simplify rag factory

metagpt/rag/engines/simple.py

@@ -14,10 +14,9 @@ from llama_index.query_engine import RetrieverQueryEngine
 from llama_index.response_synthesizers import BaseSynthesizer
 from llama_index.schema import NodeWithScore, QueryBundle, QueryType
 
+from metagpt.rag.factory import get_rankers, get_retriever
 from metagpt.rag.llm import get_default_llm
-from metagpt.rag.rankers import get_rankers
-from metagpt.rag.retrievers import get_retriever
-from metagpt.rag.retrievers.base import RAGRetriever
+from metagpt.rag.retrievers.base import ModifiableRAGRetriever
 from metagpt.rag.schema import RankerConfigType, RetrieverConfigType
 from metagpt.utils.embedding import get_embedding
 
@@ -93,8 +92,10 @@ class SimpleEngine(RetrieverQueryEngine):
         return await super().aretrieve(query_bundle)
 
     def add_docs(self, input_files: list[str]):
-        """Add docs to retriever"""
+        """Add docs to retriever. retriever must has add_nodes func"""
+        if not isinstance(self.retriever, ModifiableRAGRetriever):
+            raise TypeError(f"must be inplement to add_docs: {type(self.retriever)}")
+
         documents = SimpleDirectoryReader(input_files=input_files).load_data()
         nodes = self.index.service_context.node_parser.get_nodes_from_documents(documents)
-        retriever: RAGRetriever = self.retriever
-        retriever.add_nodes(nodes)
+        self.retriever.add_nodes(nodes)

metagpt/rag/factory.py

@@ -0,0 +1,109 @@
+"""Factory for creating retriever, ranker"""
+from typing import Any, Callable
+
+import faiss
+from llama_index import ServiceContext, StorageContext, VectorStoreIndex
+from llama_index.indices.base import BaseIndex
+from llama_index.postprocessor import LLMRerank
+from llama_index.postprocessor.types import BaseNodePostprocessor
+from llama_index.vector_stores.faiss import FaissVectorStore
+
+from metagpt.rag.retrievers.base import RAGRetriever
+from metagpt.rag.retrievers.bm25_retriever import DynamicBM25Retriever
+from metagpt.rag.retrievers.faiss_retriever import FAISSRetriever
+from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever
+from metagpt.rag.schema import (
+    BM25RetrieverConfig,
+    FAISSRetrieverConfig,
+    LLMRankerConfig,
+    RankerConfigType,
+    RetrieverConfigType,
+)
+
+
+class BaseFactory:
+    """
+    A base factory class for creating instances based on provided configurations.
+    It uses a registry of creator functions mapped to configuration types to instantiate objects dynamically.
+    """
+
+    def __init__(self, creators: dict[Any, Callable]):
+        """
+        Creators is a dictionary mapping configuration types to creator functions.
+        The first arg of Creator function should be config.
+        """
+        self.creators = creators
+
+    def get_instances(self, configs: list[Any] = None, **kwargs) -> list[Any]:
+        if not configs:
+            return [self._default_instance(**kwargs)]
+
+        return [self._get_instance(config, **kwargs) for config in configs]
+
+    def _get_instance(self, config: Any, **kwargs) -> Any:
+        create_func = self.creators.get(type(config))
+        if create_func:
+            return create_func(config, **kwargs)
+
+        raise ValueError(f"Unknown config: {config}")
+
+    def _default_instance(self, **kwargs) -> Any:
+        raise NotImplementedError("This method should be implemented by subclasses.")
+
+
+class RetrieverFactory(BaseFactory):
+    def __init__(self):
+        creators = {
+            FAISSRetrieverConfig: self._create_faiss_retriever,
+            BM25RetrieverConfig: self._create_bm25_retriever,
+        }
+        super().__init__(creators)
+
+    def get_retriever(self, index: BaseIndex, configs: list[RetrieverConfigType] = None) -> RAGRetriever:
+        """Creates and returns a retriever instance based on the provided configurations."""
+        retrievers = super().get_instances(configs, index=index)
+
+        return (
+            SimpleHybridRetriever(*retrievers, service_context=index.service_context)
+            if len(retrievers) > 1
+            else retrievers[0]
+        )
+
+    def _default_instance(self, index: BaseIndex) -> RAGRetriever:
+        return index.as_retriever()
+
+    def _create_faiss_retriever(self, config: FAISSRetrieverConfig, index: BaseIndex, **kwargs) -> FAISSRetriever:
+        vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(config.dimensions))
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+        vector_index = VectorStoreIndex(
+            nodes=list(index.docstore.docs.values()),
+            storage_context=storage_context,
+            service_context=index.service_context,
+        )
+        return FAISSRetriever(**config.model_dump(), index=vector_index)
+
+    def _create_bm25_retriever(self, config: BM25RetrieverConfig, index: BaseIndex, **kwargs) -> DynamicBM25Retriever:
+        return DynamicBM25Retriever.from_defaults(**config.model_dump(), index=index)
+
+
+class RankerFactory(BaseFactory):
+    def __init__(self):
+        creators = {
+            LLMRankerConfig: self._create_llm_ranker,
+        }
+        super().__init__(creators)
+
+    def get_rankers(
+        self, configs: list[RankerConfigType] = None, service_context: ServiceContext = None
+    ) -> list[BaseNodePostprocessor]:
+        return super().get_instances(configs, service_context=service_context)
+
+    def _default_instance(self, service_context: ServiceContext = None) -> LLMRerank:
+        return LLMRerank(top_n=LLMRankerConfig().top_n, service_context=service_context)
+
+    def _create_llm_ranker(self, config: LLMRankerConfig, service_context=None, **kwargs) -> LLMRerank:
+        return LLMRerank(**config.model_dump(), service_context=service_context)
+
+
+get_retriever = RetrieverFactory().get_retriever
+get_rankers = RankerFactory().get_rankers

