mirror of
https://github.com/meilisearch/MeiliSearch
synced 2025-07-04 04:17:10 +02:00
Move crates under a sub folder to clean up the code
This commit is contained in:
Clément Renault
parent
30f3c30389
commit
9c1e54a2c8
1062 changed files with 19 additions and 20 deletions
503
crates/milli/src/error.rs
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503
crates/milli/src/error.rs
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@ -0,0 +1,503 @@
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use std::collections::BTreeSet;
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use std::convert::Infallible;
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use std::fmt::Write;
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use std::{io, str};
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use heed::{Error as HeedError, MdbError};
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use rayon::ThreadPoolBuildError;
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use rhai::EvalAltResult;
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use serde_json::Value;
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use thiserror::Error;
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use crate::documents::{self, DocumentsBatchCursorError};
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use crate::thread_pool_no_abort::PanicCatched;
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use crate::{CriterionError, DocumentId, FieldId, Object, SortError};
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pub fn is_reserved_keyword(keyword: &str) -> bool {
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["_geo", "_geoDistance", "_geoPoint", "_geoRadius", "_geoBoundingBox"].contains(&keyword)
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}
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#[derive(Error, Debug)]
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pub enum Error {
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#[error("internal: {0}.")]
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InternalError(#[from] InternalError),
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#[error(transparent)]
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IoError(#[from] io::Error),
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#[error(transparent)]
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UserError(#[from] UserError),
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}
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#[derive(Error, Debug)]
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pub enum InternalError {
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#[error("{}", HeedError::DatabaseClosing)]
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DatabaseClosing,
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#[error("Missing {} in the {db_name} database.", key.unwrap_or("key"))]
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DatabaseMissingEntry { db_name: &'static str, key: Option<&'static str> },
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#[error("Missing {key} in the fieldids weights mapping.")]
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FieldidsWeightsMapMissingEntry { key: FieldId },
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#[error(transparent)]
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FieldIdMapMissingEntry(#[from] FieldIdMapMissingEntry),
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#[error("Missing {key} in the field id mapping.")]
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FieldIdMappingMissingEntry { key: FieldId },
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#[error(transparent)]
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Fst(#[from] fst::Error),
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#[error(transparent)]
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DocumentsError(#[from] documents::Error),
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#[error("Invalid compression type have been specified to grenad")]
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GrenadInvalidCompressionType,
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#[error("Invalid grenad file with an invalid version format")]
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GrenadInvalidFormatVersion,
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#[error("Invalid merge while processing {process}")]
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IndexingMergingKeys { process: &'static str },
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#[error(transparent)]
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RayonThreadPool(#[from] ThreadPoolBuildError),
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#[error(transparent)]
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PanicInThreadPool(#[from] PanicCatched),
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#[error(transparent)]
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SerdeJson(#[from] serde_json::Error),
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#[error(transparent)]
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Serialization(#[from] SerializationError),
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#[error(transparent)]
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Store(#[from] MdbError),
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#[error(transparent)]
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Utf8(#[from] str::Utf8Error),
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#[error("An indexation process was explicitly aborted")]
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AbortedIndexation,
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#[error("The matching words list contains at least one invalid member")]
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InvalidMatchingWords,
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#[error(transparent)]
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ArroyError(#[from] arroy::Error),
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#[error(transparent)]
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VectorEmbeddingError(#[from] crate::vector::Error),
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}
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#[derive(Error, Debug)]
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pub enum SerializationError {
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#[error("{}", match .db_name {
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Some(name) => format!("decoding from the {name} database failed"),
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None => "decoding failed".to_string(),
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})]
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Decoding { db_name: Option<&'static str> },
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#[error("{}", match .db_name {
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Some(name) => format!("encoding into the {name} database failed"),
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None => "encoding failed".to_string(),
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})]
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Encoding { db_name: Option<&'static str> },
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#[error("number is not a valid finite number")]
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InvalidNumberSerialization,
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}
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#[derive(Error, Debug)]
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pub enum FieldIdMapMissingEntry {
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#[error("unknown field id {field_id} coming from the {process} process")]
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FieldId { field_id: FieldId, process: &'static str },
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#[error("unknown field name {field_name} coming from the {process} process")]
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FieldName { field_name: String, process: &'static str },
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}
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#[derive(Error, Debug)]
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pub enum UserError {
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#[error("A document cannot contain more than 65,535 fields.")]
