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Add support for retrieving user preferences and memories using Mem0 (#1209)

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* Integrate Mem0

* Update src/crewai/memory/contextual/contextual_memory.py

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* pending commit for _fetch_user_memories

* update poetry.lock

* fixes mypy issues

* fix mypy checks

* New fixes for user_id

* remove memory_provider

* handle memory_provider

* checks for memory_config

* add mem0 to dependency

* Update pyproject.toml

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* update docs

* update doc

* bump mem0 version

* fix api error msg and mypy issue

* mypy fix

* resolve comments

* fix memory usage without mem0

* mem0 version bump

* lazy import mem0

---------

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
This commit is contained in:
Dev Khant
2024-11-15 00:29:24 +05:30
committed by GitHub
parent 9285ebf8a2
commit e70bc94ab6
17 changed files with 619 additions and 34 deletions
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47
src/crewai/memory/contextual/contextual_memory.py
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@@ -1,13 +1,25 @@
from typing import Optional
from typing import Optional, Dict, Any
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
class ContextualMemory:
def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
def __init__(
self,
memory_config: Optional[Dict[str, Any]],
stm: ShortTermMemory,
ltm: LongTermMemory,
em: EntityMemory,
um: UserMemory,
):
if memory_config is not None:
self.memory_provider = memory_config.get("provider")
else:
self.memory_provider = None
self.stm = stm
self.ltm = ltm
self.em = em
self.um = um
def build_context_for_task(self, task, context) -> str:
"""
@@ -23,6 +35,8 @@ class ContextualMemory:
context.append(self._fetch_ltm_context(task.description))
context.append(self._fetch_stm_context(query))
context.append(self._fetch_entity_context(query))
if self.memory_provider == "mem0":
context.append(self._fetch_user_context(query))
return "\n".join(filter(None, context))
def _fetch_stm_context(self, query) -> str:
@@ -32,7 +46,10 @@ class ContextualMemory:
"""
stm_results = self.stm.search(query)
formatted_results = "\n".join(
[f"- {result['context']}" for result in stm_results]
[
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
for result in stm_results
]
)
return f"Recent Insights:\n{formatted_results}" if stm_results else ""
@@ -62,6 +79,26 @@ class ContextualMemory:
"""
em_results = self.em.search(query)
formatted_results = "\n".join(
[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
[
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
for result in em_results
] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
)
return f"Entities:\n{formatted_results}" if em_results else ""
def _fetch_user_context(self, query: str) -> str:
"""
Fetches and formats relevant user information from User Memory.
Args:
query (str): The search query to find relevant user memories.
Returns:
str: Formatted user memories as bullet points, or an empty string if none found.
"""
user_memories = self.um.search(query)
if not user_memories:
return ""
formatted_memories = "\n".join(
f"- {result['memory']}" for result in user_memories
)
return f"User memories/preferences:\n{formatted_memories}"