openai-harmony

from openai_harmony import ( load_harmony_encoding, HarmonyEncodingName, Role, Message, Conversation, DeveloperContent, SystemContent, ) # Load the Harmony encoding for GPT-OSS models enc = load_harmony_encoding(HarmonyEncodingName.HARMONY_GPT_OSS) # Create a conversation with system, developer instructions, and a user message convo = Conversation.from_messages([ Message.from_role_and_content( Role.SYSTEM, SystemContent.new(), ), Message.from_role_and_content( Role.DEVELOPER, DeveloperContent.new().with_instructions("Talk like a pirate!") ), Message.from_role_and_content(Role.USER, "Arrr, how be you?"), ]) # Render the conversation into tokens for completion. This generates the prompt. tokens_for_model = enc.render_conversation_for_completion(convo, Role.ASSISTANT) print("Prompt Tokens (to send to model):", tokens_for_model) # --- Simulate a model response --- # In a real scenario, `tokens_for_model` would be sent to a gpt-oss model, # and the model would generate a continuation. For this quickstart, we'll # append a sample assistant response to the prompt tokens. model_raw_completion = tokens_for_model + "<|start|>assistant<|message|>Ahoy there, matey! I be shipshape and Bristol fashion. <|end|>" # Parse the model's raw completion back into structured messages parsed_messages = enc.parse_messages_from_completion_tokens(model_raw_completion, role=Role.ASSISTANT) print("\nParsed Messages (including original prompt and simulated response):") for msg in parsed_messages: # Accessing content might depend on the content type (e.g., TextContent, DeveloperContent) content_value = msg.content.text if hasattr(msg.content, 'text') else str(msg.content) print(f"Role: {msg.role.name}, Content: {content_value}")