metagpt/rag/rankers/__init__.py

@@ -1,6 +1 @@
 """Rankers init"""
-
-from metagpt.rag.rankers.factory import get_rankers
-
-
-__all__ = ["get_rankers"]

metagpt/rag/rankers/factory.py

@@ -1,37 +0,0 @@
-"""Rankers Factory"""
-from llama_index import ServiceContext
-from llama_index.postprocessor import LLMRerank
-from llama_index.postprocessor.types import BaseNodePostprocessor
-
-from metagpt.rag.schema import LLMRankerConfig, RankerConfigType
-
-
-class RankerFactory:
-    def __init__(self):
-        self.ranker_creators = {
-            LLMRankerConfig: self._create_llm_ranker,
-        }
-
-    def get_rankers(
-        self, configs: list[RankerConfigType] = None, service_context: ServiceContext = None
-    ) -> list[BaseNodePostprocessor]:
-        if not configs:
-            return [self._default_ranker(service_context)]
-
-        return [self._get_ranker(config, service_context) for config in configs]
-
-    def _default_ranker(self, service_context: ServiceContext = None) -> LLMRerank:
-        return LLMRerank(top_n=LLMRankerConfig().top_n, service_context=service_context)
-
-    def _get_ranker(self, config: RankerConfigType, service_context: ServiceContext = None) -> BaseNodePostprocessor:
-        create_func = self.ranker_creators.get(type(config))
-        if create_func:
-            return create_func(config, service_context)
-
-        raise ValueError(f"Unknown ranker config: {config}")
-
-    def _create_llm_ranker(self, config: LLMRankerConfig, service_context=None) -> LLMRerank:
-        return LLMRerank(**config.model_dump(), service_context=service_context)
-
-
-get_rankers = RankerFactory().get_rankers

metagpt/rag/retrievers/__init__.py

@@ -1,6 +1,5 @@
 """Retrievers init"""
 
 from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever
-from metagpt.rag.retrievers.factory import get_retriever
 
-__all__ = ["SimpleHybridRetriever", "get_retriever"]
+__all__ = ["SimpleHybridRetriever"]

metagpt/rag/retrievers/base.py

@@ -17,5 +17,16 @@ class RAGRetriever(BaseRetriever):
     def _retrieve(self, query: QueryType) -> list[NodeWithScore]:
         """Retrieve nodes"""
 
+
+class ModifiableRAGRetriever(RAGRetriever):
+    """Support modification."""
+
+    @classmethod
+    def __subclasshook__(cls, C):
+        if any("add_nodes" in B.__dict__ for B in C.__mro__):
+            return True
+        return NotImplemented
+
+    @abstractmethod
     def add_nodes(self, nodes: list[BaseNode], **kwargs) -> None:
         """To support add docs, must inplement this func"""

metagpt/rag/retrievers/factory.py

@@ -1,62 +0,0 @@
-"""Retriever Factory"""
-import faiss
-from llama_index import StorageContext, VectorStoreIndex
-from llama_index.indices.base import BaseIndex
-from llama_index.vector_stores.faiss import FaissVectorStore
-
-from metagpt.rag.retrievers.base import RAGRetriever
-from metagpt.rag.retrievers.bm25_retriever import DynamicBM25Retriever
-from metagpt.rag.retrievers.faiss_retriever import FAISSRetriever
-from metagpt.rag.retrievers.hybrid_retriever import SimpleHybridRetriever
-from metagpt.rag.schema import (
-    BM25RetrieverConfig,
-    FAISSRetrieverConfig,
-    RetrieverConfigType,
-)
-
-
-class RetrieverFactory:
-    def __init__(self):
-        self.retriever_creators = {
-            FAISSRetrieverConfig: self._create_faiss_retriever,
-            BM25RetrieverConfig: self._create_bm25_retriever,
-        }
-
-    def get_retriever(self, index: BaseIndex, configs: list[RetrieverConfigType] = None) -> RAGRetriever:
-        """Creates and returns a retriever instance based on the provided configurations."""
-        if not configs:
-            return self._default_retriever(index)
-
-        retrievers = [self._get_retriever(index, config) for config in configs]
-
-        return (
-            SimpleHybridRetriever(*retrievers, service_context=index.service_context)
-            if len(retrievers) > 1
-            else retrievers[0]
-        )
-
-    def _default_retriever(self, index: BaseIndex) -> RAGRetriever:
-        return index.as_retriever()
-
-    def _get_retriever(self, index: BaseIndex, config: RetrieverConfigType) -> RAGRetriever:
-        create_func = self.retriever_creators.get(type(config))
-        if create_func:
-            return create_func(index, config)
-
-        raise ValueError(f"Unknown retriever config: {config}")
-
-    def _create_faiss_retriever(self, index: BaseIndex, config: FAISSRetrieverConfig):
-        vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(config.dimensions))
-        storage_context = StorageContext.from_defaults(vector_store=vector_store)
-        vector_index = VectorStoreIndex(
-            nodes=list(index.docstore.docs.values()),
-            storage_context=storage_context,
-            service_context=index.service_context,
-        )
-        return FAISSRetriever(vector_index, **config.model_dump())
-
-    def _create_bm25_retriever(self, index: BaseIndex, config: BM25RetrieverConfig):
-        return DynamicBM25Retriever.from_defaults(**config.model_dump(), index=index)
-
-
-get_retriever = RetrieverFactory().get_retriever