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memory_storage use rag_pipeline

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betterwang 2024-03-07 22:07:04 +08:00
parent 93e61ec2da
commit f14fee9b0d
7 changed files with 111 additions and 19 deletions
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5
metagpt/memory/longterm_memory.py
View file

@ -32,7 +32,7 @@ class LongTermMemory(Memory):
self.memory_storage.recover_memory(role_id)
self.rc = rc
if not self.memory_storage.is_initialized:
logger.warning(f"It may the first time to run Agent {role_id}, the long-term memory is empty")
logger.warning(f"It may the first time to run Role {role_id}, the long-term memory is empty")
else:
logger.warning(f"Role {role_id} has existing memory storage and has recovered them.")
self.msg_from_recover = True
@ -66,6 +66,9 @@ class LongTermMemory(Memory):
ltm_news.append(mem)
return ltm_news[-k:]
def persit(self):
self.memory_storage.persit()
def delete(self, message: Message):
super().delete(message)
# TODO delete message in memory_storage

6
metagpt/memory/memory_storage.py
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@ -43,7 +43,7 @@ class MemoryStorage(object):
if self.role_mem_path.joinpath("default__vector_store.json").exists():
self.faiss_engine = SimpleEngine.from_index(
index_config=[FAISSIndexConfig(persist_path=self.cache_dir)],
index_config=FAISSIndexConfig(persist_path=self.cache_dir),
retriever_configs=[FAISSRetrieverConfig()],
embed_model=self.embedding,
)
@ -73,3 +73,7 @@ class MemoryStorage(object):
def clean(self):
shutil.rmtree(self.cache_dir, ignore_errors=True)
self._initialized = False
def persit(self):
if self.faiss_engine:
self.faiss_engine.index.storage_context.persist(self.cache_dir)

1
metagpt/rag/schema.py
View file

@ -104,6 +104,7 @@ class ObjectNode(TextNode):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.excluded_llm_metadata_keys = list(ObjectNodeMetadata.model_fields.keys())
self.excluded_embed_metadata_keys = self.excluded_llm_metadata_keys
@staticmethod
def get_obj_metadata(obj: RAGObject) -> dict:

29
tests/metagpt/document_store/test_faiss_store.py
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@ -6,6 +6,8 @@
@File : test_faiss_store.py
"""
import numpy as np
import pytest
from metagpt.const import EXAMPLE_PATH
@ -14,8 +16,23 @@ from metagpt.logs import logger
from metagpt.roles import Sales
def mock_openai_embed_documents(self, texts: list[str], show_progress: bool = False) -> list[list[float]]:
num = len(texts)
embeds = np.random.randint(1, 100, size=(num, 1536)) # 1536: openai embedding dim
embeds = (embeds - embeds.mean(axis=0)) / embeds.std(axis=0)
return embeds.tolist()
def mock_openai_embed_document(self, text: str) -> list[float]:
embeds = mock_openai_embed_documents(self, [text])
return embeds[0]
@pytest.mark.asyncio
async def test_search_json():
async def test_search_json(mocker):
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
store = FaissStore(EXAMPLE_PATH / "data/search_kb/example.json")
role = Sales(profile="Sales", store=store)
query = "Which facial cleanser is good for oily skin?"
@ -24,7 +41,10 @@ async def test_search_json():
@pytest.mark.asyncio
async def test_search_xlsx():
async def test_search_xlsx(mocker):
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
store = FaissStore(EXAMPLE_PATH / "data/search_kb/example.xlsx", meta_col="Answer", content_col="Question")
role = Sales(profile="Sales", store=store)
query = "Which facial cleanser is good for oily skin?"
@ -33,7 +53,10 @@ async def test_search_xlsx():
@pytest.mark.asyncio
async def test_write():
async def test_write(mocker):
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
store = FaissStore(EXAMPLE_PATH / "data/search_kb/example.xlsx", meta_col="Answer", content_col="Question")
_faiss_store = store.write()
assert _faiss_store.storage_context.docstore

42
tests/metagpt/memory/mock_text_embed.py Normal file
View file

@ -0,0 +1,42 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Desc :
import numpy as np
dim = 1536 # openai embedding dim
embed_zeros_arrr = np.zeros(shape=[1, dim]).tolist()
embed_ones_arrr = np.ones(shape=[1, dim]).tolist()
text_embed_arr = [
{"text": "Write a cli snake game", "embed": embed_zeros_arrr}, # mock data, same as below
{"text": "Write a game of cli snake", "embed": embed_zeros_arrr},
{"text": "Write a 2048 web game", "embed": embed_ones_arrr},
{"text": "Write a Battle City", "embed": embed_ones_arrr},
{
"text": "The user has requested the creation of a command-line interface (CLI) snake game",
"embed": embed_zeros_arrr,
},
{"text": "The request is command-line interface (CLI) snake game", "embed": embed_zeros_arrr},
{
"text": "Incorporate basic features of a snake game such as scoring and increasing difficulty",
"embed": embed_ones_arrr,
},
]
text_idx_dict = {item["text"]: idx for idx, item in enumerate(text_embed_arr)}
def mock_openai_embed_documents(self, texts: list[str], show_progress: bool = False) -> list[list[float]]:
idx = text_idx_dict.get(texts[0])
embed = text_embed_arr[idx].get("embed")
return embed
def mock_openai_embed_document(self, text: str) -> list[float]:
embeds = mock_openai_embed_documents(self, [text])
return embeds[0]
async def mock_openai_aembed_document(self, text: str) -> list[float]:
return mock_openai_embed_document(self, text)

