RAGObject interface add model_dump method; modify by pylint

metagpt/rag/engines/simple.py

@@ -87,9 +87,9 @@ class SimpleEngine(RetrieverQueryEngine):
         """Inplement tools.SearchInterface"""
         return await self.aquery(content)
 
-    async def aretrieve(self, query: QueryType) -> list[NodeWithScore]:
+    async def aretrieve(self, query_bundle: QueryType) -> list[NodeWithScore]:
         """Allow query to be str"""
-        query_bundle = QueryBundle(query) if isinstance(query, str) else query
+        query_bundle = QueryBundle(query_bundle) if isinstance(query_bundle, str) else query_bundle
         return await super().aretrieve(query_bundle)
 
     def add_docs(self, input_files: list[str]):
@@ -104,7 +104,7 @@ class SimpleEngine(RetrieverQueryEngine):
         """Adds objects to the retriever, storing each object's original form in metadata for future reference."""
         self._ensure_retriever_modifiable()
 
-        nodes = [TextNode(text=obj.rag_key(), metadata={"obj": obj}) for obj in objs]
+        nodes = [TextNode(text=obj.rag_key(), metadata={"obj": obj.model_dump()}) for obj in objs]
         self.retriever.add_nodes(nodes)
 
     def _ensure_retriever_modifiable(self):

metagpt/rag/factory.py

@@ -28,16 +28,11 @@ class BaseFactory:
     """
 
     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.
-        """
+        """Creators is a dictionary mapping configuration types to creator functions."""
         self.creators = creators
 
     def get_instances(self, configs: list[Any] = None, **kwargs) -> list[Any]:
-        if not configs:
-            return [self._default_instance(**kwargs)]
-
+        """Get instances by configs"""
         return [self._get_instance(config, **kwargs) for config in configs]
 
     def _get_instance(self, config: Any, **kwargs) -> Any:
@@ -47,13 +42,11 @@ class BaseFactory:
 
         raise ValueError(f"Unknown config: {config}")
 
-    def _default_instance(self, **kwargs) -> Any:
-        raise NotImplementedError("This method should be implemented by subclasses.")
-
 
 class RetrieverFactory(BaseFactory):
+    """Modify creators for dynamically instance implementation"""
+
     def __init__(self):
-        # Dynamically add configuration and corresponding instance implementation.
         creators = {
             FAISSRetrieverConfig: self._create_faiss_retriever,
             BM25RetrieverConfig: self._create_bm25_retriever,
@@ -61,7 +54,12 @@ class RetrieverFactory(BaseFactory):
         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."""
+        """Creates and returns a retriever instance based on the provided configurations.
+        If multiple retrievers, using SimpleHybridRetriever
+        """
+        if not configs:
+            return self._default_instance(index)
+
         retrievers = super().get_instances(configs, index=index)
 
         return (
@@ -73,7 +71,7 @@ class RetrieverFactory(BaseFactory):
     def _default_instance(self, index: BaseIndex) -> RAGRetriever:
         return index.as_retriever()
 
-    def _create_faiss_retriever(self, config: FAISSRetrieverConfig, index: BaseIndex, **kwargs) -> FAISSRetriever:
+    def _create_faiss_retriever(self, config: FAISSRetrieverConfig, index: BaseIndex) -> FAISSRetriever:
         vector_store = FaissVectorStore(faiss_index=faiss.IndexFlatL2(config.dimensions))
         storage_context = StorageContext.from_defaults(vector_store=vector_store)
         vector_index = VectorStoreIndex(
@@ -83,13 +81,14 @@ class RetrieverFactory(BaseFactory):
         )
         return FAISSRetriever(**config.model_dump(), index=vector_index)
 
-    def _create_bm25_retriever(self, config: BM25RetrieverConfig, index: BaseIndex, **kwargs) -> DynamicBM25Retriever:
+    def _create_bm25_retriever(self, config: BM25RetrieverConfig, index: BaseIndex) -> DynamicBM25Retriever:
         return DynamicBM25Retriever.from_defaults(**config.model_dump(), index=index)
 
 
 class RankerFactory(BaseFactory):
+    """Modify creators for dynamically instance implementation"""
+
     def __init__(self):
-        # Dynamically add configuration and corresponding instance implementation.
         creators = {
             LLMRankerConfig: self._create_llm_ranker,
         }
@@ -98,12 +97,16 @@ class RankerFactory(BaseFactory):
     def get_rankers(
         self, configs: list[RankerConfigType] = None, service_context: ServiceContext = None
     ) -> list[BaseNodePostprocessor]:
+        """Creates and returns a retriever instance based on the provided configurations."""
+        if not configs:
+            return [self._default_instance(service_context)]
+
         return super().get_instances(configs, service_context=service_context)
 
