Usage Pattern#
Get Started#
Build a query engine from index:
query_engine = index.as_query_engine()
Tip
To learn how to build an index, see Indexing
Ask a question over your data
response = query_engine.query("Who is Paul Graham?")
Configuring a Query Engine#
High-Level API#
You can directly build and configure a query engine from an index in 1 line of code:
query_engine = index.as_query_engine(
response_mode="tree_summarize",
verbose=True,
)
Note: While the high-level API optimizes for ease-of-use, it does NOT expose full range of configurability.
See Response Modes for a full list of response modes and what they do.
Low-Level Composition API#
You can use the low-level composition API if you need more granular control.
Concretely speaking, you would explicitly construct a QueryEngine
object instead of calling index.as_query_engine(...)
.
Note: You may need to look at API references or example notebooks.
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)
Streaming#
To enable streaming, you simply need to pass in a streaming=True
flag
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()
Read the full streaming guide
See an end-to-end example
Defining a Custom Query Engine#
You can also define a custom query engine. Simply subclass the CustomQueryEngine
class, define any attributes you’d want to have (similar to defining a Pydantic class), and implement a custom_query
function that returns either a Response
object or a string.
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
See the Custom Query Engine guide for more details.