Python SDK Changelog

Updates to the Arize Python SDK

Version 7.11

7.11.1 (Mar 5, 2024)

🐛 Bug Fixes

• Fix a bug that caused ImportError when importing Client from arize.api

7.11.0 (Feb 23, 2024)

🎁 New Features

• Optional strict typing in pandas logger Schema

• Optional strict typing in record-at-a-time logger

❗ Dependency Changes

• Add optional extra dependencies if the Arize package is installed as pip install arize[NLP_Metrics]:

  • nltk>=3.0.0, <4

  • sacrebleu>=2.3.1, <3

  • rouge-score>=0.1.2, <1

  • evaluate>=0.3, <1

Version 7.10

7.10.2 (Feb 14, 2024)

✅ Validation Changes

• Check that space and API keys are of string type

🐛 Bug Fixes

• Address backward compatibility issue for batch logging via Pandas for on-prem customers

7.10.1 (Feb 6, 2024)

❗Dependency Updates:

• Our Tracing extra requirements now include deprecated , a dependency coming from opentelemetry-semantic-conventions, which absence produced an ImportError

  • Tracing =
        opentelemetry-semantic-conventions==0.43b0
        openinference-semantic-conventions>=0.1.2, <1
        deprecated #opentelemetry-semantic-conventions requires it

7.10.0 (Feb 1, 2024)

🎁 New Features:

• New batch ingestion via Pandas DataFrames for MULTICLASS model type

• New TRACING environment. You can now log spans & traces for your LLM applications into Arize using batch ingestion via Pandas DataFrames

• Removed size limitation on our Schema. You can now log wider models (more columns in your DataFrame)

✅ Validation Changes

• Prediction ID and Ranking Group ID have an increased character limit from 128 to 512

❗Dependency Updates:

• Our MimicExplainer extra requirements are now more relaxed.

  • We only require interpret-community[mimic]>=0.22.0,<1

Version 7.9

7.9.0 (Dec 28, 2023)

🎁 New Features:

• New MULTICLASS model type available for record-at-a-time ingestion

Version 7.8

7.8.1 (Dec 18, 2023)

🐛 Bug Fixes:

• Fix a bug that caused missing columns validation feedback to have repeated columns in the message

• Fix a bug that caused a KeyError when llm_params is not found in the dataframe. Improved feedback to the user was included.

7.8.0 (Dec 13, 2023)

🎁 New Features

• Enable latent actuals for GENERATIVE_LLM models

💬 Feedback Enhancements

• Enable feedback when files are too large for better user experience and troubleshooting

❗ Dependency Changes

• Updated pandas requirement. We now accept pandas 2.x

Version 7.7

7.7.2 (Nov 9, 2023)

🐛 Bug Fixes:

• Default prediction sent as string for GENERATIVE_LLM single-record-logger (before it was incorrectly set as an integer, resulting in it being categorized as prediction score instead of prediction label)

7.7.1 (Nov 8, 2023)

🐛 Bug Fixes:

• Check the value of prompt/response raw_data only if not None

7.7.0 (Nov 2, 2023)

🎁 New Features

• Add CORPUS support

• Accept strings for prompt and response

• Make prompt and response optional

• Add support for a list of strings features in single-record-logger

🐛 Bug Fixes:

• Avoid creating a view of a Pandas dataframe

Version 7.6

7.6.1 (Oct 24, 2023)

✅ Validation Changes

• Add validation on embedding raw data for batch and record-at-a-time loggers

• Raise validation string limits for string fields

• Add truncation warnings for long string fields

7.6.0 (Oct 12, 2023)

🎁 New Features

• New ability to send features with type list[str]

• New fields available to send token usage to Arize, both using our pandas batch logger and the single record logger

Version 7.5

7.5.1 (Oct 5, 2023)

✅ Validation Changes

• Increase time interval validation from 2 years to 5 years

❗Dependency Changes

• Require python>=3.6 (as opposed to python>=3.8) for our core SDK. Our extras still require python>=3.8. See Python SDK for more details.

• Require pyarrow>=0.15.0 (as opposed to pyarrow>=5.0.0)

7.5.0 (Sep 2, 2023)

🎁 New Features

• Add prompt templates and LLM config fields to the single log and pandas batch ingestion. These fields are used in the Arize Prompt Template Playground

✅ Validation Changes

• Add a validation check that fails if there are more than 30 embedding features sent

Version 7.4

7.4.0 (Aug 15, 2023)

🎁 New Features

• Add filtering via the keyword where to the Exporter client

Version 7.3

7.3.0 (Aug 1, 2023)

🎁 New Features

• AutoEmbeddings supports any model in the HuggingFace Hub, public or private.

