base on OpenAI API + Ruby! 🤖❤️ # Ruby OpenAI [](https://rubygems.org/gems/ruby-openai) [](https://github.com/alexrudall/ruby-openai/blob/main/LICENSE.txt) [](https://circleci.com/gh/alexrudall/ruby-openai) Use the [OpenAI API](https://openai.com/blog/openai-api/) with Ruby! 🤖❤️ Stream text with GPT-4o, transcribe and translate audio with Whisper, or create images with DALL·E... [🚀 Hire me to build your Rails+AI app](https://insertrobot.com) | [📚 Rails AI](https://railsai.com) | [🎮 Ruby AI Builders Discord](https://discord.gg/k4Uc224xVD) | [🐦 X](https://x.com/alexrudall) | [🧠 Anthropic Gem](https://github.com/alexrudall/anthropic) | [🚂 Midjourney Gem](https://github.com/alexrudall/midjourney) ## Contents - [Ruby OpenAI](#ruby-openai) - [Table of Contents](#table-of-contents) - [Installation](#installation) - [Bundler](#bundler) - [Gem install](#gem-install) - [Usage](#usage) - [Quickstart](#quickstart) - [With Config](#with-config) - [Custom timeout or base URI](#custom-timeout-or-base-uri) - [Extra Headers per Client](#extra-headers-per-client) - [Logging](#logging) - [Errors](#errors) - [Faraday middleware](#faraday-middleware) - [Azure](#azure) - [Ollama](#ollama) - [Groq](#groq) - [Counting Tokens](#counting-tokens) - [Models](#models) - [Chat](#chat) - [Streaming Chat](#streaming-chat) - [Vision](#vision) - [JSON Mode](#json-mode) - [Functions](#functions) - [Completions](#completions) - [Embeddings](#embeddings) - [Batches](#batches) - [Files](#files) - [For fine-tuning purposes](#for-fine-tuning-purposes) - [For assistant purposes](#for-assistant-purposes) - [Finetunes](#finetunes) - [Vector Stores](#vector-stores) - [Vector Store Files](#vector-store-files) - [Vector Store File Batches](#vector-store-file-batches) - [Assistants](#assistants) - [Threads and Messages](#threads-and-messages) - [Runs](#runs) - [Create and Run](#create-and-run) - [Vision in a thread](#vision-in-a-thread) - [Runs involving function tools](#runs-involving-function-tools) - [Exploring chunks used in File Search](#exploring-chunks-used-in-file-search) - [Image Generation](#image-generation) - [DALL·E 2](#dalle-2) - [DALL·E 3](#dalle-3) - [Image Edit](#image-edit) - [Image Variations](#image-variations) - [Moderations](#moderations) - [Whisper](#whisper) - [Translate](#translate) - [Transcribe](#transcribe) - [Speech](#speech) - [Errors](#errors-1) - [Development](#development) - [Release](#release) - [Contributing](#contributing) - [License](#license) - [Code of Conduct](#code-of-conduct) ## Installation ### Bundler Add this line to your application's Gemfile: ```ruby gem "ruby-openai" ``` And then execute: ```bash $ bundle install ``` ### Gem install Or install with: ```bash $ gem install ruby-openai ``` and require with: ```ruby require "openai" ``` ## Usage - Get your API key from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys) - If you belong to multiple organizations, you can get your Organization ID from [https://platform.openai.com/account/org-settings](https://platform.openai.com/account/org-settings) ### Quickstart For a quick test you can pass your token directly to a new client: ```ruby client = OpenAI::Client.new( access_token: "access_token_goes_here", log_errors: true # Highly recommended in development, so you can see what errors OpenAI is returning. Not recommended in production because it could leak private data to your logs. ) ``` ### With Config For a more robust setup, you can configure the gem with your API keys, for example in an `openai.rb` initializer file. Never hardcode secrets into your codebase - instead use something like [dotenv](https://github.com/motdotla/dotenv) to pass the keys safely into your environments. ```ruby OpenAI.configure do |config| config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN") config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional config.log_errors = true # Highly recommended in development, so you can see what errors OpenAI is returning. Not recommended in production because it could leak private data to your logs. end ``` Then you can create a client like this: ```ruby client = OpenAI::Client.new ``` You can still override the config defaults when making new clients; any options not included will fall back to any global config set with OpenAI.configure. e.g. in this example the organization_id, request_timeout, etc. will fallback to any set globally using OpenAI.configure, with only the access_token overridden: ```ruby client = OpenAI::Client.new(access_token: "access_token_goes_here") ``` #### Custom timeout or base URI - The default timeout for any request using this library is 120 seconds. You can change that by passing a number of seconds to the `request_timeout` when initializing the client. - You can also change the base URI used for all requests, eg. to use observability tools like [Helicone](https://docs.helicone.ai/quickstart/integrate-in-one-line-of-code) or [Velvet](https://docs.usevelvet.com/docs/getting-started) - You can also add arbitrary other headers e.g. for [openai-caching-proxy-worker](https://github.com/6/openai-caching-proxy-worker), eg.: ```ruby client = OpenAI::Client.new( access_token: "access_token_goes_here", uri_base: "https://oai.hconeai.com/", request_timeout: 240, extra_headers: { "X-Proxy-TTL" => "43200", # For https://github.com/6/openai-caching-proxy-worker#specifying-a-cache-ttl "X-Proxy-Refresh": "true", # For