MeiliSearch/milli/src/vector/error.rs

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use std::path::PathBuf;
use hf_hub::api::sync::ApiError;
use crate::error::FaultSource;
#[derive(Debug, thiserror::Error)]
#[error("Error while generating embeddings: {inner}")]
pub struct Error {
pub inner: Box<ErrorKind>,
}
impl<I: Into<ErrorKind>> From<I> for Error {
fn from(value: I) -> Self {
Self { inner: Box::new(value.into()) }
}
}
impl Error {
pub fn fault(&self) -> FaultSource {
match &*self.inner {
ErrorKind::NewEmbedderError(inner) => inner.fault,
ErrorKind::EmbedError(inner) => inner.fault,
}
}
}
#[derive(Debug, thiserror::Error)]
pub enum ErrorKind {
#[error(transparent)]
NewEmbedderError(#[from] NewEmbedderError),
#[error(transparent)]
EmbedError(#[from] EmbedError),
}
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct EmbedError {
pub kind: EmbedErrorKind,
pub fault: FaultSource,
}
#[derive(Debug, thiserror::Error)]
pub enum EmbedErrorKind {
#[error("could not tokenize: {0}")]
Tokenize(Box<dyn std::error::Error + Send + Sync>),
#[error("unexpected tensor shape: {0}")]
TensorShape(candle_core::Error),
#[error("unexpected tensor value: {0}")]
TensorValue(candle_core::Error),
#[error("could not run model: {0}")]
ModelForward(candle_core::Error),
#[error("attempt to embed the following text in a configuration where embeddings must be user provided: {0:?}")]
ManualEmbed(String),
#[error("model not found. Meilisearch will not automatically download models from the Ollama library, please pull the model manually: {0:?}")]
OllamaModelNotFoundError(Option<String>),
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#[error("error deserialization the response body as JSON: {0}")]
RestResponseDeserialization(std::io::Error),
#[error("component `{0}` not found in path `{1}` in response: `{2}`")]
RestResponseMissingEmbeddings(String, String, String),
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#[error("unexpected format of the embedding response: {0}")]
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RestResponseFormat(serde_json::Error),
#[error("expected a response containing {0} embeddings, got only {1}")]
RestResponseEmbeddingCount(usize, usize),
#[error("could not authenticate against embedding server: {0:?}")]
RestUnauthorized(Option<String>),
#[error("sent too many requests to embedding server: {0:?}")]
RestTooManyRequests(Option<String>),
#[error("sent a bad request to embedding server: {0:?}")]
RestBadRequest(Option<String>),
#[error("received internal error from embedding server: {0:?}")]
RestInternalServerError(u16, Option<String>),
#[error("received HTTP {0} from embedding server: {0:?}")]
RestOtherStatusCode(u16, Option<String>),
#[error("could not reach embedding server: {0}")]
RestNetwork(ureq::Transport),
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#[error("was expected '{}' to be an object in query '{0}'", .1.join("."))]
RestNotAnObject(serde_json::Value, Vec<String>),
#[error("while embedding tokenized, was expecting embeddings of dimension `{0}`, got embeddings of dimensions `{1}`")]
OpenAiUnexpectedDimension(usize, usize),
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#[error("no embedding was produced")]
MissingEmbedding,
}
impl EmbedError {
pub fn tokenize(inner: Box<dyn std::error::Error + Send + Sync>) -> Self {
Self { kind: EmbedErrorKind::Tokenize(inner), fault: FaultSource::Runtime }
}
pub fn tensor_shape(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::TensorShape(inner), fault: FaultSource::Bug }
}
pub fn tensor_value(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::TensorValue(inner), fault: FaultSource::Bug }
}
pub fn model_forward(inner: candle_core::Error) -> Self {
Self { kind: EmbedErrorKind::ModelForward(inner), fault: FaultSource::Runtime }
}
pub(crate) fn embed_on_manual_embedder(texts: String) -> EmbedError {
Self { kind: EmbedErrorKind::ManualEmbed(texts), fault: FaultSource::User }
}
pub(crate) fn ollama_model_not_found(inner: Option<String>) -> EmbedError {
Self { kind: EmbedErrorKind::OllamaModelNotFoundError(inner), fault: FaultSource::User }
}
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pub(crate) fn rest_response_deserialization(error: std::io::Error) -> EmbedError {
Self {
kind: EmbedErrorKind::RestResponseDeserialization(error),
fault: FaultSource::Runtime,
}
}
pub(crate) fn rest_response_missing_embeddings<S: AsRef<str>>(
response: serde_json::Value,
component: &str,
response_field: &[S],
) -> EmbedError {
let response_field: Vec<&str> = response_field.iter().map(AsRef::as_ref).collect();
let response_field = response_field.join(".");
Self {
kind: EmbedErrorKind::RestResponseMissingEmbeddings(
component.to_owned(),
response_field,
serde_json::to_string_pretty(&response).unwrap_or_default(),
),
fault: FaultSource::Undecided,
}
}
pub(crate) fn rest_response_format(error: serde_json::Error) -> EmbedError {
Self { kind: EmbedErrorKind::RestResponseFormat(error), fault: FaultSource::Undecided }
}
pub(crate) fn rest_response_embedding_count(expected: usize, got: usize) -> EmbedError {
Self {
