Merge branch 'feat-exp-pool-bm25' into 'mgx_ops'

Feat exp pool bm25

See merge request pub/MetaGPT!327

metagpt/configs/exp_pool_config.py

@@ -1,8 +1,15 @@
+from enum import Enum
+
 from pydantic import Field
 
 from metagpt.utils.yaml_model import YamlModel
 
 
+class ExperiencePoolRetrievalType(Enum):
+    BM25 = "bm25"
+    CHROMA = "chroma"
+
+
 class ExperiencePoolConfig(YamlModel):
     enabled: bool = Field(
         default=False,
@@ -11,3 +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.")
+    retrieval_type: ExperiencePoolRetrievalType = Field(
+        default=ExperiencePoolRetrievalType.BM25, description="The retrieval type for experience pool."
+    )

metagpt/exp_pool/decorator.py

@@ -134,14 +134,14 @@ 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

@@ -1,10 +1,12 @@
 """Experience Manager."""
 
+from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
 from metagpt.config2 import Config
+from metagpt.configs.exp_pool_config import ExperiencePoolRetrievalType
 from metagpt.exp_pool.schema import (
     DEFAULT_COLLECTION_NAME,
     DEFAULT_SIMILARITY_TOP_K,
@@ -15,7 +17,7 @@ from metagpt.logs import logger
 from metagpt.utils.exceptions import handle_exception
 
 if TYPE_CHECKING:
-    from llama_index.vector_stores.chroma import ChromaVectorStore
+    from metagpt.rag.engines import SimpleEngine
 
 
 class ExperienceManager(BaseModel):
@@ -32,40 +34,16 @@ class ExperienceManager(BaseModel):
     config: Config = Field(default_factory=Config.default)
 
     _storage: Any = None
-    _vector_store: Any = None
 
     @property
     def storage(self):
         if self._storage is None:
-            try:
-                from metagpt.rag.engines import SimpleEngine
-                from metagpt.rag.schema import ChromaRetrieverConfig, LLMRankerConfig
-            except ImportError:
-                raise ImportError("To use the experience pool, you need to install the rag module.")
-
-            retriever_configs = [
-                ChromaRetrieverConfig(
-                    persist_path=self.config.exp_pool.persist_path,
-                    collection_name=DEFAULT_COLLECTION_NAME,
-                    similarity_top_k=DEFAULT_SIMILARITY_TOP_K,
-                )
-            ]
-            ranker_configs = [LLMRankerConfig(top_n=DEFAULT_SIMILARITY_TOP_K)]
-
-            self._storage: SimpleEngine = SimpleEngine.from_objs(
-                retriever_configs=retriever_configs, ranker_configs=ranker_configs
-            )
             logger.info(f"exp_pool config: {self.config.exp_pool}")
 
+            self._storage = self._resolve_storage()
+
         return self._storage
 
-    @property
-    def vector_store(self):
-        if not self._vector_store:
-            self._vector_store: ChromaVectorStore = self.storage._retriever._vector_store
-
-        return self._vector_store
-
     @handle_exception
     def create_exp(self, exp: Experience):
         """Adds an experience to the storage if writing is enabled.
@@ -78,6 +56,7 @@ class ExperienceManager(BaseModel):
             return
 
         self.storage.add_objs([exp])
+        self.storage.persist(self.config.exp_pool.persist_path)
 
     @handle_exception(default_return=[])
     async def query_exps(self, req: str, tag: str = "", query_type: QueryType = QueryType.SEMANTIC) -> list[Experience]:
@@ -110,7 +89,92 @@ class ExperienceManager(BaseModel):
     def get_exps_count(self) -> int:
         """Get the total number of experiences."""
 
-        return self.vector_store._collection.count()
+        return self.storage.count()
+
+    def _resolve_storage(self) -> "SimpleEngine":
+        """Selects the appropriate storage creation method based on the configured retrieval type."""
+
+        storage_creators = {
+            ExperiencePoolRetrievalType.BM25: self._create_bm25_storage,
+            ExperiencePoolRetrievalType.CHROMA: self._create_chroma_storage,
+        }
+
+        return storage_creators[self.config.exp_pool.retrieval_type]()
+
+    def _create_bm25_storage(self) -> "SimpleEngine":
+        """Creates or loads BM25 storage.
+
+        This function attempts to create a new BM25 storage if the specified
+        document store path does not exist. If the path exists, it loads the
+        existing BM25 storage.
+
+        Returns:
+            SimpleEngine: An instance of SimpleEngine configured with BM25 storage.
+
+        Raises:
+            ImportError: If required modules are not installed.
+        """
+
+        try:
+            from metagpt.rag.engines import SimpleEngine
+            from metagpt.rag.schema import (
+                BM25IndexConfig,
+                BM25RetrieverConfig,
+                LLMRankerConfig,
+            )
+        except ImportError:
+            raise ImportError("To use the experience pool, you need to install the rag module.")
+
+        persist_path = Path(self.config.exp_pool.persist_path)
+        docstore_path = persist_path / "docstore.json"
+
+        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")]
+
+            retriever_configs = [BM25RetrieverConfig(create_index=True)]
+            ranker_configs = [LLMRankerConfig(top_n=DEFAULT_SIMILARITY_TOP_K)]
+
+            storage = SimpleEngine.from_objs(
+                objs=exps, retriever_configs=retriever_configs, ranker_configs=ranker_configs
+            )
+            return storage
+
+        logger.debug(f"Path `{docstore_path}` exists, try to load bm25 storage.")
+        storage = SimpleEngine.from_index(
+            BM25IndexConfig(persist_path=persist_path), retriever_configs=[BM25RetrieverConfig()]
+        )
+
+        return storage
+
+    def _create_chroma_storage(self) -> "SimpleEngine":
+        """Creates Chroma storage.
+
+        Returns:
+            SimpleEngine: An instance of SimpleEngine configured with Chroma storage.
+
+        Raises:
+            ImportError: If required modules are not installed.
+        """
+
+        try:
+            from metagpt.rag.engines import SimpleEngine
+            from metagpt.rag.schema import ChromaRetrieverConfig, LLMRankerConfig
+        except ImportError:
+            raise ImportError("To use the experience pool, you need to install the rag module.")
+
+        retriever_configs = [
+            ChromaRetrieverConfig(
+                persist_path=self.config.exp_pool.persist_path,
+                collection_name=DEFAULT_COLLECTION_NAME,
+                similarity_top_k=DEFAULT_SIMILARITY_TOP_K,
+            )
+        ]
+        ranker_configs = [LLMRankerConfig(top_n=DEFAULT_SIMILARITY_TOP_K)]
+
+        storage = SimpleEngine.from_objs(retriever_configs=retriever_configs, ranker_configs=ranker_configs)
+
+        return storage
 