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AttributeLimitReached,
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#[error(transparent)]
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CriterionError(#[from] CriterionError),
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#[error("Maximum number of documents reached.")]
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DocumentLimitReached,
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#[error(
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"Document identifier `{}` is invalid. \
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A document identifier can be of type integer or string, \
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only composed of alphanumeric characters (a-z A-Z 0-9), hyphens (-) and underscores (_), \
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and can not be more than 512 bytes.", .document_id.to_string()
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)]
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InvalidDocumentId { document_id: Value },
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#[error("Invalid facet distribution, {}", format_invalid_filter_distribution(.invalid_facets_name, .valid_facets_name))]
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InvalidFacetsDistribution {
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invalid_facets_name: BTreeSet<String>,
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valid_facets_name: BTreeSet<String>,
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},
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#[error(transparent)]
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InvalidGeoField(#[from] GeoError),
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#[error("Invalid vector dimensions: expected: `{}`, found: `{}`.", .expected, .found)]
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InvalidVectorDimensions { expected: usize, found: usize },
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#[error("The `_vectors` field in the document with id: `{document_id}` is not an object. Was expecting an object with a key for each embedder with manually provided vectors, but instead got `{value}`")]
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InvalidVectorsMapType { document_id: String, value: Value },
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#[error("Bad embedder configuration in the document with id: `{document_id}`. {error}")]
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InvalidVectorsEmbedderConf { document_id: String, error: deserr::errors::JsonError },
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#[error("{0}")]
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InvalidFilter(String),
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#[error("Invalid type for filter subexpression: expected: {}, found: {1}.", .0.join(", "))]
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InvalidFilterExpression(&'static [&'static str], Value),
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#[error("Attribute `{}` is not sortable. {}",
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.field,
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match .valid_fields.is_empty() {
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true => "This index does not have configured sortable attributes.".to_string(),
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false => format!("Available sortable attributes are: `{}{}`.",
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valid_fields.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", "),
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.hidden_fields.then_some(", <..hidden-attributes>").unwrap_or(""),
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),
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}
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)]
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InvalidSortableAttribute { field: String, valid_fields: BTreeSet<String>, hidden_fields: bool },
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#[error("Attribute `{}` is not filterable and thus, cannot be used as distinct attribute. {}",
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.field,
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match .valid_fields.is_empty() {
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true => "This index does not have configured filterable attributes.".to_string(),
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false => format!("Available filterable attributes are: `{}{}`.",
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valid_fields.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", "),
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.hidden_fields.then_some(", <..hidden-attributes>").unwrap_or(""),
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),
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}
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)]
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InvalidDistinctAttribute { field: String, valid_fields: BTreeSet<String>, hidden_fields: bool },
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#[error("Attribute `{}` is not facet-searchable. {}",
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.field,
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match .valid_fields.is_empty() {
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true => "This index does not have configured facet-searchable attributes. To make it facet-searchable add it to the `filterableAttributes` index settings.".to_string(),
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false => format!("Available facet-searchable attributes are: `{}{}`. To make it facet-searchable add it to the `filterableAttributes` index settings.",
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valid_fields.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", "),
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.hidden_fields.then_some(", <..hidden-attributes>").unwrap_or(""),
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),
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}
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)]
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InvalidFacetSearchFacetName {
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field: String,
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valid_fields: BTreeSet<String>,
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hidden_fields: bool,
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},
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#[error("Attribute `{}` is not searchable. Available searchable attributes are: `{}{}`.",
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.field,
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.valid_fields.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", "),
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.hidden_fields.then_some(", <..hidden-attributes>").unwrap_or(""),
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)]
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InvalidSearchableAttribute {
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field: String,
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valid_fields: BTreeSet<String>,
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hidden_fields: bool,
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},
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#[error("an environment is already opened with different options")]
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InvalidLmdbOpenOptions,
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#[error("You must specify where `sort` is listed in the rankingRules setting to use the sort parameter at search time.")]
