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https://github.com/meilisearch/meilisearch.git
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Error changes
This commit is contained in:
Louis Dureuil
parent
ecee0c922f
commit
1a297c048e
@ -4,6 +4,7 @@ use std::path::PathBuf;
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use hf_hub::api::sync::ApiError;
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use super::parsed_vectors::ParsedVectorsDiff;
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use super::rest::ConfigurationSource;
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use crate::error::FaultSource;
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use crate::{FieldDistribution, PanicCatched};
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@ -45,48 +46,57 @@ pub struct EmbedError {
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#[derive(Debug, thiserror::Error)]
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pub enum EmbedErrorKind {
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#[error("could not tokenize: {0}")]
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#[error("could not tokenize:\n - {0}")]
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Tokenize(Box<dyn std::error::Error + Send + Sync>),
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#[error("unexpected tensor shape: {0}")]
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#[error("unexpected tensor shape:\n - {0}")]
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TensorShape(candle_core::Error),
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#[error("unexpected tensor value: {0}")]
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#[error("unexpected tensor value:\n - {0}")]
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TensorValue(candle_core::Error),
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#[error("could not run model: {0}")]
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#[error("could not run model:\n - {0}")]
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ModelForward(candle_core::Error),
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#[error("attempt to embed the following text in a configuration where embeddings must be user provided: {0:?}")]
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#[error("attempt to embed the following text in a configuration where embeddings must be user provided:\n - `{0}`")]
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ManualEmbed(String),
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#[error("model not found. Meilisearch will not automatically download models from the Ollama library, please pull the model manually: {0:?}")]
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#[error("model not found. Meilisearch will not automatically download models from the Ollama library, please pull the model manually{}", option_info(.0.as_deref(), "server replied with "))]
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OllamaModelNotFoundError(Option<String>),
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#[error("error deserialization the response body as JSON: {0}")]
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#[error("error deserialization the response body as JSON:\n - {0}")]
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RestResponseDeserialization(std::io::Error),
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#[error("component `{0}` not found in path `{1}` in response: `{2}`")]
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RestResponseMissingEmbeddings(String, String, String),
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#[error("unexpected format of the embedding response: {0}")]
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RestResponseFormat(serde_json::Error),
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#[error("expected a response containing {0} embeddings, got only {1}")]
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RestResponseEmbeddingCount(usize, usize),
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#[error("could not authenticate against embedding server: {0:?}")]
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#[error("could not authenticate against embedding server{}", option_info(.0.as_deref(), "server replied with "))]
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RestUnauthorized(Option<String>),
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#[error("sent too many requests to embedding server: {0:?}")]
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#[error("sent too many requests to embedding server{}", option_info(.0.as_deref(), "server replied with "))]
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RestTooManyRequests(Option<String>),
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#[error("sent a bad request to embedding server: {0:?}")]
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RestBadRequest(Option<String>),
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#[error("received internal error from embedding server: {0:?}")]
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#[error("sent a bad request to embedding server{}{}",
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if ConfigurationSource::User == *.1 {
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"\n - Hint: check that the `request` in the embedder configuration matches the remote server's API"
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} else {
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""
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},
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option_info(.0.as_deref(), "server replied with "))]
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RestBadRequest(Option<String>, ConfigurationSource),
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#[error("received internal error HTTP {0} from embedding server{}", option_info(.1.as_deref(), "server replied with "))]
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RestInternalServerError(u16, Option<String>),
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#[error("received HTTP {0} from embedding server: {0:?}")]
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#[error("received unexpected HTTP {0} from embedding server{}", option_info(.1.as_deref(), "server replied with "))]
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RestOtherStatusCode(u16, Option<String>),
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#[error("could not reach embedding server: {0}")]
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#[error("could not reach embedding server:\n - {0}")]
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RestNetwork(ureq::Transport),
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#[error("was expected '{}' to be an object in query '{0}'", .1.join("."))]
