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使用模式

从索引构建查询引擎:

query_engine = index.as_query_engine()

向您的数据提问

response = query_engine.query("Who is Paul Graham?")

你可以用一行代码直接从索引中构建并配置一个查询引擎:

query_engine = index.as_query_engine(
response_mode="tree_summarize",
verbose=True,
)

注意:虽然高级API优化了易用性,但它并未暴露完整的可配置范围。

查看响应模式获取完整的响应模式列表及其功能说明。

如果您需要更细粒度的控制,可以使用底层组合API。 具体来说,您需要显式构造一个QueryEngine对象,而不是调用index.as_query_engine(...)

注意:您可能需要查阅API参考或示例笔记本。

from llama_index.core import VectorStoreIndex, get_response_synthesizer
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
# build index
index = VectorStoreIndex.from_documents(documents)
# configure retriever
retriever = VectorIndexRetriever(
index=index,
similarity_top_k=2,
)
# configure response synthesizer
response_synthesizer = get_response_synthesizer(
response_mode="tree_summarize",
)
# assemble query engine
query_engine = RetrieverQueryEngine(
retriever=retriever,
response_synthesizer=response_synthesizer,
)
# query
response = query_engine.query("What did the author do growing up?")
print(response)

要启用流式传输,您只需传入一个 streaming=True 标志

query_engine = index.as_query_engine(
streaming=True,
)
streaming_response = query_engine.query(
"What did the author do growing up?",
)
streaming_response.print_response_stream()

您也可以定义一个自定义查询引擎。只需继承 CustomQueryEngine 类,定义您希望拥有的任何属性(类似于定义 Pydantic 类),并实现一个 custom_query 函数,该函数返回一个 Response 对象或字符串。

from llama_index.core.query_engine import CustomQueryEngine
from llama_index.core.retrievers import BaseRetriever
from llama_index.core import get_response_synthesizer
from llama_index.core.response_synthesizers import BaseSynthesizer
class RAGQueryEngine(CustomQueryEngine):
"""RAG Query Engine."""
retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)
response_obj = self.response_synthesizer.synthesize(query_str, nodes)
return response_obj

查看自定义查询引擎指南获取更多详情。