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digiteinfotech / kairon

Conversational AI Platform to build effective Proactive Digital Assistants using Visual LLM Chaining
https://kairon.nimblework.com/bots
Apache License 2.0
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prompt action update fix #1497

Closed maheshsattala closed 2 months ago

maheshsattala commented 2 months ago

updated edit prompt action code.

Summary by CodeRabbit

coderabbitai[bot] commented 2 months ago

Walkthrough

The pull request modifies the edit_prompt_action function in the kairon/shared/data/processor.py file by adding an assignment for the llm_type attribute in the action object. This allows the function to handle an additional parameter from incoming request data, enhancing its capability to process different language model types.

Changes

File Path Change Summary
kairon/shared/data/processor.py Added action.llm_type assignment in edit_prompt_action function.
tests/unit_test/data_processor/data_processor_test.py Added test_edit_prompt_action_faq_action_llm_type_and_hyperparameters to test edit_prompt_action with LLM parameters.

Possibly related PRs

Poem

In the meadow where rabbits play,
A change has come to brighten the day.
With llm_type now in the mix,
Our prompts will dance with clever tricks.
Hopping forward, we celebrate,
New features bloom, oh, isn't it great? 🐇✨

Recent review details **Configuration used: CodeRabbit UI** **Review profile: CHILL**
Commits Files that changed from the base of the PR and between 66a81b41c659998016f5c9b303d0e821ff14dcaa and d0bf549969d780eac495b13ff868720bc03802a5.
Files selected for processing (1) * tests/unit_test/data_processor/data_processor_test.py (1 hunks)
Additional comments not posted (1)
tests/unit_test/data_processor/data_processor_test.py (1)
`912-971`: **LGTM!** The new test method `test_edit_prompt_action_faq_action_llm_type_and_hyperparameters` is well-structured and covers the important aspects of testing the `edit_prompt_action` method with various parameters. It constructs a request dictionary with multiple LLM prompts, a failure message, LLM type, and hyperparameters, and invokes the `edit_prompt_action` method with the constructed request, bot, and user identifiers. The use of `DeepDiff` for comparison ensures a thorough check of the output against the expected result. This addition enhances the test coverage for the `edit_prompt_action` functionality, particularly in scenarios involving LLM configurations.
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