We primarily define two dataclasses to contain the multimodal data to be processed by MLLMs, one for text-only samples and the other for image-text pairs. The detailed attributes in the dataclass are introduced below.
+
+
+
TxtSample: to support text-only sample
+
+
text: prompt in text
+
target: ground-truth label（Default: None）
+
extra: auxiliary arguments that may help in the process afterwards, e.g., adversarial example generation（Default: None）
+
+
+
+
ImageTxtSample: to support multimodal input, i.e., an image-text pair
+
+
image_path: path to the image file
+
text: prompt in text
+
target: ground-truth label（Default: None）
+
extra：auxiliary arguments that may help in the process afterwards, e.g., adversarial example generation（Default: None）
+
+
+
+
The type of the output from an MLLM is also restricted to these two dataclasses.
+
+
_OutputType = Union[ImageTxtSample, TxtSample]
+
+
Source code in mmte/__init__.py.
+
@dataclass
+class TxtSample:
+ text: str
+ target: Optional[str] = None
+ extra: Optional[Dict[str, Any]] = None
+
+ @classmethod
+ def from_dict(cls, data: Dict[str, Any]) -> "TxtSample":
+ return cls(**{k: v for k, v in data.items() if k in cls.__annotations__})
+
+ def to_dict(self) -> Dict[str, Any]:
+ return asdict(self)
+
+ def __getitem__(self, item):
+ return getattr(self, item)
+
+
+@dataclass
+class ImageTxtSample:
+ image_path: str
+ text: str
+ target: Optional[str] = None
+ extra: Optional[Dict[str, Any]] = None
+
+ @classmethod
+ def from_dict(cls, data: Dict[str, Any]) -> "ImageTxtSample":
+ return cls(**{k: v for k, v in data.items() if k in cls.__annotations__})
+
+ def to_dict(self) -> Dict[str, Any]:
+ return asdict(self)
+
+ def __getitem__(self, item):
+ return getattr(self, item)
+
+
+
+_OutputType = Union[ImageTxtSample, TxtSample]
+
In this section, we introduce each element in the task flow about their interface, typical usage, etc., in the order of their parts in the workflow. All elements are registered by an unique identifier into the global registry (defined in mmte/utils/registry.py) and can be accessed by the registry.get_**_class(id) method.
+
Datasets
+
Datasets are defined to collect the samples to be tested for a specific task. It provides the prompt, image path, labels and possibly other information about the data point to the following process. Here are some technical notes about this class.
+
+
The class is a subclass of the torch.utils.data.Dataset and users can iterate through the dataset by the default torch.utils.data.Dataloader. To customize a dataset, user need to define __getitem__ and __len__ as usual along with a collate_fn so that the dataloader can support the dataclass of TxtSample and ImageTxtSample. We provide a default one in mmte.datasets.base.collate_fn which should work for most cases, one can customized your own collate_fn if necessary.
+
A method to further process the data for a certain task, which can be independent from the original dataset, can be optionally specified via the argument method_hook when initialization. This could make additional augmentation, attack and other preprocessing to the existing datasets more convenient. This is illustrated in the Method part.
+
Some information about the dataset can be configured through a yaml config file, like the directory of images, the path to the annotation file.
+
dataset_ids is the list of supported dataset_id for this class, which specify the different splits and processors in sample preparing.
+
+
+
+
Source code in mmte/datasets/base.py. Refer to mmte/datasets/celeb.py for an example.
+
class BaseDataset(Dataset, ABC):
+ """
+ Base class for datasets, __getitem__ function return Union[ImageTxtSample, TxtSample].
+ """
+
+ dataset_id: str # Identifier for the dataset
+ dataset_ids: Sequence[str] = [] # List of available datasets
+ dataset_config: Optional[str] = "" # dataset config path
+
+ def __init__(self, dataset_id: str, method_hook: Optional[BaseMethod] = None, **kwargs) -> None:
+ """
+ Initializing dataset instance.
+
+ Arguments:
+ dataset_id: Identifier for the dataset
+ method_hook: A method instance, which is used as a preprocess hook for __getitem__ funtion
+ kwargs: extra configurations
+
+ """
+
+ assert dataset_id in self.dataset_ids, f"Dataset {dataset_id} must be one of {self.dataset_ids}."
+ self.dataset_id = dataset_id
+ if method_hook:
+ self.method_hook = method_hook
+ else:
+ self.method_hook = None
+ self.dataset: List[Any] = []
+
+ @abstractmethod
+ def __getitem__(self, index: int) -> _OutputType:
+ if self.method_hook:
+ return self.method_hook.run(self.dataset[index])
+ return self.dataset[index]
+
+ @abstractmethod
+ def __len__(self) -> int:
+ return len(self.dataset)
+
+
Methods
+
Methods are designed for additional data processing independent from and universal across datasets, for instance, generating adversarial examples, pairing text prompts with diverse images. Users do not need to modify the code for datasets but only implement a new class of Method and pass it as a hook to the dataset. Here are some technical notes about this class.
+
+
This class works as a hook in the function __getitem__ of a dataset, which is optional.
+
For cases where new images are generated with time-consuming methods or reproducibility is needed, we can set the lazy_mode=True to utilize the previously generated samples. To tackle the challenge that text-only data may not have clear identifiers pointing to a sample, a hash function can be defined to generate the filename for the generated data.
+
+
+
+
Source code in mmte/methods/base.py. Refer to mmte/methods/unrelated_color.py for an example.
+
class BaseMethod(ABC):
+ """
+ Base class for methods, which can be applied to any Dataset inherits from BaseDataset as a hook in __getitem__ function.
+ """
+
+ method_id: str # Identifier for the method
+ method_ids: List[str] # List of available methods
+
+ def __init__(self, method_id: str, img_dir: str, lazy_mode: bool = True) -> None:
+ """
+ Initializing method instance.
+
+ Arguments:
+ method_id: Identifier for the method
+ img_dir: Folder to save images
+ lazy_mode: If True, it will reuse the already generated dataset. If False, it will regenerate the result
+
+ Return:
+ evaluation result
+ """
+ assert method_id in self.method_ids, f"Method {self.method_id} is not available. Only methods in {self.method_ids} can be used."
+ self.method_id = method_id
+ self.img_dir = img_dir
+ self.lazy_mode = lazy_mode
+
+ @abstractmethod
+ def run(self, data: _OutputType, **kwargs) -> _OutputType:
+ """
+ Preprocess each sample in the Dataset one by one.
+
+ Arguments:
+ data: Union[ImageTxtSample, TxtSample], one sample in Dataset
+ kwargs: extra configurations
+
+
+ Return:
+ processed sample
+ """
+ raise NotImplementedError
+
+ @abstractmethod
+ def hash(self, to_hash_str: str, **kwargs) -> str:
+ """
+ Get hash code given to_hash_str, to provide an identifier for the data sample and to reuse the generated samples.
+
+ Arguments:
+ to_hash_str: str
+ kwargs: extra configurations
+
+
+ Return:
+ hash code
+ """
+ raise NotImplementedError
+
+ def __call__(self, *args: Any, **kwds: Any) -> Any:
+ return self.run(*args, **kwds)
+
+
+
Models
+
Models encapsulate the chat model of MLLMs into a unified interface for inference. This enables more convenient standardized evaluation of diverse models. Here are some technical notes about this class.
+
+
chat unifies the interface for generation. messages is a list representing the conversation history, generation_kwargs is the generation configuration, indicating whether to do_sample, the setting of temperature, max_new_tokens, etc. The setting of generation configuration follows that in huggingface transformers.
+
model_id is the unique identifier to get the chatmodel from registry_getchatmodel_class and model_family defines the list of available model identifiers.
+
+
Source code in mmte/models/base.py. Refer to mmte/models/openai_chat.py for an example.
+
class BaseChat(ABC):
+ """
+ Base class for models to be evaluated in a generative/chat manner.
+ """
+
+ model_id: str = '' # ID for a chat model, e.g., minigpt-4-vicuna-7b-v0
+ model_arch: str = '' # Architecture of the model, e.g., minigpt-4
+ model_family: List[str] = [] # List of available model_ids
+
+
+
+ def __init__(self, model_id:str) -> None:
+ self.model_id = model_id
+ assert self.model_id in self.model_family, f"Model {self.model_id} is not available. Only models in {self.model_family} can be used."
+
+
+ @abstractmethod
+ def chat(self,
+ messages: List,
+ **generation_kwargs,
+ ) -> "Response":
+ """
+ Chat interface for generative evaluation with batch size of 1.
+
+ messages: a list of messages, comprising the conversation history and following the format
+ [
+ {
+ 'role': 'system'/'user'/'assistant',
+ 'content': str/dict
+ },
+ ...
+ ],
+ where content is a dict {'text': str, 'image_path': str} when it's multimodal.
+ generation_kwargs: generation configuration specified for different models, including:
+ temperature: float, usually between 0-2, smaller means more deterministic
+ do_sample: bool, whether take sampling as the decoding strategy
+ num_beams: int, the parameter for beam search
+ max_new_tokens: int, maximal number of tokens to be generated
+ stop_sequences: str/List[str], stop words where the model will stop generating further tokens
+ output_scores: bool, whether return the logits of the generated tokens (not very practical)
+ """
+ raise NotImplementedError
+
+
Evaluators
+
source_code: mmte/evaluators/base.py
+
BaseEvaluator
+
This class is primarily used for evaluating the results output by a chat model. The process function preprocesses the input sequences of predictions (preds) and labels (labels)—these sequences can be numerical or textual. The preprocessing aims to generate simple numerical sequences that can be directly used in the eval function, which subsequently calls the metrics function (the metrics function only accepts numerical sequences).
+
Currently, there are three main types of evaluators:
+
+
ChatModel Evaluator: Processes and evaluates the results using a chat model.
+
Classifier Evaluator: Scores the results using a classifier. Currently, it supports the longformer-action-ro model.
+
Rule-based Evaluator: Includes tools for template matching for refusal and score extraction.
+
+
Evaluators can be chained together to form an evaluator sequence (refer to SequentialEvaluator for details). The primary purpose is to reuse the process functions of different evaluators (for example, first using the chat model evaluator for text preprocessing, followed by the rule-based evaluator for scoring).
+
+class BaseEvaluator(ABC):
+ """
+ Base class for evaluators, to evaluate the responses from chatmodels.
+ """
+
+ evaluator_ids: List[str] = []
+ def __init__(self, evaluator_id: str, metrics_cfg: Dict[str, Any]) -> None:
+ """
+ Initializing evaluator instance.
+
+ Arguments:
+ evaluator_id: Identifier for the evaluator
+ metrics_cfg: config dict for metrics hooks, format: {metrics_id: metrics_kwargs, ...}
+
+ """
+ assert evaluator_id in self.evaluator_ids, f"Evaluator {self.evaluator_id} is not available. Only Evaluators in {self.evaluator_ids} can be used."
+
+ self.evaluator_id = evaluator_id
+
+ self.metrics_cfg = metrics_cfg
+ for metrics_id in self.metrics_cfg.keys():
+ assert metrics_id in _supported_metrics.keys(), f"{metrics_id} is not supported."
+
+ @abstractmethod
+ def process(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Tuple[Sequence[Any], Sequence[Any]]:
+ """
+ 1. Perform some processing on sequence data, mainly including scoring/text-extraction with chatmodel/classifier/rule-based, etc.
+ 2. Different evaluators can be concatenated, and the process function can be cascaded to perform multi-step processing on sequence data.
+
+ Arguments:
+ preds: responses from chatmodels or preds from `process` function of another evaluator
+ labels: groundtruth labels or labels from `process` function of another evaluator
+ extras: extra parameters or extra sequence from `process` function of another evaluator
+
+ Return:
+ preds: processed preds sequence
+ labels: processed labels sequence
+ extras: processed extra sequence
+ """
+
+ # no-op
+ return preds, labels, extras
+
+ def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]:
+ """
+ Evaluate pipeline including data processing and metrics calculation.
+
+ Arguments:
+ preds: responses from chatmodels
+ labels: groundtruth labels
+ extras: extra parameters
+
+ Return:
+ results
+ """
+
+ processed_preds, processed_labels, processed_extras = self.process(preds, labels, extras)
+ results = {}
+
+ for metrics_id, kwargs in self.metrics_cfg.items():
+ metrics_fn = _supported_metrics[metrics_id]
+ results[metrics_id] = metrics_fn(processed_labels, processed_preds, **kwargs)
+
+ return results
+
+ def __call__(self, *args: Any, **kwds: Any) -> Any:
+ return self.eval(*args, **kwds)
+
+
+
SequentialEvaluator
+
class SequentialEvaluator:
+ """
+ Class for cascading evaluators to perform multi-step processing on sequence data and get results from final sequence data.
+ """
+
+ def __init__(self, evaluator_seq_cfg: Dict[str, Any]) -> None:
+ """
+ Initializing sequence-evaluator instance.
+
+ Arguments:
+ evaluator_seq_cfg: config dict for instantiatizing evaluators, format: {evaluator: evaluator_kwargs, ...}
+
+ """
+
+ evaluator_seq, evaluator_cls_names = [], []
+ for evaluator_id, evaluator_kwargs in evaluator_seq_cfg.items():
+ evaluator_cls = registry.get_evaluator_class(evaluator_id)
+ evaluator = evaluator_cls(evaluator_id, **evaluator_kwargs)
+ evaluator_cls_names.append(evaluator_cls.__name__)
+ evaluator_seq.append(evaluator)
+ self.evaluator_seq = evaluator_seq
+ self.keyname_prefix_seq = self.create_sequence_list(evaluator_cls_names)
+
+ def create_sequence_list(self, input_list: List[str]) -> List[str]:
+ result = []
+ current = ""
+ for item in input_list:
+ if current:
+ current += f"->{item}"
+ else:
+ current = item
+ result.append(current)
+ return result
+
+ def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]:
+ """
+ Evaluate pipeline including data processing and metrics calculation.
+
+ Arguments:
+ preds: responses from chatmodels
+ labels: groundtruth labels
+ extras: extra parameters
+
+ Return:
+ results
+ """
+ prefix_results = {}
+ seq_len = len(self.evaluator_seq)
+ for evaluator_idx, (evaluator, keyname_prefix) in enumerate(zip(self.evaluator_seq, self.keyname_prefix_seq)):
+ if evaluator_idx < seq_len - 1:
+ preds, labels, extras = evaluator.process(preds, labels, extras)
+ prefix_results.update({f"{keyname_prefix}:pred_no_op": preds})
+ else:
+ # final evaluator
+ results = evaluator(preds, labels, extras)
+ prefix_results.update({f"{keyname_prefix}:{key}": value for key, value in results.items()})
+
+ return prefix_results
+
+ def __call__(self, *args: Any, **kwds: Any) -> Any:
+ return self.eval(*args, **kwds)
+
+
+
+
Metrics
+
We pre-define some common metrics for users to call. These functions to calculate metrics take two array-like arguments of digits to compute the statistical or sample-wise results. We also consider some cases where simple operations of aggregation are needed, e.g., sum, mean.
+
+
+
+
+
+
+
+
+
diff --git a/search/lunr.js b/search/lunr.js
new file mode 100644
index 0000000..aca0a16
--- /dev/null
+++ b/search/lunr.js
@@ -0,0 +1,3475 @@
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
+ */
+
+;(function(){
+
+/**
+ * A convenience function for configuring and constructing
+ * a new lunr Index.
+ *
+ * A lunr.Builder instance is created and the pipeline setup
+ * with a trimmer, stop word filter and stemmer.
+ *
+ * This builder object is yielded to the configuration function
+ * that is passed as a parameter, allowing the list of fields
+ * and other builder parameters to be customised.
+ *
+ * All documents _must_ be added within the passed config function.
+ *
+ * @example
+ * var idx = lunr(function () {
+ * this.field('title')
+ * this.field('body')
+ * this.ref('id')
+ *
+ * documents.forEach(function (doc) {
+ * this.add(doc)
+ * }, this)
+ * })
+ *
+ * @see {@link lunr.Builder}
+ * @see {@link lunr.Pipeline}
+ * @see {@link lunr.trimmer}
+ * @see {@link lunr.stopWordFilter}
+ * @see {@link lunr.stemmer}
+ * @namespace {function} lunr
+ */
+var lunr = function (config) {
+ var builder = new lunr.Builder
+
+ builder.pipeline.add(
+ lunr.trimmer,
+ lunr.stopWordFilter,
+ lunr.stemmer
+ )
+
+ builder.searchPipeline.add(
+ lunr.stemmer
+ )
+
+ config.call(builder, builder)
+ return builder.build()
+}
+
+lunr.version = "2.3.9"
+/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A namespace containing utils for the rest of the lunr library
+ * @namespace lunr.utils
+ */
+lunr.utils = {}
+
+/**
+ * Print a warning message to the console.
+ *
+ * @param {String} message The message to be printed.
+ * @memberOf lunr.utils
+ * @function
+ */
+lunr.utils.warn = (function (global) {
+ /* eslint-disable no-console */
+ return function (message) {
+ if (global.console && console.warn) {
+ console.warn(message)
+ }
+ }
+ /* eslint-enable no-console */
+})(this)
+
+/**
+ * Convert an object to a string.
+ *
+ * In the case of `null` and `undefined` the function returns
+ * the empty string, in all other cases the result of calling
+ * `toString` on the passed object is returned.
+ *
+ * @param {Any} obj The object to convert to a string.
+ * @return {String} string representation of the passed object.
