update exp_pool manager

config/config2.example.yaml

@@ -79,7 +79,7 @@ exp_pool:
   enable_read: false 
   enable_write: false
   persist_path: .chroma_exp_data # The directory.
-  storage_type: bm25 # Default is `bm25`, can be set to `chroma` for vector storage, which requires setting up embedding.
+  retrieval_type: bm25 # Default is `bm25`, can be set to `chroma` for vector storage, which requires setting up embedding.
 
 azure_tts_subscription_key: "YOUR_SUBSCRIPTION_KEY"
 azure_tts_region: "eastus"

metagpt/configs/exp_pool_config.py

@@ -5,7 +5,7 @@ from pydantic import Field
 from metagpt.utils.yaml_model import YamlModel
 
 
-class ExperiencePoolStorageType(Enum):
+class ExperiencePoolRetrievalType(Enum):
     BM25 = "bm25"
     CHROMA = "chroma"
 
@@ -18,6 +18,6 @@ class ExperiencePoolConfig(YamlModel):
     enable_read: bool = Field(default=False, description="Enable to read from experience pool.")
     enable_write: bool = Field(default=False, description="Enable to write to experience pool.")
     persist_path: str = Field(default=".chroma_exp_data", description="The persist path for experience pool.")
-    storage_type: ExperiencePoolStorageType = Field(
-        default=ExperiencePoolStorageType.BM25, description="The storage type for experience pool."
+    retrieval_type: ExperiencePoolRetrievalType = Field(
+        default=ExperiencePoolRetrievalType.BM25, description="The retrieval type for experience pool."
     )

metagpt/exp_pool/decorator.py

@@ -132,14 +132,14 @@ class ExpCacheHandler(BaseModel):
         """Fetch experiences by query_type."""
 
         self._exps = await self.exp_manager.query_exps(self._req, query_type=self.query_type, tag=self.tag)
-        logger.debug(f"Found {len(self._exps)} experiences for req '{self._req[:20]}...' and tag '{self.tag}'")
+        logger.info(f"Found {len(self._exps)} experiences for tag '{self.tag}'")
 
     async def get_one_perfect_exp(self) -> Optional[Any]:
         """Get a potentially perfect experience, and resolve resp."""
 
         for exp in self._exps:
             if await self.exp_perfect_judge.is_perfect_exp(exp, self._req, *self.args, **self.kwargs):
-                logger.debug(f"Got one perfect experience for req '{exp.req[:20]}...'")
+                logger.info(f"Got one perfect experience for req '{exp.req[:20]}...'")
                 return self.serializer.deserialize_resp(exp.resp)
 
         return None

metagpt/exp_pool/manager.py

@@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, Any
 from pydantic import BaseModel, ConfigDict, Field
 
 from metagpt.config2 import Config
-from metagpt.configs.exp_pool_config import ExperiencePoolStorageType
+from metagpt.configs.exp_pool_config import ExperiencePoolRetrievalType
 from metagpt.exp_pool.schema import (
     DEFAULT_COLLECTION_NAME,
     DEFAULT_SIMILARITY_TOP_K,
@@ -95,11 +95,11 @@ class ExperienceManager(BaseModel):
         """Selects the appropriate storage creation method based on the configured storage type."""
 
         storage_creators = {
-            ExperiencePoolStorageType.BM25: self._create_bm25_storage,
-            ExperiencePoolStorageType.CHROMA: self._create_chroma_storage,
+            ExperiencePoolRetrievalType.BM25: self._create_bm25_storage,
+            ExperiencePoolRetrievalType.CHROMA: self._create_chroma_storage,
         }
 
-        return storage_creators[self.config.exp_pool.storage_type]()
+        return storage_creators[self.config.exp_pool.retrieval_type]()
 
     def _create_bm25_storage(self) -> "SimpleEngine":
         """Creates or loads BM25 storage.
@@ -116,8 +116,6 @@ class ExperienceManager(BaseModel):
         """
 
         try:
-            from llama_index.core import VectorStoreIndex
-
             from metagpt.rag.engines import SimpleEngine
             from metagpt.rag.schema import (
                 BM25IndexConfig,
@@ -133,11 +131,8 @@ class ExperienceManager(BaseModel):
         if not docstore_path.exists():
             logger.debug(f"Path `{docstore_path}` not exists, try to create a new bm25 storage.")
             exps = [Experience(req="req", resp="resp")]
-            nodes = SimpleEngine.get_obj_nodes(exps)
-            embed_model = SimpleEngine._resolve_embed_model(configs=[BM25RetrieverConfig()])
-            index = VectorStoreIndex(nodes, embed_model=embed_model)
 
