Documentation Index
Fetch the complete documentation index at: https://docs.archetypeai.app/llms.txt
Use this file to discover all available pages before exploring further.
Lens processors are components within a lens’s model_pipeline that define how data is processed. They are configured in the lens_config when registering or modifying a lens.
Types of Lens Processors
The following processors are available:
| Processor Name | Purpose | File Types |
|---|
lens_camera_processor | Processes camera/video data for activity monitoring | .mp4 videos |
lens_timeseries_state_processor | Processes time-series sensor data for machine state classification | .csv files |
lens_sensor_logs_processor | Processes sensor logs | .jsonl files |
Configuration Example
A processor can be selected by passing in the processor name key within the model pipeline in a lens configuration:
model_pipeline:
- processor_name: lens_camera_processor
processor_config can be skipped if it is empty (it will automatically be added by the backend).
Processor Parameters
Camera Processor
| Parameter | Description | Default | Notes |
|---|
camera_buffer_size | Number of frames kept in buffer before processing | 5 | Buffer size of 5 means the model sees 5 frames per inference |
camera_buffer_step_size | Step size for frame sampling | 5 | Step of 5 frames means the next window starts 5 frames later |
Timeseries State Processor
| Parameter | Description | Default | Notes |
|---|
window_size | Number of rows analyzed together | 1024 | Smaller values (256) give more frequent predictions; larger values (2048) provide more context |
step_size | Rows to advance between predictions | 1024 | Controls prediction frequency |
input_n_shot | Labeled CSV examples for each machine state | — | At least one example required per state; format must match incoming data |
Sensor Logs Processor
| Parameter | Description | Default | Notes |
|---|
sensor_buffer_size | Number of sensor log entries kept in the buffer | 5 | The processor waits until the buffer contains this many entries before running inference. When the buffer exceeds this size, older entries are removed. |
input_buffer_step_size | Step size for sampling incoming sensor events | 1 | Controls how many incoming events are skipped between samples. A value of 1 means every event is buffered; a value of 3 means only every 3rd event is buffered. Must be greater than 0. |
Output Writers
A lens’s model_pipeline ends with an output writer processor that decides where inference results are sent. Each lens uses exactly one output channel — outputs do not appear on both at once.
| Writer | Where outputs go | How to consume |
|---|
server_sent_events_writer | The session’s Server-Sent Events stream | GET /lens/sessions/consumer/{session_id} — used by the official SDK’s lens.create_sse_consumer() |
| Default mailbox writer | The session’s WebSocket message mailbox | session.read events on the session WebSocket |
server_sent_events_writer for output. To check which writer a specific lens uses, inspect its model_pipeline via GET /lens/metadata.
If you are reading from session.read but seeing event_data: null while GET /lens/sessions/metadata reports non-zero num_outputs, the lens is writing to the SSE channel — switch to the consumer endpoint.
Best Practices
- Always destroy sessions when done to free resources
- Use appropriate buffer sizes for your data type
- Match input data format to processor requirements
The documentation focuses more on Lenses as the primary interface, with processors being internal components. For troubleshooting, check session status via the API and ensure proper authentication.
Additional Resources:
