3ba79cdf 100644
--- a/snippets/snippets/python-async/embed-jobs-post.py
+++ b/snippets/snippets/python-async/embed-jobs-post.py
@@ -6,8 +6,8 @@
 
 async def main():
     # start an embed job
-    response = await co.embed_jobs.create(
-        dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
+    job = await co.embed_jobs.create(
+        dataset_id="my-dataset-id", input_type="search_document", model="embed-english-v3.0"
     )
 
     # poll the server until the job is complete
diff --git a/snippets/snippets/python-async/finetuning/create-finetuned-model.py b/snippets/snippets/python-async/finetuning/create-finetuned-model.py
index eea9e7dd..19d8dfef 100644
--- a/snippets/snippets/python-async/finetuning/create-finetuned-model.py
+++ b/snippets/snippets/python-async/finetuning/create-finetuned-model.py
@@ -16,7 +16,7 @@ async def main():
             name="test-finetuned-model",
             settings=Settings(
                 base_model=BaseModel(
-                    base_type=BaseType.BASE_TYPE_GENERATIVE,
+                    base_type=BaseType.BASE_TYPE_CHAT,
                 ),
                 dataset_id="my-dataset-id",
             ),
diff --git a/snippets/snippets/python/chat-v2-post/default.py b/snippets/snippets/python/chat-v2-post/default.py
new file mode 100644
index 00000000..fa156470
--- /dev/null
+++ b/snippets/snippets/python/chat-v2-post/default.py
@@ -0,0 +1,14 @@
+import cohere
+
+co = cohere.ClientV2("<>")
+
+response = co.chat(
+    model="command-r-plus",
+    messages=[
+        cohere.v2.ChatMessage2_User(
+            content="hello world!"
+        )
+    ]
+)
+
+print(response)
diff --git a/snippets/snippets/python/chat-v2-post/documents.py b/snippets/snippets/python/chat-v2-post/documents.py
new file mode 100644
index 00000000..c174e1b4
--- /dev/null
+++ b/snippets/snippets/python/chat-v2-post/documents.py
@@ -0,0 +1,21 @@
+import cohere
+
+co = cohere.ClientV2("<>")
+
+response = co.chat(
+    model="command-r-plus",
+    messages=[
+        cohere.v2.ChatMessage2_User(
+            content=[
+                cohere.v2.DocumentContent(
+                    id=1,
+                    document={'title': 'The best',
+                              'text': 'Cohere is the best!'}
+                ),
+                cohere.v2.TextContent(text="Who's the best?"),
+            ]
+        )
+    ]
+)
+
+print(response)
diff --git a/snippets/snippets/python/chat-v2-post/stream.py b/snippets/snippets/python/chat-v2-post/stream.py
new file mode 100644
index 00000000..05d21cf3
--- /dev/null
+++ b/snippets/snippets/python/chat-v2-post/stream.py
@@ -0,0 +1,16 @@
+import cohere
+
+co = cohere.ClientV2("<>")
+
+response = co.chat_stream(
+    model="command-r-plus",
+    messages=[
+        cohere.v2.ChatMessage2_User(
+            content="hello world!"
+        )
+    ]
+)
+
+for event in response:
+    if event.event_type == "text-generation":
+        print(event.text, end='')
diff --git a/snippets/snippets/python/chat-v2-post/tools.py b/snippets/snippets/python/chat-v2-post/tools.py
new file mode 100644
index 00000000..2798e18b
--- /dev/null
+++ b/snippets/snippets/python/chat-v2-post/tools.py
@@ -0,0 +1,38 @@
+import cohere
+
+co = cohere.Client("<>")
+
+response = co.chat(
+    model="command-r-plus",
+    tools=[
+        cohere.v2.Tool2(type='function', function={
+            "name": 'query_daily_sales_report',
+                    "description": 'Connects to a database to retrieve overall sales volumes and sales information for a given day.',
+                    "parameters": {
+                        "day": {
+                            "description": 'Retrieves sales data for this day, formatted as YYYY-MM-DD.',
+                            "type": 'str',
+                            "required": True,
+                        },
+                    }
+        }),
+        cohere.v2.Tool2(type='function', function={
+            "name": 'query_product_catalog',
+                    "description": 'Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.',
+                    "parameters": {
+                        "category": {
+                            "description": 'Retrieves product information data for all products in this category.',
+                            "type": 'str',
+                            "required": True,
+                        },
+                    }
+        })
+    ],
+    messages=[
+        cohere.v2.ChatMessage2_User(
+            content="Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?"
+        )
+    ]
+)
+
+print(response)
diff --git a/snippets/snippets/python/dataset-post.py b/snippets/snippets/python/dataset-post.py
index bf5d577c..549a7707 100644
--- a/snippets/snippets/python/dataset-post.py
+++ b/snippets/snippets/python/dataset-post.py
@@ -3,7 +3,7 @@
 co = cohere.Client("<>")
 
 # upload a dataset
-response = co.datasets.create(
+my_dataset = co.datasets.create(
     name="prompt-completion-dataset",
     data=open("./prompt-completion.jsonl", "rb"),
     type="prompt-completion-finetune-input",
diff --git a/snippets/snippets/python/embed-jobs-post.py b/snippets/snippets/python/embed-jobs-post.py
index 13c42263..90cd470b 100644
--- a/snippets/snippets/python/embed-jobs-post.py
+++ b/snippets/snippets/python/embed-jobs-post.py
@@ -3,8 +3,8 @@
 co = cohere.Client("<>")
 
 # start an embed job
-response = co.embed_jobs.create(
-    dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
+job = co.embed_jobs.create(
+    dataset_id="my-dataset-id", input_type="search_document", model="embed-english-v3.0"
 )
 
 # poll the server until the job is complete
diff --git a/snippets/snippets/python/finetuning/create-finetuned-model.py b/snippets/snippets/python/finetuning/create-finetuned-model.py
index c1ab45f9..d1e5d869 100644
--- a/snippets/snippets/python/finetuning/create-finetuned-model.py
+++ b/snippets/snippets/python/finetuning/create-finetuned-model.py
@@ -3,6 +3,7 @@
     FinetunedModel,
     Hyperparameters,
     Settings,
+    WandbConfig
 )
 import cohere
 
@@ -14,6 +15,11 @@
     train_epoch=1,
     learning_rate=0.01,
 )
+wnb_config = WandbConfig(
+    project="test-project",
+    api_key="<>",
+    entity="test-entity",
+)
 finetuned_model = co.finetuning.create_finetuned_model(
     request=FinetunedModel(
         name="test-finetuned-model",
@@ -23,7 +29,8 @@
             ),
             dataset_id="my-dataset-id",
             hyperparameters=hp,
+            wandb=wnb_config,
         ),
     )
 )
-print(response)
+print(finetuned_model)
diff --git a/snippets/snippets/python/finetuning/update-finetuned-model.py b/snippets/snippets/python/finetuning/update-finetuned-model.py
index 51b3b498..938fbf20 100644
--- a/snippets/snippets/python/finetuning/update-finetuned-model.py
+++ b/snippets/snippets/python/finetuning/update-finetuned-model.py
@@ -1,3 +1,7 @@
+from cohere.finetuning import (
+    BaseModel,
+    Settings,
+)
 import cohere
 
 co = cohere.Client("<>")


     
       

         
 
 
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