https://github.com/6/openai-caching-proxy-worker#refreshing-the-cache "Helicone-Auth": "Bearer HELICONE_API_KEY", # For https://docs.helicone.ai/getting-started/integration-method/openai-proxy "helicone-stream-force-format" => "true", # Use this with Helicone otherwise streaming drops chunks # https://github.com/alexrudall/ruby-openai/issues/251 } ) ``` or when configuring the gem: ```ruby OpenAI.configure do |config| config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN") config.log_errors = true # Optional config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional config.uri_base = "https://oai.hconeai.com/" # Optional config.request_timeout = 240 # Optional config.extra_headers = { "X-Proxy-TTL" => "43200", # For https://github.com/6/openai-caching-proxy-worker#specifying-a-cache-ttl "X-Proxy-Refresh": "true", # For https://github.com/6/openai-caching-proxy-worker#refreshing-the-cache "Helicone-Auth": "Bearer HELICONE_API_KEY" # For https://docs.helicone.ai/getting-started/integration-method/openai-proxy } # Optional end ``` #### Extra Headers per Client You can dynamically pass headers per client object, which will be merged with any headers set globally with OpenAI.configure: ```ruby client = OpenAI::Client.new(access_token: "access_token_goes_here") client.add_headers("X-Proxy-TTL" => "43200") ``` #### Logging ##### Errors By default, `ruby-openai` does not log any `Faraday::Error`s encountered while executing a network request to avoid leaking data (e.g. 400s, 500s, SSL errors and more - see [here](https://www.rubydoc.info/github/lostisland/faraday/Faraday/Error) for a complete list of subclasses of `Faraday::Error` and what can cause them). If you would like to enable this functionality, you can set `log_errors` to `true` when configuring the client: ```ruby client = OpenAI::Client.new(log_errors: true) ``` ##### Faraday middleware You can pass [Faraday middleware](https://lostisland.github.io/faraday/#/middleware/index) to the client in a block, eg. to enable verbose logging with Ruby's [Logger](https://ruby-doc.org/3.2.2/stdlibs/logger/Logger.html): ```ruby client = OpenAI::Client.new do |f| f.response :logger, Logger.new($stdout), bodies: true end ``` #### Azure To use the [Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) API, you can configure the gem like this: ```ruby OpenAI.configure do |config| config.access_token = ENV.fetch("AZURE_OPENAI_API_KEY") config.uri_base = ENV.fetch("AZURE_OPENAI_URI") config.api_type = :azure config.api_version = "2023-03-15-preview" end ``` where `AZURE_OPENAI_URI` is e.g. `https://custom-domain.openai.azure.com/openai/deployments/gpt-35-turbo` #### Ollama Ollama allows you to run open-source LLMs, such as Llama 3, locally. It [offers chat compatibility](https://github.com/ollama/ollama/blob/main/docs/openai.md) with the OpenAI API. You can download Ollama [here](https://ollama.com/). On macOS you can install and run Ollama like this: ```bash brew install ollama ollama serve ollama pull llama3:latest # In new terminal tab. ``` Create a client using your Ollama server and the pulled model, and stream a conversation for free: ```ruby client = OpenAI::Client.new( uri_base: "http://localhost:11434" ) client.chat( parameters: { model: "llama3", # Required. messages: [{ role: "user", content: "Hello!"}], # Required. temperature: 0.7, stream: proc do |chunk, _bytesize| print chunk.dig("choices", 0, "delta", "content") end } ) # => Hi! It's nice to meet you. Is there something I can help you with, or would you like to chat? ``` #### Groq [Groq API Chat](https://console.groq.com/docs/quickstart) is broadly compatible with the OpenAI API, with a [few minor differences](https://console.groq.com/docs/openai). Get an access token from [here](https://console.groq.com/keys), then: ```ruby client = OpenAI::Client.new( access_token: "groq_access_token_goes_here", uri_base: "https://api.groq.com/openai" ) client.chat( parameters: { model: "llama3-8b-8192", # Required. messages: [{ role: "user", content: "Hello!"}], # Required. temperature: 0.7, stream: proc do |chunk, _bytesize| print chunk.dig("choices", 0, "delta", "content") end } ) ``` ### Counting Tokens OpenAI parses prompt text into [tokens](https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them), which are words or portions of words. (These tokens are unrelated to your API access_token.) Counting tokens can help you estimate your [costs](https://openai.com/pricing). It can also help you ensure your prompt text size is within the max-token limits of your model's context window, and choose an appropriate [`max_tokens`](https://platform.openai.com/docs/api-reference/chat/create#chat/create-max_tokens) completion parameter so your response will fit as well. To estimate the token-count of your text: ```ruby OpenAI.rough_token_count("Your text") ``` If you need a more accurate count, try [tiktoken_ruby](https://github.com/IAPark/tiktoken_ruby). ### Models There are different models that can be used to generate text. For a full list and to retrieve information about a single model: ```ruby client.models.list client.models.retrieve(id: "gpt-4o") ``` ### Chat GPT is a model that can be used to generate text in a conversational style. You can use it to [generate a response](https://platform.openai.com/docs/api-reference/chat/create) to a sequence of [messages](https://platform.openai.com/docs/guides/chat/introduction): ```ruby response = client.chat( parameters: { model: "gpt-4o", # Required. messages: [{ role: "user", content: "Hello!"