Input configuration for BatchPredict Action.
The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise.
The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:
One or more CSV files where each line is a single column:
::
GCS_FILE_PATH
GCS_FILE_PATH is the Google Cloud Storage location of a text file.
Supported file extensions: .TXT, .PDF Text files can be no larger than
10MB in size.
Sample rows:
::
gs://folder/text1.txt
gs://folder/text2.pdf
One or more CSV files where each line is a single column:
::
GCS_FILE_PATH
GCS_FILE_PATH is the Google Cloud Storage location of a text file.
Supported file extensions: .TXT, .PDF Text files can be no larger than
128kB in size.
Sample rows:
::
gs://folder/text1.txt
gs://folder/text2.pdf
One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict].
Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique.
Each document JSONL file contains, per line, a proto that wraps a
Document proto with input_config set. Only PDF documents are
currently supported, and each PDF document cannot exceed 2MB in size.
Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed.
Sample inline JSONL file (Shown with artificial line breaks. Actual line
breaks are denoted by "\n".):
::
{
"id": "my_first_id",
"text_snippet": { "content": "dog car cat"},
"text_features": [
{
"text_segment": {"start_offset": 4, "end_offset": 6},
"structural_type": PARAGRAPH,
"bounding_poly": {
"normalized_vertices": [
{"x": 0.1, "y": 0.1},
{"x": 0.1, "y": 0.3},
{"x": 0.3, "y": 0.3},
{"x": 0.3, "y": 0.1},
]
},
}
],
}\n
{
"id": "2",
"text_snippet": {
"content": "Extended sample content",
"mime_type": "text/plain"
}
}
Sample document JSONL file (Shown with artificial line breaks. Actual
line breaks are denoted by "\n".):
::
{
"document": {
"input_config": {
"gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
}
}
}
}\n
{
"document": {
"input_config": {
"gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
}
}
}
}
Input field definitions:
GCS_FILE_PATH
The path to a file on Google Cloud Storage. For example,
"gs://folder/video.avi".
Errors:
If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.
Required. The Google Cloud Storage location for the input content.