-            retriever_configs = [BM25RetrieverConfig(index=index)]
+            retriever_configs = [BM25RetrieverConfig(create_index=True)]
             ranker_configs = [LLMRankerConfig(top_n=DEFAULT_SIMILARITY_TOP_K)]
 
             storage = SimpleEngine.from_objs(

metagpt/rag/factories/retriever.py

@@ -7,6 +7,7 @@ import chromadb
 import faiss
 from llama_index.core import StorageContext, VectorStoreIndex
 from llama_index.core.embeddings import BaseEmbedding
+from llama_index.core.embeddings.mock_embed_model import MockEmbedding
 from llama_index.core.schema import BaseNode
 from llama_index.core.vector_stores.types import BasePydanticVectorStore
 from llama_index.vector_stores.chroma import ChromaVectorStore
@@ -84,9 +85,13 @@ class RetrieverFactory(ConfigBasedFactory):
     def _create_bm25_retriever(self, config: BM25RetrieverConfig, **kwargs) -> DynamicBM25Retriever:
         index = self._extract_index(config, **kwargs)
         nodes = list(index.docstore.docs.values()) if index else self._extract_nodes(config, **kwargs)
+
         if index and not config.index:
             config.index = index
 
+        if not config.index and config.create_index:
+            config.index = VectorStoreIndex(nodes, embed_model=MockEmbedding(embed_dim=1))
+
         return DynamicBM25Retriever(nodes=nodes, **config.model_dump())
 
     def _create_chroma_retriever(self, config: ChromaRetrieverConfig, **kwargs) -> ChromaRetriever:

metagpt/rag/schema.py

@@ -60,6 +60,11 @@ class FAISSRetrieverConfig(IndexRetrieverConfig):
 class BM25RetrieverConfig(IndexRetrieverConfig):
     """Config for BM25-based retrievers."""
 
+    create_index: bool = Field(
+        default=False,
+        description="Indicates whether to create an index for the nodes. It is useful when you need to persist data while only using BM25.",
+        exclude=True,
+    )
     _no_embedding: bool = PrivateAttr(default=True)
 
 

tests/metagpt/exp_pool/test_manager.py

@@ -3,7 +3,7 @@ import pytest
 from metagpt.config2 import Config
 from metagpt.configs.exp_pool_config import (
     ExperiencePoolConfig,
-    ExperiencePoolStorageType,
+    ExperiencePoolRetrievalType,
 )
 from metagpt.configs.llm_config import LLMConfig
 from metagpt.exp_pool.manager import Experience, ExperienceManager
@@ -16,7 +16,7 @@ class TestExperienceManager:
         return Config(
             llm=LLMConfig(),
             exp_pool=ExperiencePoolConfig(
-                enable_write=True, enable_read=True, enabled=True, storage_type=ExperiencePoolStorageType.BM25
+                enable_write=True, enable_read=True, enabled=True, retrieval_type=ExperiencePoolRetrievalType.BM25
             ),
         )
 
@@ -96,7 +96,7 @@ class TestExperienceManager:
         assert exp_manager.get_exps_count() == 10
 
     def test_resolve_storage_bm25(self, mocker, mock_config):
-        mock_config.exp_pool.storage_type = ExperiencePoolStorageType.BM25
+        mock_config.exp_pool.retrieval_type = ExperiencePoolRetrievalType.BM25
         mocker.patch.object(ExperienceManager, "_create_bm25_storage", return_value=mocker.MagicMock())
         manager = ExperienceManager(config=mock_config)
         storage = manager._resolve_storage()
@@ -104,7 +104,7 @@ class TestExperienceManager:
         assert storage is not None
 
     def test_resolve_storage_chroma(self, mocker, mock_config):
-        mock_config.exp_pool.storage_type = ExperiencePoolStorageType.CHROMA
+        mock_config.exp_pool.retrieval_type = ExperiencePoolRetrievalType.CHROMA
         mocker.patch.object(ExperienceManager, "_create_chroma_storage", return_value=mocker.MagicMock())
         manager = ExperienceManager(config=mock_config)
         storage = manager._resolve_storage()

tests/metagpt/rag/engines/test_simple.py

@@ -75,7 +75,7 @@ class TestSimpleEngine:
         )
 
         # Assert
-        mock_simple_directory_reader.assert_called_once_with(input_dir=input_dir, input_files=input_files)
+        mock_simple_directory_reader.assert_called_once_with(input_dir=input_dir, input_files=input_files, fs=None)
         mock_get_retriever.assert_called_once()
         mock_get_rankers.assert_called_once()
         mock_get_response_synthesizer.assert_called_once_with(llm=llm)

tests/metagpt/rag/factories/test_embedding.py

@@ -1,5 +1,6 @@
 import pytest
 
+from metagpt.config2 import Config
 from metagpt.configs.embedding_config import EmbeddingType
 from metagpt.configs.llm_config import LLMType
 from metagpt.rag.factories.embedding import RAGEmbeddingFactory
@@ -12,7 +13,10 @@ class TestRAGEmbeddingFactory:
 
     @pytest.fixture
     def mock_config(self, mocker):
-        return mocker.patch("metagpt.rag.factories.embedding.config")
+        config = Config.default().model_copy(deep=True)
+        default = mocker.patch("metagpt.config2.Config.default")
+        default.return_value = config
+        return config
 
     @staticmethod
     def mock_openai_embedding(mocker):