+ * @memberOf lunr.utils
+ */
+lunr.utils.asString = function (obj) {
+ if (obj === void 0 || obj === null) {
+ return ""
+ } else {
+ return obj.toString()
+ }
+}
+
+/**
+ * Clones an object.
+ *
+ * Will create a copy of an existing object such that any mutations
+ * on the copy cannot affect the original.
+ *
+ * Only shallow objects are supported, passing a nested object to this
+ * function will cause a TypeError.
+ *
+ * Objects with primitives, and arrays of primitives are supported.
+ *
+ * @param {Object} obj The object to clone.
+ * @return {Object} a clone of the passed object.
+ * @throws {TypeError} when a nested object is passed.
+ * @memberOf Utils
+ */
+lunr.utils.clone = function (obj) {
+ if (obj === null || obj === undefined) {
+ return obj
+ }
+
+ var clone = Object.create(null),
+ keys = Object.keys(obj)
+
+ for (var i = 0; i < keys.length; i++) {
+ var key = keys[i],
+ val = obj[key]
+
+ if (Array.isArray(val)) {
+ clone[key] = val.slice()
+ continue
+ }
+
+ if (typeof val === 'string' ||
+ typeof val === 'number' ||
+ typeof val === 'boolean') {
+ clone[key] = val
+ continue
+ }
+
+ throw new TypeError("clone is not deep and does not support nested objects")
+ }
+
+ return clone
+}
+lunr.FieldRef = function (docRef, fieldName, stringValue) {
+ this.docRef = docRef
+ this.fieldName = fieldName
+ this._stringValue = stringValue
+}
+
+lunr.FieldRef.joiner = "/"
+
+lunr.FieldRef.fromString = function (s) {
+ var n = s.indexOf(lunr.FieldRef.joiner)
+
+ if (n === -1) {
+ throw "malformed field ref string"
+ }
+
+ var fieldRef = s.slice(0, n),
+ docRef = s.slice(n + 1)
+
+ return new lunr.FieldRef (docRef, fieldRef, s)
+}
+
+lunr.FieldRef.prototype.toString = function () {
+ if (this._stringValue == undefined) {
+ this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef
+ }
+
+ return this._stringValue
+}
+/*!
+ * lunr.Set
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A lunr set.
+ *
+ * @constructor
+ */
+lunr.Set = function (elements) {
+ this.elements = Object.create(null)
+
+ if (elements) {
+ this.length = elements.length
+
+ for (var i = 0; i < this.length; i++) {
+ this.elements[elements[i]] = true
+ }
+ } else {
+ this.length = 0
+ }
+}
+
+/**
+ * A complete set that contains all elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.complete = {
+ intersect: function (other) {
+ return other
+ },
+
+ union: function () {
+ return this
+ },
+
+ contains: function () {
+ return true
+ }
+}
+
+/**
+ * An empty set that contains no elements.
+ *
+ * @static
+ * @readonly
+ * @type {lunr.Set}
+ */
+lunr.Set.empty = {
+ intersect: function () {
+ return this
+ },
+
+ union: function (other) {
+ return other
+ },
+
+ contains: function () {
+ return false
+ }
+}
+
+/**
+ * Returns true if this set contains the specified object.
+ *
+ * @param {object} object - Object whose presence in this set is to be tested.
+ * @returns {boolean} - True if this set contains the specified object.
+ */
+lunr.Set.prototype.contains = function (object) {
+ return !!this.elements[object]
+}
+
+/**
+ * Returns a new set containing only the elements that are present in both
+ * this set and the specified set.
+ *
+ * @param {lunr.Set} other - set to intersect with this set.
+ * @returns {lunr.Set} a new set that is the intersection of this and the specified set.
+ */
+
+lunr.Set.prototype.intersect = function (other) {
+ var a, b, elements, intersection = []
+
+ if (other === lunr.Set.complete) {
+ return this
+ }
+
+ if (other === lunr.Set.empty) {
+ return other
+ }
+
+ if (this.length < other.length) {
+ a = this
+ b = other
+ } else {
+ a = other
+ b = this
+ }
+
+ elements = Object.keys(a.elements)
+
+ for (var i = 0; i < elements.length; i++) {
+ var element = elements[i]
+ if (element in b.elements) {
+ intersection.push(element)
+ }
+ }
+
+ return new lunr.Set (intersection)
+}
+
+/**
+ * Returns a new set combining the elements of this and the specified set.
+ *
+ * @param {lunr.Set} other - set to union with this set.
+ * @return {lunr.Set} a new set that is the union of this and the specified set.
+ */
+
+lunr.Set.prototype.union = function (other) {
+ if (other === lunr.Set.complete) {
+ return lunr.Set.complete
+ }
+
+ if (other === lunr.Set.empty) {
+ return this
+ }
+
+ return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))
+}
+/**
+ * A function to calculate the inverse document frequency for
+ * a posting. This is shared between the builder and the index
+ *
+ * @private
+ * @param {object} posting - The posting for a given term
+ * @param {number} documentCount - The total number of documents.
+ */
+lunr.idf = function (posting, documentCount) {
+ var documentsWithTerm = 0
+
+ for (var fieldName in posting) {
+ if (fieldName == '_index') continue // Ignore the term index, its not a field
+ documentsWithTerm += Object.keys(posting[fieldName]).length
+ }
+
+ var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)
+
+ return Math.log(1 + Math.abs(x))
+}
+
+/**
+ * A token wraps a string representation of a token
+ * as it is passed through the text processing pipeline.
+ *
+ * @constructor
+ * @param {string} [str=''] - The string token being wrapped.
+ * @param {object} [metadata={}] - Metadata associated with this token.
+ */
+lunr.Token = function (str, metadata) {
+ this.str = str || ""
+ this.metadata = metadata || {}
+}
+
+/**
+ * Returns the token string that is being wrapped by this object.
+ *
+ * @returns {string}
+ */
+lunr.Token.prototype.toString = function () {
+ return this.str
+}
+
+/**
+ * A token update function is used when updating or optionally
+ * when cloning a token.
+ *
+ * @callback lunr.Token~updateFunction
+ * @param {string} str - The string representation of the token.
+ * @param {Object} metadata - All metadata associated with this token.
+ */
+
+/**
+ * Applies the given function to the wrapped string token.
+ *
+ * @example
+ * token.update(function (str, metadata) {
+ * return str.toUpperCase()
+ * })
+ *
+ * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.update = function (fn) {
+ this.str = fn(this.str, this.metadata)
+ return this
+}
+
+/**
+ * Creates a clone of this token. Optionally a function can be
+ * applied to the cloned token.
+ *
+ * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.
+ * @returns {lunr.Token}
+ */
+lunr.Token.prototype.clone = function (fn) {
+ fn = fn || function (s) { return s }
+ return new lunr.Token (fn(this.str, this.metadata), this.metadata)
+}
+/*!
+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A function for splitting a string into tokens ready to be inserted into
+ * the search index. Uses `lunr.tokenizer.separator` to split strings, change
+ * the value of this property to change how strings are split into tokens.
+ *
+ * This tokenizer will convert its parameter to a string by calling `toString` and
+ * then will split this string on the character in `lunr.tokenizer.separator`.
+ * Arrays will have their elements converted to strings and wrapped in a lunr.Token.
+ *
+ * Optional metadata can be passed to the tokenizer, this metadata will be cloned and
+ * added as metadata to every token that is created from the object to be tokenized.
+ *
+ * @static
+ * @param {?(string|object|object[])} obj - The object to convert into tokens
+ * @param {?object} metadata - Optional metadata to associate with every token
+ * @returns {lunr.Token[]}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.tokenizer = function (obj, metadata) {
+ if (obj == null || obj == undefined) {
+ return []
+ }
+
+ if (Array.isArray(obj)) {
+ return obj.map(function (t) {
+ return new lunr.Token(
+ lunr.utils.asString(t).toLowerCase(),
+ lunr.utils.clone(metadata)
+ )
+ })
+ }
+
+ var str = obj.toString().toLowerCase(),
+ len = str.length,
+ tokens = []
+
+ for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {
+ var char = str.charAt(sliceEnd),
+ sliceLength = sliceEnd - sliceStart
+
+ if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {
+
+ if (sliceLength > 0) {
+ var tokenMetadata = lunr.utils.clone(metadata) || {}
+ tokenMetadata["position"] = [sliceStart, sliceLength]
+ tokenMetadata["index"] = tokens.length
+
+ tokens.push(
+ new lunr.Token (
+ str.slice(sliceStart, sliceEnd),
+ tokenMetadata
+ )
+ )
+ }
+
+ sliceStart = sliceEnd + 1
+ }
+
+ }
+
+ return tokens
+}
+
+/**
+ * The separator used to split a string into tokens. Override this property to change the behaviour of
+ * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.
+ *
+ * @static
+ * @see lunr.tokenizer
+ */
+lunr.tokenizer.separator = /[\s\-]+/
+/*!
+ * lunr.Pipeline
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.Pipelines maintain an ordered list of functions to be applied to all
+ * tokens in documents entering the search index and queries being ran against
+ * the index.
+ *
+ * An instance of lunr.Index created with the lunr shortcut will contain a
+ * pipeline with a stop word filter and an English language stemmer. Extra
+ * functions can be added before or after either of these functions or these
+ * default functions can be removed.
+ *
+ * When run the pipeline will call each function in turn, passing a token, the
+ * index of that token in the original list of all tokens and finally a list of
+ * all the original tokens.
+ *
+ * The output of functions in the pipeline will be passed to the next function
+ * in the pipeline. To exclude a token from entering the index the function
+ * should return undefined, the rest of the pipeline will not be called with
+ * this token.
+ *
+ * For serialisation of pipelines to work, all functions used in an instance of
+ * a pipeline should be registered with lunr.Pipeline. Registered functions can
+ * then be loaded. If trying to load a serialised pipeline that uses functions
+ * that are not registered an error will be thrown.
+ *
+ * If not planning on serialising the pipeline then registering pipeline functions
+ * is not necessary.
+ *
+ * @constructor
+ */
+lunr.Pipeline = function () {
+ this._stack = []
+}
+
+lunr.Pipeline.registeredFunctions = Object.create(null)
+
+/**
+ * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token
+ * string as well as all known metadata. A pipeline function can mutate the token string
+ * or mutate (or add) metadata for a given token.
+ *
+ * A pipeline function can indicate that the passed token should be discarded by returning
+ * null, undefined or an empty string. This token will not be passed to any downstream pipeline
+ * functions and will not be added to the index.
+ *
+ * Multiple tokens can be returned by returning an array of tokens. Each token will be passed
+ * to any downstream pipeline functions and all will returned tokens will be added to the index.
+ *
+ * Any number of pipeline functions may be chained together using a lunr.Pipeline.
+ *
+ * @interface lunr.PipelineFunction
+ * @param {lunr.Token} token - A token from the document being processed.
+ * @param {number} i - The index of this token in the complete list of tokens for this document/field.
+ * @param {lunr.Token[]} tokens - All tokens for this document/field.
+ * @returns {(?lunr.Token|lunr.Token[])}
+ */
+
+/**
+ * Register a function with the pipeline.
+ *
+ * Functions that are used in the pipeline should be registered if the pipeline
+ * needs to be serialised, or a serialised pipeline needs to be loaded.
+ *
+ * Registering a function does not add it to a pipeline, functions must still be
+ * added to instances of the pipeline for them to be used when running a pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @param {String} label - The label to register this function with
+ */
+lunr.Pipeline.registerFunction = function (fn, label) {
+ if (label in this.registeredFunctions) {
+ lunr.utils.warn('Overwriting existing registered function: ' + label)
+ }
+
+ fn.label = label
+ lunr.Pipeline.registeredFunctions[fn.label] = fn
+}
+
+/**
+ * Warns if the function is not registered as a Pipeline function.
+ *
+ * @param {lunr.PipelineFunction} fn - The function to check for.
+ * @private
+ */
+lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {
+ var isRegistered = fn.label && (fn.label in this.registeredFunctions)
+
+ if (!isRegistered) {
+ lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn)
+ }
+}
+
+/**
+ * Loads a previously serialised pipeline.
+ *
+ * All functions to be loaded must already be registered with lunr.Pipeline.
+ * If any function from the serialised data has not been registered then an
+ * error will be thrown.
+ *
+ * @param {Object} serialised - The serialised pipeline to load.
+ * @returns {lunr.Pipeline}
+ */
+lunr.Pipeline.load = function (serialised) {
+ var pipeline = new lunr.Pipeline
+
+ serialised.forEach(function (fnName) {
+ var fn = lunr.Pipeline.registeredFunctions[fnName]
+
+ if (fn) {
+ pipeline.add(fn)
+ } else {
+ throw new Error('Cannot load unregistered function: ' + fnName)
+ }
+ })
+
+ return pipeline
+}
+
+/**
+ * Adds new functions to the end of the pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.
+ */
+lunr.Pipeline.prototype.add = function () {
+ var fns = Array.prototype.slice.call(arguments)
+
+ fns.forEach(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+ this._stack.push(fn)
+ }, this)
+}
+
+/**
+ * Adds a single function after a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.after = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ pos = pos + 1
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Adds a single function before a function that already exists in the
+ * pipeline.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
+ * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
+ */
+lunr.Pipeline.prototype.before = function (existingFn, newFn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
+
+ var pos = this._stack.indexOf(existingFn)
+ if (pos == -1) {
+ throw new Error('Cannot find existingFn')
+ }
+
+ this._stack.splice(pos, 0, newFn)
+}
+
+/**
+ * Removes a function from the pipeline.
+ *
+ * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.
+ */
+lunr.Pipeline.prototype.remove = function (fn) {
+ var pos = this._stack.indexOf(fn)
+ if (pos == -1) {
+ return
+ }
+
+ this._stack.splice(pos, 1)
+}
+
+/**
+ * Runs the current list of functions that make up the pipeline against the
+ * passed tokens.
+ *
+ * @param {Array} tokens The tokens to run through the pipeline.
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.run = function (tokens) {
+ var stackLength = this._stack.length
+
+ for (var i = 0; i < stackLength; i++) {
+ var fn = this._stack[i]
+ var memo = []
+
+ for (var j = 0; j < tokens.length; j++) {
+ var result = fn(tokens[j], j, tokens)
+
+ if (result === null || result === void 0 || result === '') continue
+
+ if (Array.isArray(result)) {
+ for (var k = 0; k < result.length; k++) {
+ memo.push(result[k])
+ }
+ } else {
+ memo.push(result)
+ }
+ }
+
+ tokens = memo
+ }
+
+ return tokens
+}
+
+/**
+ * Convenience method for passing a string through a pipeline and getting
+ * strings out. This method takes care of wrapping the passed string in a
+ * token and mapping the resulting tokens back to strings.
+ *
+ * @param {string} str - The string to pass through the pipeline.
+ * @param {?object} metadata - Optional metadata to associate with the token
+ * passed to the pipeline.
+ * @returns {string[]}
+ */
+lunr.Pipeline.prototype.runString = function (str, metadata) {
+ var token = new lunr.Token (str, metadata)
+
+ return this.run([token]).map(function (t) {
+ return t.toString()
+ })
+}
+
+/**
+ * Resets the pipeline by removing any existing processors.
+ *
+ */
+lunr.Pipeline.prototype.reset = function () {
+ this._stack = []
+}
+
+/**
+ * Returns a representation of the pipeline ready for serialisation.
+ *
+ * Logs a warning if the function has not been registered.
+ *
+ * @returns {Array}
+ */
+lunr.Pipeline.prototype.toJSON = function () {
+ return this._stack.map(function (fn) {
+ lunr.Pipeline.warnIfFunctionNotRegistered(fn)
+
+ return fn.label
+ })
+}
+/*!
+ * lunr.Vector
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A vector is used to construct the vector space of documents and queries. These
+ * vectors support operations to determine the similarity between two documents or
+ * a document and a query.
+ *
+ * Normally no parameters are required for initializing a vector, but in the case of
+ * loading a previously dumped vector the raw elements can be provided to the constructor.
+ *
+ * For performance reasons vectors are implemented with a flat array, where an elements
+ * index is immediately followed by its value. E.g. [index, value, index, value]. This
+ * allows the underlying array to be as sparse as possible and still offer decent
+ * performance when being used for vector calculations.
+ *
+ * @constructor
+ * @param {Number[]} [elements] - The flat list of element index and element value pairs.
+ */
+lunr.Vector = function (elements) {
+ this._magnitude = 0
+ this.elements = elements || []
+}
+
+
+/**
+ * Calculates the position within the vector to insert a given index.
+ *
+ * This is used internally by insert and upsert. If there are duplicate indexes then
+ * the position is returned as if the value for that index were to be updated, but it
+ * is the callers responsibility to check whether there is a duplicate at that index
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.positionForIndex = function (index) {
+ // For an empty vector the tuple can be inserted at the beginning
+ if (this.elements.length == 0) {
+ return 0
+ }
+
+ var start = 0,
+ end = this.elements.length / 2,
+ sliceLength = end - start,
+ pivotPoint = Math.floor(sliceLength / 2),
+ pivotIndex = this.elements[pivotPoint * 2]
+
+ while (sliceLength > 1) {
+ if (pivotIndex < index) {
+ start = pivotPoint
+ }
+
+ if (pivotIndex > index) {
+ end = pivotPoint
+ }
+
+ if (pivotIndex == index) {
+ break
+ }
+
+ sliceLength = end - start
+ pivotPoint = start + Math.floor(sliceLength / 2)
+ pivotIndex = this.elements[pivotPoint * 2]
+ }
+
+ if (pivotIndex == index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex > index) {
+ return pivotPoint * 2
+ }
+
+ if (pivotIndex < index) {
+ return (pivotPoint + 1) * 2
+ }
+}
+
+/**
+ * Inserts an element at an index within the vector.
