[Question]: while using SQLAutoVectorQueryEngine as query engine, how to stream responses from openai llm? about llama_index HOT 7 CLOSED

from llama_index.llms.openai import OpenAI
from llama_index.core.query_engine import SQLAutoVectorQueryEngine
from llama_index.core import get_response_synthesizer

# Initialize the OpenAI LLM with streaming enabled
llm = OpenAI(model="gpt-4", stream=True)

# Create a response synthesizer with streaming enabled
synth = get_response_synthesizer(streaming=True)

# Define the SQLAutoVectorQueryEngine with the streaming LLM and response synthesizer
query_engine = SQLAutoVectorQueryEngine(
    sql_tool, vector_tool, llm=llm, response_synthesizer=synth
)

# Query the engine and handle the streaming response
streaming_response = query_engine.query(
    "Tell me about the arts and culture of the city with the highest population"
)

# Print the streaming response as it arrives
streaming_response.print_response_stream()

query_engine = index.as_query_engine(streaming=True, similarity_top_k=1)

from llama_index.core import get_response_synthesizer

synth = get_response_synthesizer(streaming=True, ...)
query_engine = RetrieverQueryEngine(response_synthesizer=synth, ...)

streaming_response = query_engine.query(
    "What did the author do growing up?",
)

for text in streaming_response.response_gen:
    # do something with text as they arrive.
    pass

streaming_response.print_response_stream()

from llama_index.legacy.query_engine.sql_vector_query_engine import SQLAutoVectorQueryEngine
from llama_index.legacy.indices.struct_store.sql_query import BaseSQLTableQueryEngine
from llama_index.legacy.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.legacy.tools.query_engine import QueryEngineTool

sql_query_engine = BaseSQLTableQueryEngine(...)
vector_query_engine = RetrieverQueryEngine(...)

sql_query_tool = QueryEngineTool.from_defaults(
    sql_query_engine, name="SQL Tool", description="SQL Query Engine Tool"
)
vector_query_tool = QueryEngineTool.from_defaults(
    vector_query_engine, name="Vector Tool", description="Vector Query Engine Tool"
)

query_engine = SQLAutoVectorQueryEngine.from_sql_and_vector_query_engines(
    sql_query_engine=sql_query_engine,
    sql_tool_name="SQL Tool",
    sql_tool_description="SQL Query Engine Tool",
    vector_auto_retriever=vector_query_engine,
    vector_tool_name="Vector Tool",
    vector_tool_description="Vector Query Engine Tool",
    streaming=True
)

from llama_index.llms.openai import OpenAI

# Initialize the OpenAI LLM with streaming enabled
llm = OpenAI(model="gpt-4", stream=True)

from llama_index.legacy.indices.struct_store.sql_query import BaseSQLTableQueryEngine
from llama_index.legacy.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.legacy.indices.vector_store.retrievers.auto_retriever import VectorIndexAutoRetriever

# Create SQL query engine
sql_query_engine = BaseSQLTableQueryEngine(...)

# Create vector auto retriever
vector_auto_retriever = VectorIndexAutoRetriever(...)

# Create vector query engine
vector_query_engine = RetrieverQueryEngine(retriever=vector_auto_retriever, llm=llm)

from llama_index.legacy.tools.query_engine import QueryEngineTool

sql_query_tool = QueryEngineTool.from_defaults(
    sql_query_engine, name="SQL Tool", description="SQL Query Engine Tool"
)
vector_query_tool = QueryEngineTool.from_defaults(
    vector_query_engine, name="Vector Tool", description="Vector Query Engine Tool"
)

from llama_index.legacy.query_engine.sql_vector_query_engine import SQLAutoVectorQueryEngine

query_engine = SQLAutoVectorQueryEngine.from_sql_and_vector_query_engines(
    sql_query_engine=sql_query_engine,
    sql_tool_name="SQL Tool",
    sql_tool_description="SQL Query Engine Tool",
    vector_auto_retriever=vector_query_engine,
    vector_tool_name="Vector Tool",
    vector_tool_description="Vector Query Engine Tool",
    streaming=True  # Enable streaming
)

streaming_response = query_engine.query(
    "Tell me about the arts and culture of the city with the highest population"
)

# Print the streaming response as it arrives
for text in streaming_response.response_gen:
    print(text, end="")

from llama_index.core import get_response_synthesizer
from llama_index.query_engine import SQLAutoVectorQueryEngine
from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.core.indices.struct_store.sql_query import BaseSQLTableQueryEngine
from llama_index.legacy.tools.query_engine import QueryEngineTool
from llama_index.llms.openai import OpenAI

# Set up the OpenAI LLM with streaming enabled
llm = OpenAI(model="gpt-3.5-turbo")

# Construct the response synthesizer with streaming enabled
synth = get_response_synthesizer(streaming=True, llm=llm, ...)

# Create the SQL and vector query engines
sql_query_engine = BaseSQLTableQueryEngine(...)
vector_query_engine = RetrieverQueryEngine(...)

# Create the SQLAutoVectorQueryEngine using the class method
query_engine = SQLAutoVectorQueryEngine.from_sql_and_vector_query_engines(
    sql_query_engine=sql_query_engine,
    sql_tool_name="SQL Tool",
    sql_tool_description="Description of SQL Tool",
    vector_auto_retriever=vector_query_engine,
    vector_tool_name="Vector Tool",
    vector_tool_description="Description of Vector Tool",
    response_synthesizer=synth,
    ...
)

# Perform a query and get a streaming response
streaming_response = query_engine.query("What did the author do growing up?")

# Iterate over the tokens as they arrive
for text in streaming_response.response_gen:
    # do something with text as they arrive.
    pass

# Alternatively, print the text as they arrive
streaming_response.print_response_stream()