I want to build a custom retriever that accesses the rerankes nodes for multiple vector st

from llama_index.core.retrievers import BaseRetriever
from llama_index.core.schema import NodeWithScore, QueryBundle
from llama_index.postprocessor.cohere_rerank import CohereRerank

class CustomMultiVectorRetriever(BaseRetriever):
    def __init__(
        self,
        vector_retrievers,
        reranker,
        top_n=5
    ):
        self.vector_retrievers = vector_retrievers
        self.reranker = reranker
        self.top_n = top_n
        super().__init__()

    def _retrieve(self, query_bundle: QueryBundle):
        all_nodes = []
        for retriever in self.vector_retrievers:
            nodes = retriever.retrieve(query_bundle)
            all_nodes.extend(nodes)
        
        reranked_nodes = self.reranker.postprocess_nodes(
            all_nodes, query_bundle=query_bundle
        )
        
        return reranked_nodes[:self.top_n]

from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core import VectorStoreIndex

# Assume you have multiple vector store indexes
index1 = VectorStoreIndex.from_documents(documents1)
index2 = VectorStoreIndex.from_documents(documents2)

retriever1 = VectorIndexRetriever(index=index1, similarity_top_k=10)
retriever2 = VectorIndexRetriever(index=index2, similarity_top_k=10)

reranker = CohereRerank(api_key=cohere_api_key, top_n=5)

custom_retriever = CustomMultiVectorRetriever(
    vector_retrievers=[retriever1, retriever2],
    reranker=reranker,
    top_n=5
)

from llama_index.core import QueryBundle

query_str = "Your query here"
query_bundle = QueryBundle(query_str=query_str)
retrieved_nodes = custom_retriever.retrieve(query_bundle)

from llama_index.agent.openai import OpenAIAgent

top_agent = OpenAIAgent.from_tools(
    tool_retriever=custom_retriever,
    system_prompt="""\
You are an agent designed to answer queries about the documentation.
Please always use the tools provided to answer a question. Do not rely on prior knowledge.
""",
    llm=llm,
    verbose=True,
)

response = top_agent.chat("Your question here")
print(str(response))

from llama_index.core.agent import CustomSimpleAgentWorker

class MyCustomAgentWorker(CustomSimpleAgentWorker):
    def __init__(self, custom_retriever, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.custom_retriever = custom_retriever

    def run_step(self, step, task, **kwargs):
        # Use your custom retriever here
        retrieved_data = self.custom_retriever.retrieve(step.input)
        
        # Process the retrieved data and incorporate it into your step execution
        # ...

        # Then call the parent method to execute the step
        return super().run_step(step, task, **kwargs)

In both cases, you're overriding the `run_step` method to incorporate your custom retriever. You can do something similar for the `arun_step` method if you need asynchronous execution.

To use these custom workers:

   from llama_index.core.indices.query.query_transform.base import (
       StepDecomposeQueryTransform,
   )
   from llama_index.core.query_engine import MultiStepQueryEngine

   step_decompose_transform = StepDecomposeQueryTransform(llm=gpt4, verbose=True)

   query_engine = index.as_query_engine()
   query_engine = MultiStepQueryEngine(
       query_engine=query_engine,
       query_transform=step_decompose_transform,
       index_summary="Used to answer questions about the author"
   )
   

   from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter

   # Create a filter for a specific collection
   filters = MetadataFilters(
       filters=[ExactMatchFilter(key="collection_name", value="your_collection_name")]
   )

   # Use the filter with your retriever
   results = retriever.retrieve("your query", filters=filters)
   

   retriever = index.as_retriever(
       vector_store_kwargs={"filter": {"collection_name": "your_collection_name"}}
   )
   results = retriever.retrieve("your query")
   

2. [DuckDB metadata filter example](<https://docs.llamaindex.ai/en/latest/examples/vector_stores/DuckDBDemo/#metadata-filter-example>) demonstrates how to use metadata filters with DuckDB:

   

from pydantic import BaseModel

class YourStructuredOutput(BaseModel):
    field1: str
    field2: int
    # Add more fields as needed

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo")
structured_llm = llm.as_structured_llm(YourStructuredOutput)

import asyncio

async def get_structured_output():
    response = await structured_llm.acomplete("Your prompt here")
    return response.raw  # This will be an instance of YourStructuredOutput

# Run the async function
result = asyncio.run(get_structured_output())
print(result)

from pydantic import BaseModel

class YourStructuredOutput(BaseModel):
    field1: str
    field2: int
    # Add more fields as needed

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo")
structured_llm = llm.as_structured_llm(YourStructuredOutput)

import asyncio

async def get_structured_output():
    response = await structured_llm.acomplete("Your prompt here")
    return response.raw  # This will be an instance of YourStructuredOutput

# Run the async function
result = asyncio.run(get_structured_output())
print(result)