}], # Required. temperature: 0.7, } ) puts response.dig("choices", 0, "message", "content") # => "Hello! How may I assist you today?" ``` #### Streaming Chat [Quick guide to streaming Chat with Rails 7 and Hotwire](https://gist.github.com/alexrudall/cb5ee1e109353ef358adb4e66631799d) You can stream from the API in realtime, which can be much faster and used to create a more engaging user experience. Pass a [Proc](https://ruby-doc.org/core-2.6/Proc.html) (or any object with a `#call` method) to the `stream` parameter to receive the stream of completion chunks as they are generated. Each time one or more chunks is received, the proc will be called once with each chunk, parsed as a Hash. If OpenAI returns an error, `ruby-openai` will raise a Faraday error. ```ruby client.chat( parameters: { model: "gpt-4o", # Required. messages: [{ role: "user", content: "Describe a character called Anna!"}], # Required. temperature: 0.7, stream: proc do |chunk, _bytesize| print chunk.dig("choices", 0, "delta", "content") end } ) # => "Anna is a young woman in her mid-twenties, with wavy chestnut hair that falls to her shoulders..." ``` Note: In order to get usage information, you can provide the [`stream_options` parameter](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stream_options) and OpenAI will provide a final chunk with the usage. Here is an example: ```ruby stream_proc = proc { |chunk, _bytesize| puts "--------------"; puts chunk.inspect; } client.chat( parameters: { model: "gpt-4o", stream: stream_proc, stream_options: { include_usage: true }, messages: [{ role: "user", content: "Hello!"}], } ) # => -------------- # => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{"role"=>"assistant", "content"=>""}, "logprobs"=>nil, "finish_reason"=>nil}], "usage"=>nil} # => -------------- # => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{"content"=>"Hello"}, "logprobs"=>nil, "finish_reason"=>nil}], "usage"=>nil} # => -------------- # => ... more content chunks # => -------------- # => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{}, "logprobs"=>nil, "finish_reason"=>"stop"}], "usage"=>nil} # => -------------- # => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[], "usage"=>{"prompt_tokens"=>9, "completion_tokens"=>9, "total_tokens"=>18}} ``` #### Vision You can use the GPT-4 Vision model to generate a description of an image: ```ruby messages = [ { "type": "text", "text": "What’s in this image?"}, { "type": "image_url", "image_url": { "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", }, } ] response = client.chat( parameters: { model: "gpt-4-vision-preview", # Required. messages: [{ role: "user", content: messages}], # Required. } ) puts response.dig("choices", 0, "message", "content") # => "The image depicts a serene natural landscape featuring a long wooden boardwalk extending straight ahead" ``` #### JSON Mode You can set the response_format to ask for responses in JSON: ```ruby response = client.chat( parameters: { model: "gpt-4o", response_format: { type: "json_object" }, messages: [{ role: "user", content: "Hello! Give me some JSON please."}], temperature: 0.7, }) puts response.dig("choices", 0, "message", "content") # => # { # "name": "John", # "age": 30, # "city": "New York", # "hobbies": ["reading", "traveling", "hiking"], # "isStudent": false # } ``` You can stream it as well! ```ruby response = client.chat( parameters: { model: "gpt-4o", messages: [{ role: "user", content: "Can I have some JSON please?"}], response_format: { type: "json_object" }, stream: proc do |chunk, _bytesize| print chunk.dig("choices", 0, "delta", "content") end } ) # => # { # "message": "Sure, please let me know what specific JSON data you are looking for.", # "JSON_data": { # "example_1": { # "key_1": "value_1", # "key_2": "value_2", # "key_3": "value_3" # }, # "example_2": { # "key_4": "value_4", # "key_5": "value_5", # "key_6": "value_6" # } # } # } ``` ### Functions You can describe and pass in functions and the model will intelligently choose to output a JSON object containing arguments to call them - eg., to use your method `get_current_weather` to get the weather in a given location. Note that tool_choice is optional, but if you exclude it, the model will choose whether to use the function or not ([see here](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice)). ```ruby def get_current_weather(location:, unit: "fahrenheit") # Here you could use a weather api to fetch the weather. "The weather in #{location} is nice 🌞 #{unit}" end messages = [ { "role": "user", "content": "What is the weather like in San Francisco?", }, ] response = client.chat( parameters: { model: "gpt-4o", messages: messages, # Defined above because we'll use it again tools: [ { type: "function", function: { name: "get_current_weather", description: "Get the current weather in a given location", parameters: { # Format: https://json-schema.org/understanding-json-schema type: :object, properties: { location: { type: :string, description: "The city and state, e.g. San Francisco, CA", }, unit: { type: "string", enum: %w[celsius fahrenheit], }, }, required: ["location"], }, }, } ], # Optional, defaults to "auto" # Can also put "none" or specific functions, see docs tool_choice: "required" }, ) message = response.dig("choices", 0, "message") if message["role"] == "assistant" && message["tool_calls"] message["tool_calls"].each