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- ONNXEmbeddings(newdata=None, modeldata=None, tokenizerdata=None, accumulate=None, model_output_tensor=None, encode_max_length=512, show_model_properties=False, output_column_prefix='emb_', output_format='VARBYTE(3072)', overwrite_cached_models='false', is_debug=False, enable_memory_check=False, **generic_arguments)
- DESCRIPTION:
The ONNXEmbeddings() function is used to calculate embeddings values in
Vantage with a HuggingFace model that has been created outside Vantage
and exported to Vantage using ONNX format.
PARAMETERS:
newdata:
Required Argument.
Specifies the input teradataml DataFrame that contains
the data to be scored.
Types: teradataml DataFrame
modeldata:
Required Argument.
Specifies the model teradataml DataFrame to be used for
scoring.
Note:
* Use `retrieve_byom()` to get the teradataml DataFrame that contains the model.
Types: teradataml DataFrame
tokenizerdata:
Required Argument.
Specifies the tokenizer teradataml DataFrame
which contains the tokenizer json file.
Types: teradataml DataFrame
accumulate:
Required Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to
copy to the output. By default, the function copies all input
teradataml DataFrame columns to the output.
Types: str OR list of Strings (str) OR Feature OR list of Features
model_output_tensor:
Required Argument.
Specifies the column of the model's possible output fields
that the user wants to calculate and output.
Types: str
encode_max_length:
Optional Argument.
Specifies the maximum length of the tokenizer output token
encodings(only applies for models with symbolic dimensions).
Default Value: 512
Types: int
show_model_properties:
Optional Argument.
Specifies the default or expanded "model_input_fields_map" based on
input model for defaults or "model_input_fields_map" for expansion.
Default Value: False
Types: bool
output_column_prefix:
Optional Argument.
Specifies the column prefix for each of the output columns
when using float32 "output_format".
Default Value: "emb_"
Types: str
output_format:
Optional Argument.
Specifies the output format for the model embeddings output.
Default Value: "VARBYTE(3072)"
Types: str
overwrite_cached_models:
Optional Argument.
Specifies the model name that needs to be removed from the cache.
When a model loaded into the memory of the node fits in the cache,
it stays in the cache until being evicted to make space for another
model that needs to be loaded. Therefore, a model can remain in the
cache even after the completion of function execution. Other functions
that use the same model can use it, saving the cost of reloading it
into memory. User should overwrite a cached model only when it is updated,
to make sure that the Predict function uses the updated model instead
of the cached model.
Note:
Do not use the "overwrite_cached_models" argument except when user
is trying to replace a previously cached model. Using the argument
in other cases, including in concurrent queries or multiple times
within a short period of time lead to an OOM error.
Default Value: "false"
Permitted Values: true, t, yes, y, 1, false, f, no, n, 0, *,
current_cached_model
Types: str
is_debug:
Optional Argument.
Specifies whether debug statements are added to a trace table or not.
When set to True, debug statements are added to a trace table that must
be created beforehand.
Notes:
* Only available with BYOM version 3.00.00.02 and later.
* To save logs for debugging, user can create an error log by using
the is_debug=True parameter in the predict functions.
A database trace table is used to collect this information which
does impact performance of the function, so using small data input
sizes is recommended.
* To generate this log, user must do the following:
1. Create a global trace table with columns vproc_ID BYTE(2),
Sequence INTEGER, Trace_Output VARCHAR(31000)
2. Turn on session function tracing:
SET SESSION FUNCTION TRACE USING '' FOR TABLE <trace_table_name_created_in_step_1>;
3. Execute function with "is_debug" set to True.
4. Debug information is logged to the table created in step 1.
5. To turn off the logging, either disconnect from the session or
run following SQL:
SET SESSION FUNCTION TRACE OFF;
The trace table is temporary and the information is deleted if user
logs off from the session. If long term persistence is necessary,
user can copy the table to a permanent table before leaving the
session.
Default Value: False
Types: bool
enable_memory_check:
Optional Argument.
Specifies whether there is enough native memory for large models.
Default Value: True
Types: bool
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept. Below
are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile table or not. When set to
True, results are stored in a volatile table,
otherwise not.
Default Value: False
Types: bool
Function allows the user to partition, hash, order or local
order the input data. These generic arguments are available
for each argument that accepts teradataml DataFrame as
input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or
list of str (Strings) or PartitionKind
* "<input_data_arg_name>_hash_column" accepts str or list
of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list
of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if
the underlying SQL Engine function supports, else an
exception is raised.
RETURNS:
Instance of ONNXEmbeddings.
Output teradataml DataFrame can be accessed using attribute
references, such as ONNXEmbeddings.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# 4. To execute BYOM functions, set 'configure.byom_install_location' to the
# database name where BYOM functions are installed.
# Import required libraries / functions.
import os, teradataml
from teradataml import get_connection, DataFrame
from teradataml import save_byom, retrieve_byom, load_example_data
from teradataml import configure, display_analytic_functions, execute_sql
# Load example data.
load_example_data("byom", "amazon_reviews_25")
# Create teradataml DataFrame objects.
amazon_reviews_25 = DataFrame.from_table("amazon_reviews_25")
# Assigning txt column name to rev_txt column.
amazon_reviews_25 = amazon_reviews_25.assign(txt=amazon_reviews_25.rev_text)
# Set install location of BYOM functions.
configure.byom_install_location = "td_mldb"
# Check the list of available analytic functions.
display_analytic_functions(type="BYOM")
# Retrieve model.
modeldata = retrieve_byom("bge-small-en-v1.5", table_name="onnx_models")
tokenizerdata = retrieve_byom("bge-small-en-v1.5", table_name="embeddings_tokenizers")
# Assigning tokenizer_id, tokenizer to model_id, model in embeddings_tokenizers.
tokenizerdata_a1 = tokenizerdata.assign(tokenizer_id=tokenizerdata.model_id)
tokenizerdata_a2 = tokenizerdata_a1.assign(tokenizer=tokenizerdata_a1.model)
# Example 1: Calculate embedding values in Vantage with a bge-small-en-v1.5
# model that has been created outside the Vantage by removing all
# the all cached models.
ONNXEmbeddings_out_1 = ONNXEmbeddings(modeldata=modeldata,
tokenizerdata=tokenizerdata_a2.select(['tokenizer_id', 'tokenizer']),
newdata=amazon_reviews_25.select(["rev_id", "txt"]),
accumulate='rev_id',
model_output_tensor='sentence_embedding'
)
# Print the results.
print(ONNXEmbeddings_out_1.result)
# Example 2: Showcasing the model properties of bge-small-en-v1.5 model that has been
# created outside the Vantage by showcasing.
ONNXEmbeddings_out_2 = ONNXEmbeddings(modeldata=modeldata,
tokenizerdata=tokenizerdata_a2.select(['tokenizer_id', 'tokenizer']),
newdata=amazon_reviews_25.select(["rev_id", "txt"]),
accumulate='rev_id',
model_output_tensor='sentence_embedding',
show_model_properties=True
)
# Print the results.
print(ONNXEmbeddings_out_2.result)
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