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RestNotAnObject(serde_json::Value, Vec<String>),
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#[error("while embedding tokenized, was expecting embeddings of dimension `{0}`, got embeddings of dimensions `{1}`")]
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OpenAiUnexpectedDimension(usize, usize),
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#[error("error extracting embeddings from the response:\n - {0}")]
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RestExtractionError(String),
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#[error("was expecting embeddings of dimension `{0}`, got embeddings of dimensions `{1}`")]
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UnexpectedDimension(usize, usize),
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#[error("no embedding was produced")]
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MissingEmbedding,
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#[error(transparent)]
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PanicInThreadPool(#[from] PanicCatched),
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}
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fn option_info(info: Option<&str>, prefix: &str) -> String {
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match info {
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Some(info) => format!("\n - {prefix}`{info}`"),
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None => String::new(),
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}
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}
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impl EmbedError {
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pub fn tokenize(inner: Box<dyn std::error::Error + Send + Sync>) -> Self {
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Self { kind: EmbedErrorKind::Tokenize(inner), fault: FaultSource::Runtime }
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@ -119,28 +129,6 @@ impl EmbedError {
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}
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}
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pub(crate) fn rest_response_missing_embeddings<S: AsRef<str>>(
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response: serde_json::Value,
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component: &str,
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response_field: &[S],
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) -> EmbedError {
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let response_field: Vec<&str> = response_field.iter().map(AsRef::as_ref).collect();
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let response_field = response_field.join(".");
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Self {
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kind: EmbedErrorKind::RestResponseMissingEmbeddings(
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component.to_owned(),
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response_field,
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serde_json::to_string_pretty(&response).unwrap_or_default(),
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),
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fault: FaultSource::Undecided,
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}
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}
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pub(crate) fn rest_response_format(error: serde_json::Error) -> EmbedError {
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Self { kind: EmbedErrorKind::RestResponseFormat(error), fault: FaultSource::Undecided }
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}
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pub(crate) fn rest_response_embedding_count(expected: usize, got: usize) -> EmbedError {
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Self {
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kind: EmbedErrorKind::RestResponseEmbeddingCount(expected, got),
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@ -159,8 +147,14 @@ impl EmbedError {
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}
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}
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pub(crate) fn rest_bad_request(error_response: Option<String>) -> EmbedError {
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Self { kind: EmbedErrorKind::RestBadRequest(error_response), fault: FaultSource::User }
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pub(crate) fn rest_bad_request(
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error_response: Option<String>,
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configuration_source: ConfigurationSource,
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) -> EmbedError {
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Self {
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kind: EmbedErrorKind::RestBadRequest(error_response, configuration_source),
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fault: FaultSource::User,
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}
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}
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pub(crate) fn rest_internal_server_error(
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@ -184,22 +178,19 @@ impl EmbedError {
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Self { kind: EmbedErrorKind::RestNetwork(transport), fault: FaultSource::Runtime }
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}
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pub(crate) fn rest_not_an_object(
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query: serde_json::Value,
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input_path: Vec<String>,
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) -> EmbedError {
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Self { kind: EmbedErrorKind::RestNotAnObject(query, input_path), fault: FaultSource::User }
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}
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pub(crate) fn openai_unexpected_dimension(expected: usize, got: usize) -> EmbedError {
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pub(crate) fn rest_unexpected_dimension(expected: usize, got: usize) -> EmbedError {
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Self {
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kind: EmbedErrorKind::OpenAiUnexpectedDimension(expected, got),
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kind: EmbedErrorKind::UnexpectedDimension(expected, got),
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fault: FaultSource::Runtime,
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}
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}
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pub(crate) fn missing_embedding() -> EmbedError {
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Self { kind: EmbedErrorKind::MissingEmbedding, fault: FaultSource::Undecided }
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}
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pub(crate) fn rest_extraction_error(error: String) -> EmbedError {
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Self { kind: EmbedErrorKind::RestExtractionError(error), fault: FaultSource::Runtime }
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}
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}
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#[derive(Debug, thiserror::Error)]
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@ -290,10 +281,17 @@ impl NewEmbedderError {
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fault: FaultSource::Runtime,
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}
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}
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pub(crate) fn rest_could_not_parse_template(message: String) -> NewEmbedderError {
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Self {
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kind: NewEmbedderErrorKind::CouldNotParseTemplate(message),
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fault: FaultSource::User,
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}
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}
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}
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#[derive(Debug, thiserror::Error)]
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#[error("could not open config at {filename:?}: {inner}")]
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#[error("could not open config at {filename}: {inner}")]
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pub struct OpenConfig {
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pub filename: PathBuf,
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pub inner: std::io::Error,
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@ -339,18 +337,20 @@ pub enum NewEmbedderErrorKind {
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UnsupportedModel(UnsupportedModel),
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#[error(transparent)]
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OpenTokenizer(OpenTokenizer),
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#[error("could not build weights from Pytorch weights: {0}")]
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#[error("could not build weights from Pytorch weights:\n - {0}")]
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PytorchWeight(candle_core::Error),
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#[error("could not build weights from Safetensor weights: {0}")]
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#[error("could not build weights from Safetensor weights:\n - {0}")]
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SafetensorWeight(candle_core::Error),
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#[error("could not spawn HG_HUB API client: {0}")]
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#[error("could not spawn HG_HUB API client:\n - {0}")]
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NewApiFail(ApiError),
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#[error("fetching file from HG_HUB failed: {0}")]
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#[error("fetching file from HG_HUB failed:\n - {0}")]
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ApiGet(ApiError),
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#[error("could not determine model dimensions: test embedding failed with {0}")]
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#[error("could not determine model dimensions:\n - test embedding failed with {0}")]
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CouldNotDetermineDimension(EmbedError),
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#[error("loading model failed: {0}")]
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#[error("loading model failed:\n - {0}")]
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LoadModel(candle_core::Error),
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#[error("{0}")]
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CouldNotParseTemplate(String),
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}
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pub struct PossibleEmbeddingMistakes {
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