• Add AutoEmbeddings UseCase for Object Detection

• Add EmbeddingGenerator.list_default_models() method

💀 Deprecations

• Computer Vision AutoEmbeddings switched from using FeatureExtractor(deprecated from HuggingFace) to ImageProcessor class

Version 7.2

7.2.0 (Jul 22, 2023)

🎁 New Features

• Authenticating Arize Client using environment variables

🐛 Bug Fixes

• A bug causing permission errors for pandas logging using Windows machines

• A bug forcing tags to be strings

Version 7.1

7.1.0 (Jun 26, 2023)

🎁 New Features

• Add Generative LLM model-type support for single-record logging

Version 7.0

7.0.6 (Jun 24, 2023)

❗Dependency Changes

• Removed dependency on interpret for the MimicExplainer

7.0.5 (Jun 23, 2023)

➕ Enhancements

• Add a progress bar to the Exporter client

• Sort exported dataframe by time

• Update reserved headers

✅ Validation Changes

• Add validation check to Exporter client that will fail if start_time > end_time

🐛 Bug Fixes

• Add bug causing to return an error when a query returns no data. Instead, return an empty response

• A bug causing the Exporter client to return empty columns in the dataframe if there was no data in them

• A bug causing incorrect parsing of GENERATIVE_LLM model fields: prompt & response

❗ Dependency Changes

• Add missing dependency for Exporter: tqdm>=4.60.0,<5

7.0.4 (Jun 13, 2023)

❗ Dependency Changes

• Relax protobuf requirements from protobuf~=3.12 to protobuf>=3.12, <5

7.0.3 (Jun 2, 2023)

🎁 New Features

• Python Export Client, you can now export data from Arize using the Python SDK

🐛 Bug Fixes

• A bug preventing REGRESSION models from using the MimicExplainer

✅ Validation Changes

• Remove null value validation for prediction_label and actual_label from single-record logging

• Add model mapping rules validation for OBJECT_DETECTION models

7.0.2 (May 12, 2023)

💬 Feedback Enhancements

• Improve error messages around prediction ID, prediction labels, and tags

🐛 Bug Fixes

• A bug causing predictions to be sent as scores instead of labels for NUMERIC model types

✅ Validation Changes

• Add a validation check that will fail if the character limit on tags (1000 max) is exceeded

• Add a validation check that will fail if actuals are sent without prediction ID information (for single-record logging). This would result in a delayed record being sent without a prediction ID, which is necessary for the latent join

• Add a validation check that will fail if the Schema, without prediction columns, does not contain a prediction ID column (for pandas logging). This would result in a delayed record being sent without a prediction ID, which is necessary for the latent join

• Add a validation check that will fail if the Schema points to an empty string as a column name

• Add check for invalid index in AutoEmbeddings: DataFrames must have a sorted, continuous index starting at 0

• Remove label requirements & accept null values on SCORE_CATEGORICAL, NUMERIC, and RANKING models

• Allow feature and tag columns to contain null values for pandas logging

• Allow to send delayed actuals for RANKING models, it is no longer enforced the presence of rank and prediction_group_id columns in the Schema. However, if the columns are sent, they must not have nulls, since we cannot construct predictions with either value null

❗ Dependency Changes

• Change optional dependency for MimicExplainer, raise the version ceiling of lightgbm from 3.3.4 to 4

7.0.1 (Apr 25, 2023)

🐛 Bug Fixes

• A bug causing GENERATIVE_LLM models to be sent as SCORE_CATEGORICAL models

7.0.0 (Apr 13, 2023)

🎁 New Features

• Add Object Detection model-type support

• Add Generative LLM model-type support for pandas logging

• Add evaluation metrics generation for Generative LLM models

• Make prediction IDs optional

• Add summarization UseCase to AutoEmbeddings

• Add optional, additional custom headers to Client instantiation

💬 Feedback Enhancements

• Add a warning message when only actuals are sent

• Add a descriptive error message when embedding features are sent without a vector

• Add warning when prediction label or prediction ID will be defaulted

🐛 Bug Fixes

• A bug causing skipped validation checks when the new REGRESSION and CATEGORICAL model types are selected

✅ Validation Changes

• Add a validation check that will fail if the character limit on prediction ID (128 max) is exceeded

• Add a validation check that will fail if there are duplicated columns in the dataframe

• Changed time range requirements to -2/+1 (two years in the past, and 1 future year)

❗ Dependency Changes

• Require Python >= 3.8

• Add optional extra dependencies if the Arize package is installed as pip install arize[LLM_Evaluation]:

  • nltk>=3.0.0, <4

  • sacrebleu>=2.3.1, <3

  • rouge-score>=0.1.2, <1

  • evaluate>=0.3, <1

💀 Deprecations

• Remove numeric_sequence support