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chore(docs): updated ML documentation (#4063)

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docs/docs/install/environment-variables.md
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@ -188,15 +188,19 @@ Typesense URL example JSON before encoding:
## Machine Learning
| Variable | Description | Default | Services |
| :----------------------------------------------- | :----------------------------------------- | :-----------------: | :--------------- |
| `MACHINE_LEARNING_MODEL_TTL` | Model TTL | `300` | machine learning |
| `MACHINE_LEARNING_CACHE_FOLDER` | ML Cache Location | `/cache` | machine learning |
| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Request thread pool size | number of CPU cores | machine learning |
| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
| Variable | Description | Default | Services |
| :----------------------------------------------- | :---------------------------------------------------------------- | :-----------------: | :--------------- |
| `MACHINE_LEARNING_MODEL_TTL`<sup>\*1</sup> | Inactivity time (s) before a model is unloaded (disabled if <= 0) | `0` | machine learning |
| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning |
| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*2</sup> | Thread count of the request thread pool (disabled if <= 0) | number of CPU cores | machine learning |
| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
| `MACHINE_LEARNING_WORKERS`<sup>\*3</sup> | Number of worker processes to spawn | `1` | machine learning |
| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` | machine learning |
\*1: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones.
\*1: This is an experimental feature. It may result in increased memory use over time when loading models repeatedly.
\*2: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones.
\*3: Since each process duplicates models in memory, changing this is not recommended unless you have abundant memory to go around.
:::info