do |tool_call| tool_call_id = tool_call.dig("id") function_name = tool_call.dig("function", "name") function_args = JSON.parse( tool_call.dig("function", "arguments"), { symbolize_names: true }, ) function_response = case function_name when "get_current_weather" get_current_weather(**function_args) # => "The weather is nice 🌞" else # decide how to handle end # For a subsequent message with the role "tool", OpenAI requires the preceding message to have a tool_calls argument. messages << message messages << { tool_call_id: tool_call_id, role: "tool", name: function_name, content: function_response } # Extend the conversation with the results of the functions end second_response = client.chat( parameters: { model: "gpt-4o", messages: messages } ) puts second_response.dig("choices", 0, "message", "content") # At this point, the model has decided to call functions, you've called the functions # and provided the response back, and the model has considered this and responded. end # => "It looks like the weather is nice and sunny in San Francisco! If you're planning to go out, it should be a pleasant day." ``` ### Completions Hit the OpenAI API for a completion using other GPT-3 models: ```ruby response = client.completions( parameters: { model: "gpt-4o", prompt: "Once upon a time", max_tokens: 5 } ) puts response["choices"].map { |c| c["text"] } # => [", there lived a great"] ``` ### Embeddings You can use the embeddings endpoint to get a vector of numbers representing an input. You can then compare these vectors for different inputs to efficiently check how similar the inputs are. ```ruby response = client.embeddings( parameters: { model: "text-embedding-ada-002", input: "The food was delicious and the waiter..." } ) puts response.dig("data", 0, "embedding") # => Vector representation of your embedding ``` ### Batches The Batches endpoint allows you to create and manage large batches of API requests to run asynchronously. Currently, the supported endpoints for batches are `/v1/chat/completions` (Chat Completions API) and `/v1/embeddings` (Embeddings API). To use the Batches endpoint, you need to first upload a JSONL file containing the batch requests using the Files endpoint. The file must be uploaded with the purpose set to `batch`. Each line in the JSONL file represents a single request and should have the following format: ```json { "custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": { "model": "gpt-4o", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "What is 2+2?" } ] } } ``` Once you have uploaded the JSONL file, you can create a new batch by providing the file ID, endpoint, and completion window: ```ruby response = client.batches.create( parameters: { input_file_id: "file-abc123", endpoint: "/v1/chat/completions", completion_window: "24h" } ) batch_id = response["id"] ``` You can retrieve information about a specific batch using its ID: ```ruby batch = client.batches.retrieve(id: batch_id) ``` To cancel a batch that is in progress: ```ruby client.batches.cancel(id: batch_id) ``` You can also list all the batches: ```ruby client.batches.list ``` Once the batch["completed_at"] is present, you can fetch the output or error files: ```ruby batch = client.batches.retrieve(id: batch_id) output_file_id = batch["output_file_id"] output_response = client.files.content(id: output_file_id) error_file_id = batch["error_file_id"] error_response = client.files.content(id: error_file_id) ``` These files are in JSONL format, with each line representing the output or error for a single request. The lines can be in any order: ```json { "id": "response-1", "custom_id": "request-1", "response": { "id": "chatcmpl-abc123", "object": "chat.completion", "created": 1677858242, "model": "gpt-4o", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "2+2 equals 4." } } ] } } ``` If a request fails with a non-HTTP error, the error object will contain more information about the cause of the failure. ### Files #### For fine-tuning purposes Put your data in a `.jsonl` file like this: ```json {"prompt":"Overjoyed with my new phone! ->", "completion":" positive"} {"prompt":"@lakers disappoint for a third straight night ->", "completion":" negative"} ``` and pass the path (or a StringIO object) to `client.files.upload` to upload it to OpenAI, and then interact with it: ```ruby client.files.upload(parameters: { file: "path/to/sentiment.jsonl", purpose: "fine-tune" }) client.files.list client.files.retrieve(id: "file-123") client.files.content(id: "file-123") client.files.delete(id: "file-123") ``` #### For assistant purposes You can send a file path: ```ruby client.files.upload(parameters: { file: "path/to/file.pdf", purpose: "assistants" }) ``` or a File object ```ruby my_file = File.open("path/to/file.pdf", "rb") client.files.upload(parameters: { file: my_file, purpose: "assistants" }) ``` See supported file types on [API documentation](https://platform.openai.com/docs/assistants/tools/file-search/supported-files). ### Finetunes Upload your fine-tuning data in a `.jsonl` file as above and get its ID: ```ruby response = client.files.upload(parameters: { file: "path/to/sarcasm.jsonl", purpose: "fine-tune" }) file_id = JSON.parse(response.body)["id"] ``` You can then use this file ID to create a fine tuning job: ```ruby response = client.finetunes.create( parameters: { training_file: file_id, model: "gpt-4o" }) fine_tune_id = response["id"] ``` That will give you the fine-tune ID. If you made a mistake you can cancel the fine-tune model before it is processed: ```ruby client.finetunes.cancel(id: fine_tune_id) ``` You may need to wait a short time for processing to complete. Once processed, you can use list or retrieve to get the name of the fine-tuned model: ```ruby client.finetunes.list response = client.finetunes.retrieve(id: fine_tune_id) fine_tuned_model = response["fine_tuned_model"] ``` This fine-tuned model name can then be used in chat completions: ```ruby response = client.chat( parameters: { model: fine_tuned_model, messages: [{ role: "user", content: "I love Mondays!" }] } ) response.dig("choices", 0, "message", "content") ``` You can also capture the events for a job: ```ruby client.finetunes.list_events(id: fine_tune_id) ``` ### Vector Stores Vector Store objects give the File Search tool the ability to search your files. You can create a new vector store: ```ruby response = client.vector_stores.create( parameters: { name: "my vector store", file_ids: ["file-abc123", "file-def456"] } ) vector_store_id = response["id"] ``` Given a `vector_store_id` you can `retrieve` the current field values: ```ruby client.vector_stores.retrieve(id: vector_store_id) ``` You can get a `list` of all vector stores currently available under the organization: ```ruby client.vector_stores.list ``` You can modify an existing vector store, except for the `file_ids`: ```ruby response = client.vector_stores.modify( id: vector_store_id, parameters: { name: "Modified Test Vector Store", } ) ``` You can delete vector stores: ```ruby client.vector_stores.delete(id: vector_store_id) ``` ### Vector Store Files Vector store files represent files inside a vector store. You can create a new vector store file by attaching a File to a vector store. ```ruby response = client.vector_store_files.create( vector_store_id: "vector-store-abc123", parameters: { file_id: "file-abc123" } ) vector_store_file_id = response["id"] ``` Given a `vector_store_file_id` you can `retrieve` the current field values: ```ruby client.vector_store_files.retrieve( vector_store_id: "vector-store-abc123", id: vector_store_file_id ) ``` You can get a `list` of all vector store files currently available under the vector store: ```ruby client.vector_store_files.list(vector_store_id: "vector-store-abc123") ``` You can delete a vector store file: ```ruby client.vector_store_files.delete( vector_store_id: "vector-store-abc123", id: vector_store_file_id ) ``` Note: This will remove the file from the vector store but the file itself will not be deleted. To delete the file, use the delete file endpoint. ### Vector Store File Batches Vector store file batches represent operations to add multiple files to a vector store. You can create a new vector store file batch by attaching multiple Files to a vector store. ```ruby response = client.vector_store_file_batches.create( vector_store_id: "vector-store-abc123", parameters: { file_ids: ["file-abc123", "file-def456"] } ) file_batch_id = response["id"] ``` Given a `file_batch_id` you can `retrieve` the current field values: ```ruby client.vector_store_file_batches.retrieve( vector_store_id: "vector-store-abc123", id: file_batch_id ) ``` You can get a `list` of all vector store files in a batch currently available under the vector store: ```ruby client.vector_store_file_batches.list( vector_store_id: "vector-store-abc123", id: file_batch_id ) ``` You can cancel a vector store file batch (This attempts to cancel the processing of files in this batch as soon as possible): ```ruby client.vector_store_file_batches.cancel( vector_store_id: "vector-store-abc123", id: file_batch_id ) ``` ### Assistants Assistants are stateful actors that can have many conversations and use tools to perform tasks (see [Assistant Overview](https://platform.openai.com/docs/assistants/overview)). To create a new assistant: ```ruby response = client.assistants.create( parameters: { model: "gpt-4o", name: "OpenAI-Ruby test assistant", description: nil, instructions: "You are a Ruby dev bot. When asked a question, write and run Ruby code to answer the question", tools: [ { type: "code_interpreter" }, { type: "file_search" } ], tool_resources: { code_interpreter: { file_ids: [] # See Files section above for how to upload files }, file_search: { vector_store_ids: [] # See Vector Stores section above for how to add vector stores } }, "metadata": { my_internal_version_id: "1.0.0" } } ) assistant_id = response["id"] ``` Given an `assistant_id` you can `retrieve` the current field values: ```ruby client.assistants.retrieve(id: assistant_id) ``` You can get a `list` of all assistants currently available under the organization: ```ruby client.assistants.list ``` You can modify an existing assistant using the assistant's id (see [API documentation](https://platform.openai.com/docs/api-reference/assistants/modifyAssistant)): ```ruby response = client.assistants.modify( id: assistant_id, parameters: { name: "Modified Test Assistant for OpenAI-Ruby", metadata: { my_internal_version_id: '1.0.1' } } ) ``` You can delete assistants: ```ruby client.assistants.delete(id: assistant_id) ``` ### Threads and Messages Once you have created an assistant as described above, you need to prepare a `Thread` of `Messages` for the assistant to work on (see [introduction on Assistants](https://platform.openai.com/docs/assistants/how-it-works)). For