Anthropic support #222
Anthropic support #222
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README.md
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| @@ -7,7 +7,10 @@ | |||
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| # Initial Project Setup | |||
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| 1. Install Poetry | |||
| 1. Install Python version 3.10.13 and make sure it is being used the next step and later during development | |||
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Other 3.10.x versions are also fine. The exact version is added for consistency
| input_variables={'description': self.description}, | ||
| tracked_user=self.user, | ||
| tags=['manual_contribution_assessment'], | ||
| **make_prompt_kwargs(settings.GITHUB_MANUAL_CONTRIBUTION_ASSESSMENT_PROMPT_NAME), |
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This helps development and debug where a developer can use a prompt with a particular tag by overriding a value in settings.dev.py
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| def is_ia(author): | ||
| return author.id == settings.IA_DISCORD_USER_ID |
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This fixes the issue where anyone impersonate for IA just by having _ai in their username. It also allows not to force developers to have a bot with _ai string in their name
| content = message.content | ||
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| if (role := map_author_structured(message.author)) == prev_role: | ||
| # We need to merge messages to prevent the following error from Anthropic |
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Anthropic has its limitations. We may need similar thing in the other place of IA interaction if we decide to use Anthropic there
| @@ -32,28 +75,25 @@ async def on_message_implementation(message): | |||
| await message.reply('Please, register at https://thenewboston.com') | |||
| return | |||
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| # TODO(dmu) MEDIUM: Request message history just once and convert it to necessary format before LLM call | |||
| plain_text_message_history = await get_plain_text_message_history(message.channel) | |||
| messages = (await get_historical_messages(message.channel))[::-1] | |||
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Now we query historical messages just once
| @@ -20,6 +20,49 @@ | |||
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| bot = commands.Bot('/', intents=intents) | |||
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| # TODO(dmu) HIGH: Cover bot logic with unittests: it is already complex enough | |||
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Saving time on not having unittests is overrated
| from thenewboston.general.commands import CustomCommand | ||
| from thenewboston.general.utils.json import ResponseEncoder | ||
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| class Command(CustomCommand): |
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Was very handy for developing and debugging Anthropic integration
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Implementation of #221
Both Anthropic and OpenAI are supported on LLMClient level. Other prompts can be switched to Anthropic just by changing prompt parameters in PromptLayer (did not test, but should work)