Welcome to the Getting Started guide, This is what you will achieve by following the next steps:
Hardware
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Hardware prerequisite above is just a starting point. It depends a lot on how you configure Saga and what is your processing time expectation.
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You'll be able to get it from binaries from here (SCA Artifactory repository).
From this version and on, we provide docker images:
Saga image (lightweight):
docker.repository.sca.accenture.com/docker/saga-server:1.3.4-javacio17-base
docker.repository.sca.accenture.com/docker/saga-server:1.3.4-alpine3.19
Saga image containing the tensorFlow model used in FAQ recognizer:
docker.repository.sca.accenture.com/docker/saga-server:1.3.4-tensor-javacio17-base
docker.repository.sca.accenture.com/docker/saga-server:1.3.4-tensor-alpine3.19
Python Bridge:
docker.repository.sca.accenture.com/docker/saga-python-bridge:1.3.4-ubuntu22.04cio-base-basic
docker.repository.sca.accenture.com/docker/saga-python-bridge:1.3.4-ubuntu22.04cio-base-all
docker.repository.sca.accenture.com/docker/saga-python-bridge:1.3.4-debian12-basic
docker.repository.sca.accenture.com/docker/saga-python-bridge:1.3.4-debian12-all
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You will need permissions to download binaries or docker images. Please send your access request to [email protected] |
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The Saga team has a "Teams" Team here. |
You can also find documentation here (Saga MS Teams site):
Creators and users of Saga are subscribed to this Saga team, so you can always publish a message to get some help if you have questions or comments. |
$> java -version
{SAGA_HOME}
.bin
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If you have never ran Saga against your Elasticsearch, your Elasticsearch will be empty. That's ok because Saga will generate all the necessary indexes with the minimum default data (base pipeline, executors,...); although you need to add new tags and resources. |
Once you have Saga in {SAGA_HOME}
validate the following:
There is a {SAGA_HOME}/lib
folder containing the following JARs:
saga-classification-trainer-stage-1.3.14
saga-faq-stage-1.3.4
saga-intent-stage-1.jar3.4
saga-lang-detector-stage-1.3.1.jar4
saga-name-trainer-stage-1.3.1.jar4
saga-parts-of-speech-stage-1.3.1.jar4
saga-sentence-breaker-stage-1.3.1.jar4
saga-spellchecking-stage-1.3.1.jar4
Check the configuration base on what NoSQL DB provider used.
ES : How To Connect To Elasticsearch
Opensearch: How To Connect To OpenSearch
Check the basic configuration on {SAGA_HOME}/config/config.json
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For more information about Saga Configuration, check this. |
If you have some valid "models" you'd like to include them on the server:
{SAGA-HOME}/nt-models
folder for "name trainers" and copy the model there.{SAGA-HOME}/ct-models
folder for "classification trainers" and copy the model there.{SAGA-HOME}/tf-models
folder for "FAQ" (uses TensorFlow) and copy the model there.To add datasets:
{SAGA-HOME}/datasets
folder.To run Saga:
Check that ElasticSearch Elasticsearch is running.
Use the bundled startup script on {SAGA_HOME}/bin (either startup.bat
for Windows or startup.sh
for Linux and Mac).
If you didn't change the default port on the configuration, you should be able to access Saga UI at http://localhost:8080/.
If not, then check your configuration for the right port.
Saga has a python recognizer and python stage that can be used to process text using machine learning python models like Bert.
In case you need this, follow instruction on how to setup and run the python bridge here.