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feat(reme_ai): implement memory retrieval and merging functionality

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- Add BuildQueryOp to construct query for memory retrieval-Implement MergeMemoryOp to combine retrieved memories
- Create RecallVectorStoreOp to fetch memories from vector store
- Develop memory representation and conversion methods
- Establish initial project structure and dependencies
This commit is contained in:
jinli.yl 2025-08-25 16:09:54 +08:00
parent a3f281f365
commit 4739d39f3f
24 changed files with 500 additions and 211 deletions
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10
.gitignore vendored
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@ -29,12 +29,4 @@ cookbook/appworld/experiments/*
cookbook/appworld/exp_result/*
file_vector_store/*
cookbook/appworld/file_vector_store/*
experiencemaker/tool/web_search_cach/*
experiencemaker/cookbook/bfcl/exp_result
experiencemaker/cookbook/bfcl/old_exp_result
experiencemaker/cookbook/bfcl/data
experiencemaker/cookbook/bfcl/gorilla
experiencemaker/*.sh
experiencemaker/file_vector_store
experiencemaker/*.egg-info
experiencemaker/experiment_library
experiencemaker/tool/web_search_cach/*

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experiencemaker/LICENSE → LICENSE
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@ -186,7 +186,7 @@
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2024 Alibaba Group
Copyright 2025 Alibaba Group
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.

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# 代码框架
1. flowllm: 通过op pipeline的配置实现mcp接口的生成。
2. 重写Remy readme.
3. 迁移memoryscope/experiencemaker到flowllm的框架下
4. 迁移到新的op框架下
5. 重写memoryscope的cli-chat前端
6. 迁移memoryscope的文档
7. 迁移experiencemaker的文档
#

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OPENAI_API_KEY=sk-xxxx
OPENAI_BASE_URL=https://xxxx/v1
EMBEDDING_API_KEY=sk-xxxx
EMBEDDING_BASE_URL=https://xxxx/v1
LLM_API_KEY=sk-xxxx
LLM_BASE_URL=https://xxxx/v1
ES_HOSTS=http://0.0.0.0:9200
DASHSCOPE_API_KEY=sk-xxxx

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@ -1,201 +0,0 @@
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[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "Reme-AI"
version = "0.1.0"
description = "Manage Experience, Navigate Tasks & Optimize Reuse"
authors = [{ name = "reme-ai team", email = "reme-ai@alibaba-inc.com" }]
license = { file = "LICENSE" }
readme = "README.md"
requires-python = ">=3.12"
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
]
dependencies = [
"dashscope>=1.19.1",
"elasticsearch>=8.14.0",
"fastapi>=0.115.13",
"fastmcp>=2.10.6",
"loguru>=0.7.3",
"mcp>=1.9.4",
"numpy>=2.3.0",
"openai>=1.88.0",
"pydantic>=2.11.7",
"PyYAML>=6.0.2",
"Requests>=2.32.4",
"uvicorn>=0.34.3",
"setuptools>=75.0",
]
[tool.setuptools.packages.find]
where = ["."]
include = ["experiencemaker*"]
exclude = ["memoryscope*"]
[tool.setuptools.package-data]
experiencemaker = [
"config/*.yaml",
"op/**/*.yaml",
]
[project.scripts]
reme = "experiencemaker.app:main"

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__version__ = "0.1.0"

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reme_ai/app.py Normal file
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import sys
from flowllm.service.base_service import BaseService
def main():
with BaseService.get_service(*sys.argv[1:]) as service:
service()
if __name__ == "__main__":
main()
# python -m build
# twine upload dist/*

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reme_ai/config/__init__.py Normal file
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reme_ai/config/config_parser.py Normal file
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from flowllm.config.pydantic_config_parser import PydanticConfigParser
class ConfigParser(PydanticConfigParser):
current_file: str = __file__
default_config_name: str = "default"

