Enrichments

For a complete list of all LLM enrichments with output columns and sub-metrics, see the LLM Observability Metrics Reference.

Introduction to Enrichments

Enrichments are specialized evaluation features that augment your LLM application data with automatically generated trust and safety metrics. By defining enrichments during model onboarding, you instruct Fiddler to analyze published prompts and responses using purpose-built models that assess dimensions like faithfulness, toxicity, relevance, and safety compliance. The enrichment framework processes your application’s inputs and outputs to generate quantitative scores that integrate directly with Fiddler’s monitoring dashboards, alerting systems, and root cause analysis tools. This approach enables proactive detection of model drift, content safety violations, and performance degradation without requiring manual evaluation or external API dependencies.

Enrichments are custom features designed to augment the data provided in events
Enrichments augment existing columns with new metrics that are defined during model onboarding
The new metrics are available for use within the analysis, charting, and alerting functionalities in Fiddler

The following example demonstrates how to configure a TextEmbedding enrichment to enable vector-based monitoring and visualization of your LLM application’s text inputs. TextEmbedding enrichments convert unstructured text into high-dimensional vector representations that capture semantic meaning, enabling Fiddler to detect drift in the topics and themes of your prompts or responses over time. In this configuration, the enrichment transforms text from the question column into numerical embeddings stored in question_embedding. These embeddings power Fiddler’s 3D UMAP visualizations, allowing you to visually identify clusters of similar content, detect outliers, and spot shifts in user behavior patterns. The TextEmbedding feature also enables drift detection by comparing the distribution of embeddings between your baseline and production data, providing early warning when your application encounters significantly different types of content than expected.

import fiddler as fdl

# Define a TextEmbedding enrichment for vector-based monitoring
fiddler_custom_features = [
    fdl.TextEmbedding(
        name='question_cf',          # Internal name for the custom feature
        source_column='question',    # Original text column to analyze
        column='question_embedding', # Generated embedding vector column
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Embedding

Embeddings are numerical representations (vectors) generated by a model for input text. Each number within the vector represents a different dimension of the text input. The meaning of each number depends on how the embedding generating model was trained. Fiddler uses publicly available embeddings to power the 3D UMAP experience. Because the same model generates all embeddings, the points will naturally cluster, enabling quick visual anomaly detection. To create embeddings and leverage them for the UMAP visualization, you must create a new TextEmbedding enrichment on your unstructured text column. If you want to bring your own embeddings onto the Fiddler platform, you can direct Fiddler to consume the embeddings vector directly from your data.

View Usage Examples

Example 1: Fiddler-Generated EmbeddingsThis example automatically generates text embeddings on a text column called prompt:

import fiddler as fdl

fiddler_custom_features = [
    fdl.TextEmbedding(
        name='Prompt TextEmbedding',  # name of generated column (for internal use, required)
        source_column='prompt',  # source - raw text
        column='Enrichment Prompt Embedding',  # name of the vector output
        n_clusters=5,  # Number of clusters for k-means clustering
        n_tags=5,  # Top n tags used as summary tags per cluster
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
Enrichment Prompt Embedding	vector	Embeddings corresponding to string column `prompt`
Prompt Text Embedding	integer	Column for internal use
Prompt Text Embedding - Centroid Distance	float	Centroid Distance to string column `prompt`

Example 2: User-Provided EmbeddingsThis example demonstrates leveraging an existing vector column of text embeddings called pre_existing_prompt_embedding:

Vector embeddings must use data type List(float) for compatibility. Using CSV for source data often requires preprocessing after initial loading into a pandas dataframe.

import ast
import numpy as np
import pandas as pd
import fiddler as fdl

df = pd.read_csv(PATH_TO_SAMPLE_CSV)
# Convert string representation to list
df['pre_existing_prompt_embedding'] = df['pre_existing_prompt_embedding'].apply(
    ast.literal_eval
)

fiddler_custom_features = [
    fdl.TextEmbedding(
        name='User-provided Text Embedding',  # name of the generated column
        source_column='prompt',  # source - raw text
        column='pre_existing_prompt_embedding',  # name of your vector column
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt'],
    metadata=['pre_existing_prompt_embedding'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
User-provided Text Embedding	integer	Column for internal use
User-provided Text Embedding - Centroid Distance	float	Centroid Distance to string column `prompt`

Centroid Distance

Fiddler uses KMeans to determine cluster membership for a given enrichment. The Centroid Distance enrichment provides information about the distance between the selected point and the closest centroid. Centroid Distance is automatically added if the TextEmbedding enrichment is created for any given model.