example, as an initial setup you could do: ```ruby # Create thread response = client.threads.create # Note: Once you create a thread, there is no way to list it # or recover it currently (as of 2023-12-10). So hold onto the `id` thread_id = response["id"] # Add initial message from user (see https://platform.openai.com/docs/api-reference/messages/createMessage) message_id = client.messages.create( thread_id: thread_id, parameters: { role: "user", # Required for manually created messages content: "Can you help me write an API library to interact with the OpenAI API please?" } )["id"] # Retrieve individual message message = client.messages.retrieve(thread_id: thread_id, id: message_id) # Review all messages on the thread messages = client.messages.list(thread_id: thread_id) ``` To clean up after a thread is no longer needed: ```ruby # To delete the thread (and all associated messages): client.threads.delete(id: thread_id) client.messages.retrieve(thread_id: thread_id, id: message_id) # -> Fails after thread is deleted ``` ### Runs To submit a thread to be evaluated with the model of an assistant, create a `Run` as follows: ```ruby # Create run (will use instruction/model/tools from Assistant's definition) response = client.runs.create( thread_id: thread_id, parameters: { assistant_id: assistant_id, max_prompt_tokens: 256, max_completion_tokens: 16 } ) run_id = response['id'] ``` You can stream the message chunks as they come through: ```ruby client.runs.create( thread_id: thread_id, parameters: { assistant_id: assistant_id, max_prompt_tokens: 256, max_completion_tokens: 16, stream: proc do |chunk, _bytesize| if chunk["object"] == "thread.message.delta" print chunk.dig("delta", "content", 0, "text", "value") end end } ) ``` To get the status of a Run: ```ruby response = client.runs.retrieve(id: run_id, thread_id: thread_id) status = response['status'] ``` The `status` response can include the following strings `queued`, `in_progress`, `requires_action`, `cancelling`, `cancelled`, `failed`, `completed`, or `expired` which you can handle as follows: ```ruby while true do response = client.runs.retrieve(id: run_id, thread_id: thread_id) status = response['status'] case status when 'queued', 'in_progress', 'cancelling' puts 'Sleeping' sleep 1 # Wait one second and poll again when 'completed' break # Exit loop and report result to user when 'requires_action' # Handle tool calls (see below) when 'cancelled', 'failed', 'expired' puts response['last_error'].inspect break # or `exit` else puts "Unknown status response: #{status}" end end ``` If the `status` response indicates that the `run` is `completed`, the associated `thread` will have one or more new `messages` attached: ```ruby # Either retrieve all messages in bulk again, or... messages = client.messages.list(thread_id: thread_id, parameters: { order: 'asc' }) # Alternatively retrieve the `run steps` for the run which link to the messages: run_steps = client.run_steps.list(thread_id: thread_id, run_id: run_id, parameters: { order: 'asc' }) new_message_ids = run_steps['data'].filter_map do |step| if step['type'] == 'message_creation' step.dig('step_details', "message_creation", "message_id") end # Ignore tool calls, because they don't create new messages. end # Retrieve the individual messages new_messages = new_message_ids.map do |msg_id| client.messages.retrieve(id: msg_id, thread_id: thread_id) end # Find the actual response text in the content array of the messages new_messages.each do |msg| msg['content'].each do |content_item| case content_item['type'] when 'text' puts content_item.dig('text', 'value') # Also handle annotations when 'image_file' # Use File endpoint to retrieve file contents via id id = content_item.dig('image_file', 'file_id') end end end ``` You can also update the metadata on messages, including messages that come from the assistant. ```ruby metadata = { user_id: "abc123" } message = client.messages.modify( id: message_id, thread_id: thread_id, parameters: { metadata: metadata }, ) ``` At any time you can list all runs which have been performed on a particular thread or are currently running: ```ruby client.runs.list(thread_id: thread_id, parameters: { order: "asc", limit: 3 }) ``` #### Create and Run You can also create a thread and run in one call like this: ```ruby response = client.runs.create_thread_and_run(parameters: { assistant_id: assistant_id }) run_id = response['id'] thread_id = response['thread_id'] ``` #### Vision in a thread You can include images in a thread and they will be described & read by the LLM. In this example I'm using [this file](https://upload.wikimedia.org/wikipedia/commons/7/70/Example.png): ```ruby require "openai" # Make a client client = OpenAI::Client.new( access_token: "access_token_goes_here", log_errors: true # Don't log errors in production. ) # Upload image as a file file_id = client.files.upload( parameters: { file: "path/to/example.png", purpose: "assistants", } )["id"] # Create assistant (You could also use an existing one here) assistant_id = client.assistants.create( parameters: { model: "gpt-4o", name: "Image reader", instructions: "You are an image describer. You describe the contents of images.", } )["id"] # Create thread thread_id = client.threads.create["id"] # Add image in message client.messages.create( thread_id: thread_id, parameters: { role: "user", # Required for manually created messages content: [ { "type": "text", "text": "What's in this image?" }, { "type": "image_file", "image_file": { "file_id": file_id } } ] } ) # Run thread run_id = client.runs.create( thread_id: thread_id, parameters: { assistant_id: assistant_id } )["id"] # Wait until run in complete status = nil until status == "completed" do sleep(0.1) status = client.runs.retrieve(id: run_id, thread_id: thread_id)['status'] end # Get the response messages = client.messages.list(thread_id: thread_id, parameters: { order: 'asc' }) messages.dig("data", -1, "content", 0, "text", "value") => "The image contains a placeholder graphic with a tilted, stylized representation of a postage stamp in the top part, which includes an abstract landscape with hills and a sun. Below the stamp, in the middle of the image, there is italicized text in a light golden color that reads, \"This is just an example.\" The background is a light pastel shade, and a yellow border frames the entire image." ``` #### Runs involving function tools In case you are allowing the assistant to access `function` tools (they are defined in the same way as functions during chat completion), you might get a status code of `requires_action` when the assistant wants you to evaluate one or more function tools: ```ruby def get_current_weather(location:, unit: "celsius") # Your function code goes here if location =~ /San Francisco/i return unit == "celsius" ? "The weather is nice 🌞 at 27°C" : "The weather is nice 🌞 at 80°F" else return unit == "celsius" ? "The weather is icy 🥶 at -5°C" : "The weather is icy 🥶 at 23°F" end end if status == 'requires_action' tools_to_call = response.dig('required_action', 'submit_tool_outputs', 'tool_calls') my_tool_outputs = tools_to_call.map { |tool| # Call the functions based on the tool's name function_name = tool.dig('function', 'name') arguments = JSON.parse( tool.dig("function", "arguments"), { symbolize_names: true }, ) tool_output = case function_name when "get_current_weather" get_current_weather(**arguments) end { tool_call_id: tool['id'], output: tool_output, } } client.runs.submit_tool_outputs( thread_id: thread_id, run_id: run_id, parameters: { tool_outputs: my_tool_outputs } ) end ``` Note that you have 10 minutes to submit your tool output before the run expires. #### Exploring chunks used in File Search Take a deep breath. You might need a drink for this one. It's possible for OpenAI to share what chunks it used in its internal RAG Pipeline to create its filesearch results. An example spec can be found [here](https://github.com/alexrudall/ruby-openai/blob/main/spec/openai/client/assistant_file_search_spec.rb) that does this, just so you know it's possible. Here's how to get the chunks used in a file search. In this example I'm using [this file](https://css4.pub/2015/textbook/somatosensory.pdf): ```ruby require "openai" # Make a client client = OpenAI::Client.new( access_token: "access_token_goes_here", log_errors: true # Don't log errors in production. ) # Upload your file(s) file_id = client.files.upload( parameters: { file: "path/to/somatosensory.pdf", purpose: "assistants" } )["id"] # Create a vector store to store the vectorised file(s) vector_store_id = client.vector_stores.create(parameters: {})["id"] # Vectorise the file(s) vector_store_file_id = client.vector_store_files.create( vector_store_id: vector_store_id, parameters: { file_id: file_id } )["id"] # Check that the file is vectorised (wait for status to be "completed") client.vector_store_files.retrieve(vector_store_id: vector_store_id, id: vector_store_file_id)["status"] # Create an assistant, referencing the vector store assistant_id = client.assistants.create( parameters: { model: "gpt-4o", name: "Answer finder", instructions: "You are a file search tool. Find the answer in the given files, please.", tools: [ { type: "file_search" } ], tool_resources: { file_search: { vector_store_ids: [vector_store_id] } } } )["id"] # Create a thread with your question thread_id = client.threads.create(parameters: { messages: [ { role: "user", content: "Find the description of a nociceptor." } ] })["id"] # Run the thread to generate the response. Include the "GIVE ME THE CHUNKS" incantation. run_id = client.runs.create( thread_id: thread_id, parameters: { assistant_id: assistant_id }, query_parameters: { include: ["step_details.tool_calls[*].file_search.results[*].content"] } # incantation )["id"] # Get the steps that happened in the run steps = client.run_steps.list( thread_id: thread_id, run_id: run_id, parameters: { order: "asc" } ) # Retrieve all the steps. Include the "GIVE ME THE CHUNKS" incantation again. steps = steps["data"].map do |step| client.run_steps.retrieve( thread_id: thread_id, run_id: run_id, id: step["id"], parameters: { include: ["step_details.tool_calls[*].file_search.results[*].content"] } # incantation ) end # Now we've got the chunk info, buried deep. Loop through the steps and find chunks if included: chunks = steps.flat_map do |step| included_results = step.dig("step_details", "tool_calls", 0, "file_search", "results") next if included_results.nil? || included_results.empty? included_results.flat_map do |result| result["content"].map do |content| content["text"] end end end.compact # The first chunk will be the closest match to the prompt. Finally, if you want to view the completed message(s): client.messages.list(thread_id: thread_id) ``` ### Image Generation Generate images using DALL·E 2 or DALL·E 