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# default config.yaml
backend: mcp
language: ""
thread_pool_max_workers: 32
ray_max_workers: 1
mcp:
transport: sse
host: "0.0.0.0"
port: 8002
http:
host: "0.0.0.0"
port: 8002
timeout_keep_alive: 600
limit_concurrency: 64
flow:
task_retrieve:
flow_content: build_query_op->recall_vector_store_op->merge_experience_op
# task_summarizer: simple_summary_op->update_vector_store_op
# vector_store: vector_store_action_op
# agent: react_op
mock_expression_flow:
flow_content: mock1_op>>((mock4_op>>mock2_op)|mock5_op)>>(mock3_op|mock6_op)
description: "mock flow"
input_schema:
a:
type: "str"
description: "mock attr a"
required: true
b:
type: "str"
description: "mock attr b"
required: true
op:
mock1_op:
backend: mock1_op
llm: default
vector_store: default
llm:
default:
backend: openai_compatible
model_name: qwen3-30b-a3b-thinking-2507
params:
temperature: 0.6
qwen3_30b_instruct:
backend: openai_compatible
model_name: qwen3-30b-a3b-instruct-2507
embedding_model:
default:
backend: openai_compatible
model_name: text-embedding-v4
params:
dimensions: 1024
vector_store:
default:
backend: elasticsearch
embedding_model: default
params:
hosts: "http://localhost:9200"

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from reme_ai.retrieve import task

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from reme_ai.retrieve.task.build_query_op import BuildQueryOp
from reme_ai.retrieve.task.merge_memory_op import MergeMemoryOp

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reme_ai/retrieve/task/build_query_op.py Normal file
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from flowllm import C, BaseLLMOp
from flowllm.utils.llm_utils import merge_messages_content
from loguru import logger
@C.register_op()
class BuildQueryOp(BaseLLMOp):
current_path: str = __file__
def execute(self):
if "query" in self.context:
query = self.context.query
elif "messages" in self.context:
if self.op_params.get("enable_llm_build", True):
execution_process = merge_messages_content(self.context.messages)
query = self.prompt_format(prompt_name="query_build", execution_process=execution_process)
else:
context_parts = []
message_summaries = []
for message in self.context.messages[-3:]: # Last 3 messages
content = message.content[:200] + "..." if len(message.content) > 200 else message.content
message_summaries.append(f"- {message.role.value}: {content}")
if message_summaries:
context_parts.append("Recent messages:\n" + "\n".join(message_summaries))
query = "\n\n".join(context_parts)
else:
raise RuntimeError("query or messages is required!")
logger.info(f"build.query={query}")
self.context.query = query

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query_build: |
# Execution Process
{execution_process}
Read through the entire execution process to understand which part is currently being executed.
Generate a `query` that reflects the current state, which will later be used to search for similar problems in the database and help resolve the issue at hand.

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reme_ai/retrieve/task/merge_memory_op.py Normal file
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from typing import List
from flowllm import C, BaseOp
from loguru import logger
from reme_ai.schema.memory import BaseMemory
@C.register_op()
class MergeMemoryOp(BaseOp):
def execute(self):
memory_list: List[BaseMemory] = self.context.response.metadata["memory_list"]
if not memory_list:
return
content_collector = ["Previous Memory"]
for memory in memory_list:
if not memory.content:
continue
content_collector.append(f"- when_to_use: {memory.when_to_use}\n"
f"content: {memory.content}\n")
content_collector.append("Please consider the helpful parts from these in answering the question, "
"to make the response more comprehensive and substantial.")
self.context.response.answer = "\n".join(content_collector)
logger.info(f"response.answer={self.context.response.answer}")