18
tests/metagpt/memory/test_longterm_memory.py
View file

@ -13,12 +13,17 @@ from metagpt.config2 import config
from metagpt.memory.longterm_memory import LongTermMemory
from metagpt.roles.role import RoleContext
from metagpt.schema import Message
from tests.metagpt.memory.mock_text_embed import text_embed_arr
os.environ.setdefault("OPENAI_API_KEY", config.get_openai_llm().api_key)
@pytest.mark.asyncio
async def test_ltm_search():
async def test_ltm_search(mocker):
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._aget_query_embedding", mock_openai_aembed_document)
role_id = "UTUserLtm(Product Manager)"
from metagpt.environment import Environment
@ -28,24 +33,25 @@ async def test_ltm_search():
ltm = LongTermMemory()
ltm.recover_memory(role_id, rc)
idea = "Write a cli snake game"
idea = text_embed_arr[0].get("text", "Write a cli snake game")
message = Message(role="User", content=idea, cause_by=UserRequirement)
news = await ltm.find_news([message])
assert len(news) == 1
assert len(news) == 0
ltm.add(message)
sim_idea = "Write a game of cli snake"
sim_idea = text_embed_arr[1].get("text", "Write a game of cli snake")
sim_message = Message(role="User", content=sim_idea, cause_by=UserRequirement)
news = await ltm.find_news([sim_message])
assert len(news) == 0
ltm.add(sim_message)
new_idea = "Write a 2048 web game"
new_idea = text_embed_arr[2].get("text", "Write a 2048 web game")
new_message = Message(role="User", content=new_idea, cause_by=UserRequirement)
news = await ltm.find_news([new_message])
assert len(news) == 1
ltm.add(new_message)
ltm.persit()
# restore from local index
ltm_new = LongTermMemory()
@ -57,7 +63,7 @@ async def test_ltm_search():
news = await ltm_new.find_news([sim_message])
assert len(news) == 0
new_idea = "Write a Battle City"
new_idea = text_embed_arr[3].get("text", "Write a Battle City")
new_message = Message(role="User", content=new_idea, cause_by=UserRequirement)
news = await ltm_new.find_news([new_message])
assert len(news) == 1

29
tests/metagpt/memory/test_memory_storage.py
View file

@ -17,13 +17,18 @@ from metagpt.config2 import config
from metagpt.const import DATA_PATH
from metagpt.memory.memory_storage import MemoryStorage
from metagpt.schema import Message
from tests.metagpt.memory.mock_text_embed import text_embed_arr
os.environ.setdefault("OPENAI_API_KEY", config.get_openai_llm().api_key)
@pytest.mark.asyncio
async def test_idea_message():
idea = "Write a cli snake game"
async def test_idea_message(mocker):
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._aget_query_embedding", mock_openai_aembed_document)
idea = text_embed_arr[0].get("text", "Write a cli snake game")
role_id = "UTUser1(Product Manager)"
message = Message(role="User", content=idea, cause_by=UserRequirement)
@ -35,12 +40,12 @@ async def test_idea_message():
memory_storage.add(message)
assert memory_storage.is_initialized is True
sim_idea = idea # "Write a game of cli snake"
sim_idea = text_embed_arr[1].get("text", "Write a game of cli snake")
sim_message = Message(role="User", content=sim_idea, cause_by=UserRequirement)
new_messages = await memory_storage.search_dissimilar(sim_message)
assert len(new_messages) == 0 # similar, return []
new_idea = "Write a 2048 web game"
new_idea = text_embed_arr[2].get("text", "Write a 2048 web game")
new_message = Message(role="User", content=new_idea, cause_by=UserRequirement)
new_messages = await memory_storage.search_dissimilar(new_message)
assert new_messages[0].content == message.content
@ -50,13 +55,19 @@ async def test_idea_message():
@pytest.mark.asyncio
async def test_actionout_message():
async def test_actionout_message(mocker):
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embeddings", mock_openai_embed_documents)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._get_text_embedding", mock_openai_embed_document)
# mocker.patch("llama_index.embeddings.openai.base.OpenAIEmbedding._aget_query_embedding", mock_openai_aembed_document)
out_mapping = {"field1": (str, ...), "field2": (List[str], ...)}
out_data = {"field1": "field1 value", "field2": ["field2 value1", "field2 value2"]}
ic_obj = ActionNode.create_model_class("prd", out_mapping)
role_id = "UTUser2(Architect)"
content = "The user has requested the creation of a command-line interface (CLI) snake game"
content = text_embed_arr[4].get(
"text", "The user has requested the creation of a command-line interface (CLI) snake game"
)
message = Message(
content=content, instruct_content=ic_obj(**out_data), role="user", cause_by=WritePRD
) # WritePRD as test action
@ -69,12 +80,14 @@ async def test_actionout_message():
memory_storage.add(message)
assert memory_storage.is_initialized is True
sim_conent = "The request is command-line interface (CLI) snake game"
sim_conent = text_embed_arr[5].get("text", "The request is command-line interface (CLI) snake game")
sim_message = Message(content=sim_conent, instruct_content=ic_obj(**out_data), role="user", cause_by=WritePRD)
new_messages = await memory_storage.search_dissimilar(sim_message)
assert len(new_messages) == 0 # similar, return []
new_conent = "Incorporate basic features of a snake game such as scoring and increasing difficulty"
new_conent = text_embed_arr[6].get(
"text", "Incorporate basic features of a snake game such as scoring and increasing difficulty"
)
new_message = Message(content=new_conent, instruct_content=ic_obj(**out_data), role="user", cause_by=WritePRD)
new_messages = await memory_storage.search_dissimilar(new_message)
assert new_messages[0].content == message.content