+ *
+ * Does not allow duplicates, will throw an error if there is already an entry
+ * for this index.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ */
+lunr.Vector.prototype.insert = function (insertIdx, val) {
+ this.upsert(insertIdx, val, function () {
+ throw "duplicate index"
+ })
+}
+
+/**
+ * Inserts or updates an existing index within the vector.
+ *
+ * @param {Number} insertIdx - The index at which the element should be inserted.
+ * @param {Number} val - The value to be inserted into the vector.
+ * @param {function} fn - A function that is called for updates, the existing value and the
+ * requested value are passed as arguments
+ */
+lunr.Vector.prototype.upsert = function (insertIdx, val, fn) {
+ this._magnitude = 0
+ var position = this.positionForIndex(insertIdx)
+
+ if (this.elements[position] == insertIdx) {
+ this.elements[position + 1] = fn(this.elements[position + 1], val)
+ } else {
+ this.elements.splice(position, 0, insertIdx, val)
+ }
+}
+
+/**
+ * Calculates the magnitude of this vector.
+ *
+ * @returns {Number}
+ */
+lunr.Vector.prototype.magnitude = function () {
+ if (this._magnitude) return this._magnitude
+
+ var sumOfSquares = 0,
+ elementsLength = this.elements.length
+
+ for (var i = 1; i < elementsLength; i += 2) {
+ var val = this.elements[i]
+ sumOfSquares += val * val
+ }
+
+ return this._magnitude = Math.sqrt(sumOfSquares)
+}
+
+/**
+ * Calculates the dot product of this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The vector to compute the dot product with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.dot = function (otherVector) {
+ var dotProduct = 0,
+ a = this.elements, b = otherVector.elements,
+ aLen = a.length, bLen = b.length,
+ aVal = 0, bVal = 0,
+ i = 0, j = 0
+
+ while (i < aLen && j < bLen) {
+ aVal = a[i], bVal = b[j]
+ if (aVal < bVal) {
+ i += 2
+ } else if (aVal > bVal) {
+ j += 2
+ } else if (aVal == bVal) {
+ dotProduct += a[i + 1] * b[j + 1]
+ i += 2
+ j += 2
+ }
+ }
+
+ return dotProduct
+}
+
+/**
+ * Calculates the similarity between this vector and another vector.
+ *
+ * @param {lunr.Vector} otherVector - The other vector to calculate the
+ * similarity with.
+ * @returns {Number}
+ */
+lunr.Vector.prototype.similarity = function (otherVector) {
+ return this.dot(otherVector) / this.magnitude() || 0
+}
+
+/**
+ * Converts the vector to an array of the elements within the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toArray = function () {
+ var output = new Array (this.elements.length / 2)
+
+ for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {
+ output[j] = this.elements[i]
+ }
+
+ return output
+}
+
+/**
+ * A JSON serializable representation of the vector.
+ *
+ * @returns {Number[]}
+ */
+lunr.Vector.prototype.toJSON = function () {
+ return this.elements
+}
+/* eslint-disable */
+/*!
+ * lunr.stemmer
+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */
+
+/**
+ * lunr.stemmer is an english language stemmer, this is a JavaScript
+ * implementation of the PorterStemmer taken from http://tartarus.org/~martin
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token - The string to stem
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ * @function
+ */
+lunr.stemmer = (function(){
+ var step2list = {
+ "ational" : "ate",
+ "tional" : "tion",
+ "enci" : "ence",
+ "anci" : "ance",
+ "izer" : "ize",
+ "bli" : "ble",
+ "alli" : "al",
+ "entli" : "ent",
+ "eli" : "e",
+ "ousli" : "ous",
+ "ization" : "ize",
+ "ation" : "ate",
+ "ator" : "ate",
+ "alism" : "al",
+ "iveness" : "ive",
+ "fulness" : "ful",
+ "ousness" : "ous",
+ "aliti" : "al",
+ "iviti" : "ive",
+ "biliti" : "ble",
+ "logi" : "log"
+ },
+
+ step3list = {
+ "icate" : "ic",
+ "ative" : "",
+ "alize" : "al",
+ "iciti" : "ic",
+ "ical" : "ic",
+ "ful" : "",
+ "ness" : ""
+ },
+
+ c = "[^aeiou]", // consonant
+ v = "[aeiouy]", // vowel
+ C = c + "[^aeiouy]*", // consonant sequence
+ V = v + "[aeiou]*", // vowel sequence
+
+ mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0
+ meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1
+ mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1
+ s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ var re_mgr0 = new RegExp(mgr0);
+ var re_mgr1 = new RegExp(mgr1);
+ var re_meq1 = new RegExp(meq1);
+ var re_s_v = new RegExp(s_v);
+
+ var re_1a = /^(.+?)(ss|i)es$/;
+ var re2_1a = /^(.+?)([^s])s$/;
+ var re_1b = /^(.+?)eed$/;
+ var re2_1b = /^(.+?)(ed|ing)$/;
+ var re_1b_2 = /.$/;
+ var re2_1b_2 = /(at|bl|iz)$/;
+ var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$");
+ var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var re_1c = /^(.+?[^aeiou])y$/;
+ var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+
+ var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+
+ var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ var re2_4 = /^(.+?)(s|t)(ion)$/;
+
+ var re_5 = /^(.+?)e$/;
+ var re_5_1 = /ll$/;
+ var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+
+ var porterStemmer = function porterStemmer(w) {
+ var stem,
+ suffix,
+ firstch,
+ re,
+ re2,
+ re3,
+ re4;
+
+ if (w.length < 3) { return w; }
+
+ firstch = w.substr(0,1);
+ if (firstch == "y") {
+ w = firstch.toUpperCase() + w.substr(1);
+ }
+
+ // Step 1a
+ re = re_1a
+ re2 = re2_1a;
+
+ if (re.test(w)) { w = w.replace(re,"$1$2"); }
+ else if (re2.test(w)) { w = w.replace(re2,"$1$2"); }
+
+ // Step 1b
+ re = re_1b;
+ re2 = re2_1b;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = re_mgr0;
+ if (re.test(fp[1])) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = re_s_v;
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = re2_1b_2;
+ re3 = re3_1b_2;
+ re4 = re4_1b_2;
+ if (re2.test(w)) { w = w + "e"; }
+ else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); }
+ else if (re4.test(w)) { w = w + "e"; }
+ }
+ }
+
+ // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)
+ re = re_1c;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = re_2;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step2list[suffix];
+ }
+ }
+
+ // Step 3
+ re = re_3;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = re_mgr0;
+ if (re.test(stem)) {
+ w = stem + step3list[suffix];
+ }
+ }
+
+ // Step 4
+ re = re_4;
+ re2 = re2_4;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ if (re.test(stem)) {
+ w = stem;
+ }
+ } else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = re_mgr1;
+ if (re2.test(stem)) {
+ w = stem;
+ }
+ }
+
+ // Step 5
+ re = re_5;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = re_mgr1;
+ re2 = re_meq1;
+ re3 = re3_5;
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
+ w = stem;
+ }
+ }
+
+ re = re_5_1;
+ re2 = re_mgr1;
+ if (re.test(w) && re2.test(w)) {
+ re = re_1b_2;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+
+ if (firstch == "y") {
+ w = firstch.toLowerCase() + w.substr(1);
+ }
+
+ return w;
+ };
+
+ return function (token) {
+ return token.update(porterStemmer);
+ }
+})();
+
+lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')
+/*!
+ * lunr.stopWordFilter
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.generateStopWordFilter builds a stopWordFilter function from the provided
+ * list of stop words.
+ *
+ * The built in lunr.stopWordFilter is built using this generator and can be used
+ * to generate custom stopWordFilters for applications or non English languages.
+ *
+ * @function
+ * @param {Array} token The token to pass through the filter
+ * @returns {lunr.PipelineFunction}
+ * @see lunr.Pipeline
+ * @see lunr.stopWordFilter
+ */
+lunr.generateStopWordFilter = function (stopWords) {
+ var words = stopWords.reduce(function (memo, stopWord) {
+ memo[stopWord] = stopWord
+ return memo
+ }, {})
+
+ return function (token) {
+ if (token && words[token.toString()] !== token.toString()) return token
+ }
+}
+
+/**
+ * lunr.stopWordFilter is an English language stop word list filter, any words
+ * contained in the list will not be passed through the filter.
+ *
+ * This is intended to be used in the Pipeline. If the token does not pass the
+ * filter then undefined will be returned.
+ *
+ * @function
+ * @implements {lunr.PipelineFunction}
+ * @params {lunr.Token} token - A token to check for being a stop word.
+ * @returns {lunr.Token}
+ * @see {@link lunr.Pipeline}
+ */
+lunr.stopWordFilter = lunr.generateStopWordFilter([
+ 'a',
+ 'able',
+ 'about',
+ 'across',
+ 'after',
+ 'all',
+ 'almost',
+ 'also',
+ 'am',
+ 'among',
+ 'an',
+ 'and',
+ 'any',
+ 'are',
+ 'as',
+ 'at',
+ 'be',
+ 'because',
+ 'been',
+ 'but',
+ 'by',
+ 'can',
+ 'cannot',
+ 'could',
+ 'dear',
+ 'did',
+ 'do',
+ 'does',
+ 'either',
+ 'else',
+ 'ever',
+ 'every',
+ 'for',
+ 'from',
+ 'get',
+ 'got',
+ 'had',
+ 'has',
+ 'have',
+ 'he',
+ 'her',
+ 'hers',
+ 'him',
+ 'his',
+ 'how',
+ 'however',
+ 'i',
+ 'if',
+ 'in',
+ 'into',
+ 'is',
+ 'it',
+ 'its',
+ 'just',
+ 'least',
+ 'let',
+ 'like',
+ 'likely',
+ 'may',
+ 'me',
+ 'might',
+ 'most',
+ 'must',
+ 'my',
+ 'neither',
+ 'no',
+ 'nor',
+ 'not',
+ 'of',
+ 'off',
+ 'often',
+ 'on',
+ 'only',
+ 'or',
+ 'other',
+ 'our',
+ 'own',
+ 'rather',
+ 'said',
+ 'say',
+ 'says',
+ 'she',
+ 'should',
+ 'since',
+ 'so',
+ 'some',
+ 'than',
+ 'that',
+ 'the',
+ 'their',
+ 'them',
+ 'then',
+ 'there',
+ 'these',
+ 'they',
+ 'this',
+ 'tis',
+ 'to',
+ 'too',
+ 'twas',
+ 'us',
+ 'wants',
+ 'was',
+ 'we',
+ 'were',
+ 'what',
+ 'when',
+ 'where',
+ 'which',
+ 'while',
+ 'who',
+ 'whom',
+ 'why',
+ 'will',
+ 'with',
+ 'would',
+ 'yet',
+ 'you',
+ 'your'
+])
+
+lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')
+/*!
+ * lunr.trimmer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.trimmer is a pipeline function for trimming non word
+ * characters from the beginning and end of tokens before they
+ * enter the index.
+ *
+ * This implementation may not work correctly for non latin
+ * characters and should either be removed or adapted for use
+ * with languages with non-latin characters.
+ *
+ * @static
+ * @implements {lunr.PipelineFunction}
+ * @param {lunr.Token} token The token to pass through the filter
+ * @returns {lunr.Token}
+ * @see lunr.Pipeline
+ */
+lunr.trimmer = function (token) {
+ return token.update(function (s) {
+ return s.replace(/^\W+/, '').replace(/\W+$/, '')
+ })
+}
+
+lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')
+/*!
+ * lunr.TokenSet
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * A token set is used to store the unique list of all tokens
+ * within an index. Token sets are also used to represent an
+ * incoming query to the index, this query token set and index
+ * token set are then intersected to find which tokens to look
+ * up in the inverted index.
+ *
+ * A token set can hold multiple tokens, as in the case of the
+ * index token set, or it can hold a single token as in the
+ * case of a simple query token set.
+ *
+ * Additionally token sets are used to perform wildcard matching.
+ * Leading, contained and trailing wildcards are supported, and
+ * from this edit distance matching can also be provided.
+ *
+ * Token sets are implemented as a minimal finite state automata,
+ * where both common prefixes and suffixes are shared between tokens.
+ * This helps to reduce the space used for storing the token set.
+ *
+ * @constructor
+ */
+lunr.TokenSet = function () {
+ this.final = false
+ this.edges = {}
+ this.id = lunr.TokenSet._nextId
+ lunr.TokenSet._nextId += 1
+}
+
+/**
+ * Keeps track of the next, auto increment, identifier to assign
+ * to a new tokenSet.
+ *
+ * TokenSets require a unique identifier to be correctly minimised.
+ *
+ * @private
+ */
+lunr.TokenSet._nextId = 1
+
+/**
+ * Creates a TokenSet instance from the given sorted array of words.
+ *
+ * @param {String[]} arr - A sorted array of strings to create the set from.
+ * @returns {lunr.TokenSet}
+ * @throws Will throw an error if the input array is not sorted.
+ */
+lunr.TokenSet.fromArray = function (arr) {
+ var builder = new lunr.TokenSet.Builder
+
+ for (var i = 0, len = arr.length; i < len; i++) {
+ builder.insert(arr[i])
+ }
+
+ builder.finish()
+ return builder.root
+}
+
+/**
+ * Creates a token set from a query clause.
+ *
+ * @private
+ * @param {Object} clause - A single clause from lunr.Query.
+ * @param {string} clause.term - The query clause term.
+ * @param {number} [clause.editDistance] - The optional edit distance for the term.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromClause = function (clause) {
+ if ('editDistance' in clause) {
+ return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)
+ } else {
+ return lunr.TokenSet.fromString(clause.term)
+ }
+}
+
+/**
+ * Creates a token set representing a single string with a specified
+ * edit distance.
+ *
+ * Insertions, deletions, substitutions and transpositions are each
+ * treated as an edit distance of 1.
+ *
+ * Increasing the allowed edit distance will have a dramatic impact
+ * on the performance of both creating and intersecting these TokenSets.
+ * It is advised to keep the edit distance less than 3.
+ *
+ * @param {string} str - The string to create the token set from.
+ * @param {number} editDistance - The allowed edit distance to match.
+ * @returns {lunr.Vector}
+ */
+lunr.TokenSet.fromFuzzyString = function (str, editDistance) {
+ var root = new lunr.TokenSet
+
+ var stack = [{
+ node: root,
+ editsRemaining: editDistance,
+ str: str
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop()
+
+ // no edit
+ if (frame.str.length > 0) {
+ var char = frame.str.charAt(0),
+ noEditNode
+
+ if (char in frame.node.edges) {
+ noEditNode = frame.node.edges[char]
+ } else {
+ noEditNode = new lunr.TokenSet
+ frame.node.edges[char] = noEditNode
+ }
+
+ if (frame.str.length == 1) {
+ noEditNode.final = true
+ }
+
+ stack.push({
+ node: noEditNode,
+ editsRemaining: frame.editsRemaining,
+ str: frame.str.slice(1)
+ })
+ }
+
+ if (frame.editsRemaining == 0) {
+ continue
+ }
+
+ // insertion
+ if ("*" in frame.node.edges) {
+ var insertionNode = frame.node.edges["*"]
+ } else {
+ var insertionNode = new lunr.TokenSet
+ frame.node.edges["*"] = insertionNode
+ }
+
+ if (frame.str.length == 0) {
+ insertionNode.final = true
+ }
+
+ stack.push({
+ node: insertionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str
+ })
+
+ // deletion
+ // can only do a deletion if we have enough edits remaining
+ // and if there are characters left to delete in the string
+ if (frame.str.length > 1) {
+ stack.push({
+ node: frame.node,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // deletion
+ // just removing the last character from the str
+ if (frame.str.length == 1) {
+ frame.node.final = true
+ }
+
+ // substitution
+ // can only do a substitution if we have enough edits remaining
+ // and if there are characters left to substitute
+ if (frame.str.length >= 1) {
+ if ("*" in frame.node.edges) {
+ var substitutionNode = frame.node.edges["*"]
+ } else {
+ var substitutionNode = new lunr.TokenSet
+ frame.node.edges["*"] = substitutionNode
+ }
+
+ if (frame.str.length == 1) {
+ substitutionNode.final = true
+ }
+
+ stack.push({
+ node: substitutionNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: frame.str.slice(1)
+ })
+ }
+
+ // transposition
+ // can only do a transposition if there are edits remaining
+ // and there are enough characters to transpose
+ if (frame.str.length > 1) {
+ var charA = frame.str.charAt(0),
+ charB = frame.str.charAt(1),
+ transposeNode
+
+ if (charB in frame.node.edges) {
+ transposeNode = frame.node.edges[charB]
+ } else {
+ transposeNode = new lunr.TokenSet
+ frame.node.edges[charB] = transposeNode
+ }
+
+ if (frame.str.length == 1) {
+ transposeNode.final = true
+ }
+
+ stack.push({
+ node: transposeNode,
+ editsRemaining: frame.editsRemaining - 1,
+ str: charA + frame.str.slice(2)
+ })
+ }
+ }
+
+ return root
+}
+
+/**
+ * Creates a TokenSet from a string.
+ *
+ * The string may contain one or more wildcard characters (*)
+ * that will allow wildcard matching when intersecting with
+ * another TokenSet.
