Adding long term, short term, entity and contextual memory

src/crewai/memory/contextual/contextual_memory.py

@@ -0,0 +1,58 @@
+from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
+
+
+class ContextualMemory:
+    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
+        self.stm = stm
+        self.ltm = ltm
+        self.em = em
+
+    def build_context_for_task(self, task, context) -> str:
+        """
+        Automatically builds a minimal, highly relevant set of contextual information
+        for a given task.
+        """
+        query = f"{task.description} {context}".strip()
+
+        if query == "":
+            return ""
+
+        context = []
+        context.append(self._fetch_ltm_context(task.description))
+        context.append(self._fetch_stm_context(query))
+        context.append(self._fetch_entity_context(query))
+        return "\n".join(filter(None, context))
+
+    def _fetch_stm_context(self, query) -> str:
+        """
+        Fetches recent relevant insights from STM related to the task's description and expected_output,
+        formatted as bullet points.
+        """
+        stm_results = self.stm.search(query)
+        formatted_results = "\n".join([f"- {result}" for result in stm_results])
+        return f"Recent Insights:\n{formatted_results}" if stm_results else ""
+
+    def _fetch_ltm_context(self, task) -> str:
+        """
+        Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,
+        formatted as bullet points.
+        """
+        ltm_results = self.ltm.search(task)
+        if not ltm_results:
+            return None
+        formatted_results = "\n".join(
+            [f"{result['metadata']['suggestions']}" for result in ltm_results]
+        )
+        formatted_results = list(set(formatted_results))
+        return f"Historical Data:\n{formatted_results}" if ltm_results else ""
+
+    def _fetch_entity_context(self, query) -> str:
+        """
+        Fetches relevant entity information from Entity Memory related to the task's description and expected_output,
+        formatted as bullet points.
+        """
+        em_results = self.em.search(query)
+        formatted_results = "\n".join(
+            [f"- {result['context']}" for result in em_results]
+        )
+        return f"Entities:\n{formatted_results}" if em_results else ""