Implement Pydantic models from neon-data-models
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| unit_tests: | ||
| strategy: | ||
| matrix: | ||
| python-version: [ 3.9, "3.10", "3.11", "3.12" ] |
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we do not intend to support Python 3.8 in our system anymore?
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I've been dropping it from tests as it recently reached EoL
| request_data=request_data.model_dump(), | ||
| target_queue=queue, | ||
| response_queue=f"{queue}.response") | ||
| return LLMProposeResponse(**resp_data) |
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I would omit kwargs-like argument passing here, to make it more flexible to any potential updates to the callback body of send_mq_request
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This should handle any mis-match of keys as part of the Pydantic validation. My intent with the Pydantic models here is that anything moving across MQ (and HTTP and Messagebus) is defined in neon-data-models; any added parameters will have defaults defined there, so the defaults are consistent across different handlers.
requirements/requirements.txt
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| pydantic~=2.6 | ||
| neon-data-models@git+https://github.com/neongeckocom/neon-data-models@FEAT_LLMRequestModels |
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lets first merge this PR and then publish it to PyPi to further get the actual version there
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PyPI upload will fail with git dependencies, so the neon-data-models PR must be merged before this one.
Outline unit tests with automation Add test extra dependencies
Add tests of MQ request/response handling in `chatbot` module Update `chatbot` module to use Pydantic models in place of `dict` objects for MQ message validation
…ser` Refactor `chatbot` methods to safely handle missing context
Include `routing_key` in LLM responses to associate inputs/responses
Update tests to reference constant strings for more specific testing
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Description
Implements shared models for data sent via MQ (NeonGeckoCom/neon-data-models#4)
Refactors
personahandling to validate configured personasIssues
Other Notes
This refactoring allows MQ consumers to more easily decode received messages into Pydantic models so that passed parameters are consistent and new parameters have clearly-defined defaults