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SortRankingRuleMissing,
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#[error("The database file is in an invalid state.")]
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InvalidStoreFile,
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#[error("Maximum database size has been reached.")]
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MaxDatabaseSizeReached,
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#[error("Document doesn't have a `{}` attribute: `{}`.", .primary_key, serde_json::to_string(.document).unwrap())]
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MissingDocumentId { primary_key: String, document: Object },
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#[error("Document have too many matching `{}` attribute: `{}`.", .primary_key, serde_json::to_string(.document).unwrap())]
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TooManyDocumentIds { primary_key: String, document: Object },
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#[error("The primary key inference failed as the engine did not find any field ending with `id` in its name. Please specify the primary key manually using the `primaryKey` query parameter.")]
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NoPrimaryKeyCandidateFound,
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#[error("The primary key inference failed as the engine found {} fields ending with `id` in their names: '{}' and '{}'. Please specify the primary key manually using the `primaryKey` query parameter.", .candidates.len(), .candidates.first().unwrap(), .candidates.get(1).unwrap())]
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MultiplePrimaryKeyCandidatesFound { candidates: Vec<String> },
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#[error("There is no more space left on the device. Consider increasing the size of the disk/partition.")]
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NoSpaceLeftOnDevice,
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#[error("Index already has a primary key: `{0}`.")]
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PrimaryKeyCannotBeChanged(String),
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#[error(transparent)]
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SerdeJson(serde_json::Error),
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#[error(transparent)]
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SortError(#[from] SortError),
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#[error("An unknown internal document id have been used: `{document_id}`.")]
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UnknownInternalDocumentId { document_id: DocumentId },
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#[error("`minWordSizeForTypos` setting is invalid. `oneTypo` and `twoTypos` fields should be between `0` and `255`, and `twoTypos` should be greater or equals to `oneTypo` but found `oneTypo: {0}` and twoTypos: {1}`.")]
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InvalidMinTypoWordLenSetting(u8, u8),
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#[error(transparent)]
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VectorEmbeddingError(#[from] crate::vector::Error),
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#[error(transparent)]
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MissingDocumentField(#[from] crate::prompt::error::RenderPromptError),
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#[error(transparent)]
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InvalidPrompt(#[from] crate::prompt::error::NewPromptError),
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#[error("`.embedders.{0}.documentTemplate`: Invalid template: {1}.")]
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InvalidPromptForEmbeddings(String, crate::prompt::error::NewPromptError),
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#[error("Too many embedders in the configuration. Found {0}, but limited to 256.")]
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TooManyEmbedders(usize),
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#[error("Cannot find embedder with name `{0}`.")]
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InvalidEmbedder(String),
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#[error("Too many vectors for document with id {0}: found {1}, but limited to 256.")]