Centroid Distance columns are automatically generated when you create a TextEmbedding enrichment. See the Embedding section above for complete usage examples.

Personally Identifiable Information

The PII (Personally Identifiable Information) enrichment tool is a critical tool for detecting and flagging sensitive information in textual data. Whether user-entered or system-generated, this enrichment aims to identify instances where PII may be exposed, helping prevent privacy breaches and misuse of personal data. In an era where digital privacy concerns are paramount, mishandling or unintentionally leaking PII can have serious repercussions, including privacy violations, identity theft, and significant legal and reputational damage.

PII enrichment is integrated with Presidio for entity detection.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Rag PII',
        enrichment='pii',
        columns=['question'],  # one or more columns
        allow_list=['fiddler'],  # Optional: list of strings that are white listed
        score_threshold=0.85,  # Optional: float value for minimum possible confidence
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Rag PII (question)	bool	Whether any PII was detected
FDL Rag PII (question) Matches	str	What matches in raw text were flagged as potential PII (ex. ‘Douglas MacArthur,Korean’)
FDL Rag PII (question) Entities	str	What entities these matches were tagged as (ex. ‘PERSON’)

Supported PII Entity Types:CREDIT_CARD, CRYPTO, DATE_TIME, EMAIL_ADDRESS, IBAN_CODE, IP_ADDRESS, LOCATION, PERSON, PHONE_NUMBER, URL, US_SSN, US_DRIVER_LICENSE, US_ITIN, US_PASSPORT

Evaluate

This enrichment provides n-gram-based metrics for comparing two passages of text, such as BLEU, ROUGE, and METEOR. Created initially to compare an AI-generated translation or summary to a human-generated one, these metrics have some use in RAG summarization tasks. They score highest when the reference and generated texts contain overlapping sequences. Additionally, these metrics are not as effective for long passages of text.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='QA Evaluate',
        enrichment='evaluate',
        columns=['correct_answer', 'generated_answer'],
        config={
            'reference_col': 'correct_answer',  # required
            'prediction_col': 'generated_answer',  # required
            'metrics': ['bleu', 'rouge', 'meteor'],  # optional, this is the default
        }
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL QA Evaluate (bleu)	float	BLEU score: Measures precision of word n-grams
FDL QA Evaluate (rouge1)	float	ROUGE-1 score: Unigram recall
FDL QA Evaluate (rouge2)	float	ROUGE-2 score: Bigram recall
FDL QA Evaluate (rougel)	float	ROUGE-L score: Longest common subsequence
FDL QA Evaluate (rougelsum)	float	ROUGE-L summary score
FDL QA Evaluate (meteor)	float	METEOR score: Precision, recall, and semantic matching

Textstat

The Textstat enrichment generates various text statistics such as character/letter count, Flesch-Kincaid, and other metrics on the target text column.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Text Statistics',
        enrichment='textstat',
        columns=['question'],
        config={
            'statistics': [
                'char_count',
                'dale_chall_readability_score',
            ]
        },
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Text Statistics (question) char_count	int	Character count of string in `question` column
FDL Text Statistics (question) dale_chall_readability_score	float	Readability score of string in `question` column

Supported Statistics:char_count, letter_count, miniword_count, words_per_sentence, polysyllabcount, lexicon_count, syllable_count, sentence_count, flesch_reading_ease, smog_index, flesch_kincaid_grade, coleman_liau_index, automated_readability_index, dale_chall_readability_score, difficult_words, linsear_write_formula, gunning_fog, long_word_count, monosyllabcount

Sentiment

The Sentiment enrichment uses NLTK’s VADER lexicon to generate a score and corresponding sentiment for all specified columns. To enable, set the enrichment parameter to sentiment.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Question Sentiment',
        enrichment='sentiment',
        columns=['question'],
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Question Sentiment (question) compound	float	Raw score of sentiment
FDL Question Sentiment (question) sentiment	string	One of `positive`, `negative`, or `neutral`

Profanity

The Profanity enrichment is designed to detect and flag the use of offensive or inappropriate language within textual content. This enrichment is essential for maintaining the integrity and professionalism of digital platforms, forums, social media, and any user-generated content areas. The profanity enrichment searches the target text for words from the two sources below:

