forked from platypush/platypush
Added openai plugin.
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docs/source/platypush/plugins/openai.rst
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docs/source/platypush/plugins/openai.rst
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``openai``
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==========
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.. automodule:: platypush.plugins.openai
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:members:
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@ -95,6 +95,7 @@ Plugins
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platypush/plugins/ngrok.rst
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platypush/plugins/nmap.rst
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platypush/plugins/ntfy.rst
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platypush/plugins/openai.rst
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platypush/plugins/otp.rst
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platypush/plugins/pihole.rst
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platypush/plugins/ping.rst
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283
platypush/plugins/openai/__init__.py
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platypush/plugins/openai/__init__.py
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import os
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from dataclasses import dataclass
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from datetime import datetime as dt
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from enum import Enum
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from threading import RLock
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from typing import Iterable, List, Optional
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import requests
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from platypush.plugins import Plugin, action
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class ContextEntryRole(Enum):
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"""
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Roles for context entries.
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"""
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ASSISTANT = "assistant"
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SYSTEM = "system"
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USER = "user"
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@dataclass
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class ContextEntry:
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"""
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A context entry.
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"""
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timestamp: dt
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role: ContextEntryRole
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content: str
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@classmethod
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def from_dict(cls, data: dict):
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return cls(
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timestamp=dt.fromisoformat(data.get("timestamp", dt.now().isoformat())),
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role=ContextEntryRole(data["role"]),
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content=data["content"],
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)
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def to_dict(self):
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return {
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"role": self.role.value,
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"content": self.content,
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}
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class OpenaiPlugin(Plugin):
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"""
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Plugin to interact with OpenAI services.
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So far only ChatGPT is supported.
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Contexts
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--------
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The plugin also supports the implementation of custom assistant
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*contexts*/environment.
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Contexts can be used to:
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- Customize the model's behavior based on a set of inputs - going from
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a generic "*You are a helpful assistant*" to a more specific "*You
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are a Star Trek fan*", or "*You are a 16th century noble lady who
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talks in Shakespearean English to her peers*".
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- Pre-configure the model with a set of previous interactions in order
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to either pre-load information that we expect the model to remember,
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or to provide a set of previous interactions that the model can use
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to generate responses that are consistent with the conversation
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history.
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The plugin provides two types of contexts:
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- **Default context**: This is a set of context entries that are
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provided at plugin initialization and that will be used to initialize
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the model with a configuration or set of previous interactions that
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will be remembered when generating all responses.
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- **Runtime context**: This is a set of context entries that can be
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passed at runtime at :meth:`.get_response`. All the interactions
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(both user prompts and assistant responses) that are processed
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through :meth:`.get_response` will also be added to the runtime
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context, and remembered for the next ``context_expiry`` seconds. This
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allows you to generate responses that are consistent with the recent
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conversation history.
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Each context entry is a dictionary with the following keys:
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- ``role``: The role of the message. Can be one of:
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- ``system``: A system message provided to the model to set
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up its initial state - e.g. "you are a helpful
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assistant".
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- ``user``: A user message, as provided by a previous (real
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or synthetic) user interaction.
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- ``assistant``: An assistant message, as provided by a
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previous (real or synthetic) assistant response.
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- ``content``: The content of the message.
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An example of context:
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.. code-block:: yaml
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context:
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- role: system
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content: >
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You are a 16th century noble lady who talks in
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Shakespearean English to her peers.
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- role: user
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content: What is a telephone?
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- role: assistant
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content: >
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Pray tell, noble companion, a telephone is a device
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of modern innovation that doth permit one to speak
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with a distant acquaintance by means of magical pink
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waves that do carry the sound of thine voice to the
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ear of the listener.
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Given such context, if you call :meth:`.get_response` with a
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prompt such as "*How does it work?*", the model may generate a
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response such as "*Fair lady, to use a telephone, thou must first
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lift the receiver and place it to thine ear. Then, thou must speak
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into the mouthpiece as though conversing with a companion in
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another room. The magical pink waves shall carry thy words to the
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recipient, who shall hear them on their own device. 'Tis a wondrous
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invention indeed!*".
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Note that the model will remember the previous interactions and
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also generate responses, so you can ask it direct questions such as "How
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does it work" while remembering what "it" is likely to mean. And it'll
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provide responses which are in the same style initialized through the
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``system`` context.