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TooManyVectors(String, usize),
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#[error("`.embedders.{embedder_name}`: Field `{field}` unavailable for source `{source_}` (only available for sources: {}). Available fields: {}",
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allowed_sources_for_field
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.iter()
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.map(|accepted| format!("`{}`", accepted))
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.collect::<Vec<String>>()
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.join(", "),
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allowed_fields_for_source
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.iter()
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.map(|accepted| format!("`{}`", accepted))
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.collect::<Vec<String>>()
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.join(", ")
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)]
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InvalidFieldForSource {
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embedder_name: String,
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source_: crate::vector::settings::EmbedderSource,
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field: &'static str,
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allowed_fields_for_source: &'static [&'static str],
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allowed_sources_for_field: &'static [crate::vector::settings::EmbedderSource],
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},
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#[error("`.embedders.{embedder_name}.model`: Invalid model `{model}` for OpenAI. Supported models: {:?}", crate::vector::openai::EmbeddingModel::supported_models())]
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InvalidOpenAiModel { embedder_name: String, model: String },
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#[error("`.embedders.{embedder_name}`: Missing field `{field}` (note: this field is mandatory for source {source_})")]
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MissingFieldForSource {
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field: &'static str,
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source_: crate::vector::settings::EmbedderSource,
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embedder_name: String,
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},
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#[error("`.embedders.{embedder_name}.dimensions`: Model `{model}` does not support overriding its native dimensions of {expected_dimensions}. Found {dimensions}")]
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InvalidOpenAiModelDimensions {
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embedder_name: String,
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model: &'static str,
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dimensions: usize,
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expected_dimensions: usize,
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},
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#[error("`.embedders.{embedder_name}.dimensions`: Model `{model}` does not support overriding its dimensions to a value higher than {max_dimensions}. Found {dimensions}")]
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InvalidOpenAiModelDimensionsMax {
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embedder_name: String,
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model: &'static str,
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dimensions: usize,
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max_dimensions: usize,
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},
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#[error("`.embedders.{embedder_name}.dimensions`: `dimensions` cannot be zero")]
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InvalidSettingsDimensions { embedder_name: String },
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#[error(
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"`.embedders.{embedder_name}.binaryQuantized`: Cannot disable the binary quantization.\n - Note: Binary quantization is a lossy operation that cannot be reverted.\n - Hint: Add a new embedder that is non-quantized and regenerate the vectors."
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)]
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InvalidDisableBinaryQuantization { embedder_name: String },
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#[error("`.embedders.{embedder_name}.documentTemplateMaxBytes`: `documentTemplateMaxBytes` cannot be zero")]
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InvalidSettingsDocumentTemplateMaxBytes { embedder_name: String },
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#[error("`.embedders.{embedder_name}.url`: could not parse `{url}`: {inner_error}")]
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InvalidUrl { embedder_name: String, inner_error: url::ParseError, url: String },
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#[error("Document editions cannot modify a document's primary key")]
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DocumentEditionCannotModifyPrimaryKey,
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#[error("Document editions must keep documents as objects")]
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DocumentEditionDocumentMustBeObject,
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#[error("Document edition runtime error encountered while running the function: {0}")]
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DocumentEditionRuntimeError(Box<EvalAltResult>),
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#[error("Document edition runtime error encountered while compiling the function: {0}")]
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DocumentEditionCompilationError(rhai::ParseError),
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#[error("{0}")]
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DocumentEmbeddingError(String),
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}
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impl From<crate::vector::Error> for Error {
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fn from(value: crate::vector::Error) -> Self {
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match value.fault() {
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FaultSource::User => Error::UserError(value.into()),
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FaultSource::Runtime => Error::UserError(value.into()),
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FaultSource::Bug => Error::InternalError(value.into()),
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FaultSource::Undecided => Error::UserError(value.into()),
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}
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}
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}
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impl From<arroy::Error> for Error {
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fn from(value: arroy::Error) -> Self {
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match value {
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arroy::Error::Heed(heed) => heed.into(),
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arroy::Error::Io(io) => io.into(),
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arroy::Error::InvalidVecDimension { expected, received } => {
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Error::UserError(UserError::InvalidVectorDimensions { expected, found: received })
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}
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arroy::Error::DatabaseFull
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| arroy::Error::InvalidItemAppend
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| arroy::Error::UnmatchingDistance { .. }
|
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| arroy::Error::NeedBuild(_)
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| arroy::Error::MissingKey { .. }
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| arroy::Error::MissingMetadata(_) => {
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Error::InternalError(InternalError::ArroyError(value))
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}
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}
|
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}
|
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}
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|
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#[derive(Error, Debug)]
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pub enum GeoError {
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#[error("The `_geo` field in the document with the id: `{document_id}` is not an object. Was expecting an object with the `_geo.lat` and `_geo.lng` fields but instead got `{value}`.")]