The Obscenity List from SurgeAI
Google banned words <https://github.com/coffee-and-fun/google-profanity-words/blob/main/data/en.txt>

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Profanity',
        enrichment='profanity',
        columns=['prompt', 'response'],
        config={'output_column_name': 'contains_profanity'},
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Profanity (prompt) contains_profanity	bool	Indicates if input contains profanity in the value of the prompt column
FDL Profanity (response) contains_profanity	bool	Indicates if input contains profanity in the value of the response column

Regex Match

The Regex Match enrichment evaluates text responses or content for adherence to specific patterns defined by regular expressions (regex). By accepting a regex as input, this metric offers a highly customizable way to check if a string column in the dataset matches the given pattern. This functionality is essential for scenarios requiring precise formatting, specific keyword inclusion, or adherence to particular linguistic structures.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Regex - only digits',
        enrichment='regex_match',
        columns=['prompt', 'response'],
        config={
            'regex': '^\d+$',
        }
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt', 'doc_0', 'doc_1', 'doc_2', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Regex - only digits	category	Match or No Match, depending on the regex specified in the config matching in the string

Topic

The Topic enrichment leverages the capabilities of Zero Shot Classifier models to categorize textual inputs into a predefined list of topics, even without having been explicitly trained on those topics. This approach to text classification is known as zero-shot learning, a groundbreaking method in natural language processing (NLP) that enables models to intelligently classify text they haven’t encountered during training. It’s beneficial for applications that require understanding and organizing content dynamically across a broad range of subjects or themes.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Topics',
        enrichment='topic_model',
        columns=['response'],
        config={'topics': ['politics', 'economy', 'astronomy']},
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt', 'doc_0', 'doc_1', 'doc_2', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Topics (response) topic_model_scores	list[float]	Probability of the given column in each of the topics specified in the Enrichment config. Each float value indicates the probability of the given input being classified in the corresponding topic, in the same order as topics. Each value will be between 0 and 1. The sum of values does not equal 1, as each classification is performed independently of other topics.
FDL Topics (response) max_score_topic	string	Topic with the maximum score from the list of topic names specified in the Enrichment config

Banned Keyword Detector

The Banned Keyword Detector enrichment is designed to scrutinize textual inputs for the presence of specified terms, with a particular focus on identifying content that includes potentially undesirable or restricted keywords. This enrichment operates based on a list of terms defined in its configuration, making it highly adaptable to various content moderation, compliance, and content filtering needs.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Banned KW',
        enrichment='banned_keywords',
        columns=['prompt', 'response'],
        config={
            'output_column_name': 'contains_banned_kw',
            'banned_keywords': ['nike', 'adidas', 'puma'],
        },
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt', 'doc_0', 'doc_1', 'doc_2', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Banned KW (prompt) contains_banned_kw	bool	Indicates if input contains one of the specified banned keywords in the value of the prompt column
FDL Banned KW (response) contains_banned_kw	bool	Indicates if input contains one of the specified banned keywords in the value of the response column

Language Detector

The Language Detector enrichment identifies the language of the source text. This enrichment is based on a pretrained text identification model and leverages fasttext models for language detection.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Language',
        enrichment='language_detection',
        columns=['prompt'],
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Language (prompt) language	string	Language prediction for input text
FDL Language (prompt) language_probability	float	Confidence probability of language prediction

Answer Relevance

The Answer Relevance enrichment evaluates the pertinence of AI-generated responses to their corresponding prompts. This enrichment assesses whether a response accurately addresses the question or topic posed by the initial prompt, providing a simple yet effective binary outcome: relevant or not. Its primary function is to ensure that the output of AI systems, such as chatbots, virtual assistants, and content generation models, remains aligned with the user’s informational needs and intentions.

Looking for more granular relevance scoring? The Evals SDK and Agentic Monitoring offer Answer Relevance 2.0 with ordinal scoring (High/Medium/Low) as part of the RAG Health Metrics diagnostic triad.