3! #### DALL·E 2 For DALL·E 2 the size of any generated images must be one of `256x256`, `512x512` or `1024x1024` - if not specified the image will default to `1024x1024`. ```ruby response = client.images.generate( parameters: { prompt: "A baby sea otter cooking pasta wearing a hat of some sort", size: "256x256", } ) puts response.dig("data", 0, "url") # => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..." ```  #### DALL·E 3 For DALL·E 3 the size of any generated images must be one of `1024x1024`, `1024x1792` or `1792x1024`. Additionally the quality of the image can be specified to either `standard` or `hd`. ```ruby response = client.images.generate( parameters: { prompt: "A springer spaniel cooking pasta wearing a hat of some sort", model: "dall-e-3", size: "1024x1792", quality: "standard", } ) puts response.dig("data", 0, "url") # => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..." ```  ### Image Edit Fill in the transparent part of an image, or upload a mask with transparent sections to indicate the parts of an image that can be changed according to your prompt... ```ruby response = client.images.edit( parameters: { prompt: "A solid red Ruby on a blue background", image: "image.png", mask: "mask.png", } ) puts response.dig("data", 0, "url") # => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..." ```  ### Image Variations Create n variations of an image. ```ruby response = client.images.variations(parameters: { image: "image.png", n: 2 }) puts response.dig("data", 0, "url") # => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..." ```   ### Moderations Pass a string to check if it violates OpenAI's Content Policy: ```ruby response = client.moderations(parameters: { input: "I'm worried about that." }) puts response.dig("results", 0, "category_scores", "hate") # => 5.505014632944949e-05 ``` ### Whisper Whisper is a speech to text model that can be used to generate text based on audio files: #### Translate The translations API takes as input the audio file in any of the supported languages and transcribes the audio into English. ```ruby response = client.audio.translate( parameters: { model: "whisper-1", file: File.open("path_to_file", "rb"), } ) puts response["text"] # => "Translation of the text" ``` #### Transcribe The transcriptions API takes as input the audio file you want to transcribe and returns the text in the desired output file format. You can pass the language of the audio file to improve transcription quality. Supported languages are listed [here](https://github.com/openai/whisper#available-models-and-languages). You need to provide the language as an ISO-639-1 code, eg. "en" for English or "ne" for Nepali. You can look up the codes [here](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes). ```ruby response = client.audio.transcribe( parameters: { model: "whisper-1", file: File.open("path_to_file", "rb"), language: "en", # Optional } ) puts response["text"] # => "Transcription of the text" ``` #### Speech The speech API takes as input the text and a voice and returns the content of an audio file you can listen to. ```ruby response = client.audio.speech( parameters: { model: "tts-1", input: "This is a speech test!", voice: "alloy", response_format: "mp3", # Optional speed: 1.0, # Optional } ) File.binwrite('demo.mp3', response) # => mp3 file that plays: "This is a speech test!" ``` ### Errors HTTP errors can be caught like this: ```ruby begin OpenAI::Client.new.models.retrieve(id: "gpt-4o") rescue Faraday::Error => e raise "Got a Faraday error: #{e}" end ``` ## Development After checking out the repo, run `bin/setup` to install dependencies. You can run `bin/console` for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run `bundle exec rake install`. To run all tests, execute the command `bundle exec rake`, which will also run the linter (Rubocop). This repository uses [VCR](https://github.com/vcr/vcr) to log API requests. > [!WARNING] > If you have an `OPENAI_ACCESS_TOKEN` in your `ENV`, running the specs will use this to run the specs against the actual API, which will be slow and cost you money - 2 cents or more! Remove it from your environment with `unset` or similar if you just want to run the specs against the stored VCR responses. ## Release First run the specs without VCR so they actually hit the API. This will cost 2 cents or more. Set OPENAI_ACCESS_TOKEN in your environment or pass it in like this: ``` OPENAI_ACCESS_TOKEN=123abc bundle exec rspec ``` Then update the version number in `version.rb`, update `CHANGELOG.md`, run `bundle install` to update Gemfile.lock, and then run `bundle exec rake release`, which will create a git tag for the version, push git commits and tags, and push the `.gem` file to [rubygems.org](https://rubygems.org). ## Contributing Bug reports and pull requests are welcome on GitHub at <https://github.com/alexrudall/ruby-openai>. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [code of conduct](https://github.com/alexrudall/ruby-openai/blob/main/CODE_OF_CONDUCT.md). ## License The gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT). ## Code of Conduct Everyone interacting in the Ruby OpenAI project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the [code of conduct](https://github.com/alexrudall/ruby-openai/blob/main/CODE_OF_CONDUCT.md). ", Assign "at most 3 tags" to the expected json: {"id":"419","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"