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NotAnObject { document_id: Value, value: Value },
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#[error("The `_geo` field in the document with the id: `{document_id}` contains the following unexpected fields: `{value}`.")]
|
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UnexpectedExtraFields { document_id: Value, value: Value },
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#[error("Could not find latitude nor longitude in the document with the id: `{document_id}`. Was expecting `_geo.lat` and `_geo.lng` fields.")]
|
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MissingLatitudeAndLongitude { document_id: Value },
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#[error("Could not find latitude in the document with the id: `{document_id}`. Was expecting a `_geo.lat` field.")]
|
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MissingLatitude { document_id: Value },
|
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#[error("Could not find longitude in the document with the id: `{document_id}`. Was expecting a `_geo.lng` field.")]
|
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MissingLongitude { document_id: Value },
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#[error("Could not parse latitude nor longitude in the document with the id: `{document_id}`. Was expecting finite numbers but instead got `{lat}` and `{lng}`.")]
|
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BadLatitudeAndLongitude { document_id: Value, lat: Value, lng: Value },
|
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#[error("Could not parse latitude in the document with the id: `{document_id}`. Was expecting a finite number but instead got `{value}`.")]
|
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BadLatitude { document_id: Value, value: Value },
|
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#[error("Could not parse longitude in the document with the id: `{document_id}`. Was expecting a finite number but instead got `{value}`.")]
|
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BadLongitude { document_id: Value, value: Value },
|
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}
|
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|
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fn format_invalid_filter_distribution(
|
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invalid_facets_name: &BTreeSet<String>,
|
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valid_facets_name: &BTreeSet<String>,
|
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) -> String {
|
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if valid_facets_name.is_empty() {
|
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return "this index does not have configured filterable attributes.".into();
|
||||
}
|
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|
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let mut result = String::new();
|
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|
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match invalid_facets_name.len() {
|
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0 => (),
|
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1 => write!(
|
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result,
|
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"attribute `{}` is not filterable.",
|
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invalid_facets_name.first().unwrap()
|
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)
|
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.unwrap(),
|
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_ => write!(
|
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result,
|
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"attributes `{}` are not filterable.",
|
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invalid_facets_name.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", ")
|
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)
|
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.unwrap(),
|
||||
};
|
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|
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match valid_facets_name.len() {
|
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1 => write!(
|
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result,
|
||||
" The available filterable attribute is `{}`.",
|
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valid_facets_name.first().unwrap()
|
||||
)
|
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.unwrap(),
|
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_ => write!(
|
||||
result,
|
||||
" The available filterable attributes are `{}`.",
|
||||
valid_facets_name.iter().map(AsRef::as_ref).collect::<Vec<&str>>().join(", ")
|
||||
)
|
||||
.unwrap(),
|
||||
}
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// A little macro helper to autogenerate From implementation that needs two `Into`.