View Usage Example

Python Configuration:

import fiddler as fdl

answer_relevance_config = {
    'prompt': 'prompt_col',
    'response': 'response_col',
}

fiddler_custom_features = [
    fdl.Enrichment(
        name='Answer Relevance',
        enrichment='answer_relevance',
        columns=['prompt_col', 'response_col'],
        config=answer_relevance_config,
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt_col', 'response_col'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Answer Relevance	bool	Binary metric, which is True if `response` is relevant to the `prompt`

Faithfulness

The Faithfulness (Groundedness) enrichment is a binary indicator that evaluates the accuracy and reliability of the facts presented in AI-generated text responses. It specifically assesses whether the information used in the response aligns with and is grounded in the provided context, often through referenced documents or data. This enrichment plays a critical role in ensuring that the AI’s outputs are not only relevant but also factually accurate, given the context it was provided.

Faithfulness (LLM-as-a-Judge) vs Faithfulness (Centor Model): This LLM-based Faithfulness enrichment (faithfulness) uses context and response config keys. Faithfulness (Centor Model) (ftl_response_faithfulness) uses Fiddler’s proprietary Centor Model for Faithfulness for lower latency. For RAG pipeline diagnostics with detailed reasoning, see RAG Faithfulness in the Evals SDK and Agentic Monitoring.

View Usage Example

Python Configuration:

import fiddler as fdl

faithfulness_config = {
    'context': ['doc_0', 'doc_1', 'doc_2'],
    'response': 'response_col',
}

fiddler_custom_features = [
    fdl.Enrichment(
        name='Faithfulness',
        enrichment='faithfulness',
        columns=['doc_0', 'doc_1', 'doc_2', 'response_col'],
        config=faithfulness_config,
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['doc_0', 'doc_1', 'doc_2', 'response_col'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Faithfulness	bool	Binary metric, which is True if the facts used in `response` are correctly used from the `context` columns

Coherence

The Coherence enrichment assesses the logical flow and clarity of AI-generated text responses, ensuring they are structured in a way that makes sense from start to finish. This enrichment is crucial for evaluating whether the content produced by AI maintains a consistent theme, argument, or narrative, without disjointed thoughts or abrupt shifts in topic. Coherence is key to making AI-generated content not only understandable but also engaging and informative for the reader.

View Usage Example

Python Configuration:

import fiddler as fdl

coherence_config = {
    'response': 'response_col',
}

fiddler_custom_features = [
    fdl.Enrichment(
        name='Coherence',
        enrichment='coherence',
        columns=['response_col'],
        config=coherence_config,
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['doc_0', 'doc_1', 'doc_2', 'response_col'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Coherence	bool	Binary metric, which is True if `response` makes coherent arguments that flow well

Conciseness

The Conciseness enrichment evaluates the brevity and clarity of AI-generated text responses, ensuring that the information is presented in a straightforward and efficient manner. This enrichment identifies and rewards responses that effectively communicate their message without unnecessary elaboration or redundancy. In the realm of AI-generated content, where verbosity can dilute the message’s impact or confuse the audience, maintaining conciseness is crucial for enhancing readability and user engagement.

View Usage Example

Python Configuration:

import fiddler as fdl

conciseness_config = {
    'response': 'response_col',
}

fiddler_custom_features = [
    fdl.Enrichment(
        name='Conciseness',
        enrichment='conciseness',
        columns=['response'],
        config=conciseness_config,
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt', 'doc_0', 'doc_1', 'doc_2', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Conciseness	bool	Binary metric, which is True if `response` is concise and not overly verbose

Safety

The Safety enrichment evaluates the safety of the text along eleven different dimensions: illegal, hateful, harassing, racist, sexist, violent, sexual, harmful, unethical, jailbreaking, roleplaying. The Safety enrichment is powered by the Centor Model for Safety.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Prompt Safety',
        enrichment='ftl_prompt_safety',
        columns=['prompt'],
        config={'classifiers': ['jailbreaking', 'illegal']}  # Optional: specify dimensions
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['prompt'],
    custom_features=fiddler_custom_features,
)

Generated Columns:For each dimension specified (or all 11 dimensions if not specified):

Column	Type	Description
FDL Prompt Safety (prompt) `dimension`	bool	Binary metric, which is True if the input is deemed unsafe, False otherwise
FDL Prompt Safety (prompt) `dimension` score	float	Confidence probability of safety prediction

Supported Dimensions: illegal, hateful, harassing, racist, sexist, violent, sexual, harmful, unethical, jailbreaking, roleplaying

Faithfulness (Centor Model)

The Faithfulness (Centor Model) enrichment is designed to evaluate the accuracy and reliability of facts presented in AI-generated text responses. It is powered by the Centor Model for Faithfulness.