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"""
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def __init__(
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self,
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api_key: Optional[str],
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model: str = "gpt-3.5-turbo",
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timeout: float = 30,
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context: Optional[Iterable[dict]] = None,
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context_expiry: Optional[float] = 600,
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max_tokens: int = 500,
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**kwargs,
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):
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"""
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:param api_key: OpenAI API key. If not set, it will be read from the
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``OPENAI_API_KEY`` environment variable.
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:param model: The model to use. Default: ``gpt-3.5-turbo``.
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:param timeout: Default timeout for API requests (default: 30 seconds).
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:param max_tokens: Maximum number of tokens to generate in the response
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(default: 500).
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:param context: Default context to use for completions, as a list of
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dictionaries with ``role`` and ``content`` keys. Default: None.
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:param context_expiry: Default expiry time for the context in seconds.
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After this time since the last interaction, the context will be
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cleared.
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This means that any follow-up interactions happening within the
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expiry window will remember the past prompts, but any interaction
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that happens after the expiry window (calculated from the time of
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the last interaction) will start fresh.
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Note that ``context_expiry`` is only applied to the runtime
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context. The default context will never expire unless it's removed
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from the plugin configuration.
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Set to 0 to disable context expiry - i.e. all messages stay in the
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context until the plugin is restarted or the context is cleared
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explicitly via :meth:`.clear_context`. Default: 600 seconds (10
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minutes).
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"""
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super().__init__(**kwargs)
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api_key = api_key or os.getenv('OPENAI_API_KEY')
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assert api_key, 'OpenAI API key not provided'
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self._api_key = api_key
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self._context_lock = RLock()
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self._runtime_context: List[ContextEntry] = []
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self._default_context = [
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ContextEntry.from_dict(entries) for entries in (context or [])
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]
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self.max_tokens = max_tokens
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self.context_expiry = context_expiry
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self.model = model
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self.timeout = timeout
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def _rotate_context(self):
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"""
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Rotate the context by removing any entries older than the configured
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``context_expiry``.
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"""
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if not self.context_expiry:
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return
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with self._context_lock:
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now = dt.now()
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self._runtime_context = [
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entry
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for entry in self._runtime_context
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if (now - entry.timestamp).total_seconds() < self.context_expiry
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]
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@action
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def get_response(
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self,
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prompt: str,
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model: Optional[str] = None,
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context: Optional[Iterable[dict]] = None,
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timeout: Optional[float] = None,
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max_tokens: Optional[int] = None,
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) -> Optional[str]:
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"""
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Get completions for a given prompt using ChatGPT.
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:param prompt: The prompt/question to complete/answer.
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:param model: Override the default model to use.
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:param context: Extend the default context with these extra messages.
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:param max_tokens: Override the default maximum number of tokens to
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generate in the response.
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:param timeout: Override the default timeout for the API request.
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:return: The completion for the prompt - or, better, the message
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associted to the highest scoring completion choice.
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"""
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self._rotate_context()
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context = [
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*(context or []),
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{
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"role": "user",
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"content": prompt,
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},
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]
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resp = requests.post(
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"https://api.openai.com/v1/chat/completions",
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timeout=timeout or self.timeout,
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headers={
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"Authorization": f"Bearer {self._api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": model or self.model,
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"messages": [
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*(
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entry.to_dict()
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for entry in (
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*(self._default_context or []),
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*self._runtime_context,
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)
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),
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*context,
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],
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"max_tokens": max_tokens or self.max_tokens,
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},
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)
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resp.raise_for_status()
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self._update_context(*context)
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choices = resp.json()["choices"]
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self.logger.debug("OpenAI response: %s", resp.json())
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if not choices:
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return None
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msg = choices[0]["message"]
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self._update_context(msg)
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return msg["content"]
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def _update_context(self, *entries: dict):
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"""
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Update the context with a new entry.
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"""
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with self._context_lock:
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for entry in entries:
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self._runtime_context.append(ContextEntry.from_dict(entry))
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self._rotate_context()
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@action
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def clear_context(self):
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"""
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Clear the runtime context.
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"""
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with self._context_lock:
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self._runtime_context = []
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3
platypush/plugins/openai/manifest.yaml
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3
platypush/plugins/openai/manifest.yaml
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manifest:
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package: platypush.plugins.openai
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type: plugin
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