Bases: BaseLlamaPack
自动合并检索器包。
从一组文档构建一个分层节点图，并运行我们的自动合并检索器。
Source code in llama_index/packs/auto_merging_retriever/base.py
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66 | class AutoMergingRetrieverPack(BaseLlamaPack):
"""自动合并检索器包。
从一组文档构建一个分层节点图，并运行我们的自动合并检索器。"""
def __init__(
self,
docs: List[Document] = None,
**kwargs: Any,
) -> None:
"""初始化参数。"""
# create the sentence window node parser w/ default settings
self.node_parser = HierarchicalNodeParser.from_defaults()
nodes = self.node_parser.get_nodes_from_documents(docs)
leaf_nodes = get_leaf_nodes(nodes)
docstore = SimpleDocumentStore()
# insert nodes into docstore
docstore.add_documents(nodes)
# define storage context (will include vector store by default too)
storage_context = StorageContext.from_defaults(docstore=docstore)
service_context = ServiceContext.from_defaults(
llm=OpenAI(model="gpt-3.5-turbo")
)
self.base_index = VectorStoreIndex(
leaf_nodes,
storage_context=storage_context,
service_context=service_context,
)
base_retriever = self.base_index.as_retriever(similarity_top_k=6)
self.retriever = AutoMergingRetriever(
base_retriever, storage_context, verbose=True
)
self.query_engine = RetrieverQueryEngine.from_args(self.retriever)
def get_modules(self) -> Dict[str, Any]:
"""获取模块。"""
return {
"node_parser": self.node_parser,
"retriever": self.retriever,
"query_engine": self.query_engine,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""运行流水线。"""
return self.query_engine.query(*args, **kwargs)
|
get_modules
get_modules() -> Dict[str, Any]
获取模块。
Source code in llama_index/packs/auto_merging_retriever/base.py
| def get_modules(self) -> Dict[str, Any]:
"""获取模块。"""
return {
"node_parser": self.node_parser,
"retriever": self.retriever,
"query_engine": self.query_engine,
}
|
run
run(*args: Any, **kwargs: Any) -> Any
运行流水线。
Source code in llama_index/packs/auto_merging_retriever/base.py
| def run(self, *args: Any, **kwargs: Any) -> Any:
"""运行流水线。"""
return self.query_engine.query(*args, **kwargs)
|