kind: EmbedErrorKind::RestResponseEmbeddingCount(expected, got),
fault: FaultSource::Runtime,
}
}
pub(crate) fn rest_unauthorized(error_response: Option<String>) -> EmbedError {
Self { kind: EmbedErrorKind::RestUnauthorized(error_response), fault: FaultSource::User }
}
pub(crate) fn rest_too_many_requests(error_response: Option<String>) -> EmbedError {
Self {
kind: EmbedErrorKind::RestTooManyRequests(error_response),
fault: FaultSource::Runtime,
}
}
pub(crate) fn rest_bad_request(error_response: Option<String>) -> EmbedError {
Self { kind: EmbedErrorKind::RestBadRequest(error_response), fault: FaultSource::User }
}
pub(crate) fn rest_internal_server_error(
code: u16,
error_response: Option<String>,
) -> EmbedError {
Self {
kind: EmbedErrorKind::RestInternalServerError(code, error_response),
fault: FaultSource::Runtime,
}
}
pub(crate) fn rest_other_status_code(code: u16, error_response: Option<String>) -> EmbedError {
Self {
kind: EmbedErrorKind::RestOtherStatusCode(code, error_response),
fault: FaultSource::Undecided,
}
}
pub(crate) fn rest_network(transport: ureq::Transport) -> EmbedError {
Self { kind: EmbedErrorKind::RestNetwork(transport), fault: FaultSource::Runtime }
}
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pub(crate) fn rest_not_an_object(
query: serde_json::Value,
input_path: Vec<String>,
) -> EmbedError {
Self { kind: EmbedErrorKind::RestNotAnObject(query, input_path), fault: FaultSource::User }
}
pub(crate) fn openai_unexpected_dimension(expected: usize, got: usize) -> EmbedError {
Self {
kind: EmbedErrorKind::OpenAiUnexpectedDimension(expected, got),
fault: FaultSource::Runtime,
}
}
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pub(crate) fn missing_embedding() -> EmbedError {
Self { kind: EmbedErrorKind::MissingEmbedding, fault: FaultSource::Undecided }
}
}
#[derive(Debug, thiserror::Error)]
#[error("{fault}: {kind}")]
pub struct NewEmbedderError {
pub kind: NewEmbedderErrorKind,
pub fault: FaultSource,
}
impl NewEmbedderError {
pub fn open_config(config_filename: PathBuf, inner: std::io::Error) -> NewEmbedderError {
let open_config = OpenConfig { filename: config_filename, inner };
Self { kind: NewEmbedderErrorKind::OpenConfig(open_config), fault: FaultSource::Runtime }
}
pub fn deserialize_config(
config: String,
config_filename: PathBuf,
inner: serde_json::Error,
) -> NewEmbedderError {
let deserialize_config = DeserializeConfig { config, filename: config_filename, inner };
Self {
kind: NewEmbedderErrorKind::DeserializeConfig(deserialize_config),
fault: FaultSource::Runtime,
}
}
pub fn open_tokenizer(
tokenizer_filename: PathBuf,
inner: Box<dyn std::error::Error + Send + Sync>,
) -> NewEmbedderError {
let open_tokenizer = OpenTokenizer { filename: tokenizer_filename, inner };
Self {
kind: NewEmbedderErrorKind::OpenTokenizer(open_tokenizer),
fault: FaultSource::Runtime,
}
}
pub fn new_api_fail(inner: ApiError) -> Self {
Self { kind: NewEmbedderErrorKind::NewApiFail(inner), fault: FaultSource::Bug }
}
pub fn api_get(inner: ApiError) -> Self {
Self { kind: NewEmbedderErrorKind::ApiGet(inner), fault: FaultSource::Undecided }
}
pub fn pytorch_weight(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::PytorchWeight(inner), fault: FaultSource::Runtime }
}
pub fn safetensor_weight(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::PytorchWeight(inner), fault: FaultSource::Runtime }
}
pub fn load_model(inner: candle_core::Error) -> Self {
Self { kind: NewEmbedderErrorKind::LoadModel(inner), fault: FaultSource::Runtime }
}
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pub fn could_not_determine_dimension(inner: EmbedError) -> NewEmbedderError {
Self {
kind: NewEmbedderErrorKind::CouldNotDetermineDimension(inner),
fault: FaultSource::Runtime,
}
}
}
#[derive(Debug, thiserror::Error)]
#[error("could not open config at {filename:?}: {inner}")]
pub struct OpenConfig {
pub filename: PathBuf,
pub inner: std::io::Error,
}
#[derive(Debug, thiserror::Error)]
#[error("could not deserialize config at {filename}: {inner}. Config follows:\n{config}")]
pub struct DeserializeConfig {
pub config: String,
pub filename: PathBuf,
pub inner: serde_json::Error,
}
#[derive(Debug, thiserror::Error)]
#[error("could not open tokenizer at {filename}: {inner}")]
pub struct OpenTokenizer {
pub filename: PathBuf,
#[source]
pub inner: Box<dyn std::error::Error + Send + Sync>,
}
#[derive(Debug, thiserror::Error)]
pub enum NewEmbedderErrorKind {
// hf
#[error(transparent)]
OpenConfig(OpenConfig),
#[error(transparent)]
DeserializeConfig(DeserializeConfig),
#[error(transparent)]
OpenTokenizer(OpenTokenizer),
#[error("could not build weights from Pytorch weights: {0}")]
PytorchWeight(candle_core::Error),
#[error("could not build weights from Safetensor weights: {0}")]
SafetensorWeight(candle_core::Error),
#[error("could not spawn HG_HUB API client: {0}")]
NewApiFail(ApiError),
#[error("fetching file from HG_HUB failed: {0}")]
ApiGet(ApiError),
#[error("could not determine model dimensions: test embedding failed with {0}")]
CouldNotDetermineDimension(EmbedError),
#[error("loading model failed: {0}")]
LoadModel(candle_core::Error),
}