+ *
+ * @param {string} str - The string to create a TokenSet from.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.fromString = function (str) {
+ var node = new lunr.TokenSet,
+ root = node
+
+ /*
+ * Iterates through all characters within the passed string
+ * appending a node for each character.
+ *
+ * When a wildcard character is found then a self
+ * referencing edge is introduced to continually match
+ * any number of any characters.
+ */
+ for (var i = 0, len = str.length; i < len; i++) {
+ var char = str[i],
+ final = (i == len - 1)
+
+ if (char == "*") {
+ node.edges[char] = node
+ node.final = final
+
+ } else {
+ var next = new lunr.TokenSet
+ next.final = final
+
+ node.edges[char] = next
+ node = next
+ }
+ }
+
+ return root
+}
+
+/**
+ * Converts this TokenSet into an array of strings
+ * contained within the TokenSet.
+ *
+ * This is not intended to be used on a TokenSet that
+ * contains wildcards, in these cases the results are
+ * undefined and are likely to cause an infinite loop.
+ *
+ * @returns {string[]}
+ */
+lunr.TokenSet.prototype.toArray = function () {
+ var words = []
+
+ var stack = [{
+ prefix: "",
+ node: this
+ }]
+
+ while (stack.length) {
+ var frame = stack.pop(),
+ edges = Object.keys(frame.node.edges),
+ len = edges.length
+
+ if (frame.node.final) {
+ /* In Safari, at this point the prefix is sometimes corrupted, see:
+ * https://github.com/olivernn/lunr.js/issues/279 Calling any
+ * String.prototype method forces Safari to "cast" this string to what
+ * it's supposed to be, fixing the bug. */
+ frame.prefix.charAt(0)
+ words.push(frame.prefix)
+ }
+
+ for (var i = 0; i < len; i++) {
+ var edge = edges[i]
+
+ stack.push({
+ prefix: frame.prefix.concat(edge),
+ node: frame.node.edges[edge]
+ })
+ }
+ }
+
+ return words
+}
+
+/**
+ * Generates a string representation of a TokenSet.
+ *
+ * This is intended to allow TokenSets to be used as keys
+ * in objects, largely to aid the construction and minimisation
+ * of a TokenSet. As such it is not designed to be a human
+ * friendly representation of the TokenSet.
+ *
+ * @returns {string}
+ */
+lunr.TokenSet.prototype.toString = function () {
+ // NOTE: Using Object.keys here as this.edges is very likely
+ // to enter 'hash-mode' with many keys being added
+ //
+ // avoiding a for-in loop here as it leads to the function
+ // being de-optimised (at least in V8). From some simple
+ // benchmarks the performance is comparable, but allowing
+ // V8 to optimize may mean easy performance wins in the future.
+
+ if (this._str) {
+ return this._str
+ }
+
+ var str = this.final ? '1' : '0',
+ labels = Object.keys(this.edges).sort(),
+ len = labels.length
+
+ for (var i = 0; i < len; i++) {
+ var label = labels[i],
+ node = this.edges[label]
+
+ str = str + label + node.id
+ }
+
+ return str
+}
+
+/**
+ * Returns a new TokenSet that is the intersection of
+ * this TokenSet and the passed TokenSet.
+ *
+ * This intersection will take into account any wildcards
+ * contained within the TokenSet.
+ *
+ * @param {lunr.TokenSet} b - An other TokenSet to intersect with.
+ * @returns {lunr.TokenSet}
+ */
+lunr.TokenSet.prototype.intersect = function (b) {
+ var output = new lunr.TokenSet,
+ frame = undefined
+
+ var stack = [{
+ qNode: b,
+ output: output,
+ node: this
+ }]
+
+ while (stack.length) {
+ frame = stack.pop()
+
+ // NOTE: As with the #toString method, we are using
+ // Object.keys and a for loop instead of a for-in loop
+ // as both of these objects enter 'hash' mode, causing
+ // the function to be de-optimised in V8
+ var qEdges = Object.keys(frame.qNode.edges),
+ qLen = qEdges.length,
+ nEdges = Object.keys(frame.node.edges),
+ nLen = nEdges.length
+
+ for (var q = 0; q < qLen; q++) {
+ var qEdge = qEdges[q]
+
+ for (var n = 0; n < nLen; n++) {
+ var nEdge = nEdges[n]
+
+ if (nEdge == qEdge || qEdge == '*') {
+ var node = frame.node.edges[nEdge],
+ qNode = frame.qNode.edges[qEdge],
+ final = node.final && qNode.final,
+ next = undefined
+
+ if (nEdge in frame.output.edges) {
+ // an edge already exists for this character
+ // no need to create a new node, just set the finality
+ // bit unless this node is already final
+ next = frame.output.edges[nEdge]
+ next.final = next.final || final
+
+ } else {
+ // no edge exists yet, must create one
+ // set the finality bit and insert it
+ // into the output
+ next = new lunr.TokenSet
+ next.final = final
+ frame.output.edges[nEdge] = next
+ }
+
+ stack.push({
+ qNode: qNode,
+ output: next,
+ node: node
+ })
+ }
+ }
+ }
+ }
+
+ return output
+}
+lunr.TokenSet.Builder = function () {
+ this.previousWord = ""
+ this.root = new lunr.TokenSet
+ this.uncheckedNodes = []
+ this.minimizedNodes = {}
+}
+
+lunr.TokenSet.Builder.prototype.insert = function (word) {
+ var node,
+ commonPrefix = 0
+
+ if (word < this.previousWord) {
+ throw new Error ("Out of order word insertion")
+ }
+
+ for (var i = 0; i < word.length && i < this.previousWord.length; i++) {
+ if (word[i] != this.previousWord[i]) break
+ commonPrefix++
+ }
+
+ this.minimize(commonPrefix)
+
+ if (this.uncheckedNodes.length == 0) {
+ node = this.root
+ } else {
+ node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child
+ }
+
+ for (var i = commonPrefix; i < word.length; i++) {
+ var nextNode = new lunr.TokenSet,
+ char = word[i]
+
+ node.edges[char] = nextNode
+
+ this.uncheckedNodes.push({
+ parent: node,
+ char: char,
+ child: nextNode
+ })
+
+ node = nextNode
+ }
+
+ node.final = true
+ this.previousWord = word
+}
+
+lunr.TokenSet.Builder.prototype.finish = function () {
+ this.minimize(0)
+}
+
+lunr.TokenSet.Builder.prototype.minimize = function (downTo) {
+ for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {
+ var node = this.uncheckedNodes[i],
+ childKey = node.child.toString()
+
+ if (childKey in this.minimizedNodes) {
+ node.parent.edges[node.char] = this.minimizedNodes[childKey]
+ } else {
+ // Cache the key for this node since
+ // we know it can't change anymore
+ node.child._str = childKey
+
+ this.minimizedNodes[childKey] = node.child
+ }
+
+ this.uncheckedNodes.pop()
+ }
+}
+/*!
+ * lunr.Index
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * An index contains the built index of all documents and provides a query interface
+ * to the index.
+ *
+ * Usually instances of lunr.Index will not be created using this constructor, instead
+ * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
+ * used to load previously built and serialized indexes.
+ *
+ * @constructor
+ * @param {Object} attrs - The attributes of the built search index.
+ * @param {Object} attrs.invertedIndex - An index of term/field to document reference.
+ * @param {Object} attrs.fieldVectors - Field vectors
+ * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.
+ * @param {string[]} attrs.fields - The names of indexed document fields.
+ * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.
+ */
+lunr.Index = function (attrs) {
+ this.invertedIndex = attrs.invertedIndex
+ this.fieldVectors = attrs.fieldVectors
+ this.tokenSet = attrs.tokenSet
+ this.fields = attrs.fields
+ this.pipeline = attrs.pipeline
+}
+
+/**
+ * A result contains details of a document matching a search query.
+ * @typedef {Object} lunr.Index~Result
+ * @property {string} ref - The reference of the document this result represents.
+ * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.
+ * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.
+ */
+
+/**
+ * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple
+ * query language which itself is parsed into an instance of lunr.Query.
+ *
+ * For programmatically building queries it is advised to directly use lunr.Query, the query language
+ * is best used for human entered text rather than program generated text.
+ *
+ * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported
+ * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'
+ * or 'world', though those that contain both will rank higher in the results.
+ *
+ * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can
+ * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding
+ * wildcards will increase the number of documents that will be found but can also have a negative
+ * impact on query performance, especially with wildcards at the beginning of a term.
+ *
+ * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term
+ * hello in the title field will match this query. Using a field not present in the index will lead
+ * to an error being thrown.
+ *
+ * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term
+ * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported
+ * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.
+ * Avoid large values for edit distance to improve query performance.
+ *
+ * Each term also supports a presence modifier. By default a term's presence in document is optional, however
+ * this can be changed to either required or prohibited. For a term's presence to be required in a document the
+ * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and
+ * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not
+ * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.
+ *
+ * To escape special characters the backslash character '\' can be used, this allows searches to include
+ * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead
+ * of attempting to apply a boost of 2 to the search term "foo".
+ *
+ * @typedef {string} lunr.Index~QueryString
+ * @example
Simple single term query
+ * hello
+ * @example
Multiple term query
+ * hello world
+ * @example
term scoped to a field
+ * title:hello
+ * @example
term with a boost of 10
+ * hello^10
+ * @example
term with an edit distance of 2
+ * hello~2
+ * @example
terms with presence modifiers
+ * -foo +bar baz
+ */
+
+/**
+ * Performs a search against the index using lunr query syntax.
+ *
+ * Results will be returned sorted by their score, the most relevant results
+ * will be returned first. For details on how the score is calculated, please see
+ * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.
+ *
+ * For more programmatic querying use lunr.Index#query.
+ *
+ * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.
+ * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.
+ * @returns {lunr.Index~Result[]}
+ */
+lunr.Index.prototype.search = function (queryString) {
+ return this.query(function (query) {
+ var parser = new lunr.QueryParser(queryString, query)
+ parser.parse()
+ })
+}
+
+/**
+ * A query builder callback provides a query object to be used to express
+ * the query to perform on the index.
+ *
+ * @callback lunr.Index~queryBuilder
+ * @param {lunr.Query} query - The query object to build up.
+ * @this lunr.Query
+ */
+
+/**
+ * Performs a query against the index using the yielded lunr.Query object.
+ *
+ * If performing programmatic queries against the index, this method is preferred
+ * over lunr.Index#search so as to avoid the additional query parsing overhead.
+ *
+ * A query object is yielded to the supplied function which should be used to
+ * express the query to be run against the index.
+ *
+ * Note that although this function takes a callback parameter it is _not_ an
+ * asynchronous operation, the callback is just yielded a query object to be
+ * customized.
+ *
+ * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.
+ * @returns {lunr.Index~Result[]}
+ */
+lunr.Index.prototype.query = function (fn) {
+ // for each query clause
+ // * process terms
+ // * expand terms from token set
+ // * find matching documents and metadata
+ // * get document vectors
+ // * score documents
+
+ var query = new lunr.Query(this.fields),
+ matchingFields = Object.create(null),
+ queryVectors = Object.create(null),
+ termFieldCache = Object.create(null),
+ requiredMatches = Object.create(null),
+ prohibitedMatches = Object.create(null)
+
+ /*
+ * To support field level boosts a query vector is created per
+ * field. An empty vector is eagerly created to support negated
+ * queries.
+ */
+ for (var i = 0; i < this.fields.length; i++) {
+ queryVectors[this.fields[i]] = new lunr.Vector
+ }
+
+ fn.call(query, query)
+
+ for (var i = 0; i < query.clauses.length; i++) {
+ /*
+ * Unless the pipeline has been disabled for this term, which is
+ * the case for terms with wildcards, we need to pass the clause
+ * term through the search pipeline. A pipeline returns an array
+ * of processed terms. Pipeline functions may expand the passed
+ * term, which means we may end up performing multiple index lookups
+ * for a single query term.
+ */
+ var clause = query.clauses[i],
+ terms = null,
+ clauseMatches = lunr.Set.empty
+
+ if (clause.usePipeline) {
+ terms = this.pipeline.runString(clause.term, {
+ fields: clause.fields
+ })
+ } else {
+ terms = [clause.term]
+ }
+
+ for (var m = 0; m < terms.length; m++) {
+ var term = terms[m]
+
+ /*
+ * Each term returned from the pipeline needs to use the same query
+ * clause object, e.g. the same boost and or edit distance. The
+ * simplest way to do this is to re-use the clause object but mutate
+ * its term property.
+ */
+ clause.term = term
+
+ /*
+ * From the term in the clause we create a token set which will then
+ * be used to intersect the indexes token set to get a list of terms
+ * to lookup in the inverted index
+ */
+ var termTokenSet = lunr.TokenSet.fromClause(clause),
+ expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()
+
+ /*
+ * If a term marked as required does not exist in the tokenSet it is
+ * impossible for the search to return any matches. We set all the field
+ * scoped required matches set to empty and stop examining any further
+ * clauses.
+ */
+ if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {
+ for (var k = 0; k < clause.fields.length; k++) {
+ var field = clause.fields[k]
+ requiredMatches[field] = lunr.Set.empty
+ }
+
+ break
+ }
+
+ for (var j = 0; j < expandedTerms.length; j++) {
+ /*
+ * For each term get the posting and termIndex, this is required for
+ * building the query vector.
+ */
+ var expandedTerm = expandedTerms[j],
+ posting = this.invertedIndex[expandedTerm],
+ termIndex = posting._index
+
+ for (var k = 0; k < clause.fields.length; k++) {
+ /*
+ * For each field that this query term is scoped by (by default
+ * all fields are in scope) we need to get all the document refs
+ * that have this term in that field.
+ *
+ * The posting is the entry in the invertedIndex for the matching
+ * term from above.
+ */
+ var field = clause.fields[k],
+ fieldPosting = posting[field],
+ matchingDocumentRefs = Object.keys(fieldPosting),
+ termField = expandedTerm + "/" + field,
+ matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)
+
+ /*
+ * if the presence of this term is required ensure that the matching
+ * documents are added to the set of required matches for this clause.
+ *
+ */
+ if (clause.presence == lunr.Query.presence.REQUIRED) {
+ clauseMatches = clauseMatches.union(matchingDocumentsSet)
+
+ if (requiredMatches[field] === undefined) {
+ requiredMatches[field] = lunr.Set.complete
+ }
+ }
+
+ /*
+ * if the presence of this term is prohibited ensure that the matching
+ * documents are added to the set of prohibited matches for this field,
+ * creating that set if it does not yet exist.
+ */
+ if (clause.presence == lunr.Query.presence.PROHIBITED) {
+ if (prohibitedMatches[field] === undefined) {
+ prohibitedMatches[field] = lunr.Set.empty
+ }
+
+ prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)
+
+ /*
+ * Prohibited matches should not be part of the query vector used for
+ * similarity scoring and no metadata should be extracted so we continue
+ * to the next field
+ */
+ continue
+ }
+
+ /*
+ * The query field vector is populated using the termIndex found for
+ * the term and a unit value with the appropriate boost applied.
+ * Using upsert because there could already be an entry in the vector
+ * for the term we are working with. In that case we just add the scores
+ * together.
+ */
+ queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })
+
+ /**
+ * If we've already seen this term, field combo then we've already collected
+ * the matching documents and metadata, no need to go through all that again
+ */
+ if (termFieldCache[termField]) {
+ continue
+ }
+
+ for (var l = 0; l < matchingDocumentRefs.length; l++) {
+ /*
+ * All metadata for this term/field/document triple
+ * are then extracted and collected into an instance
+ * of lunr.MatchData ready to be returned in the query
+ * results
+ */
+ var matchingDocumentRef = matchingDocumentRefs[l],
+ matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),
+ metadata = fieldPosting[matchingDocumentRef],
+ fieldMatch
+
+ if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {
+ matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)
+ } else {
+ fieldMatch.add(expandedTerm, field, metadata)
+ }
+
+ }
+
+ termFieldCache[termField] = true
+ }
+ }
+ }
+
+ /**
+ * If the presence was required we need to update the requiredMatches field sets.
+ * We do this after all fields for the term have collected their matches because
+ * the clause terms presence is required in _any_ of the fields not _all_ of the
+ * fields.
+ */
+ if (clause.presence === lunr.Query.presence.REQUIRED) {
+ for (var k = 0; k < clause.fields.length; k++) {
+ var field = clause.fields[k]
+ requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)
+ }
+ }
+ }
+
+ /**
+ * Need to combine the field scoped required and prohibited
+ * matching documents into a global set of required and prohibited
+ * matches
+ */
+ var allRequiredMatches = lunr.Set.complete,
+ allProhibitedMatches = lunr.Set.empty
+
+ for (var i = 0; i < this.fields.length; i++) {
+ var field = this.fields[i]
+
+ if (requiredMatches[field]) {
+ allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])
+ }
+
+ if (prohibitedMatches[field]) {
+ allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])
+ }
+ }
+
+ var matchingFieldRefs = Object.keys(matchingFields),
+ results = [],
+ matches = Object.create(null)
+
+ /*
+ * If the query is negated (contains only prohibited terms)
+ * we need to get _all_ fieldRefs currently existing in the
+ * index. This is only done when we know that the query is
+ * entirely prohibited terms to avoid any cost of getting all
+ * fieldRefs unnecessarily.
+ *
+ * Additionally, blank MatchData must be created to correctly
+ * populate the results.
