module references clean
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2 changed files with 7 additions and 26 deletions
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@ -159,6 +159,8 @@ class ChatManager:
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"duration": (datetime.now(UTC) - datetime.fromisoformat(workflow.startedAt)).total_seconds()
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}
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# Get user language from service
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userLanguage = self.service.user.language
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# Prepare messages for AI context
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messages = [
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@ -188,7 +190,7 @@ class ChatManager:
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})
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# Generate feedback using AI
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feedback = await self.callAi(messages, produceUserAnswer=True, temperature=0.7)
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feedback = await self.service.callAi(messages, produceUserAnswer=True, temperature=0.7)
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return feedback
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@ -1,23 +1,4 @@
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- all AI calls to route over self service (AI basic, ai special, ai...) and to streamline
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- integrate, then remove:
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callAi(prompt, context) -> str
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callAi4Image(prompt, context, imageData)-> str
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_callAiBasic
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_callAiAdvanced
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in manageChat
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- not to manipulate workflow or messages --> to use functions from servicechatclass
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- to add CRUD for tasks to workflow
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- to move methods to methods class
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- service object to define correctly
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- folder workflow modules --> solve problems types
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Procedure for task generation:
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- prompt input:
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Prompt for task definition:
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- original user input (summary)
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- prompt for task to do
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- method list
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@ -33,12 +14,10 @@ Procedure for task generation:
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- as parameter only to use available items of documentReference and connectionReference or resultLabel of a previous method.action
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- required result format: json for TaskResult, nothing else
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- result parsing:
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- execute task actions by using data references stepwise, all documents in TaskResults to save as ChatDocuments, to be available for next action.
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TODO:
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- action output filename to be available as TaskResult with name to reference object and ChatDocument list
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TODO
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- result parsing: execute task actions by using data references stepwise, all documents in TaskResults to save as ChatDocuments, to be available for next action.
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- action execution: to use conversion functions, user DocumentReference and ConnectionReference
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service (ServiceContainer)
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