Use EnsembleRetreiver in parallel

[saswat0]

How can EnsembleRetriever be called asynchronously? I have a dataset with ~1k questions and I wish to find the documents that can best answer each of them in parallel. However, calling it sequentially takes a lot of time. Can I run the retriever in parallel for all rows? Or is there a different way to optimise the run times?

I'm calling it like this now but it gives out a segmentation fault after getting stuck for an hour

import asyncio

queries = [query1, query2, ...]

async def process_query(profile):
    result = await ensemble_retriever.aget_relevant_documents(profile)
    return result

async def process_all_queries():
        
    tasks = [process_query(query) for query in queries]
    results = await asyncio.gather(*tasks)
    return results

results = asyncio.run(process_all_queries())

Related issue

[dosubot[bot]]

🤖

Hey @saswat0! It's good to see you again. Hope you're doing well. Let's dive right into your issue!

Based on the information provided, it seems like you're trying to use the aget_relevant_documents method in an asynchronous manner. However, the EnsembleRetriever class in LangChain does not have an aget_relevant_documents method. Instead, it provides an ainvoke method for asynchronous operations.

Here's how you can use the ainvoke method to process your queries in parallel:

import asyncio

# Assuming `queries` is your list of queries and `ensemble_retriever` is an instance of EnsembleRetriever
async def process_queries(queries, ensemble_retriever):
    tasks = [ensemble_retriever.ainvoke(query) for query in queries]
    results = await asyncio.gather(*tasks)
    return results

# Run the async function
loop = asyncio.get_event_loop()
results = loop.run_until_complete(process_queries(queries, ensemble_retriever))

In this example, queries is your list of queries and ensemble_retriever is an instance of EnsembleRetriever. The process_queries function creates a list of tasks, where each task is an asynchronous invocation of the EnsembleRetriever on a query. The asyncio.gather function is then used to run these tasks in parallel. The results are returned as a list of Document lists.

Please note that the actual implementation may vary depending on the structure of your dataset and how you want to handle the results.

You can find more information about the ainvoke method in the EnsembleRetriever class.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Sources

• libs/langchain/langchain/retrievers/ensemble.py

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[saswat0]

@dosu-bot This answer isn't helpful. I'm getting Segmentation fault (core dumped)

[dosubot[bot]]

🤖

I'm sorry to hear that you're experiencing a segmentation fault. This error typically occurs when a program tries to access memory that it shouldn't, such as writing to read-only memory or accessing memory that has already been freed.

In your case, it seems like the aget_relevant_documents method might be causing the issue. However, the LangChain codebase does not have an aget_relevant_documents method in the EnsembleRetriever class. Instead, it provides an asynchronous method named ainvoke which you can use to retrieve and rank documents asynchronously. Here is the method definition:

async def ainvoke(
    self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> List[Document]:
    from langchain_core.callbacks.manager import AsyncCallbackManager
    config = ensure_config(config)
    callback_manager = AsyncCallbackManager.configure(
        config.get("callbacks"),
        None,
        verbose=kwargs.get("verbose", False),
        inheritable_tags=config.get("tags", []),
        local_tags=self.tags,
        inheritable_metadata=config.get("metadata", {}),
        local_metadata=self.metadata,
    )
    run_manager = await callback_manager.on_retriever_start(
        dumpd(self),
        input,
        name=config.get("run_name"),
        **kwargs,
    )
    try:
        result = await self.arank_fusion(
            input, run_manager=run_manager, config=config
        )
    except Exception as e:
        await run_manager.on_retriever_error(e)
        raise e
    else:
        await run_manager.on_retriever_end(
            result,
            **kwargs,
        )
        return result

You can find this method in the EnsembleRetriever class.

You can use this method to run the retriever asynchronously for each query in your dataset. Here is an example of how you can do this:

import asyncio

# Assuming `queries` is your list of queries and `ensemble_retriever` is an instance of EnsembleRetriever
async def run_retrievers(queries, ensemble_retriever):
    tasks = [ensemble_retriever.ainvoke(query) for query in queries]
    results = await asyncio.gather(*tasks)
    return results

# Run the async function
loop = asyncio.get_event_loop()
results = loop.run_until_complete(run_retrievers(queries, ensemble_retriever))

Please note that this code assumes that your dataset can fit into memory and that you have enough system resources to run all the retrievers in parallel. If your dataset is too large or you don't have enough system resources, you may need to batch your dataset and run the retrievers in parallel for each batch.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Sources

• libs/langchain/langchain/retrievers/ensemble.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[saswat0]

@dosu-bot This new suggestion is the same as the before snippet. It causes segmentation fault

[dosubot[bot]]

🤖

I'm sorry to hear that you're experiencing a segmentation fault when trying to run the EnsembleRetriever asynchronously. This could be due to a variety of reasons, including memory issues or improper handling of asynchronous tasks.

From the code snippet you've shared, it seems like you're using the aget_relevant_documents method in an asynchronous context. However, based on the LangChain repository, the EnsembleRetriever class doesn't seem to have an aget_relevant_documents method. Instead, it provides an ainvoke method for asynchronous operations.

Here's an example of how you can use the ainvoke method:

# Assuming you have an instance of EnsembleRetriever named ensemble_retriever
# and a query string named query

# Create a config if needed
config = RunnableConfig(callbacks=..., run_name=...)

# Run the retriever asynchronously
async def run_retriever():
    try:
        result = await ensemble_retriever.ainvoke(query, config)
    except Exception as e:
        print(f"An error occurred: {e}")
    else:
        # Process the result
        print(result)

# Run the async function
asyncio.run(run_retriever())

In this example, the ainvoke method is used to run the retriever asynchronously. If an exception occurs during the execution, it will be caught and the on_retriever_error method of the run_manager will be called. If the execution is successful, the on_retriever_end method of the run_manager will be called and the result will be returned.

Please note that you need to handle exceptions properly to avoid segmentation faults. In the provided context, exceptions are caught and handled in the ainvoke method. If an exception occurs, the on_retriever_error method of the run_manager is called and the exception is re-raised. You should ensure that you have proper error handling in place when you call the ainvoke method.

You can find more details about the ainvoke method in the EnsembleRetriever class.

If you're still experiencing issues after trying this, could you please provide more details about the error you're encountering? Specifically, it would be helpful to know the exact error message you're seeing when the segmentation fault occurs, as well as any other relevant details about your environment or the data you're working with. This will help in diagnosing the issue further.

Sources

• libs/langchain/langchain/retrievers/ensemble.py
• libs/langchain/langchain/chains/base.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[girlsending0]

do you resolve this problem?