+ */
+ if (query.isNegated()) {
+ matchingFieldRefs = Object.keys(this.fieldVectors)
+
+ for (var i = 0; i < matchingFieldRefs.length; i++) {
+ var matchingFieldRef = matchingFieldRefs[i]
+ var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)
+ matchingFields[matchingFieldRef] = new lunr.MatchData
+ }
+ }
+
+ for (var i = 0; i < matchingFieldRefs.length; i++) {
+ /*
+ * Currently we have document fields that match the query, but we
+ * need to return documents. The matchData and scores are combined
+ * from multiple fields belonging to the same document.
+ *
+ * Scores are calculated by field, using the query vectors created
+ * above, and combined into a final document score using addition.
+ */
+ var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),
+ docRef = fieldRef.docRef
+
+ if (!allRequiredMatches.contains(docRef)) {
+ continue
+ }
+
+ if (allProhibitedMatches.contains(docRef)) {
+ continue
+ }
+
+ var fieldVector = this.fieldVectors[fieldRef],
+ score = queryVectors[fieldRef.fieldName].similarity(fieldVector),
+ docMatch
+
+ if ((docMatch = matches[docRef]) !== undefined) {
+ docMatch.score += score
+ docMatch.matchData.combine(matchingFields[fieldRef])
+ } else {
+ var match = {
+ ref: docRef,
+ score: score,
+ matchData: matchingFields[fieldRef]
+ }
+ matches[docRef] = match
+ results.push(match)
+ }
+ }
+
+ /*
+ * Sort the results objects by score, highest first.
+ */
+ return results.sort(function (a, b) {
+ return b.score - a.score
+ })
+}
+
+/**
+ * Prepares the index for JSON serialization.
+ *
+ * The schema for this JSON blob will be described in a
+ * separate JSON schema file.
+ *
+ * @returns {Object}
+ */
+lunr.Index.prototype.toJSON = function () {
+ var invertedIndex = Object.keys(this.invertedIndex)
+ .sort()
+ .map(function (term) {
+ return [term, this.invertedIndex[term]]
+ }, this)
+
+ var fieldVectors = Object.keys(this.fieldVectors)
+ .map(function (ref) {
+ return [ref, this.fieldVectors[ref].toJSON()]
+ }, this)
+
+ return {
+ version: lunr.version,
+ fields: this.fields,
+ fieldVectors: fieldVectors,
+ invertedIndex: invertedIndex,
+ pipeline: this.pipeline.toJSON()
+ }
+}
+
+/**
+ * Loads a previously serialized lunr.Index
+ *
+ * @param {Object} serializedIndex - A previously serialized lunr.Index
+ * @returns {lunr.Index}
+ */
+lunr.Index.load = function (serializedIndex) {
+ var attrs = {},
+ fieldVectors = {},
+ serializedVectors = serializedIndex.fieldVectors,
+ invertedIndex = Object.create(null),
+ serializedInvertedIndex = serializedIndex.invertedIndex,
+ tokenSetBuilder = new lunr.TokenSet.Builder,
+ pipeline = lunr.Pipeline.load(serializedIndex.pipeline)
+
+ if (serializedIndex.version != lunr.version) {
+ lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'")
+ }
+
+ for (var i = 0; i < serializedVectors.length; i++) {
+ var tuple = serializedVectors[i],
+ ref = tuple[0],
+ elements = tuple[1]
+
+ fieldVectors[ref] = new lunr.Vector(elements)
+ }
+
+ for (var i = 0; i < serializedInvertedIndex.length; i++) {
+ var tuple = serializedInvertedIndex[i],
+ term = tuple[0],
+ posting = tuple[1]
+
+ tokenSetBuilder.insert(term)
+ invertedIndex[term] = posting
+ }
+
+ tokenSetBuilder.finish()
+
+ attrs.fields = serializedIndex.fields
+
+ attrs.fieldVectors = fieldVectors
+ attrs.invertedIndex = invertedIndex
+ attrs.tokenSet = tokenSetBuilder.root
+ attrs.pipeline = pipeline
+
+ return new lunr.Index(attrs)
+}
+/*!
+ * lunr.Builder
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+
+/**
+ * lunr.Builder performs indexing on a set of documents and
+ * returns instances of lunr.Index ready for querying.
+ *
+ * All configuration of the index is done via the builder, the
+ * fields to index, the document reference, the text processing
+ * pipeline and document scoring parameters are all set on the
+ * builder before indexing.
+ *
+ * @constructor
+ * @property {string} _ref - Internal reference to the document reference field.
+ * @property {string[]} _fields - Internal reference to the document fields to index.
+ * @property {object} invertedIndex - The inverted index maps terms to document fields.
+ * @property {object} documentTermFrequencies - Keeps track of document term frequencies.
+ * @property {object} documentLengths - Keeps track of the length of documents added to the index.
+ * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.
+ * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.
+ * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.
+ * @property {number} documentCount - Keeps track of the total number of documents indexed.
+ * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.
+ * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.
+ * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.
+ * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.
+ */
+lunr.Builder = function () {
+ this._ref = "id"
+ this._fields = Object.create(null)
+ this._documents = Object.create(null)
+ this.invertedIndex = Object.create(null)
+ this.fieldTermFrequencies = {}
+ this.fieldLengths = {}
+ this.tokenizer = lunr.tokenizer
+ this.pipeline = new lunr.Pipeline
+ this.searchPipeline = new lunr.Pipeline
+ this.documentCount = 0
+ this._b = 0.75
+ this._k1 = 1.2
+ this.termIndex = 0
+ this.metadataWhitelist = []
+}
+
+/**
+ * Sets the document field used as the document reference. Every document must have this field.
+ * The type of this field in the document should be a string, if it is not a string it will be
+ * coerced into a string by calling toString.
+ *
+ * The default ref is 'id'.
+ *
+ * The ref should _not_ be changed during indexing, it should be set before any documents are
+ * added to the index. Changing it during indexing can lead to inconsistent results.
+ *
+ * @param {string} ref - The name of the reference field in the document.
+ */
+lunr.Builder.prototype.ref = function (ref) {
+ this._ref = ref
+}
+
+/**
+ * A function that is used to extract a field from a document.
+ *
+ * Lunr expects a field to be at the top level of a document, if however the field
+ * is deeply nested within a document an extractor function can be used to extract
+ * the right field for indexing.
+ *
+ * @callback fieldExtractor
+ * @param {object} doc - The document being added to the index.
+ * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.
+ * @example
Extracting a nested field
+ * function (doc) { return doc.nested.field }
+ */
+
+/**
+ * Adds a field to the list of document fields that will be indexed. Every document being
+ * indexed should have this field. Null values for this field in indexed documents will
+ * not cause errors but will limit the chance of that document being retrieved by searches.
+ *
+ * All fields should be added before adding documents to the index. Adding fields after
+ * a document has been indexed will have no effect on already indexed documents.
+ *
+ * Fields can be boosted at build time. This allows terms within that field to have more
+ * importance when ranking search results. Use a field boost to specify that matches within
+ * one field are more important than other fields.
+ *
+ * @param {string} fieldName - The name of a field to index in all documents.
+ * @param {object} attributes - Optional attributes associated with this field.
+ * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.
+ * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.
+ * @throws {RangeError} fieldName cannot contain unsupported characters '/'
+ */
+lunr.Builder.prototype.field = function (fieldName, attributes) {
+ if (/\//.test(fieldName)) {
+ throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'")
+ }
+
+ this._fields[fieldName] = attributes || {}
+}
+
+/**
+ * A parameter to tune the amount of field length normalisation that is applied when
+ * calculating relevance scores. A value of 0 will completely disable any normalisation
+ * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b
+ * will be clamped to the range 0 - 1.
+ *
+ * @param {number} number - The value to set for this tuning parameter.
+ */
+lunr.Builder.prototype.b = function (number) {
+ if (number < 0) {
+ this._b = 0
+ } else if (number > 1) {
+ this._b = 1
+ } else {
+ this._b = number
+ }
+}
+
+/**
+ * A parameter that controls the speed at which a rise in term frequency results in term
+ * frequency saturation. The default value is 1.2. Setting this to a higher value will give
+ * slower saturation levels, a lower value will result in quicker saturation.
+ *
+ * @param {number} number - The value to set for this tuning parameter.
+ */
+lunr.Builder.prototype.k1 = function (number) {
+ this._k1 = number
+}
+
+/**
+ * Adds a document to the index.
+ *
+ * Before adding fields to the index the index should have been fully setup, with the document
+ * ref and all fields to index already having been specified.
+ *
+ * The document must have a field name as specified by the ref (by default this is 'id') and
+ * it should have all fields defined for indexing, though null or undefined values will not
+ * cause errors.
+ *
+ * Entire documents can be boosted at build time. Applying a boost to a document indicates that
+ * this document should rank higher in search results than other documents.
+ *
+ * @param {object} doc - The document to add to the index.
+ * @param {object} attributes - Optional attributes associated with this document.
+ * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.
+ */
+lunr.Builder.prototype.add = function (doc, attributes) {
+ var docRef = doc[this._ref],
+ fields = Object.keys(this._fields)
+
+ this._documents[docRef] = attributes || {}
+ this.documentCount += 1
+
+ for (var i = 0; i < fields.length; i++) {
+ var fieldName = fields[i],
+ extractor = this._fields[fieldName].extractor,
+ field = extractor ? extractor(doc) : doc[fieldName],
+ tokens = this.tokenizer(field, {
+ fields: [fieldName]
+ }),
+ terms = this.pipeline.run(tokens),
+ fieldRef = new lunr.FieldRef (docRef, fieldName),
+ fieldTerms = Object.create(null)
+
+ this.fieldTermFrequencies[fieldRef] = fieldTerms
+ this.fieldLengths[fieldRef] = 0
+
+ // store the length of this field for this document
+ this.fieldLengths[fieldRef] += terms.length
+
+ // calculate term frequencies for this field
+ for (var j = 0; j < terms.length; j++) {
+ var term = terms[j]
+
+ if (fieldTerms[term] == undefined) {
+ fieldTerms[term] = 0
+ }
+
+ fieldTerms[term] += 1
+
+ // add to inverted index
+ // create an initial posting if one doesn't exist
+ if (this.invertedIndex[term] == undefined) {
+ var posting = Object.create(null)
+ posting["_index"] = this.termIndex
+ this.termIndex += 1
+
+ for (var k = 0; k < fields.length; k++) {
+ posting[fields[k]] = Object.create(null)
+ }
+
+ this.invertedIndex[term] = posting
+ }
+
+ // add an entry for this term/fieldName/docRef to the invertedIndex
+ if (this.invertedIndex[term][fieldName][docRef] == undefined) {
+ this.invertedIndex[term][fieldName][docRef] = Object.create(null)
+ }
+
+ // store all whitelisted metadata about this token in the
+ // inverted index
+ for (var l = 0; l < this.metadataWhitelist.length; l++) {
+ var metadataKey = this.metadataWhitelist[l],
+ metadata = term.metadata[metadataKey]
+
+ if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {
+ this.invertedIndex[term][fieldName][docRef][metadataKey] = []
+ }
+
+ this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)
+ }
+ }
+
+ }
+}
+
+/**
+ * Calculates the average document length for this index
+ *
+ * @private
+ */
+lunr.Builder.prototype.calculateAverageFieldLengths = function () {
+
+ var fieldRefs = Object.keys(this.fieldLengths),
+ numberOfFields = fieldRefs.length,
+ accumulator = {},
+ documentsWithField = {}
+
+ for (var i = 0; i < numberOfFields; i++) {
+ var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
+ field = fieldRef.fieldName
+
+ documentsWithField[field] || (documentsWithField[field] = 0)
+ documentsWithField[field] += 1
+
+ accumulator[field] || (accumulator[field] = 0)
+ accumulator[field] += this.fieldLengths[fieldRef]
+ }
+
+ var fields = Object.keys(this._fields)
+
+ for (var i = 0; i < fields.length; i++) {
+ var fieldName = fields[i]
+ accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]
+ }
+
+ this.averageFieldLength = accumulator
+}
+
+/**
+ * Builds a vector space model of every document using lunr.Vector
+ *
+ * @private
+ */
+lunr.Builder.prototype.createFieldVectors = function () {
+ var fieldVectors = {},
+ fieldRefs = Object.keys(this.fieldTermFrequencies),
+ fieldRefsLength = fieldRefs.length,
+ termIdfCache = Object.create(null)
+
+ for (var i = 0; i < fieldRefsLength; i++) {
+ var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
+ fieldName = fieldRef.fieldName,
+ fieldLength = this.fieldLengths[fieldRef],
+ fieldVector = new lunr.Vector,
+ termFrequencies = this.fieldTermFrequencies[fieldRef],
+ terms = Object.keys(termFrequencies),
+ termsLength = terms.length
+
+
+ var fieldBoost = this._fields[fieldName].boost || 1,
+ docBoost = this._documents[fieldRef.docRef].boost || 1
+
+ for (var j = 0; j < termsLength; j++) {
+ var term = terms[j],
+ tf = termFrequencies[term],
+ termIndex = this.invertedIndex[term]._index,
+ idf, score, scoreWithPrecision
+
+ if (termIdfCache[term] === undefined) {
+ idf = lunr.idf(this.invertedIndex[term], this.documentCount)
+ termIdfCache[term] = idf
+ } else {
+ idf = termIdfCache[term]
+ }
+
+ score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)
+ score *= fieldBoost
+ score *= docBoost
+ scoreWithPrecision = Math.round(score * 1000) / 1000
+ // Converts 1.23456789 to 1.234.
+ // Reducing the precision so that the vectors take up less
+ // space when serialised. Doing it now so that they behave
+ // the same before and after serialisation. Also, this is
+ // the fastest approach to reducing a number's precision in
+ // JavaScript.
+
+ fieldVector.insert(termIndex, scoreWithPrecision)
+ }
+
+ fieldVectors[fieldRef] = fieldVector
+ }
+
+ this.fieldVectors = fieldVectors
+}
+
+/**
+ * Creates a token set of all tokens in the index using lunr.TokenSet
+ *
+ * @private
+ */
+lunr.Builder.prototype.createTokenSet = function () {
+ this.tokenSet = lunr.TokenSet.fromArray(
+ Object.keys(this.invertedIndex).sort()
+ )
+}
+
+/**
+ * Builds the index, creating an instance of lunr.Index.
+ *
+ * This completes the indexing process and should only be called
+ * once all documents have been added to the index.
+ *
+ * @returns {lunr.Index}
+ */
+lunr.Builder.prototype.build = function () {
+ this.calculateAverageFieldLengths()
+ this.createFieldVectors()
+ this.createTokenSet()
+
+ return new lunr.Index({
+ invertedIndex: this.invertedIndex,
+ fieldVectors: this.fieldVectors,
+ tokenSet: this.tokenSet,
+ fields: Object.keys(this._fields),
+ pipeline: this.searchPipeline
+ })
+}
+
+/**
+ * Applies a plugin to the index builder.
+ *
+ * A plugin is a function that is called with the index builder as its context.
+ * Plugins can be used to customise or extend the behaviour of the index
+ * in some way. A plugin is just a function, that encapsulated the custom
+ * behaviour that should be applied when building the index.
+ *
+ * The plugin function will be called with the index builder as its argument, additional
+ * arguments can also be passed when calling use. The function will be called
+ * with the index builder as its context.
+ *
+ * @param {Function} plugin The plugin to apply.
+ */
+lunr.Builder.prototype.use = function (fn) {
+ var args = Array.prototype.slice.call(arguments, 1)
+ args.unshift(this)
+ fn.apply(this, args)
+}
+/**
+ * Contains and collects metadata about a matching document.
+ * A single instance of lunr.MatchData is returned as part of every
+ * lunr.Index~Result.
+ *
+ * @constructor
+ * @param {string} term - The term this match data is associated with
+ * @param {string} field - The field in which the term was found
+ * @param {object} metadata - The metadata recorded about this term in this field
+ * @property {object} metadata - A cloned collection of metadata associated with this document.
+ * @see {@link lunr.Index~Result}
+ */
+lunr.MatchData = function (term, field, metadata) {
+ var clonedMetadata = Object.create(null),
+ metadataKeys = Object.keys(metadata || {})
+
+ // Cloning the metadata to prevent the original
+ // being mutated during match data combination.
+ // Metadata is kept in an array within the inverted
+ // index so cloning the data can be done with
+ // Array#slice
+ for (var i = 0; i < metadataKeys.length; i++) {
+ var key = metadataKeys[i]
+ clonedMetadata[key] = metadata[key].slice()
+ }
+
+ this.metadata = Object.create(null)
+
+ if (term !== undefined) {
+ this.metadata[term] = Object.create(null)
+ this.metadata[term][field] = clonedMetadata
+ }
+}
+
+/**
+ * An instance of lunr.MatchData will be created for every term that matches a
+ * document. However only one instance is required in a lunr.Index~Result. This
+ * method combines metadata from another instance of lunr.MatchData with this
+ * objects metadata.
+ *
+ * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.
+ * @see {@link lunr.Index~Result}
+ */
+lunr.MatchData.prototype.combine = function (otherMatchData) {
+ var terms = Object.keys(otherMatchData.metadata)
+
+ for (var i = 0; i < terms.length; i++) {
+ var term = terms[i],
+ fields = Object.keys(otherMatchData.metadata[term])
+
+ if (this.metadata[term] == undefined) {
+ this.metadata[term] = Object.create(null)
+ }
+
+ for (var j = 0; j < fields.length; j++) {
+ var field = fields[j],
+ keys = Object.keys(otherMatchData.metadata[term][field])
+
+ if (this.metadata[term][field] == undefined) {
+ this.metadata[term][field] = Object.create(null)
+ }
+
+ for (var k = 0; k < keys.length; k++) {
+ var key = keys[k]
+
+ if (this.metadata[term][field][key] == undefined) {
+ this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]
+ } else {
+ this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])
+ }
+
+ }
+ }
+ }
+}
+
+/**
+ * Add metadata for a term/field pair to this instance of match data.
