Closed mglslg closed 1 week ago
Hey there, @mglslg! 😄 Long time, no bug, huh? It's always a pleasure to see your name pop up, even if it's accompanied by a tricky issue. I'm diving into this headfirst to untangle it for you, so hang tight! Will get back to you with a full answer shortly.
My first suggestion is to rewrite your api endpoint using actual async, but I'm not sure how async in flask works (I typically use fastapi)
Alternatively, seems like an internal issue in the elastic search client
@logan-markewich Thank you for your help :) Yes, it is indeed an issue with Elasticsearch itself, but I also tried using a custom-created Elasticsearch synchronized client and it still doesn't work. Then I added nest_asyncio.apply() in the rest API code, but it still throw the same error. Currently, I am not very keen on switching the app to another async-supported framework. Is there any other way to solve this issue?
To address the "RuntimeError: Session and connector has to use same event loop" error in your Flask application without switching to an async-supported framework, you'll need to ensure that your Elasticsearch queries are executed in a synchronous manner. This involves avoiding direct use of asynchronous methods within Flask route handlers, which are not compatible with Flask's synchronous nature.
Here's a strategy to adapt your Elasticsearch operations to be synchronous within Flask:
Synchronous Elasticsearch Operations: Ensure that all interactions with Elasticsearch from your Flask application are done synchronously. If your ElasticsearchStore
or any related component uses asynchronous calls, you should modify these to be synchronous or use synchronous equivalents if available.
Adapting Asynchronous Calls: If you have asynchronous methods that need to be called from Flask routes, consider wrapping these calls to run them synchronously. However, be cautious as directly using asyncio.run()
in Flask routes can lead to performance issues and block the server's event loop. It's generally better to redesign the async parts to be compatible with Flask's synchronous execution model.
Example Adaptation: If your ElasticsearchStore
class or any method you're using to interact with Elasticsearch is designed to be asynchronous, you'll need to either provide a synchronous interface for it or ensure that its asynchronous operations are properly managed when called from Flask routes.
By focusing on ensuring that all Elasticsearch queries and related operations are executed in a synchronous context within your Flask application, you can avoid the event loop error and ensure compatibility with Flask's execution model.
To continue the conversation, mention @dosu.
@dosubot Then how can I specify using a synchronous Elasticsearch client in LlamaIndex?
To use a synchronous Elasticsearch client in LlamaIndex within a Flask application, you can adapt asynchronous operations to run synchronously. This involves wrapping asynchronous method calls with asyncio.get_event_loop().run_until_complete()
. Here's how you can apply this approach:
Wrap Asynchronous Calls: For any asynchronous operation you need to perform with Elasticsearch in Flask, use asyncio.get_event_loop().run_until_complete()
to execute these operations synchronously. This method allows asynchronous functions to be awaited and their results returned just like synchronous functions.
Example Implementation:
asyncio.get_event_loop().run_until_complete(self.async_add(nodes))
asyncio.get_event_loop().run_until_complete(self.adelete(ref_doc_id))
Integrate with Flask: Ensure that nest_asyncio.apply()
is called in your Flask application setup to patch the event loop for compatibility with synchronous Flask routes.
By following these steps, you can maintain your Flask application's synchronous execution model while performing asynchronous Elasticsearch operations under the hood. This method leverages the power of asynchronous IO with Elasticsearch without needing to switch your entire application to an asynchronous framework.
To continue the conversation, mention @dosu.
@dosubot I tried to use synchronized client like this:
es_client=elasticsearch.Elasticsearch(**connection_params)
es_store = ElasticsearchStore(
index_name="sk_test_index",
es_client=es_client
)
But got this exception:
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/site-packages/llama_index/vector_stores/elasticsearch/base.py", line 351, in add
return asyncio.get_event_loop().run_until_complete(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/site-packages/nest_asyncio.py", line 98, in run_until_complete
return f.result()
^^^^^^^^^^
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/asyncio/futures.py", line 203, in result
raise self._exception.with_traceback(self._exception_tb)
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/asyncio/tasks.py", line 277, in __step
result = coro.send(None)
^^^^^^^^^^^^^^^
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/site-packages/llama_index/vector_stores/elasticsearch/base.py", line 383, in async_add
await self._create_index_if_not_exists(
File "/opt/miniconda3/envs/inc-cyjr-oms-sk-gpt/lib/python3.11/site-packages/llama_index/vector_stores/elasticsearch/base.py", line 281, in _create_index_if_not_exists
if await self.client.indices.exists(index=index_name):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: object HeadApiResponse can't be used in 'await' expression
So, is llamaindex not supporting synchronous ES client?
It seems like the core of the issue you're facing with LlamaIndex and the synchronous Elasticsearch client revolves around the execution context—specifically, the mix-up between synchronous and asynchronous operations. LlamaIndex is designed to support both synchronous and asynchronous operations, which gives you the flexibility to choose the approach that best fits your application's architecture. However, this flexibility also requires careful management of execution contexts to avoid errors.
From the error message you shared, it appears that there's an attempt to use an asynchronous operation in a context that expects synchronous execution. This is evident from the TypeError: object HeadApiResponse can't be used in 'await' expression
, indicating that an asynchronous response object is being incorrectly used in a synchronous context.
To resolve this issue, you should ensure that your usage of Elasticsearch operations through LlamaIndex aligns with the execution model of your Flask application. If your Flask application is designed to operate synchronously, you should use LlamaIndex's synchronous methods (e.g., add
, query
) without await
. Conversely, if you're working within an asynchronous context or adapting parts of your application to be asynchronous, you should use the asynchronous counterparts of these methods (e.g., async_add
, aquery
) and ensure they are awaited properly.
Here's a quick recap of the approach you should take:
await
. This is suitable for Flask routes that are designed to operate synchronously.await
with the method call, ensuring that the Flask route or the context in which this operation is performed supports asynchronous execution.This distinction is crucial for avoiding errors related to execution context mismatches. If your application primarily uses Flask in a synchronous manner and you wish to keep it that way, focusing on the synchronous methods provided by LlamaIndex and ensuring any asynchronous operations are properly adapted to fit this model will be key.
Remember, the goal is to match your operations with the execution context of your application. Misalignments between synchronous and asynchronous contexts are common sources of errors when integrating asynchronous capabilities into traditionally synchronous frameworks like Flask.
To continue the conversation, mention @dosu.
Every time you use es_vector_store, just recreate the object, don't get it from the context.
Bug Description
This is ElasticsearchStore code:
This is restful api code:
And I got this error below:
How can I solve this?
Version
0.10.19
Steps to Reproduce
Start a Flask service, then use a REST request to query an index bound to Elasticsearch.
Relevant Logs/Tracbacks
No response