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https://github.com/crewAIInc/crewAI.git
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1ec4da6947
* fix: fix test actually running * fix: fix test to not send request to openai * fix: fix linting to remove cli files * fix: exclude only files that breaks black * fix: Fix all Ruff checkings on the code and Fix Test with repeated name * fix: Change linter name on yml file * feat: update pre-commit * feat: remove need for isort on the code * feat: add mypy as type checker, update code and add comment to reference * feat: remove black linter * feat: remove poetry to run the command * feat: change logic to test mypy * feat: update tests yml to try to fix the tests gh action * feat: try to add just mypy to run on gh action * feat: fix yml file * feat: add comment to avoid issue on gh action * feat: decouple pytest from the necessity of poetry install * feat: change tests.yml to test different approach * feat: change to poetry run * fix: parameter field on yml file * fix: update parameters to be on the pyproject * fix: update pyproject to remove import untyped errors
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
from typing import Optional
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from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
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class ContextualMemory:
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def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
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self.stm = stm
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self.ltm = ltm
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self.em = em
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def build_context_for_task(self, task, context) -> str:
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"""
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Automatically builds a minimal, highly relevant set of contextual information
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for a given task.
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"""
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query = f"{task.description} {context}".strip()
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if query == "":
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return ""
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context = []
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context.append(self._fetch_ltm_context(task.description))
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context.append(self._fetch_stm_context(query))
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context.append(self._fetch_entity_context(query))
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return "\n".join(filter(None, context))
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def _fetch_stm_context(self, query) -> str:
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"""
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Fetches recent relevant insights from STM related to the task's description and expected_output,
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formatted as bullet points.
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"""
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stm_results = self.stm.search(query)
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formatted_results = "\n".join([f"- {result}" for result in stm_results])
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return f"Recent Insights:\n{formatted_results}" if stm_results else ""
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def _fetch_ltm_context(self, task) -> Optional[str]:
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"""
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Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,
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formatted as bullet points.
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"""
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ltm_results = self.ltm.search(task, latest_n=2)
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if not ltm_results:
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return None
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formatted_results = [
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suggestion
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for result in ltm_results
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for suggestion in result["metadata"]["suggestions"] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
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]
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formatted_results = list(dict.fromkeys(formatted_results))
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formatted_results = "\n".join([f"- {result}" for result in formatted_results]) # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
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return f"Historical Data:\n{formatted_results}" if ltm_results else ""
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def _fetch_entity_context(self, query) -> str:
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"""
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Fetches relevant entity information from Entity Memory related to the task's description and expected_output,
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formatted as bullet points.
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"""
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em_results = self.em.search(query)
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formatted_results = "\n".join(
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[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
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)
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return f"Entities:\n{formatted_results}" if em_results else ""
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