Connecting Input Datasources

An input datasource is one logical source of sensor data — an OPC-UA endpoint, an MQTT topic group, a CSV replay file, and so on. Each source is a typed adapter with its own connection settings and tag list. You map its tags to your model’s input channels, then enable it to start inference.

Supported input sources

• OPC-UA — subscribe to tags from any compliant server.
• MQTT — bring-your-own broker, TLS supported.
• CSV — replay recorded data from a file (great for testing).
• PLC — Modbus TCP and other industrial protocols.

One input enabled at a time

At most one input datasource can be enabled at any moment. Enabling a new source automatically disables the previous one. This keeps the runtime bound to a single, predictable stream.

Walkthrough: add a source and start inference

1. Open the Datasources page

   In the dashboard, go to Datasources → Input, then click + Create.

2. Pick the adapter type and fill the connection

   Select the source type. The form adapts to the protocol you choose — fill in the endpoint, credentials, and any protocol-specific fields. Secret fields (passwords, tokens) are masked as *** after saving.

   OPC-UA

   Field	Example
   Endpoint	opc.tcp://your-opcua-host:4840/factory/line1
   Security policy	None (or a Basic256Sha256 profile with certificates)
   Authentication	Anonymous (or username/password)

   Tags are referenced by NodeId, e.g. ns=2;i=2 (numeric) or ns=2;s=Temperature (string).

   MQTT

   Field	Example
   Broker URL	mqtt://your-broker-host:1883 (or mqtts:// for TLS)
   Topic	factory/line1/temp
   JSON path	value (extracts the numeric reading from the message)
   Username / password	optional (masked on save)

   CSV

   Field	Example
   File	a recorded dataset to replay
   Columns	the sensor columns to feed as channels

   CSV is replay-only — useful for testing a model without a live plant connection.

3. Map tags to model channels

   List the tags you want to stream and assign each to a numbered input channel (0, 1, 2, …). Each channel feeds one model input. Set the window size — how many consecutive samples the model consumes per prediction — to match the model you will pair.

   Channel mapping must be complete

   Provide exactly one tag per channel, with no gaps. Validation rejects an enable if any channel is unmapped or over-filled. String tags (e.g. a RUN/STOP status) cannot be used as numeric features — skip them when defining channels.

4. Save and pair a model

   Save the datasource. Then pair it with a model you uploaded under Models. The platform runs a compatibility check — window size × feature count must agree on both sides — and rejects the pairing if they differ.

5. Enable, then start

   Toggle Enable on the row. This validates the configuration, loads the paired model, and connects the adapter. The connection handshake (OPC-UA / MQTT / Modbus) happens here, so a network or credential problem surfaces now and rolls the row back to Disabled with an error message.

   Enabling does not auto-start streaming — click Start to begin inference.

Test the connection first

Before enabling, use Test Connection on the source. It verifies the live form values reach the endpoint, so you catch a wrong host or credential before wiring tags.

Rate limiting and buffering

Each datasource has independent rate limits and a bounded buffer. When a source produces faster than the runtime consumes, backpressure is reported to the dashboard so you can spot saturation.

If something goes wrong

Symptom	Likely cause
Enable fails with model_not_paired	Pair a model with the datasource first.
Enable rolls back on connect	Endpoint unreachable, wrong credentials, or firewall — verify with Test Connection.
Row says Enabled but no predictions	By design — press Start to begin streaming.
Pairing rejected	window size × feature count mismatch between model and datasource.

See the Troubleshooting runbook for more.

Next steps

Send results out Write predictions back to a PLC, broker, or file.

Deploy a model Upload, pair, and activate models.