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 Index

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 import (
    VectorStoreIndex,
    get_response_synthesizer,
)
from llama_index.retrievers import VectorIndexRetriever
from llama_index.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() 

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.query_engine import CustomQueryEngine
from llama_index.retrievers import BaseRetriever
from llama_index.response_synthesizers import get_response_synthesizer, 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.