|
||||
/// Given the following parameters: `error_from_sub_error!(FieldIdMapMissingEntry => InternalError)`
|
||||
/// the macro will create the following code:
|
||||
/// ```ignore
|
||||
/// impl From<FieldIdMapMissingEntry> for Error {
|
||||
/// fn from(error: FieldIdMapMissingEntry) -> Error {
|
||||
/// Error::from(InternalError::from(error))
|
||||
/// }
|
||||
/// }
|
||||
/// ```
|
||||
macro_rules! error_from_sub_error {
|
||||
() => {};
|
||||
($sub:ty => $intermediate:ty) => {
|
||||
impl From<$sub> for Error {
|
||||
fn from(error: $sub) -> Error {
|
||||
Error::from(<$intermediate>::from(error))
|
||||
}
|
||||
}
|
||||
};
|
||||
($($sub:ty => $intermediate:ty $(,)?),+) => {
|
||||
$(error_from_sub_error!($sub => $intermediate);)+
|
||||
};
|
||||
}
|
||||
|
||||
error_from_sub_error! {
|
||||
FieldIdMapMissingEntry => InternalError,
|
||||
fst::Error => InternalError,
|
||||
documents::Error => InternalError,
|
||||
str::Utf8Error => InternalError,
|
||||
ThreadPoolBuildError => InternalError,
|
||||
SerializationError => InternalError,
|
||||
GeoError => UserError,
|
||||
CriterionError => UserError,
|
||||
}
|
||||
|
||||
impl<E> From<grenad::Error<E>> for Error
|
||||
where
|
||||
Error: From<E>,
|
||||
{
|
||||
fn from(error: grenad::Error<E>) -> Error {
|
||||
match error {
|
||||
grenad::Error::Io(error) => Error::IoError(error),
|
||||
grenad::Error::Merge(error) => Error::from(error),
|
||||
grenad::Error::InvalidCompressionType => {
|
||||
Error::InternalError(InternalError::GrenadInvalidCompressionType)
|
||||
}
|
||||
grenad::Error::InvalidFormatVersion => {
|
||||
Error::InternalError(InternalError::GrenadInvalidFormatVersion)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<DocumentsBatchCursorError> for Error {
|
||||
fn from(error: DocumentsBatchCursorError) -> Error {
|
||||
match error {
|
||||
DocumentsBatchCursorError::Grenad(e) => Error::from(e),
|
||||
DocumentsBatchCursorError::SerdeJson(e) => Error::from(InternalError::from(e)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl From<Infallible> for Error {
|
||||
fn from(_error: Infallible) -> Error {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
impl From<HeedError> for Error {
|
||||
fn from(error: HeedError) -> Error {
|
||||
use self::Error::*;
|
||||
use self::InternalError::*;
|
||||
use self::SerializationError::*;
|
||||
use self::UserError::*;
|
||||
|
||||
match error {
|
||||
HeedError::Io(error) => Error::from(error),
|
||||
HeedError::Mdb(MdbError::MapFull) => UserError(MaxDatabaseSizeReached),
|
||||
HeedError::Mdb(MdbError::Invalid) => UserError(InvalidStoreFile),
|
||||
HeedError::Mdb(error) => InternalError(Store(error)),
|
||||
// TODO use the encoding
|
||||
HeedError::Encoding(_) => InternalError(Serialization(Encoding { db_name: None })),
|
||||
HeedError::Decoding(_) => InternalError(Serialization(Decoding { db_name: None })),
|
||||
HeedError::DatabaseClosing => InternalError(DatabaseClosing),
|
||||
HeedError::BadOpenOptions { .. } => UserError(InvalidLmdbOpenOptions),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum FaultSource {
|
||||
User,
|
||||
Runtime,
|
||||
Bug,
|
||||
Undecided,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for FaultSource {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
let s = match self {
|
||||
FaultSource::User => "user error",
|
||||
FaultSource::Runtime => "runtime error",
|
||||
FaultSource::Bug => "coding error",
|
||||
FaultSource::Undecided => "error",
|
||||
};
|
||||
f.write_str(s)
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn conditionally_lookup_for_error_message() {
|
||||
let prefix = "Attribute `name` is not sortable.";
|
||||
let messages = vec![
|
||||
(BTreeSet::new(), "This index does not have configured sortable attributes."),
|
||||
(BTreeSet::from(["age".to_string()]), "Available sortable attributes are: `age`."),
|
||||
];
|
||||
|
||||
for (list, suffix) in messages {
|
||||
let err = UserError::InvalidSortableAttribute {
|
||||
field: "name".to_string(),
|
||||
valid_fields: list,
|
||||
hidden_fields: false,
|
||||
};
|
||||
|
||||
assert_eq!(err.to_string(), format!("{} {}", prefix, suffix));
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue