please show me an example of how to make a customqueryengine with reranker an dnode post p

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI

# Load documents and create index
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents=documents)

# Create reranker
reranker = LLMRerank(
    top_n=2,
    choice_batch_size=5
)

# Create query engine with reranker as node postprocessor
query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[reranker],
    response_mode="tree_summarize"
)

# Use the query engine
response = query_engine.query("Your question here")

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

retriever = index.as_retriever()
synthesizer = get_response_synthesizer(response_mode="compact")
query_engine = RAGQueryEngine(
    retriever=retriever, 
    response_synthesizer=synthesizer
)

response = query_engine.query("Your question here")

   document.excluded_llm_metadata_keys = ["file_name"]
   

   from llama_index.core.schema import MetadataMode
   print(document.get_content(metadata_mode=MetadataMode.LLM))
   

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.tools import QueryEngineTool
from llama_index.llms.openai import OpenAI
import asyncio

# Assuming you already have a query_engine

# Create a QueryEngineTool from your query engine
query_engine_tool = QueryEngineTool.from_defaults(
    query_engine=query_engine,
    name="document_search",
    description="Useful for answering questions about the document."
)

# Create an async function to load context and query
async def search_documents(query: str) -> str:
    """Useful for answering natural language questions about the document."""
    response = await query_engine.aquery(query)
    return str(response)

# Create an enhanced workflow with the tool
agent = AgentWorkflow.from_tools_or_functions(
    [search_documents],
    llm=OpenAI(model="gpt-4"),
    verbose=True
)

# Now you can use the agent asynchronously
async def main():
    response = await agent.run(
        "Your question about the document here"
    )
    print(response)

# Run the agent
if __name__ == "__main__":
    asyncio.run(main())

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.postprocessor import SimilarityPostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle

# Assuming you have already created an index
index = VectorStoreIndex.from_documents(documents)

# Configure retriever
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=10,
)

# Configure node postprocessors
node_postprocessors = [
    SimilarityPostprocessor(similarity_cutoff=0.7)
]

# Create a query
query = "Your query here"
query_bundle = QueryBundle(query)

# Retrieve nodes
retrieved_nodes = retriever.retrieve(query_bundle)

# Apply node postprocessors
for postprocessor in node_postprocessors:
    retrieved_nodes = postprocessor.postprocess_nodes(
        retrieved_nodes, query_bundle=query_bundle
    )

# Process the retrieved and postprocessed nodes as needed
for node in retrieved_nodes:
    print(f"Node content: {node.node.get_content()}")
    print(f"Node score: {node.score}")
    print(f"Node metadata: {node.node.metadata}")
    print("---")

from llama_index.core.retrievers import BaseRetriever
from llama_index.core.schema import NodeWithScore, QueryBundle

class CustomRetriever(BaseRetriever):
    def __init__(self, index):
        self.index = index

    def retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
        # Implement your custom retrieval logic here
        # ...
        return retrieved_nodes

   retriever = index.as_retriever(similarity_top_k=10)  # Increase this number as needed
   

   query_engine = index.as_query_engine(
       retriever=retriever,
       response_mode="tree_summarize"  # or "refine"
   )
   

   from llama_index.core.response_synthesizers import TreeSummarize

   summarizer = TreeSummarize(
       verbose=True,
       summary_template=(
           "Provide a detailed summary of the following context:\n"
           "---------------------\n"
           "{context_str}\n"
           "---------------------\n"
           "Detailed Summary: "
       )
   )

   query_engine = index.as_query_engine(
       retriever=retriever,
       response_synthesizer=summarizer
   )
   

   from llama_index.llms import OpenAI

   llm = OpenAI(model="gpt-4", temperature=0.2)
   query_engine = index.as_query_engine(
       retriever=retriever,
       llm=llm
   )
   

Here's an example that combines these approaches: