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| agent_system_prompt: str = Field( | ||
| default=( | ||
| "You are an expert at evaluating search results. " | ||
| "Analyze the provided result for the query against all given criteria and " | ||
| "select the most appropriate category for each. Provide clear reasoning." | ||
| ), | ||
| description="System prompt for the internal scoring agent", | ||
| ) |
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have this get from os.getenv as well so that we can globally modify whenever we want...and default to this value you have. something like AKD_LLM_RERANKER_SYSTEM_PROMPT
| ScoringCriterion( | ||
| name="Processing Level", | ||
| description="How well does this result match the required processing level?", | ||
| weight=0.5, | ||
| ), |
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This seems too specific to data search agent. we need to find some general criteria that can be globally applied to any usecase...
| debug: Enable debug logging | ||
| """ | ||
| super().__init__(config=config, debug=debug) | ||
| self.config: LLMRerankerToolConfig = self.config # type hint |
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we don't need this...it will automatically be done through super()...
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| class ScoringAgent(LiteLLMInstructorBaseAgent): | ||
| input_schema = DummyInput | ||
| output_schema = dynamic_scoring_model |
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move this to anotehr internal method like _setup_scoring_agent or something
and we can just do `self.scoring_agent = self._setup_scoring_agent(model....)
| Similar to relevancy-ranker.py approach - creates explicit named fields | ||
| for each criterion so LLM can see them in the JSON schema. | ||
| """ | ||
| from pydantic import create_model |
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move to top-level import
| ) | ||
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| if self.debug: | ||
| print(formatted_prompt) |
| }, | ||
| ] | ||
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| print(messages) |
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remove print stattement...or add if self.debug: logger.debug(...)
| def create_reranker( | ||
| reranker_type: RerankerType, | ||
| config: RerankerToolConfig | None = None, | ||
| config: RerankerToolConfig | LLMRerankerToolConfig | None = None, |
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Technically don't need LLMRerankerToolConfig because it's also a type of RerankerToolConfig. Redundant type hint
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| response = await self.scoring_agent.get_response_async( | ||
| messages=messages, | ||
| ) | ||
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| results = {} | ||
| response_dict = response.model_dump() |
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what if we do this at agent.arun level? since it's one-level higher abstraction. is it possible to do it with the formatted_prompt? you have to convert it to ap ydantic input schema
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❌ Tests failed (exit code: 1) 📊 Test Results
Branch: 📋 Full coverage report and logs are available in the workflow run. |
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❌ Tests failed (exit code: 1) 📊 Test Results
Branch: 📋 Full coverage report and logs are available in the workflow run. |
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Summary
- Single LLM call per result evaluates all criteria simultaneously commit hash (02eb523)
- Multiple LLM call per result (for per criteria) commit hash (166f475)