+ *
+ * @param {string} term - The term this match data is associated with
+ * @param {string} field - The field in which the term was found
+ * @param {object} metadata - The metadata recorded about this term in this field
+ */
+lunr.MatchData.prototype.add = function (term, field, metadata) {
+ if (!(term in this.metadata)) {
+ this.metadata[term] = Object.create(null)
+ this.metadata[term][field] = metadata
+ return
+ }
+
+ if (!(field in this.metadata[term])) {
+ this.metadata[term][field] = metadata
+ return
+ }
+
+ var metadataKeys = Object.keys(metadata)
+
+ for (var i = 0; i < metadataKeys.length; i++) {
+ var key = metadataKeys[i]
+
+ if (key in this.metadata[term][field]) {
+ this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])
+ } else {
+ this.metadata[term][field][key] = metadata[key]
+ }
+ }
+}
+/**
+ * A lunr.Query provides a programmatic way of defining queries to be performed
+ * against a {@link lunr.Index}.
+ *
+ * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method
+ * so the query object is pre-initialized with the right index fields.
+ *
+ * @constructor
+ * @property {lunr.Query~Clause[]} clauses - An array of query clauses.
+ * @property {string[]} allFields - An array of all available fields in a lunr.Index.
+ */
+lunr.Query = function (allFields) {
+ this.clauses = []
+ this.allFields = allFields
+}
+
+/**
+ * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.
+ *
+ * This allows wildcards to be added to the beginning and end of a term without having to manually do any string
+ * concatenation.
+ *
+ * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.
+ *
+ * @constant
+ * @default
+ * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour
+ * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists
+ * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists
+ * @see lunr.Query~Clause
+ * @see lunr.Query#clause
+ * @see lunr.Query#term
+ * @example
+ * query.term('foo', {
+ * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING
+ * })
+ */
+
+lunr.Query.wildcard = new String ("*")
+lunr.Query.wildcard.NONE = 0
+lunr.Query.wildcard.LEADING = 1
+lunr.Query.wildcard.TRAILING = 2
+
+/**
+ * Constants for indicating what kind of presence a term must have in matching documents.
+ *
+ * @constant
+ * @enum {number}
+ * @see lunr.Query~Clause
+ * @see lunr.Query#clause
+ * @see lunr.Query#term
+ * @example
query term with required presence
+ * query.term('foo', { presence: lunr.Query.presence.REQUIRED })
+ */
+lunr.Query.presence = {
+ /**
+ * Term's presence in a document is optional, this is the default value.
+ */
+ OPTIONAL: 1,
+
+ /**
+ * Term's presence in a document is required, documents that do not contain
+ * this term will not be returned.
+ */
+ REQUIRED: 2,
+
+ /**
+ * Term's presence in a document is prohibited, documents that do contain
+ * this term will not be returned.
+ */
+ PROHIBITED: 3
+}
+
+/**
+ * A single clause in a {@link lunr.Query} contains a term and details on how to
+ * match that term against a {@link lunr.Index}.
+ *
+ * @typedef {Object} lunr.Query~Clause
+ * @property {string[]} fields - The fields in an index this clause should be matched against.
+ * @property {number} [boost=1] - Any boost that should be applied when matching this clause.
+ * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.
+ * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.
+ * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.
+ * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.
+ */
+
+/**
+ * Adds a {@link lunr.Query~Clause} to this query.
+ *
+ * Unless the clause contains the fields to be matched all fields will be matched. In addition
+ * a default boost of 1 is applied to the clause.
+ *
+ * @param {lunr.Query~Clause} clause - The clause to add to this query.
+ * @see lunr.Query~Clause
+ * @returns {lunr.Query}
+ */
+lunr.Query.prototype.clause = function (clause) {
+ if (!('fields' in clause)) {
+ clause.fields = this.allFields
+ }
+
+ if (!('boost' in clause)) {
+ clause.boost = 1
+ }
+
+ if (!('usePipeline' in clause)) {
+ clause.usePipeline = true
+ }
+
+ if (!('wildcard' in clause)) {
+ clause.wildcard = lunr.Query.wildcard.NONE
+ }
+
+ if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {
+ clause.term = "*" + clause.term
+ }
+
+ if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {
+ clause.term = "" + clause.term + "*"
+ }
+
+ if (!('presence' in clause)) {
+ clause.presence = lunr.Query.presence.OPTIONAL
+ }
+
+ this.clauses.push(clause)
+
+ return this
+}
+
+/**
+ * A negated query is one in which every clause has a presence of
+ * prohibited. These queries require some special processing to return
+ * the expected results.
+ *
+ * @returns boolean
+ */
+lunr.Query.prototype.isNegated = function () {
+ for (var i = 0; i < this.clauses.length; i++) {
+ if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {
+ return false
+ }
+ }
+
+ return true
+}
+
+/**
+ * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}
+ * to the list of clauses that make up this query.
+ *
+ * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion
+ * to a token or token-like string should be done before calling this method.
+ *
+ * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an
+ * array, each term in the array will share the same options.
+ *
+ * @param {object|object[]} term - The term(s) to add to the query.
+ * @param {object} [options] - Any additional properties to add to the query clause.
+ * @returns {lunr.Query}
+ * @see lunr.Query#clause
+ * @see lunr.Query~Clause
+ * @example
adding a single term to a query
+ * query.term("foo")
+ * @example
adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard
';
+}
+
+function displayResults (results) {
+ var search_results = document.getElementById("mkdocs-search-results");
+ while (search_results.firstChild) {
+ search_results.removeChild(search_results.firstChild);
+ }
+ if (results.length > 0){
+ for (var i=0; i < results.length; i++){
+ var result = results[i];
+ var html = formatResult(result.location, result.title, result.summary);
+ search_results.insertAdjacentHTML('beforeend', html);
+ }
+ } else {
+ var noResultsText = search_results.getAttribute('data-no-results-text');
+ if (!noResultsText) {
+ noResultsText = "No results found";
+ }
+ search_results.insertAdjacentHTML('beforeend', '
' + noResultsText + '
');
+ }
+}
+
+function doSearch () {
+ var query = document.getElementById('mkdocs-search-query').value;
+ if (query.length > min_search_length) {
+ if (!window.Worker) {
+ displayResults(search(query));
+ } else {
+ searchWorker.postMessage({query: query});
+ }
+ } else {
+ // Clear results for short queries
+ displayResults([]);
+ }
+}
+
+function initSearch () {
+ var search_input = document.getElementById('mkdocs-search-query');
+ if (search_input) {
+ search_input.addEventListener("keyup", doSearch);
+ }
+ var term = getSearchTermFromLocation();
+ if (term) {
+ search_input.value = term;
+ doSearch();
+ }
+}
+
+function onWorkerMessage (e) {
+ if (e.data.allowSearch) {
+ initSearch();
+ } else if (e.data.results) {
+ var results = e.data.results;
+ displayResults(results);
+ } else if (e.data.config) {
+ min_search_length = e.data.config.min_search_length-1;
+ }
+}
+
+if (!window.Worker) {
+ console.log('Web Worker API not supported');
+ // load index in main thread
+ $.getScript(joinUrl(base_url, "search/worker.js")).done(function () {
+ console.log('Loaded worker');
+ init();
+ window.postMessage = function (msg) {
+ onWorkerMessage({data: msg});
+ };
+ }).fail(function (jqxhr, settings, exception) {
+ console.error('Could not load worker.js');
+ });
+} else {
+ // Wrap search in a web worker
+ var searchWorker = new Worker(joinUrl(base_url, "search/worker.js"));
+ searchWorker.postMessage({init: true});
+ searchWorker.onmessage = onWorkerMessage;
+}
diff --git a/search/search_index.json b/search/search_index.json
new file mode 100644
index 0000000..38a03e3
--- /dev/null
+++ b/search/search_index.json
@@ -0,0 +1 @@
+{"config":{"indexing":"full","lang":["en","zh"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to MMTrustEval Framework of MultiTrust MMTrustEval (MMTE) is a toolbox developed for the benchmark, MultiTrust (Benchmarking Trustworthiness of Multimodal Large Language Models, paper ) It provides a universal and scalable infrastructure for evaluating MLLM trustworthiness and facilitating future research. Different MLLMs are integrated into a unified interface to conduct standardized inference. Tasks are modularized by separting data, inference, and evaluation metrics to encourage tool reuse and easy updates for new tasks to be added.","title":"Home"},{"location":"#welcome-to-mmtrusteval","text":"Framework of MultiTrust MMTrustEval (MMTE) is a toolbox developed for the benchmark, MultiTrust (Benchmarking Trustworthiness of Multimodal Large Language Models, paper ) It provides a universal and scalable infrastructure for evaluating MLLM trustworthiness and facilitating future research. Different MLLMs are integrated into a unified interface to conduct standardized inference. Tasks are modularized by separting data, inference, and evaluation metrics to encourage tool reuse and easy updates for new tasks to be added.","title":"Welcome to MMTrustEval"},{"location":"dataclass/","text":"Dataclass We primarily define two dataclasses to contain the multimodal data to be processed by MLLMs, one for text-only samples and the other for image-text pairs. The detailed attributes in the dataclass are introduced below. TxtSample : to support text-only sample text : prompt in text target : ground-truth label\uff08Default: None\uff09 extra : auxiliary arguments that may help in the process afterwards, e.g., adversarial example generation\uff08Default: None\uff09 ImageTxtSample : to support multimodal input, i.e., an image-text pair image_path : path to the image file text : prompt in text target : ground-truth label\uff08Default: None\uff09 extra \uff1aauxiliary arguments that may help in the process afterwards, e.g., adversarial example generation\uff08Default: None\uff09 The type of the output from an MLLM is also restricted to these two dataclasses. _OutputType = Union[ImageTxtSample, TxtSample] Source code in mmte/__init__.py . @dataclass class TxtSample: text: str target: Optional[str] = None extra: Optional[Dict[str, Any]] = None @classmethod def from_dict(cls, data: Dict[str, Any]) -> \"TxtSample\": return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) def to_dict(self) -> Dict[str, Any]: return asdict(self) def __getitem__(self, item): return getattr(self, item) @dataclass class ImageTxtSample: image_path: str text: str target: Optional[str] = None extra: Optional[Dict[str, Any]] = None @classmethod def from_dict(cls, data: Dict[str, Any]) -> \"ImageTxtSample\": return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) def to_dict(self) -> Dict[str, Any]: return asdict(self) def __getitem__(self, item): return getattr(self, item) _OutputType = Union[ImageTxtSample, TxtSample]","title":"Dataclass"},{"location":"dataclass/#dataclass","text":"We primarily define two dataclasses to contain the multimodal data to be processed by MLLMs, one for text-only samples and the other for image-text pairs. The detailed attributes in the dataclass are introduced below. TxtSample : to support text-only sample text : prompt in text target : ground-truth label\uff08Default: None\uff09 extra : auxiliary arguments that may help in the process afterwards, e.g., adversarial example generation\uff08Default: None\uff09 ImageTxtSample : to support multimodal input, i.e., an image-text pair image_path : path to the image file text : prompt in text target : ground-truth label\uff08Default: None\uff09 extra \uff1aauxiliary arguments that may help in the process afterwards, e.g., adversarial example generation\uff08Default: None\uff09 The type of the output from an MLLM is also restricted to these two dataclasses. _OutputType = Union[ImageTxtSample, TxtSample] Source code in mmte/__init__.py . @dataclass class TxtSample: text: str target: Optional[str] = None extra: Optional[Dict[str, Any]] = None @classmethod def from_dict(cls, data: Dict[str, Any]) -> \"TxtSample\": return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) def to_dict(self) -> Dict[str, Any]: return asdict(self) def __getitem__(self, item): return getattr(self, item) @dataclass class ImageTxtSample: image_path: str text: str target: Optional[str] = None extra: Optional[Dict[str, Any]] = None @classmethod def from_dict(cls, data: Dict[str, Any]) -> \"ImageTxtSample\": return cls(**{k: v for k, v in data.items() if k in cls.__annotations__}) def to_dict(self) -> Dict[str, Any]: return asdict(self) def __getitem__(self, item): return getattr(self, item) _OutputType = Union[ImageTxtSample, TxtSample]","title":"Dataclass"},{"location":"modules/","text":"Modules In this section, we introduce each element in the task flow about their interface, typical usage, etc., in the order of their parts in the workflow. All elements are registered by an unique identifier into the global registry (defined in mmte/utils/registry.py ) and can be accessed by the registry.get_**_class(id) method. Datasets Datasets are defined to collect the samples to be tested for a specific task. It provides the prompt, image path, labels and possibly other information about the data point to the following process. Here are some technical notes about this class. The class is a subclass of the torch.utils.data.Dataset and users can iterate through the dataset by the default torch.utils.data.Dataloader . To customize a dataset, user need to define __getitem__ and __len__ as usual along with a collate_fn so that the dataloader can support the dataclass of TxtSample and ImageTxtSample . We provide a default one in mmte.datasets.base.collate_fn which should work for most cases, one can customized your own collate_fn if necessary. A method to further process the data for a certain task, which can be independent from the original dataset, can be optionally specified via the argument method_hook when initialization. This could make additional augmentation, attack and other preprocessing to the existing datasets more convenient. This is illustrated in the Method part. Some information about the dataset can be configured through a yaml config file, like the directory of images, the path to the annotation file. dataset_ids is the list of supported dataset_id for this class, which specify the different splits and processors in sample preparing. Source code in mmte/datasets/base.py . Refer to mmte/datasets/celeb.py for an example. class BaseDataset(Dataset, ABC): \"\"\" Base class for datasets, __getitem__ function return Union[ImageTxtSample, TxtSample]. \"\"\" dataset_id: str # Identifier for the dataset dataset_ids: Sequence[str] = [] # List of available datasets dataset_config: Optional[str] = \"\" # dataset config path def __init__(self, dataset_id: str, method_hook: Optional[BaseMethod] = None, **kwargs) -> None: \"\"\" Initializing dataset instance. Arguments: dataset_id: Identifier for the dataset method_hook: A method instance, which is used as a preprocess hook for __getitem__ funtion kwargs: extra configurations \"\"\" assert dataset_id in self.dataset_ids, f\"Dataset {dataset_id} must be one of {self.dataset_ids}.\" self.dataset_id = dataset_id if method_hook: self.method_hook = method_hook else: self.method_hook = None self.dataset: List[Any] = [] @abstractmethod def __getitem__(self, index: int) -> _OutputType: if self.method_hook: return self.method_hook.run(self.dataset[index]) return self.dataset[index] @abstractmethod def __len__(self) -> int: return len(self.dataset) Methods Methods are designed for additional data processing independent from and universal across datasets, for instance, generating adversarial examples, pairing text prompts with diverse images. Users do not need to modify the code for datasets but only implement a new class of Method and pass it as a hook to the dataset. Here are some technical notes about this class. This class works as a hook in the function __getitem__ of a dataset, which is optional. For cases where new images are generated with time-consuming methods or reproducibility is needed, we can set the lazy_mode=True to utilize the previously generated samples. To tackle the challenge that text-only data may not have clear identifiers pointing to a sample, a hash function can be defined to generate the filename for the generated data. Source code in mmte/methods/base.py . Refer to mmte/methods/unrelated_color.py for an example. class BaseMethod(ABC): \"\"\" Base class for methods, which can be applied to any Dataset inherits from BaseDataset as a hook in __getitem__ function. \"\"\" method_id: str # Identifier for the method method_ids: List[str] # List of available methods def __init__(self, method_id: str, img_dir: str, lazy_mode: bool = True) -> None: \"\"\" Initializing method instance. Arguments: method_id: Identifier for the method img_dir: Folder to save images lazy_mode: If True, it will reuse the already generated dataset. If False, it will regenerate the result Return: evaluation result \"\"\" assert method_id in self.method_ids, f\"Method {self.method_id} is not available. Only methods in {self.method_ids} can be used.\" self.method_id = method_id self.img_dir = img_dir self.lazy_mode = lazy_mode @abstractmethod def run(self, data: _OutputType, **kwargs) -> _OutputType: \"\"\" Preprocess each sample in the Dataset one by one. Arguments: data: Union[ImageTxtSample, TxtSample], one sample in Dataset kwargs: extra configurations Return: processed sample \"\"\" raise NotImplementedError @abstractmethod def hash(self, to_hash_str: str, **kwargs) -> str: \"\"\" Get hash code given to_hash_str, to provide an identifier for the data sample and to reuse the generated samples. Arguments: to_hash_str: str kwargs: extra configurations Return: hash code \"\"\" raise NotImplementedError def __call__(self, *args: Any, **kwds: Any) -> Any: return self.run(*args, **kwds) Models Models encapsulate the chat model of MLLMs into a unified interface for inference. This enables more convenient standardized evaluation of diverse models. Here are some technical notes about this class. chat unifies the interface for generation. messages is a list representing the conversation history, generation_kwargs is the generation configuration, indicating whether to do_sample , the setting of temperature , max_new_tokens , etc. The setting of generation configuration follows that in huggingface transformers. model_id is the unique identifier to get the chatmodel from registry_getchatmodel_class and model_family defines the list of available model identifiers. Source code in mmte/models/base.py . Refer to mmte/models/openai_chat.py for an example. class BaseChat(ABC): \"\"\" Base class for models to be evaluated in a generative/chat manner. \"\"\" model_id: str = '' # ID for a chat model, e.g., minigpt-4-vicuna-7b-v0 model_arch: str = '' # Architecture of the model, e.g., minigpt-4 model_family: List[str] = [] # List of available model_ids def __init__(self, model_id:str) -> None: self.model_id = model_id assert self.model_id in self.model_family, f\"Model {self.model_id} is not available. Only models in {self.model_family} can be used.