Package index • ellmer

Chatbots ellmer provides a simple interface to a wide range of LLM providers. Use the `chat_` functions to initialize a `Chat` object for a specific provider and model. Once created, use the methods of the `Chat` object to send messages, receive responses, manage tools and extract structured data.
`Chat`	A chat
`chat_anthropic()` `models_anthropic()`	Chat with an Anthropic Claude model
`chat_aws_bedrock()` `models_aws_bedrock()`	Chat with an AWS bedrock model
`chat_azure_openai()`	Chat with a model hosted on Azure OpenAI
`chat_cloudflare()`	Chat with a model hosted on CloudFlare
`chat_cortex_analyst()`	Create a chatbot that speaks to the Snowflake Cortex Analyst
`chat_databricks()`	Chat with a model hosted on Databricks
`chat_deepseek()`	Chat with a model hosted on DeepSeek
`chat_github()`	Chat with a model hosted on the GitHub model marketplace
`chat_google_gemini()` `chat_google_vertex()` `models_google_gemini()` `models_google_vertex()`	Chat with a Google Gemini or Vertex AI model
`chat_groq()`	Chat with a model hosted on Groq
`chat_huggingface()`	Chat with a model hosted on Hugging Face Serverless Inference API
`chat_mistral()`	Chat with a model hosted on Mistral's La Platforme
`chat_ollama()` `models_ollama()`	Chat with a local Ollama model
`chat_openai()` `models_openai()`	Chat with an OpenAI model
`chat_openrouter()`	Chat with one of the many models hosted on OpenRouter
`chat_perplexity()`	Chat with a model hosted on perplexity.ai
`chat_portkey()` `models_portkey()`	Chat with a model hosted on PortkeyAI
`chat_snowflake()`	Chat with a model hosted on Snowflake
`chat_vllm()` `models_vllm()`	Chat with a model hosted by vLLM
`token_usage()`	Report on token usage in the current session
Chat helpers
`create_tool_def()`	Create metadata for a tool
`content_image_url()` `content_image_file()` `content_image_plot()`	Encode images for chat input
`content_pdf_file()` `content_pdf_url()`	Encode PDFs content for chat input
`live_console()` `live_browser()`	Open a live chat application
`interpolate()` `interpolate_file()` `interpolate_package()`	Helpers for interpolating data into prompts
`google_upload()`	Upload a file to gemini
Parallel and batch chat
`batch_chat()` `batch_chat_structured()` `batch_chat_completed()`	Submit multiple chats in one batch
`parallel_chat()` `parallel_chat_structured()`	Submit multiple chats in parallel
Tools and structured data
`tool()`	Define a tool
`tool_annotations()`	Tool annotations
`tool_reject()`	Reject a tool call
`type_boolean()` `type_integer()` `type_number()` `type_string()` `type_enum()` `type_array()` `type_object()`	Type specifications
Objects These classes abstract away behaviour differences in chat providers so that for typical ellmer use you don’t need to worry about them. You’ll need to learn more about the objects if you’re doing something that’s only supported by one provider, or if you’re implementing a new provider.
`Provider()`	A chatbot provider
`Chat`	A chat
`Turn()`	A user or assistant turn
`Content()` `ContentText()` `ContentImage()` `ContentImageRemote()` `ContentImageInline()` `ContentToolRequest()` `ContentToolResult()` `ContentThinking()` `ContentPDF()`	Content types received from and sent to a chatbot
`TypeBasic()` `TypeEnum()` `TypeArray()` `TypeObject()`	Type definitions for function calling and structured data extraction.
Utilities
`contents_text()` `contents_html()` `contents_markdown()`	Format contents into a textual representation
`params()`	Standard model parameters
Deprecated functions
`chat_cortex()` `chat_azure()` `chat_bedrock()` `chat_claude()` `chat_gemini()`	Deprecated functions

Reference

Chatbots

Chat helpers

Parallel and batch chat

Tools and structured data

Objects

Utilities

Deprecated functions