xintongsong commented on code in PR #629:
URL: https://github.com/apache/flink-agents/pull/629#discussion_r3098285160
##########
python/flink_agents/api/memory/long_term_memory.py:
##########
@@ -15,95 +15,54 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#################################################################################
-import importlib
from abc import ABC, abstractmethod
from datetime import datetime
-from enum import Enum
-from typing import Any, Dict, List, Type
+from typing import Any, Dict, List
-from pydantic import (
- BaseModel,
- Field,
- field_serializer,
- model_validator,
-)
+from pydantic import BaseModel, Field
-from flink_agents.api.chat_message import ChatMessage
from flink_agents.api.configuration import ConfigOption
-from flink_agents.api.prompts.prompt import Prompt
-
-ItemType = str | ChatMessage
-
-
-class CompactionConfig(BaseModel):
- """Compaction configuration.
-
- Attributes:
- model: The name of the llm used for generating the summarization.
- prompt: The prompt instance or name of the prompt used for generating
- the summarization. Optional.
- limit: How many summarization should generate for the context.
Defaults to 1.
- """
-
- model: str
- prompt: str | Prompt | None = None
- limit: int = 1
-
-
-class LongTermMemoryBackend(Enum):
- """Backend for Long-Term Memory."""
-
- EXTERNAL_VECTOR_STORE = "external_vector_store"
class LongTermMemoryOptions:
- """Config options for ReActAgent."""
+ """Config options for Long-Term Memory."""
- BACKEND = ConfigOption(
- key="long-term-memory.backend",
- config_type=LongTermMemoryBackend,
+ CHAT_MODEL_SETUP = ConfigOption(
+ key="long-term-memory.chat-model-setup",
+ config_type=str,
default=None,
)
- EXTERNAL_VECTOR_STORE_NAME = ConfigOption(
- key="long-term-memory.external-vector-store-name",
+ EMBEDDING_MODEL_SETUP = ConfigOption(
+ key="long-term-memory.embedding-model-setup",
config_type=str,
default=None,
)
- ASYNC_COMPACTION = ConfigOption(
- key="long-term-memory.async-compaction",
- config_type=bool,
- default=True,
+ VECTOR_STORE = ConfigOption(
+ key="long-term-memory.vector-store",
+ config_type=dict,
+ default=None,
Review Comment:
1. I'd suggest the names `long-term-memory.mem0.xxx`, in case we introduce
more backends in future.
2. Having a `dict` type config option can be troublesome. Why is the vector
store configured differently compared to chat and embedding model?
##########
python/flink_agents/runtime/flink_runner_context.py:
##########
@@ -770,17 +769,17 @@ def create_flink_runner_context(
j_runner_context, agent_plan_json, executor, j_resource_adapter
)
- backend = ctx.config.get(LongTermMemoryOptions.BACKEND)
- # use external vector store based long term memory
- if backend == LongTermMemoryBackend.EXTERNAL_VECTOR_STORE:
- vector_store_name = ctx.config.get(
- LongTermMemoryOptions.EXTERNAL_VECTOR_STORE_NAME
- )
+ chat_model_name = ctx.config.get(LongTermMemoryOptions.CHAT_MODEL_SETUP)
+ embedding_model_name =
ctx.config.get(LongTermMemoryOptions.EMBEDDING_MODEL_SETUP)
+ if chat_model_name is not None and embedding_model_name is not None:
+ vector_store_config =
ctx.config.get(LongTermMemoryOptions.VECTOR_STORE)
ctx.set_long_term_memory(
- VectorStoreLongTermMemory(
+ Mem0LongTermMemory(
ctx=ctx,
- vector_store=vector_store_name,
job_id=job_identifier,
+ chat_model_name=chat_model_name,
+ embedding_model_name=embedding_model_name,
+ vector_store_config=vector_store_config,
Review Comment:
The initialization of ltm-beckend might worth a separate function.
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