UNDER CONSTRUCTION
Welcome to the Getting Started guide,
this This is what you will achieve by
follow following the next steps:
SAGASetup - Setting up the environment
.Deploy .Run .Check - Checking that it's working!
.Elasticsearch 6.4.1 or above.
Software
- Elasticsearch 7+ (versions 7.14 - 7.17 for 1.3.3) (Also 8.8.1 for 1.3.3/ 8.12.2 for 1.3.4)/OpenSearch v. 1.1 or above
- Java 11 or above, Java 17 for versions 1.3.
Step 1: Getting Saga
At the moment of writing, Saga version 1.0.0 has just been released. You can get it from here (Saga team in MS Teams). It is a bundle containing:
- Saga binaries
- Instance of Elasticsearch with sample data for your convenience
- Saga documentation (Introduction)
- Saga user manual
Creators and users of SAGA are subscribed to this Saga team so in case you have questions or comments Hardware
- RAM: 10 Gb free
- CPU: 2 cores
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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. - Saga comes configured by default to use from 6 to 10 GB of JVM heap. This configuration can be changed in bin\startup.bat or bin\startup.sh files. Memory consumption depends mostly on how big are the dictionaries loaded into Saga. If you are not using dictionaries at all or they are very small you can decrease memory configuration accordingly. If your dictionaries are big, let's say more than 2 million entries total for example, you may need to increase the 10 Gb limit.
- Saga is very CPU intensive, the more cores the server has the faster data will get processed. Therefore number of cores depends on your specific processing time requirements.
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Step 1: Getting Saga
You'll be able to get 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. 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. |
Step 2:
SetSet up the environment
- Check that Java 11 (or 17 for 1.3.3/1.3.4) is installed on your machine by running on terminal (or system console):
$> java -version
- Unpackage Saga in your preferred location.
This -
This is our recommended setup but you can pretty much handle the paths as you wish.
This guide will refer to Saga's working directory as {SAGA_HOME}
. - Saga uses
Elasticsearch (6.4.1 or above) and you can get it here under {SAGA_HOME}
in something like {SAGA_HOME}/Elasticsearch-6.4.1
.- or OpenSearch
- Run ES by executing the binary
on SAGA/Elasticsearch-6.4.1Saga can run on an empty ES instance, you'd
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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. |
Step 3: Deploy Saga
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-1.3.4
saga-faq-stage-1.3.4
saga-intent-stage-1.
0.0-SNAPSHOT3.4
saga-lang-detector-stage-1.3.4
saga-name-trainer-stage-1.
0.0-SNAPSHOT3.4
saga-parts-of-speech-stage-1.3.4
saga-sentence-breaker-stage-1.3.4
saga-spellchecking-stage-1.3.4
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
:
- "
airPort 8080 → this is the - 8080 → The port used by the server.
- "
ipAdress this - This IP/mask is used to restrict inbound connections, open to all connections by default.
- "
logger" → each logger level config per handler."provider" → data Resources, mainly used to specify location of resource files like dictionaries and ES configuration- New filesystem providers can be added to group different resource files.
- ES configuration includes the "port" to connect to, this is by default 9200, you may change it to fit your environment.
- security" → Security access to the Saga Server and Management UI (recommended only when ssl is also enabled)
- "enable": true → Indicates the use of security
- "encryptionKeyFile": → file containing the encryption key. A file is provided by default but it is recommended to change it.
- "users.username" → Specify the username to use
- "users.password" → Specify the password for the username. Can be either plain or encrypted
- "ssl" → Enables SSL for the communication with the Saga Server
- enable": true → Indicates the use of ssl
- keyStore → Path to the keyStore
- keyStorePassword → Password of the keyStore. Can be either plain or encrypted
- "libraryJars": ["./lib"] → Folder where library jars are located
- If you are running elasticseach in localhost then you don't need to change anything. But if your elasticsearch runs somewhere else then you'll need to adjust the "nodeUrls" property in both "elasticSearch" solution and "Elastic" provider. In case you have a cluster, you can specify them separated by comma, for example: " nodeUrls": ["elastic1:9200", "elastic2:9200", "elastic3:9200"],
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For more information about Saga Configuration, check this |
"solutions" → bundle solution schema, it values may be change to have multiple servers with different "solutions" or to switch from one to another.A solution work as a domain. By default the "saga" solution creates ES indexes using the pattern "saga-<index>" and only loads indexes with the same patter. So you could have multiple solutions on a ES server. To switch between solution you'd need to shut down the server, change the "indexName" value and restart the server
If you have some valid "models" you'd like to include them on the server:
- Create a
{SAGA-HOME}/nt-models
folder for "name trainers" and copy the model there. - Create a
{SAGA-HOME}/ct-models
folder for "classification trainers" and copy the model there. - Create a
{SAGA-HOME}/tf-models
folder for "FAQ" (uses TensorFlow) and copy the model there.
To add datasets:
- Create a
{SAGA-HOME}/datasets
folder. - Each dataset must be placed
on each , this - . This folder name will be the one displayed for "test runs".
- Each data document in the dataset must be compliant with Saga's data file JSON format.
- Each folder must contain a ".metadata" file with information about the dataset and how to read it.
You can check out the dataset format here.
Step 4: Run Saga
To run Saga:
Check that 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.
Step 5: Run Python Bridge Server
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.
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