\" @abstractmethod def chat(self, messages: List, **generation_kwargs, ) -> \"Response\": \"\"\" Chat interface for generative evaluation with batch size of 1. messages: a list of messages, comprising the conversation history and following the format [ { 'role': 'system'/'user'/'assistant', 'content': str/dict }, ... ], where content is a dict {'text': str, 'image_path': str} when it's multimodal. generation_kwargs: generation configuration specified for different models, including: temperature: float, usually between 0-2, smaller means more deterministic do_sample: bool, whether take sampling as the decoding strategy num_beams: int, the parameter for beam search max_new_tokens: int, maximal number of tokens to be generated stop_sequences: str/List[str], stop words where the model will stop generating further tokens output_scores: bool, whether return the logits of the generated tokens (not very practical) \"\"\" raise NotImplementedError Evaluators source_code: mmte/evaluators/base.py BaseEvaluator This class is primarily used for evaluating the results output by a chat model. The process function preprocesses the input sequences of predictions ( preds ) and labels ( labels )\u2014these sequences can be numerical or textual. The preprocessing aims to generate simple numerical sequences that can be directly used in the eval function, which subsequently calls the metrics function (the metrics function only accepts numerical sequences). Currently, there are three main types of evaluators: ChatModel Evaluator : Processes and evaluates the results using a chat model. Classifier Evaluator : Scores the results using a classifier. Currently, it supports the longformer-action-ro model. Rule-based Evaluator : Includes tools for template matching for refusal and score extraction. Evaluators can be chained together to form an evaluator sequence (refer to SequentialEvaluator for details). The primary purpose is to reuse the process functions of different evaluators (for example, first using the chat model evaluator for text preprocessing, followed by the rule-based evaluator for scoring). class BaseEvaluator(ABC): \"\"\" Base class for evaluators, to evaluate the responses from chatmodels. \"\"\" evaluator_ids: List[str] = [] def __init__(self, evaluator_id: str, metrics_cfg: Dict[str, Any]) -> None: \"\"\" Initializing evaluator instance. Arguments: evaluator_id: Identifier for the evaluator metrics_cfg: config dict for metrics hooks, format: {metrics_id: metrics_kwargs, ...} \"\"\" assert evaluator_id in self.evaluator_ids, f\"Evaluator {self.evaluator_id} is not available. Only Evaluators in {self.evaluator_ids} can be used.\" self.evaluator_id = evaluator_id self.metrics_cfg = metrics_cfg for metrics_id in self.metrics_cfg.keys(): assert metrics_id in _supported_metrics.keys(), f\"{metrics_id} is not supported.\" @abstractmethod def process(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Tuple[Sequence[Any], Sequence[Any]]: \"\"\" 1. Perform some processing on sequence data, mainly including scoring/text-extraction with chatmodel/classifier/rule-based, etc. 2. Different evaluators can be concatenated, and the process function can be cascaded to perform multi-step processing on sequence data. Arguments: preds: responses from chatmodels or preds from `process` function of another evaluator labels: groundtruth labels or labels from `process` function of another evaluator extras: extra parameters or extra sequence from `process` function of another evaluator Return: preds: processed preds sequence labels: processed labels sequence extras: processed extra sequence \"\"\" # no-op return preds, labels, extras def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]: \"\"\" Evaluate pipeline including data processing and metrics calculation. Arguments: preds: responses from chatmodels labels: groundtruth labels extras: extra parameters Return: results \"\"\" processed_preds, processed_labels, processed_extras = self.process(preds, labels, extras) results = {} for metrics_id, kwargs in self.metrics_cfg.items(): metrics_fn = _supported_metrics[metrics_id] results[metrics_id] = metrics_fn(processed_labels, processed_preds, **kwargs) return results def __call__(self, *args: Any, **kwds: Any) -> Any: return self.eval(*args, **kwds) SequentialEvaluator class SequentialEvaluator: \"\"\" Class for cascading evaluators to perform multi-step processing on sequence data and get results from final sequence data. \"\"\" def __init__(self, evaluator_seq_cfg: Dict[str, Any]) -> None: \"\"\" Initializing sequence-evaluator instance. Arguments: evaluator_seq_cfg: config dict for instantiatizing evaluators, format: {evaluator: evaluator_kwargs, ...} \"\"\" evaluator_seq, evaluator_cls_names = [], [] for evaluator_id, evaluator_kwargs in evaluator_seq_cfg.items(): evaluator_cls = registry.get_evaluator_class(evaluator_id) evaluator = evaluator_cls(evaluator_id, **evaluator_kwargs) evaluator_cls_names.append(evaluator_cls.__name__) evaluator_seq.append(evaluator) self.evaluator_seq = evaluator_seq self.keyname_prefix_seq = self.create_sequence_list(evaluator_cls_names) def create_sequence_list(self, input_list: List[str]) -> List[str]: result = [] current = \"\" for item in input_list: if current: current += f\"->{item}\" else: current = item result.append(current) return result def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]: \"\"\" Evaluate pipeline including data processing and metrics calculation. Arguments: preds: responses from chatmodels labels: groundtruth labels extras: extra parameters Return: results \"\"\" prefix_results = {} seq_len = len(self.evaluator_seq) for evaluator_idx, (evaluator, keyname_prefix) in enumerate(zip(self.evaluator_seq, self.keyname_prefix_seq)): if evaluator_idx < seq_len - 1: preds, labels, extras = evaluator.process(preds, labels, extras) prefix_results.update({f\"{keyname_prefix}:pred_no_op\": preds}) else: # final evaluator results = evaluator(preds, labels, extras) prefix_results.update({f\"{keyname_prefix}:{key}\": value for key, value in results.items()}) return prefix_results def __call__(self, *args: Any, **kwds: Any) -> Any: return self.eval(*args, **kwds) Metrics We pre-define some common metrics for users to call. These functions to calculate metrics take two array-like arguments of digits to compute the statistical or sample-wise results. We also consider some cases where simple operations of aggregation are needed, e.g., sum, mean. Source code in mmte/evaluators/metrics.py . \"\"\" Input Requirement y_true: 1d array-like y_pred: 1d array-like \"\"\" _supported_metrics = { # aggregation op \"pred_no_op\": pred_no_op, \"pred_sum\": pred_sum, \"pred_mean\": pred_mean, # general metrics \"accuracy_score\": accuracy_score, \"precision_score\": precision_score, \"recall_score\": recall_score, \"f1_score\": f1_score, \"pearson_corr\": pearson_corr, \"failure\": failure, }","title":"Modules"},{"location":"modules/#modules","text":"In this section, we introduce each element in the task flow about their interface, typical usage, etc., in the order of their parts in the workflow. All elements are registered by an unique identifier into the global registry (defined in mmte/utils/registry.py ) and can be accessed by the registry.get_**_class(id) method.","title":"Modules"},{"location":"modules/#datasets","text":"Datasets are defined to collect the samples to be tested for a specific task. It provides the prompt, image path, labels and possibly other information about the data point to the following process. Here are some technical notes about this class. The class is a subclass of the torch.utils.data.Dataset and users can iterate through the dataset by the default torch.utils.data.Dataloader . To customize a dataset, user need to define __getitem__ and __len__ as usual along with a collate_fn so that the dataloader can support the dataclass of TxtSample and ImageTxtSample . We provide a default one in mmte.datasets.base.collate_fn which should work for most cases, one can customized your own collate_fn if necessary. A method to further process the data for a certain task, which can be independent from the original dataset, can be optionally specified via the argument method_hook when initialization. This could make additional augmentation, attack and other preprocessing to the existing datasets more convenient. This is illustrated in the Method part. Some information about the dataset can be configured through a yaml config file, like the directory of images, the path to the annotation file. dataset_ids is the list of supported dataset_id for this class, which specify the different splits and processors in sample preparing. Source code in mmte/datasets/base.py . Refer to mmte/datasets/celeb.py for an example. class BaseDataset(Dataset, ABC): \"\"\" Base class for datasets, __getitem__ function return Union[ImageTxtSample, TxtSample]. \"\"\" dataset_id: str # Identifier for the dataset dataset_ids: Sequence[str] = [] # List of available datasets dataset_config: Optional[str] = \"\" # dataset config path def __init__(self, dataset_id: str, method_hook: Optional[BaseMethod] = None, **kwargs) -> None: \"\"\" Initializing dataset instance. Arguments: dataset_id: Identifier for the dataset method_hook: A method instance, which is used as a preprocess hook for __getitem__ funtion kwargs: extra configurations \"\"\" assert dataset_id in self.dataset_ids, f\"Dataset {dataset_id} must be one of {self.dataset_ids}.\" self.dataset_id = dataset_id if method_hook: self.method_hook = method_hook else: self.method_hook = None self.dataset: List[Any] = [] @abstractmethod def __getitem__(self, index: int) -> _OutputType: if self.method_hook: return self.method_hook.run(self.dataset[index]) return self.dataset[index] @abstractmethod def __len__(self) -> int: return len(self.dataset)","title":"Datasets"},{"location":"modules/#methods","text":"Methods are designed for additional data processing independent from and universal across datasets, for instance, generating adversarial examples, pairing text prompts with diverse images. Users do not need to modify the code for datasets but only implement a new class of Method and pass it as a hook to the dataset. Here are some technical notes about this class. This class works as a hook in the function __getitem__ of a dataset, which is optional. For cases where new images are generated with time-consuming methods or reproducibility is needed, we can set the lazy_mode=True to utilize the previously generated samples. To tackle the challenge that text-only data may not have clear identifiers pointing to a sample, a hash function can be defined to generate the filename for the generated data. Source code in mmte/methods/base.py . Refer to mmte/methods/unrelated_color.py for an example. class BaseMethod(ABC): \"\"\" Base class for methods, which can be applied to any Dataset inherits from BaseDataset as a hook in __getitem__ function. \"\"\" method_id: str # Identifier for the method method_ids: List[str] # List of available methods def __init__(self, method_id: str, img_dir: str, lazy_mode: bool = True) -> None: \"\"\" Initializing method instance. Arguments: method_id: Identifier for the method img_dir: Folder to save images lazy_mode: If True, it will reuse the already generated dataset. If False, it will regenerate the result Return: evaluation result \"\"\" assert method_id in self.method_ids, f\"Method {self.method_id} is not available. Only methods in {self.method_ids} can be used.\" self.method_id = method_id self.img_dir = img_dir self.lazy_mode = lazy_mode @abstractmethod def run(self, data: _OutputType, **kwargs) -> _OutputType: \"\"\" Preprocess each sample in the Dataset one by one. Arguments: data: Union[ImageTxtSample, TxtSample], one sample in Dataset kwargs: extra configurations Return: processed sample \"\"\" raise NotImplementedError @abstractmethod def hash(self, to_hash_str: str, **kwargs) -> str: \"\"\" Get hash code given to_hash_str, to provide an identifier for the data sample and to reuse the generated samples. Arguments: to_hash_str: str kwargs: extra configurations Return: hash code \"\"\" raise NotImplementedError def __call__(self, *args: Any, **kwds: Any) -> Any: return self.run(*args, **kwds)","title":"Methods"},{"location":"modules/#models","text":"Models encapsulate the chat model of MLLMs into a unified interface for inference. This enables more convenient standardized evaluation of diverse models. Here are some technical notes about this class. chat unifies the interface for generation. messages is a list representing the conversation history, generation_kwargs is the generation configuration, indicating whether to do_sample , the setting of temperature , max_new_tokens , etc. The setting of generation configuration follows that in huggingface transformers. model_id is the unique identifier to get the chatmodel from registry_getchatmodel_class and model_family defines the list of available model identifiers. Source code in mmte/models/base.py . Refer to mmte/models/openai_chat.py for an example. class BaseChat(ABC): \"\"\" Base class for models to be evaluated in a generative/chat manner. \"\"\" model_id: str = '' # ID for a chat model, e.g., minigpt-4-vicuna-7b-v0 model_arch: str = '' # Architecture of the model, e.g., minigpt-4 model_family: List[str] = [] # List of available model_ids def __init__(self, model_id:str) -> None: self.model_id = model_id assert self.model_id in self.model_family, f\"Model {self.model_id} is not available. Only models in {self.model_family} can be used.\" @abstractmethod def chat(self, messages: List, **generation_kwargs, ) -> \"Response\": \"\"\" Chat interface for generative evaluation with batch size of 1. messages: a list of messages, comprising the conversation history and following the format [ { 'role': 'system'/'user'/'assistant', 'content': str/dict }, ... ], where content is a dict {'text': str, 'image_path': str} when it's multimodal. generation_kwargs: generation configuration specified for different models, including: temperature: float, usually between 0-2, smaller means more deterministic do_sample: bool, whether take sampling as the decoding strategy num_beams: int, the parameter for beam search max_new_tokens: int, maximal number of tokens to be generated stop_sequences: str/List[str], stop words where the model will stop generating further tokens output_scores: bool, whether return the logits of the generated tokens (not very practical) \"\"\" raise NotImplementedError","title":"Models"},{"location":"modules/#evaluators","text":"source_code: mmte/evaluators/base.py","title":"Evaluators"},{"location":"modules/#baseevaluator","text":"This class is primarily used for evaluating the results output by a chat model. The process function preprocesses the input sequences of predictions ( preds ) and labels ( labels )\u2014these sequences can be numerical or textual. The preprocessing aims to generate simple numerical sequences that can be directly used in the eval function, which subsequently calls the metrics function (the metrics function only accepts numerical sequences). Currently, there are three main types of evaluators: ChatModel Evaluator : Processes and evaluates the results using a chat model. Classifier Evaluator : Scores the results using a classifier. Currently, it supports the longformer-action-ro model. Rule-based Evaluator : Includes tools for template matching for refusal and score extraction. Evaluators can be chained together to form an evaluator sequence (refer to SequentialEvaluator for details). The primary purpose is to reuse the process functions of different evaluators (for example, first using the chat model evaluator for text preprocessing, followed by the rule-based evaluator for scoring). class BaseEvaluator(ABC): \"\"\" Base class for evaluators, to evaluate the responses from chatmodels. \"\"\" evaluator_ids: List[str] = [] def __init__(self, evaluator_id: str, metrics_cfg: Dict[str, Any]) -> None: \"\"\" Initializing evaluator instance. Arguments: evaluator_id: Identifier for the evaluator metrics_cfg: config dict for metrics hooks, format: {metrics_id: metrics_kwargs, ...} \"\"\" assert evaluator_id in self.evaluator_ids, f\"Evaluator {self.evaluator_id} is not available. Only Evaluators in {self.evaluator_ids} can be used.\" self.evaluator_id = evaluator_id self.metrics_cfg = metrics_cfg for metrics_id in self.metrics_cfg.keys(): assert metrics_id in _supported_metrics.keys(), f\"{metrics_id} is not supported.\" @abstractmethod def process(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Tuple[Sequence[Any], Sequence[Any]]: \"\"\" 1. Perform some processing on sequence data, mainly including scoring/text-extraction with chatmodel/classifier/rule-based, etc. 2. Different evaluators can be concatenated, and the process function can be cascaded to perform multi-step processing on sequence data. Arguments: preds: responses from chatmodels or preds from `process` function of another evaluator labels: groundtruth labels or labels from `process` function of another evaluator extras: extra parameters or extra sequence from `process` function of another evaluator Return: preds: processed preds sequence labels: processed labels sequence extras: processed extra sequence \"\"\" # no-op return preds, labels, extras def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]: \"\"\" Evaluate pipeline including data processing and metrics calculation. Arguments: preds: responses from chatmodels labels: groundtruth labels extras: extra parameters Return: results \"\"\" processed_preds, processed_labels, processed_extras = self.process(preds, labels, extras) results = {} for metrics_id, kwargs in self.metrics_cfg.items(): metrics_fn = _supported_metrics[metrics_id] results[metrics_id] = metrics_fn(processed_labels, processed_preds, **kwargs) return results def __call__(self, *args: Any, **kwds: Any) -> Any: return self.eval(*args, **kwds)","title":"BaseEvaluator"},{"location":"modules/#sequentialevaluator","text":"class SequentialEvaluator: \"\"\" Class for cascading evaluators to perform multi-step processing on sequence data and get results from final sequence data. \"\"\" def __init__(self, evaluator_seq_cfg: Dict[str, Any]) -> None: \"\"\" Initializing sequence-evaluator instance. Arguments: evaluator_seq_cfg: config dict for instantiatizing evaluators, format: {evaluator: evaluator_kwargs, ...} \"\"\" evaluator_seq, evaluator_cls_names = [], [] for evaluator_id, evaluator_kwargs in evaluator_seq_cfg.items(): evaluator_cls = registry.get_evaluator_class(evaluator_id) evaluator = evaluator_cls(evaluator_id, **evaluator_kwargs) evaluator_cls_names.append(evaluator_cls.