Hi After doing the rasax upgradation, I realize that rasa chatbot response is getting slower. Moreover it is difficult to create a compete chatbot story. And challenging to add correction in conversation in interactive mode.
Any advice to resolve it.
@msaraf1 can you be more explicit about your issues? I.e.
After doing the rasax upgradation,
upgrading from what version to what version?
I realize that rasa chatbot response is getting slower.
how big is your model, i.e. how much training data?
Moreover it is difficult to create a compete chatbot story.
what do you mean by this?
And challenging to add correction in conversation in interactive mode.
how is it challenging?
I’ll chime in to say, that when I am in interactive mode, I can’t actually change the responses that the bot gives AFTER its been posted, meaning I have to enter the actual story under stories, and rewrite them in there
Here is detail of version and rasa details
Docker version: Docker version 18.09.2, build 6247962
Python version: Python 3.6.9
RASA version : Rasa 1.3.6
Host operating system: Windows 10
4.9.125-linuxkit
Size of model: 9169KB
Size of data: 15KB
While doing the conversation in rasa, the response of rasa is quite slow.
For example if I ask rasa “please help to buy pizza”
Chatbot takes long time to response the question (around 15 to 60 sec)
Sometime rasa does not respond at all (keep running …)
Once we transfer the user conversation to interactive mode. It is quite difficult to make correction on “action or utter”. Sometime rasa reshuffle all the dialogues or sometime duplicate just previous dialogue. For example How are you How are you I am gemmy I am gemmy
Did you solve this?
I’m facing the same issue. Bot takes 1–3 seconds on an average to reply. My bot has about 10 intents, 2 forms, 160 nlu inputs and 2 actions. Environment:
Hello,
I’m facing the same issue as well on rasa shell, rasa interactive, and pythonically in Jupyter notebook, starting from rasa.core.agent import Agent to await agent.handle_text(message). The bot takes 2-6 seconds to reply. My bot has 6 intents, 10 entities (2 of them from duckling and spacy), 6 featurized slots, 3 actions, and 1 form action.
Environment:
I observed the actions server on debug mode and it seems like the custom actions run quiet fast, the NLU part takes 0.08-0.09s in jupyter notebook by importing the NLU model using from rasa.nlu.model import Interpreter. The slow part is right after I input into the bot by using any of either method above (shell, interactive, or jupyter), the action server receives request to run action from rasa core 1-2 seconds after I input the message. Everything run locally with endpoint http://localhost:5055/webhook
Do you know why this is happening? Thanks before.
@tomgun132 please create a new post for your question. My question to the OP about rasa shell was to compare about their slow time in Rasa X, your issue with the python API is not the same.
I updated to RASA 1.9.3 yesterday and noticed that the response is faster than RASA 1.8 in rasa shell. However, in **RASA X** response is slow.
Hi @kiranlvs93 thanks for the update. by slow you mean ~2 seconds? If so, this is a known issue we’re working to resolve – in the meantime you can apply a workaround by disabling telemetry at this endpoint: HTTP API
Is that happening in previous version of Rasa? Has it solved?
My server also takes over 2 second to respond my POST request.
I use the following command to start a new project and run my rasa server.
rasa init
rasa run --enable-api --port 5000
and then I post a simple request in Python
data = {'message': msg, 'sender': 'Jezemy', 'token': 'None'}
headers = {'content-type': 'application/json'}
r = requests.post('http://localhost:5000/webhooks/rest/webhook', json = data, headers = headers)
I print the cost time, It takes 2.7 to response the JSON data
I have the same issue, the response is about 2.1~2.4 seconds for the “mood_bot” demo.
updated: It seems fine (response time ~180ms) when I use postman. why?
hello. I am facing the same issue with the same env. Do you solve the problom now??
Hi. Have you solved the problem？ I have the same situation with you.
I met the same problem, it’s very painful to work with rasa in local machine. I don’t know why it’s so slow, since CPU and memory usage are all low. However, in our production machine with an official docker image rasa/rasa-sdk:3.3.0, it’s very fast. I’m wondering if there are any optimizations in this image that I can reproduce in my local machine?
Any updates on this? Cheers!