The faithfulness threshold defaults to 0.5 but can be adjusted in the configuration to control the sensitivity of the faithfulness scoring. Lower thresholds result in stricter faithfulness detection, while higher thresholds are more permissive.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='Faithfulness',
        enrichment='ftl_response_faithfulness',
        columns=['context', 'response'],
        config={
            'context_field': 'context',
            'response_field': 'response',
            'threshold': '0.5'  # Optional parameter, default is 0.5
        }
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['context', 'response'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Faithfulness faithful	bool	Binary metric, which is True if the facts used in `response` are correctly used from the `context` columns
FDL Faithfulness faithful score	float	Confidence probability of faithfulness prediction

Token Count

The Token Count enrichment counts the number of tokens in a string.

This enrichment uses the tiktoken library for token counting.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='TokenCount',
        enrichment='token_count',
        columns=['question'],
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['question'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
FDL Token Counts (question)	int	Number of tokens in the string

SQL Validation

The SQL Validation enrichment is designed to evaluate different query dialects for syntax correctness.

Query validation is syntax based and does not check against any existing schema or databases for validity.

View Usage Example

Python Configuration:

import fiddler as fdl

# The following dialects are supported:
# 'athena', 'bigquery', 'clickhouse', 'databricks', 'doris', 'drill', 'duckdb',
# 'hive', 'materialize', 'mysql', 'oracle', 'postgres', 'presto', 'prql',
# 'redshift', 'risingwave', 'snowflake', 'spark', 'spark2', 'sqlite',
# 'starrocks', 'tableau', 'teradata', 'trino', 'tsql'

fiddler_custom_features = [
    fdl.Enrichment(
        name='SQLValidation',
        enrichment='sql_validation',
        columns=['query_string'],
        config={
            'dialect': 'mysql'
        }
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['query_string'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
SQL Validator valid	bool	True if the query string is syntactically valid for the specified dialect, False if not
SQL Validator errors	str	If syntax errors are found they will be present as a JSON serialized string containing a list of dictionaries describing the errors

JSON Validation

The JSON Validation enrichment is designed to validate JSON for correctness and optionally against a user-defined schema for validation.

This enrichment uses the python-jsonschema library for JSON schema validation. The defined validation_schema must be a valid python-jsonschema schema.

View Usage Example

Python Configuration:

import fiddler as fdl

fiddler_custom_features = [
    fdl.Enrichment(
        name='JSONValidation',
        enrichment='json_validation',
        columns=['json_string'],
        config={
            'strict': 'true',
            'validation_schema': {
                '$schema': 'https://json-schema.org/draft/2020-12/schema',
                'type': 'object',
                'properties': {
                    'prop_1': {'type': 'number'}
                    # ... additional properties
                },
                'required': ['prop_1'],  # ... additional required fields
                'additionalProperties': False
            }
        }
    ),
]

model_spec = fdl.ModelSpec(
    inputs=['json_string'],
    custom_features=fiddler_custom_features,
)

Generated Columns:

Column	Type	Description
JSON Validator valid	bool	String is valid JSON
JSON Validator errors	str	If the string failed to parse to JSON any parsing errors will be returned as a serialized JSON list of dictionaries

Getting Started

Concepts

Evaluate & Test

Reference

Introduction to Enrichments

Embedding

Centroid Distance

Personally Identifiable Information

Evaluate

Textstat

Sentiment

Profanity

Regex Match

Topic

Banned Keyword Detector

Language Detector

Answer Relevance

Faithfulness

Coherence

Conciseness

Safety

Faithfulness (Centor Model)

Token Count

SQL Validation

JSON Validation

​Introduction to Enrichments

​Embedding

​Centroid Distance

​Personally Identifiable Information

​Evaluate

​Textstat

​Sentiment

​Profanity

​Regex Match

​Topic

​Banned Keyword Detector

​Language Detector

​Answer Relevance

​Faithfulness

​Coherence

​Conciseness

​Safety

​Faithfulness (Centor Model)

​Token Count

​SQL Validation

​JSON Validation

Introduction to Enrichments

Embedding

Centroid Distance

Personally Identifiable Information

Evaluate

Textstat

Sentiment

Profanity

Regex Match

Topic

Banned Keyword Detector

Language Detector

Answer Relevance

Faithfulness

Coherence

Conciseness

Safety

Faithfulness (Centor Model)

Token Count

SQL Validation

JSON Validation