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reme_ai/schema/__init__.py Normal file
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reme_ai/schema/memory.py Normal file
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import datetime
from abc import ABC
from typing import List
from uuid import uuid4
from flowllm.schema.vector_node import VectorNode
from pydantic import BaseModel, Field
class BaseMemory(BaseModel, ABC):
workspace_id: str = Field(default="")
memory_id: str = Field(default_factory=lambda: uuid4().hex)
memory_type: str = Field(default=...)
when_to_use: str = Field(default="")
content: str | bytes = Field(default="")
score: float | None = Field(default=None)
created_time: str = Field(default_factory=lambda: datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
modified_time: str = Field(default_factory=lambda: datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
author: str = Field(default="")
metadata: dict = Field(default_factory=dict)
def update_modified_time(self):
self.modified_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
def to_vector_node(self) -> VectorNode:
raise NotImplementedError
@classmethod
def from_vector_node(cls, node: VectorNode):
raise NotImplementedError
class TaskMemory(BaseMemory):
memory_type: str = Field(default="task")
def to_vector_node(self) -> VectorNode:
return VectorNode(unique_id=self.memory_id,
workspace_id=self.workspace_id,
content=self.when_to_use,
metadata={
"memory_type": self.memory_type,
"content": self.content,
"score": self.score,
"created_time": self.created_time,
"modified_time": self.modified_time,
"author": self.author,
"metadata": self.metadata,
})
@classmethod
def from_vector_node(cls, node: VectorNode) -> "TaskMemory":
return cls(workspace_id=node.workspace_id,
memory_id=node.unique_id,
memory_type=node.metadata.get("memory_type"),
when_to_use=node.content,
content=node.metadata.get("content"),
score=node.metadata.get("score"),
created_time=node.metadata.get("created_time"),
modified_time=node.metadata.get("modified_time"),
author=node.metadata.get("author"),
metadata=node.metadata.get("metadata"))
class FunctionArg(BaseModel):
arg_name: str = Field(default=...)
arg_type: str = Field(default=...)
required: bool = Field(default=True)
class Function(BaseModel):
func_code: str = Field(default=..., description="function code")
func_name: str = Field(default=..., description="function name")
func_args: List[FunctionArg] = Field(default_factory=list)
class FuncMemory(BaseMemory):
memory_type: str = Field(default="function")
functions: List[Function] = Field(default_factory=list)
class PersonalMemory(BaseMemory):
memory_type: str = Field(default="personal")
target: str = Field(default="")
class PersonalTopicMemory(PersonalMemory):
memory_type: str = Field(default="personal_topic")
def vector_node_to_memory(node: VectorNode) -> BaseMemory:
memory_type = node.metadata.get("memory_type")
if memory_type == "task":
return TaskMemory.from_vector_node(node)
elif memory_type == "function":
return FuncMemory.from_vector_node(node)
elif memory_type == "personal":
return PersonalMemory.from_vector_node(node)
elif memory_type == "personal_topic":
return PersonalTopicMemory.from_vector_node(node)
else:
raise RuntimeError(f"memory_type={memory_type} not supported!")
def dict_to_experience(memory_dict: dict):
memory_type = memory_dict.get("memory_type", "task")
if memory_type == "task":
return TaskMemory(**memory_dict)
elif memory_type == "function":
return FuncMemory(**memory_dict)
elif memory_type == "personal":
return PersonalMemory(**memory_dict)
elif memory_type == "personal_topic":
return PersonalTopicMemory(**memory_dict)
else:
raise RuntimeError(f"memory_type={memory_type} not supported!")
if __name__ == "__main__":
e1 = TaskMemory(
workspace_id="w_1024",
memory_id="123",
when_to_use="test case use",
content="test content",
score=0.99,
metadata={})
print(e1.model_dump_json(indent=2))
v1 = e1.to_vector_node()
print(v1.model_dump_json(indent=2))
e2 = vector_node_to_memory(v1)
print(e2.model_dump_json(indent=2))