 
 _exp_manager = None

metagpt/rag/engines/simple.py

@@ -37,7 +37,11 @@ from metagpt.rag.factories import (
     get_retriever,
 )
 from metagpt.rag.interface import NoEmbedding, RAGObject
-from metagpt.rag.retrievers.base import ModifiableRAGRetriever, PersistableRAGRetriever
+from metagpt.rag.retrievers.base import (
+    ModifiableRAGRetriever,
+    PersistableRAGRetriever,
+    QueryableRAGRetriever,
+)
 from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever
 from metagpt.rag.schema import (
     BaseIndexConfig,
@@ -144,7 +148,7 @@ class SimpleEngine(RetrieverQueryEngine):
         if not objs and any(isinstance(config, BM25RetrieverConfig) for config in retriever_configs):
             raise ValueError("In BM25RetrieverConfig, Objs must not be empty.")
 
-        nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
+        nodes = cls.get_obj_nodes(objs)
 
         return cls._from_nodes(
             nodes=nodes,
@@ -201,7 +205,7 @@ class SimpleEngine(RetrieverQueryEngine):
         """Adds objects to the retriever, storing each object's original form in metadata for future reference."""
         self._ensure_retriever_modifiable()
 
-        nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
+        nodes = self.get_obj_nodes(objs)
         self._save_nodes(nodes)
 
     def persist(self, persist_dir: Union[str, os.PathLike], **kwargs):
@@ -210,6 +214,18 @@ class SimpleEngine(RetrieverQueryEngine):
 
         self._persist(str(persist_dir), **kwargs)
 
+    def count(self) -> int:
+        """Count."""
+        self._ensure_retriever_queryable()
+
+        return self._retriever.query_total_count()
+
+    @staticmethod
+    def get_obj_nodes(objs: Optional[list[RAGObject]] = None) -> list[ObjectNode]:
+        """Converts a list of RAGObjects to a list of ObjectNodes."""
+
+        return [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
+
     @classmethod
     def _from_nodes(
         cls,
@@ -258,6 +274,9 @@ class SimpleEngine(RetrieverQueryEngine):
     def _ensure_retriever_persistable(self):
         self._ensure_retriever_of_type(PersistableRAGRetriever)
 
+    def _ensure_retriever_queryable(self):
+        self._ensure_retriever_of_type(QueryableRAGRetriever)
+
     def _ensure_retriever_of_type(self, required_type: BaseRetriever):
         """Ensure that self.retriever is required_type, or at least one of its components, if it's a SimpleHybridRetriever.
 

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
@@ -85,6 +86,12 @@ class RetrieverFactory(ConfigBasedFactory):
         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/retrievers/base.py

@@ -45,3 +45,17 @@ class PersistableRAGRetriever(RAGRetriever):
     @abstractmethod
     def persist(self, persist_dir: str, **kwargs) -> None:
         """To support persist, must inplement this func"""
+
+
+class QueryableRAGRetriever(RAGRetriever):
+    """Support querying total count."""
+
+    @classmethod
+    def __subclasshook__(cls, C):
+        if cls is QueryableRAGRetriever:
+            return check_methods(C, "query_total_count")
+        return NotImplemented
+
+    @abstractmethod
+    def query_total_count(self) -> int:
+        """To support querying total count, must implement this func"""

metagpt/rag/retrievers/bm25_retriever.py

@@ -47,3 +47,8 @@ class DynamicBM25Retriever(BM25Retriever):
         """Support persist."""
         if self._index:
             self._index.storage_context.persist(persist_dir)
+
+    def query_total_count(self) -> int:
+        """Support query total count."""
+
+        return len(self._nodes)

metagpt/rag/retrievers/chroma_retriever.py

@@ -2,6 +2,7 @@
 
 from llama_index.core.retrievers import VectorIndexRetriever
 from llama_index.core.schema import BaseNode
+from llama_index.vector_stores.chroma import ChromaVectorStore
 
 
 class ChromaRetriever(VectorIndexRetriever):
@@ -15,3 +16,10 @@ class ChromaRetriever(VectorIndexRetriever):
         """Support persist.
 
         Chromadb automatically saves, so there is no need to implement."""
+
+    def query_total_count(self) -> int:
+        """Support query total count."""
+
+        vector_store: ChromaVectorStore = self._vector_store
+
+        return vector_store._collection.count()

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)