MeiliSearch/milli/src/vector/error.rs

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use std::path::PathBuf;
use hf_hub::api::sync::ApiError;
use crate::error::FaultSource;
use crate::vector::openai::OpenAiError;
#[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("could not reach OpenAI: {0}")]
OpenAiNetwork(reqwest::Error),
#[error("unexpected response from OpenAI: {0}")]
OpenAiUnexpected(reqwest::Error),
#[error("could not authenticate against OpenAI: {0}")]
OpenAiAuth(OpenAiError),
#[error("sent too many requests to OpenAI: {0}")]
OpenAiTooManyRequests(OpenAiError),
#[error("received internal error from OpenAI: {0}")]
OpenAiInternalServerError(OpenAiError),
#[error("sent too many tokens in a request to OpenAI: {0}")]
OpenAiTooManyTokens(OpenAiError),
#[error("received unhandled HTTP status code {0} from OpenAI")]
OpenAiUnhandledStatusCode(u16),
#[error("attempt to embed the following text in a configuration where embeddings must be user provided: {0:?}")]
ManualEmbed(String),
}
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 fn openai_network(inner: reqwest::Error) -> Self {
Self { kind: EmbedErrorKind::OpenAiNetwork(inner), fault: FaultSource::Runtime }
}
pub fn openai_unexpected(inner: reqwest::Error) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiUnexpected(inner), fault: FaultSource::Bug }
}
pub(crate) fn openai_auth_error(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiAuth(inner), fault: FaultSource::User }
}
pub(crate) fn openai_too_many_requests(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiTooManyRequests(inner), fault: FaultSource::Runtime }
}
pub(crate) fn openai_internal_server_error(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiInternalServerError(inner), fault: FaultSource::Runtime }
}
pub(crate) fn openai_too_many_tokens(inner: OpenAiError) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiTooManyTokens(inner), fault: FaultSource::Bug }
}
pub(crate) fn openai_unhandled_status_code(code: u16) -> EmbedError {
Self { kind: EmbedErrorKind::OpenAiUnhandledStatusCode(code), fault: FaultSource::Bug }
}
pub(crate) fn embed_on_manual_embedder(texts: String) -> EmbedError {
Self { kind: EmbedErrorKind::ManualEmbed(texts), fault: FaultSource::User }
}
}
#[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 }
}
pub fn hf_could_not_determine_dimension(inner: EmbedError) -> NewEmbedderError {
Self {
kind: NewEmbedderErrorKind::CouldNotDetermineDimension(inner),
fault: FaultSource::Runtime,
}
}
pub fn openai_initialize_web_client(inner: reqwest::Error) -> Self {
Self { kind: NewEmbedderErrorKind::InitWebClient(inner), fault: FaultSource::Runtime }
}
pub fn openai_invalid_api_key_format(inner: reqwest::header::InvalidHeaderValue) -> Self {
Self { kind: NewEmbedderErrorKind::InvalidApiKeyFormat(inner), fault: FaultSource::User }
}
}
#[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),
// openai
#[error("initializing web client for sending embedding requests failed: {0}")]
InitWebClient(reqwest::Error),
#[error("The API key passed to Authorization error was in an invalid format: {0}")]
InvalidApiKeyFormat(reqwest::header::InvalidHeaderValue),
}