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reme_ai/summary/__init__.py Normal file
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reme_ai/utils/__init__.py Normal file
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reme_ai/utils/op_utils.py Normal file
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import json
import re
from typing import List
from flowllm.schema.message import Message, Trajectory
from loguru import logger
def parse_json_experience_response(response: str) -> List[dict]:
"""Parse JSON formatted experience response"""
try:
# Extract JSON blocks
json_pattern = r'```json\s*([\s\S]*?)\s*```'
json_blocks = re.findall(json_pattern, response)
if json_blocks:
parsed = json.loads(json_blocks[0])
# Handle array format
if isinstance(parsed, list):
experiences = []
for exp_data in parsed:
if isinstance(exp_data, dict) and (
("when_to_use" in exp_data and "experience" in exp_data) or
("condition" in exp_data and "experience" in exp_data)
):
experiences.append(exp_data)
return experiences
# Handle single object
elif isinstance(parsed, dict) and (
("when_to_use" in parsed and "experience" in parsed) or
("condition" in parsed and "experience" in parsed)
):
return [parsed]
# Fallback: try to parse entire response
parsed = json.loads(response)
if isinstance(parsed, list):
return parsed
elif isinstance(parsed, dict):
return [parsed]
except json.JSONDecodeError as e:
logger.warning(f"Failed to parse JSON experience response: {e}")
return []
def get_trajectory_context(trajectory: Trajectory, step_sequence: List[Message]) -> str:
"""Get context of step sequence within trajectory"""
try:
# Find position of step sequence in trajectory
start_idx = 0
for i, step in enumerate(trajectory.messages):
if step == step_sequence[0]:
start_idx = i
break
# Extract before and after context
context_before = trajectory.messages[max(0, start_idx - 2):start_idx]
context_after = trajectory.messages[start_idx + len(step_sequence):start_idx + len(step_sequence) + 2]
context = f"Query: {trajectory.metadata.get('query', 'N/A')}\n"
if context_before:
context += "Previous steps:\n" + "\n".join(
[f"- {step.content[:100]}..." for step in context_before]) + "\n"
if context_after:
context += "Following steps:\n" + "\n".join([f"- {step.content[:100]}..." for step in context_after])
return context
except Exception as e:
logger.error(f"Error getting trajectory context: {e}")
return f"Query: {trajectory.metadata.get('query', 'N/A')}"

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"""
1. retrieve:
search: query(context), workspace_id(request), top_k(request)
2. summary:
insert: nodes(context), workspace_id(request)
delete: ids(context), workspace_id(request)
search: query(context), workspace_id(request), top_k(request.config.op)
3. vector:
dump: workspace_id(request), path(str), max_size(int)
load: workspace_id(request), path(str)
delete: workspace_id(request)
copy: source_id, target_id, max_size(int)
"""

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reme_ai/vector_store/recall_vector_store_op.py Normal file
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from typing import List
from flowllm import C, BaseLLMOp
from flowllm.schema.vector_node import VectorNode
from loguru import logger
from reme_ai.schema.memory import BaseMemory, vector_node_to_memory
@C.register_op()
class RecallVectorStoreOp(BaseLLMOp):
def execute(self):
recall_key: str = self.op_params.get("recall_key", "query")
query: str = self.context[recall_key]
assert query, "query should be not empty!"
top_k: int = self.context.top_k
workspace_id: str = self.context.workspace_id
nodes: List[VectorNode] = self.vector_store.search(query=query, workspace_id=workspace_id, top_k=top_k)
memory_list: List[BaseMemory] = []
memory_content_list: List[str] = []
for node in nodes:
memory: BaseMemory = vector_node_to_memory(node)
if memory.content not in memory_content_list:
memory_list.append(memory)
memory_content_list.append(memory.content)
logger.info(f"retrieve memory.size={len(memory_list)}")
threshold_score: float | None = self.op_params.get("threshold_score", None)
if threshold_score is not None:
memory_list = [mem for mem in memory_list if mem.score >= threshold_score or mem.score is None]
logger.info(f"after filter by threshold_score size={len(memory_list)}")
self.context.response.metadata["memory_list"] = memory_list