-    def _default_instance(self, service_context: ServiceContext = None) -> LLMRerank:
+    def _default_instance(self, service_context: ServiceContext) -> LLMRerank:
         return LLMRerank(top_n=LLMRankerConfig().top_n, service_context=service_context)
 
-    def _create_llm_ranker(self, config: LLMRankerConfig, service_context=None, **kwargs) -> LLMRerank:
+    def _create_llm_ranker(self, config: LLMRankerConfig, service_context: ServiceContext = None) -> LLMRerank:
         return LLMRerank(**config.model_dump(), service_context=service_context)
 
 

metagpt/rag/interface.py

@@ -1,6 +1,14 @@
-from typing import Protocol
+"""RAG Interface."""
+from typing import Any, Protocol
 
 
 class RAGObject(Protocol):
+    """Support rag add object"""
+
     def rag_key(self) -> str:
-        """for rag search"""
+        """For rag search."""
+
+    def model_dump(self) -> dict[str, Any]:
+        """For rag persist.
+        Pydantic Model don't need to implement this, as there is a built-in function named model_dump
+        """

metagpt/rag/llm.py

@@ -1,7 +1,11 @@
+"""RAG LLM
+The LLM of LlamaIndex and the LLM of MG are not the same. 
+"""
 from llama_index.llms import OpenAI
 
 from metagpt.config2 import config
 
 
 def get_default_llm() -> OpenAI:
+    """OpenAI"""
     return OpenAI(api_base=config.llm.base_url, api_key=config.llm.api_key, model=config.llm.model)

metagpt/rag/retrievers/bm25_retriever.py

@@ -1,9 +1,13 @@
+"""BM25 retriever."""
 from llama_index.retrievers import BM25Retriever
 from llama_index.schema import BaseNode
 
 
 class DynamicBM25Retriever(BM25Retriever):
+    """BM25 retriever."""
+
     def add_nodes(self, nodes: list[BaseNode], **kwargs):
+        """Support add nodes"""
         try:
             from rank_bm25 import BM25Okapi
         except ImportError:

metagpt/rag/retrievers/faiss_retriever.py

@@ -1,7 +1,11 @@
+"""FAISS retriever."""
 from llama_index.retrievers import VectorIndexRetriever
 from llama_index.schema import BaseNode
 
 
 class FAISSRetriever(VectorIndexRetriever):
+    """FAISS retriever."""
+
     def add_nodes(self, nodes: list[BaseNode], **kwargs):
+        """Support add nodes"""
         self._index.insert_nodes(nodes, **kwargs)

metagpt/rag/retrievers/hybrid_retriever.py

@@ -38,5 +38,6 @@ class SimpleHybridRetriever(RAGRetriever):
         return result
 
     def add_nodes(self, nodes: list[BaseNode]):
+        """Support add nodes"""
         for r in self.retrievers:
             r.add_nodes(nodes)

metagpt/rag/schema.py

@@ -1,28 +1,34 @@
-"""Retriever schemas"""
+"""RAG schemas"""
 
 from typing import Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class RetrieverConfig(BaseModel):
-    similarity_top_k: int = 5
+    """Common config for retrievers."""
+
+    similarity_top_k: int = Field(default=5, description="Number of top-k similar results to return during retrieval.")
 
 
 class FAISSRetrieverConfig(RetrieverConfig):
-    dimensions: int = 1536
+    """Config for FAISS-based retrievers."""
+
+    dimensions: int = Field(default=1536, description="Dimensionality of the vectors for FAISS index construction.")
 
 
 class BM25RetrieverConfig(RetrieverConfig):
-    ...
+    """Config for BM25-based retrievers."""
 
 
 class RankerConfig(BaseModel):
+    """Common config for rankers."""
+
     top_n: int = 5
 
 
 class LLMRankerConfig(RankerConfig):
-    ...
+    """Config for LLM-based rankers."""
 
 
 # If add new config, it is necessary to add the corresponding instance implementation in rag.factory