New: Streaming responses in Python

Learn how to build Open Interpreter into your application.

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messages

This property holds a list of messages between the user and the interpreter. You can use it to restore a conversation: 
interpreter.chat("Hi! Can you print hello world?")

print(interpreter.messages)

# This would output:

[
   {
      "role": "user",
      "message": "Hi! Can you print hello world?"
   },
   {
      "role": "assistant",
      "message": "Sure!"
   }
   {
      "role": "assistant",
      "language": "python",
      "code": "print('Hello, World!')",
      "output": "Hello, World!"
   }
]
You can use this to restore interpreter to a previous conversation. 
interpreter.messages = messages # A list that resembles the one above

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offline

This replaced interpreter.local in the New Computer Update (0.2.0).
This boolean flag determines whether to enable or disable some offline features like open procedures. 
interpreter.offline = True  # Check for updates, use procedures
interpreter.offline = False  # Don't check for updates, don't use procedures
Use this in conjunction with the model parameter to set your language model.

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auto_run

Setting this flag to True allows Open Interpreter to automatically run the generated code without user confirmation. 
interpreter.auto_run = True  # Don't require user confirmation
interpreter.auto_run = False  # Require user confirmation (default)

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verbose

Use this boolean flag to toggle verbose mode on or off. Verbose mode will print information at every step to help diagnose problems. 
interpreter.verbose = True  # Turns on verbose mode
interpreter.verbose = False  # Turns off verbose mode

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max_output

This property sets the maximum number of tokens for the output response. 
interpreter.max_output = 2000

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conversation_history

A boolean flag to indicate if the conversation history should be stored or not. 
interpreter.conversation_history = True  # To store history
interpreter.conversation_history = False  # To not store history

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conversation_filename

This property sets the filename where the conversation history will be stored. 
interpreter.conversation_filename = "my_conversation.json"

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conversation_history_path

You can set the path where the conversation history will be stored. 
import os
interpreter.conversation_history_path = os.path.join("my_folder", "conversations")

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model

Specifies the language model to be used. 
interpreter.llm.model = "gpt-3.5-turbo"

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temperature

Sets the randomness level of the model’s output. 
interpreter.llm.temperature = 0.7

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system_message

This stores the model’s system message as a string. Explore or modify it: 
interpreter.system_message += "\nRun all shell commands with -y."

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context_window

This manually sets the context window size in tokens. We try to guess the right context window size for you model, but you can override it with this parameter. 
interpreter.llm.context_window = 16000

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max_tokens

Sets the maximum number of tokens the model can generate in a single response. 
interpreter.llm.max_tokens = 100

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api_base

If you are using a custom API, you can specify its base URL here. 
interpreter.llm.api_base = "https://api.example.com"

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api_key

Set your API key for authentication. 
interpreter.llm.api_key = "your_api_key_here"

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max_budget

This property sets the maximum budget limit for the session in USD. 
interpreter.max_budget = 0.01 # 1 cent