__name__) evaluator_seq.append(evaluator) self.evaluator_seq = evaluator_seq self.keyname_prefix_seq = self.create_sequence_list(evaluator_cls_names) def create_sequence_list(self, input_list: List[str]) -> List[str]: result = [] current = \"\" for item in input_list: if current: current += f\"->{item}\" else: current = item result.append(current) return result def eval(self, preds: Sequence[Any], labels: Optional[Sequence[Any]] = None, extras: Optional[Sequence[Any]] = None, **kwargs) -> Dict[str, Union[Sequence, float]]: \"\"\" Evaluate pipeline including data processing and metrics calculation. Arguments: preds: responses from chatmodels labels: groundtruth labels extras: extra parameters Return: results \"\"\" prefix_results = {} seq_len = len(self.evaluator_seq) for evaluator_idx, (evaluator, keyname_prefix) in enumerate(zip(self.evaluator_seq, self.keyname_prefix_seq)): if evaluator_idx < seq_len - 1: preds, labels, extras = evaluator.process(preds, labels, extras) prefix_results.update({f\"{keyname_prefix}:pred_no_op\": preds}) else: # final evaluator results = evaluator(preds, labels, extras) prefix_results.update({f\"{keyname_prefix}:{key}\": value for key, value in results.items()}) return prefix_results def __call__(self, *args: Any, **kwds: Any) -> Any: return self.eval(*args, **kwds)","title":"SequentialEvaluator"},{"location":"modules/#metrics","text":"We pre-define some common metrics for users to call. These functions to calculate metrics take two array-like arguments of digits to compute the statistical or sample-wise results. We also consider some cases where simple operations of aggregation are needed, e.g., sum, mean. Source code in mmte/evaluators/metrics.py . \"\"\" Input Requirement y_true: 1d array-like y_pred: 1d array-like \"\"\" _supported_metrics = { # aggregation op \"pred_no_op\": pred_no_op, \"pred_sum\": pred_sum, \"pred_mean\": pred_mean, # general metrics \"accuracy_score\": accuracy_score, \"precision_score\": precision_score, \"recall_score\": recall_score, \"f1_score\": f1_score, \"pearson_corr\": pearson_corr, \"failure\": failure, }","title":"Metrics"},{"location":"structure/","text":"Structure Project Structure ./ \u251c\u2500\u2500 mmte \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 configs \u2502 \u2502 \u251c\u2500\u2500 datasets/*.yaml \u2502 \u2502 \u251c\u2500\u2500 models/*/*.yaml \u2502 \u2502 \u2514\u2500\u2500 task/*/*.yaml \u2502 \u251c\u2500\u2500 datasets \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 evaluators \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u251c\u2500\u2500 metrics.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 methods \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 models \u2502 \u2502 \u251c\u2500\u2500 [model_dependence] \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 tasks/base.py \u2502 \u2514\u2500\u2500 utils/ \u251c\u2500\u2500 scripts/ \u2502 \u251c\u2500\u2500 run/ \u2502 \u2514\u2500\u2500 score/ \u251c\u2500\u2500 docs/ \u251c\u2500\u2500 data/ \u251c\u2500\u2500 env/ \u251c\u2500\u2500 LICENSE \u251c\u2500\u2500 README.md \u2514\u2500\u2500 run_task.py Task Workflow Task Workflow The basic workflow of a task in MMTrustEval follows the pipeline above. The image-text pairs (or text-only samples) are retrieved from the customized dataset . They are likely to be further processed with a pre-defined method (e.g., pairing text with synthesized images, imposing adversarial noises to the images) by method_hook passed into the dataset. Data in multiple modalities is gathered into a dataclass, TxtSample or ImageTxtSample . The samples ready for inference are then input to MLLMs with unified interface for chat . Further, the generated content is processed by diverse evaluators (e.g., keyword extraction, GPT-4 rating, classifier) and further standardized to be computed with specified metrics (e.g., accuracy, pearson correlation coefficient).","title":"Structure"},{"location":"structure/#structure","text":"","title":"Structure"},{"location":"structure/#project-structure","text":"./ \u251c\u2500\u2500 mmte \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 configs \u2502 \u2502 \u251c\u2500\u2500 datasets/*.yaml \u2502 \u2502 \u251c\u2500\u2500 models/*/*.yaml \u2502 \u2502 \u2514\u2500\u2500 task/*/*.yaml \u2502 \u251c\u2500\u2500 datasets \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 evaluators \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u251c\u2500\u2500 metrics.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 methods \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 models \u2502 \u2502 \u251c\u2500\u2500 [model_dependence] \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 base.py \u2502 \u2502 \u2514\u2500\u2500 *.py \u2502 \u251c\u2500\u2500 tasks/base.py \u2502 \u2514\u2500\u2500 utils/ \u251c\u2500\u2500 scripts/ \u2502 \u251c\u2500\u2500 run/ \u2502 \u2514\u2500\u2500 score/ \u251c\u2500\u2500 docs/ \u251c\u2500\u2500 data/ \u251c\u2500\u2500 env/ \u251c\u2500\u2500 LICENSE \u251c\u2500\u2500 README.md \u2514\u2500\u2500 run_task.py","title":"Project Structure"},{"location":"structure/#task-workflow","text":"Task Workflow The basic workflow of a task in MMTrustEval follows the pipeline above. The image-text pairs (or text-only samples) are retrieved from the customized dataset . They are likely to be further processed with a pre-defined method (e.g., pairing text with synthesized images, imposing adversarial noises to the images) by method_hook passed into the dataset. Data in multiple modalities is gathered into a dataclass, TxtSample or ImageTxtSample . The samples ready for inference are then input to MLLMs with unified interface for chat . Further, the generated content is processed by diverse evaluators (e.g., keyword extraction, GPT-4 rating, classifier) and further standardized to be computed with specified metrics (e.g., accuracy, pearson correlation coefficient).","title":"Task Workflow"},{"location":"task/","text":"Task A task powered by MMTrustEval is a collection of the aforementioned modules, i.e., a certain model is evaluated on a certain dataset with certain metrics. Here, we introduce the basic logic of a task workflow in MMTrustEval and how to construct a task via a configuration file. Note that, the purpose of MMTrustEval is to provide modularized tools for developing new tasks to evaluate trustworthiness of MLLMs, instead of to restrict the implementation to modularized configuration. While the tasks in MultiTrust are organized with these modules, users are free to implement their own tasks in a more customized style and only use one or several modules like unified models and off-the-shelf evaluators to facilitate their evaluation. Task Pipeline We provide a BaseTask class to organize the modules in a standardized logic and provide a way for modular customization. The modules for different elements are instantiated according to the configuration from a yaml file or commandline argurments automatically and the user can simply launch the evaluation with one command. Source code in mmte/tasks/base.py class BaseTask(ABC): def __init__(self, dataset_id: str, model_id: str, method_cfg: Optional[Dict] = {}, dataset_cfg: Optional[Dict] = {}, evaluator_seq_cfgs: List = [], log_file: Optional[str] = None) -> None: self.dataset_id = dataset_id self.model_id = model_id self.method_cfg = method_cfg self.dataset_cfg = dataset_cfg self.evaluator_seq_cfgs = evaluator_seq_cfgs self.log_file = log_file def pipeline(self) -> None: self.get_handlers() # automatic module instantiation dataloader = self.get_dataloader() responses = self.generate(dataloader) # unified model inference results = self.eval(responses) # response postprocessing and metrics computation self.save_results(results) # result logging Task Configuration model_id : The ID of the model dataset_id : The ID of the dataset log_file : The file path for the output JSON file method_cfg : Configuration for the method, formatted as {method_id: method_kwargs, ...} . Default: {} dataset_cfg : Additional parameters required for initializing the dataset, formatted as dataset_kwargs . Default: {} generation_kwargs : Additional parameters used for inference with the chat model. Default: {} evaluator_seq_cfgs : Configuration for the evaluator sequence. Each list item represents a different evaluator sequence, with each sequence containing multiple evaluators, and each evaluator corresponding to multiple metrics. Default: [] List[ {evaluator_id: {metrics_cfg: {metrics_id: metrics_kwargs}, {metrics_id: metrics_kwargs}, ... }, ... }, ... ]","title":"Task"},{"location":"task/#task","text":"A task powered by MMTrustEval is a collection of the aforementioned modules, i.e., a certain model is evaluated on a certain dataset with certain metrics. Here, we introduce the basic logic of a task workflow in MMTrustEval and how to construct a task via a configuration file. Note that, the purpose of MMTrustEval is to provide modularized tools for developing new tasks to evaluate trustworthiness of MLLMs, instead of to restrict the implementation to modularized configuration. While the tasks in MultiTrust are organized with these modules, users are free to implement their own tasks in a more customized style and only use one or several modules like unified models and off-the-shelf evaluators to facilitate their evaluation.","title":"Task"},{"location":"task/#task-pipeline","text":"We provide a BaseTask class to organize the modules in a standardized logic and provide a way for modular customization. The modules for different elements are instantiated according to the configuration from a yaml file or commandline argurments automatically and the user can simply launch the evaluation with one command. Source code in mmte/tasks/base.py class BaseTask(ABC): def __init__(self, dataset_id: str, model_id: str, method_cfg: Optional[Dict] = {}, dataset_cfg: Optional[Dict] = {}, evaluator_seq_cfgs: List = [], log_file: Optional[str] = None) -> None: self.dataset_id = dataset_id self.model_id = model_id self.method_cfg = method_cfg self.dataset_cfg = dataset_cfg self.evaluator_seq_cfgs = evaluator_seq_cfgs self.log_file = log_file def pipeline(self) -> None: self.get_handlers() # automatic module instantiation dataloader = self.get_dataloader() responses = self.generate(dataloader) # unified model inference results = self.eval(responses) # response postprocessing and metrics computation self.save_results(results) # result logging","title":"Task Pipeline"},{"location":"task/#task-configuration","text":"model_id : The ID of the model dataset_id : The ID of the dataset log_file : The file path for the output JSON file method_cfg : Configuration for the method, formatted as {method_id: method_kwargs, ...} . Default: {} dataset_cfg : Additional parameters required for initializing the dataset, formatted as dataset_kwargs . Default: {} generation_kwargs : Additional parameters used for inference with the chat model. Default: {} evaluator_seq_cfgs : Configuration for the evaluator sequence. Each list item represents a different evaluator sequence, with each sequence containing multiple evaluators, and each evaluator corresponding to multiple metrics. Default: [] List[ {evaluator_id: {metrics_cfg: {metrics_id: metrics_kwargs}, {metrics_id: metrics_kwargs}, ... }, ... }, ... ]","title":"Task Configuration"},{"location":"tutorial/","text":"Tutorial to Add Tasks Using the example of Privacy Task P3: infoflow-expectation with dataset confaide-unrelated-image-color : Define the Dataset Define the dataset and register the dataset_id : confaide-unrelated-image-color (refer to mmte/datasets/confaide.py ). If you need to do some preprocessing to your custom dataset, please define Method and use it as a hook function in your dataset. Define the Evaluator Define the evaluator and register the evaluator_id : rule_match_and_score_eval (refer to mmte/evaluators/rule_eval.py ). If additional metrics are required, modify mmte/evaluators/metrics.py and register the metrics_fn in _supported_metrics . Edit task_config.yaml (refer to mmte/configs/task/privacy/infoflow.yaml ) dataset_id: \"confaide-unrelated-image-color\" model_id: \"llava-v1.5-7b\" log_file: \"./logs/privacy/infoflow.json\" evaluator_seq_cfgs: [ { \"rule_match_and_score_eval\": { metrics_cfg: { pearson_corr: {}, failure: {} } }, }, ] Run with a Single Command python run_task.py --config mmte/configs/task/privacy/infoflow.yaml To modify configurations without changing the yaml file, one can ADD or OVERWRITE configurations in yaml files using the --cfg-options parameter. For example: python run_task.py --config mmte/configs/task/privacy/infoflow.yaml --cfg-options dataset_id=confaide-image log_file=\"logs/privacy/infoflow-confaide-image.json\"","title":"Tutorial"},{"location":"tutorial/#tutorial-to-add-tasks","text":"Using the example of Privacy Task P3: infoflow-expectation with dataset confaide-unrelated-image-color :","title":"Tutorial to Add Tasks"},{"location":"tutorial/#define-the-dataset","text":"Define the dataset and register the dataset_id : confaide-unrelated-image-color (refer to mmte/datasets/confaide.py ). If you need to do some preprocessing to your custom dataset, please define Method and use it as a hook function in your dataset.","title":"Define the Dataset"},{"location":"tutorial/#define-the-evaluator","text":"Define the evaluator and register the evaluator_id : rule_match_and_score_eval (refer to mmte/evaluators/rule_eval.py ). If additional metrics are required, modify mmte/evaluators/metrics.py and register the metrics_fn in _supported_metrics .","title":"Define the Evaluator"},{"location":"tutorial/#edit-task_configyaml","text":"(refer to mmte/configs/task/privacy/infoflow.yaml ) dataset_id: \"confaide-unrelated-image-color\" model_id: \"llava-v1.5-7b\" log_file: \"./logs/privacy/infoflow.json\" evaluator_seq_cfgs: [ { \"rule_match_and_score_eval\": { metrics_cfg: { pearson_corr: {}, failure: {} } }, }, ]","title":"Edit task_config.yaml"},{"location":"tutorial/#run-with-a-single-command","text":"python run_task.py --config mmte/configs/task/privacy/infoflow.yaml To modify configurations without changing the yaml file, one can ADD or OVERWRITE configurations in yaml files using the --cfg-options parameter. For example: python run_task.py --config mmte/configs/task/privacy/infoflow.yaml --cfg-options dataset_id=confaide-image log_file=\"logs/privacy/infoflow-confaide-image.json\"","title":"Run with a Single Command"}]}
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+++ b/search/worker.js
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+var base_path = 'function' === typeof importScripts ? '.' : '/search/';
+var allowSearch = false;
+var index;
+var documents = {};
+var lang = ['en'];
+var data;
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+function getScript(script, callback) {
+ console.log('Loading script: ' + script);
+ $.getScript(base_path + script).done(function () {
+ callback();
+ }).fail(function (jqxhr, settings, exception) {
+ console.log('Error: ' + exception);
+ });
+}
+
+function getScriptsInOrder(scripts, callback) {
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+ }
+ getScript(scripts[0], function() {
+ getScriptsInOrder(scripts.slice(1), callback);
+ });
+}
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+function loadScripts(urls, callback) {
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+ scriptsToLoad.push('lunr.stemmer.support.js');
+ if (lang.length > 1) {
+ scriptsToLoad.push('lunr.multi.js');
+ }
+ if (lang.includes("ja") || lang.includes("jp")) {
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+ }
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+ scriptsToLoad.push(['lunr', lang[i], 'js'].join('.'));
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+ }
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+ loadScripts(scriptsToLoad, onScriptsLoaded);
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+function onScriptsLoaded () {
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+function search (query) {
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+ console.error('Assets for search still loading');
+ return;
+ }
+
+ var resultDocuments = [];
+ var results = index.search(query);
+ for (var i=0; i < results.length; i++){
+ var result = results[i];
+ doc = documents[result.ref];
+ doc.summary = doc.text.substring(0, 200);
+ resultDocuments.push(doc);
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+ return resultDocuments;
+}
+
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+ onmessage = function (e) {
+ if (e.data.init) {
+ init();
+ } else if (e.data.query) {
+ postMessage({ results: search(e.data.query) });
+ } else {
+ console.error("Worker - Unrecognized message: " + e);
+ }
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+}
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+ Structure - MMTrustEval Docs
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+
The basic workflow of a task in MMTrustEval follows the pipeline above. The image-text pairs (or text-only samples) are retrieved from the customized dataset. They are likely to be further processed with a pre-defined method (e.g., pairing text with synthesized images, imposing adversarial noises to the images) by method_hook passed into the dataset. Data in multiple modalities is gathered into a dataclass, TxtSample or ImageTxtSample. The samples ready for inference are then input to MLLMs with unified interface for chat. Further, the generated content is processed by diverse evaluators (e.g., keyword extraction, GPT-4 rating, classifier) and further standardized to be computed with specified metrics (e.g., accuracy, pearson correlation coefficient).
A task powered by MMTrustEval is a collection of the aforementioned modules, i.e., a certain model is evaluated on a certain dataset with certain metrics. Here, we introduce the basic logic of a task workflow in MMTrustEval and how to construct a task via a configuration file.
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Note that, the purpose of MMTrustEval is to provide modularized tools for developing new tasks to evaluate trustworthiness of MLLMs, instead of to restrict the implementation to modularized configuration. While the tasks in MultiTrust are organized with these modules, users are free to implement their own tasks in a more customized style and only use one or several modules like unified models and off-the-shelf evaluators to facilitate their evaluation.
+
Task Pipeline
+
We provide a BaseTask class to organize the modules in a standardized logic and provide a way for modular customization. The modules for different elements are instantiated according to the configuration from a yaml file or commandline argurments automatically and the user can simply launch the evaluation with one command.
method_cfg: Configuration for the method, formatted as {method_id: method_kwargs, ...}. Default: {}
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dataset_cfg: Additional parameters required for initializing the dataset, formatted as dataset_kwargs. Default: {}
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+
generation_kwargs: Additional parameters used for inference with the chat model. Default: {}
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+
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evaluator_seq_cfgs: Configuration for the evaluator sequence. Each list item represents a different evaluator sequence, with each sequence containing multiple evaluators, and each evaluator corresponding to multiple metrics. Default: []
To modify configurations without changing the yaml file, one can ADD or OVERWRITE configurations in yaml files using the --cfg-options parameter. For example:
+ 
+