from objs

metagpt/rag/engines/simple.py

@@ -38,14 +38,16 @@ from metagpt.rag.schema import (
     BaseIndexConfig,
     BaseRankerConfig,
     BaseRetrieverConfig,
+    BM25RetrieverConfig,
     ObjectNode,
 )
 from metagpt.utils.common import import_class
 
 
 class SimpleEngine(RetrieverQueryEngine):
-    """
-    SimpleEngine is a lightweight and easy-to-use search engine that integrates
+    """SimpleEngine is designed to be simple and straightforward.
+
+    It is a lightweight and easy-to-use search engine that integrates
     document reading, embedding, indexing, retrieving, and ranking functionalities
     into a single, straightforward workflow. It is designed to quickly set up a
     search engine from a collection of documents.
@@ -78,7 +80,9 @@ class SimpleEngine(RetrieverQueryEngine):
         retriever_configs: list[BaseRetrieverConfig] = None,
         ranker_configs: list[BaseRankerConfig] = None,
     ) -> "SimpleEngine":
-        """This engine is designed to be simple and straightforward
+        """From docs.
+
+        Must provide either `input_dir` or `input_files`.
 
         Args:
             input_dir: Path to the directory.
@@ -89,6 +93,9 @@ class SimpleEngine(RetrieverQueryEngine):
             retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever.
             ranker_configs: Configuration for rankers.
         """
+        if not input_dir and not input_files:
+            raise ValueError("Must provide either `input_dir` or `input_files`.")
+
         documents = SimpleDirectoryReader(input_dir=input_dir, input_files=input_files).load_data()
         index = VectorStoreIndex.from_documents(
             documents=documents,
@@ -97,6 +104,39 @@ class SimpleEngine(RetrieverQueryEngine):
         )
         return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
 
+    @classmethod
+    def from_objs(
+        cls,
+        objs: Optional[list[RAGObject]] = None,
+        transformations: Optional[list[TransformComponent]] = None,
+        embed_model: BaseEmbedding = None,
+        llm: LLM = None,
+        retriever_configs: list[BaseRetrieverConfig] = None,
+        ranker_configs: list[BaseRankerConfig] = None,
+    ) -> "SimpleEngine":
+        """From objs.
+
+        Args:
+            objs: List of RAGObject.
+            transformations: Parse documents to nodes. Default [SentenceSplitter].
+            embed_model: Parse nodes to embedding. Must supported by llama index. Default OpenAIEmbedding.
+            llm: Must supported by llama index. Default OpenAI.
+            retriever_configs: Configuration for retrievers. If more than one config, will use SimpleHybridRetriever.
+            ranker_configs: Configuration for rankers.
+        """
+        # check
+        if not retriever_configs or any(isinstance(config, BM25RetrieverConfig) for config in retriever_configs):
+            raise ValueError("Must provide retriever_configs, and BM25RetrieverConfig is not supported.")
+
+        objs = objs or []
+        nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
+        index = VectorStoreIndex(
+            nodes=nodes,
+            transformations=transformations or [SentenceSplitter()],
+            embed_model=embed_model or get_rag_embedding(),
+        )
+        return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
+
     @classmethod
     def from_index(
         cls,
@@ -110,25 +150,6 @@ class SimpleEngine(RetrieverQueryEngine):
         index = get_index(index_config, embed_model=embed_model or get_rag_embedding())
         return cls._from_index(index, llm=llm, retriever_configs=retriever_configs, ranker_configs=ranker_configs)
 
-    @classmethod
-    def _from_index(
-        cls,
-        index: BaseIndex,
-        llm: LLM = None,
-        retriever_configs: list[BaseRetrieverConfig] = None,
-        ranker_configs: list[BaseRankerConfig] = None,
-    ) -> "SimpleEngine":
-        llm = llm or get_rag_llm()
-        retriever = get_retriever(configs=retriever_configs, index=index)
-        rankers = get_rankers(configs=ranker_configs, llm=llm)
-
-        return cls(
-            retriever=retriever,
-            node_postprocessors=rankers,
-            response_synthesizer=get_response_synthesizer(llm=llm),
-            index=index,
-        )
-
     async def asearch(self, content: str, **kwargs) -> str:
         """Inplement tools.SearchInterface"""
         return await self.aquery(content)
@@ -156,6 +177,25 @@ class SimpleEngine(RetrieverQueryEngine):
         nodes = [ObjectNode(text=obj.rag_key(), metadata=ObjectNode.get_obj_metadata(obj)) for obj in objs]
         self._save_nodes(nodes)
 
+    @classmethod
+    def _from_index(
+        cls,
+        index: BaseIndex,
+        llm: LLM = None,
+        retriever_configs: list[BaseRetrieverConfig] = None,
+        ranker_configs: list[BaseRankerConfig] = None,
+    ) -> "SimpleEngine":
+        llm = llm or get_rag_llm()
+        retriever = get_retriever(configs=retriever_configs, index=index)  # Default index.as_retriever
+        rankers = get_rankers(configs=ranker_configs, llm=llm)  # Default []
+
+        return cls(
+            retriever=retriever,
+            node_postprocessors=rankers,
+            response_synthesizer=get_response_synthesizer(llm=llm),
+            index=index,
+        )
+
     def _ensure_retriever_modifiable(self):
         if not isinstance(self.retriever, ModifiableRAGRetriever):
             raise TypeError(f"the retriever is not modifiable: {type(self.retriever)}")