searchengines-web/MeiliSearch
searchengines-web/MeiliSearch
1
0
Fork 0
mirror of https://github.com/meilisearch/MeiliSearch synced 2024-11-25 22:34:28 +01:00
Code Issues Releases Wiki Activity

Expose REST embedder to the API

Browse Source
This commit is contained in:
Louis Dureuil 2024-03-25 10:05:38 +01:00
parent f87747f4d3
commit a1db342f01
No known key found for this signature in database
7 changed files with 357 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
milli/src/update/index_documents/mod.rs
View File

@ -2646,6 +2646,12 @@ mod tests {
api_key: Setting::NotSet,
dimensions: Setting::Set(3),
document_template: Setting::NotSet,
url: Setting::NotSet,
query: Setting::NotSet,
input_field: Setting::NotSet,
path_to_embeddings: Setting::NotSet,
embedding_object: Setting::NotSet,
input_type: Setting::NotSet,
}),
);
settings.set_embedder_settings(embedders);

74
milli/src/update/settings.rs
View File

@ -1140,6 +1140,12 @@ fn validate_prompt(
api_key,
dimensions,
document_template: Setting::Set(template),
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
}) => {
// validate
let template = crate::prompt::Prompt::new(template)
@ -1153,6 +1159,12 @@ fn validate_prompt(
api_key,
dimensions,
document_template: Setting::Set(template),
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
}))
}
new => Ok(new),
@ -1165,8 +1177,20 @@ pub fn validate_embedding_settings(
) -> Result<Setting<EmbeddingSettings>> {
let settings = validate_prompt(name, settings)?;
let Setting::Set(settings) = settings else { return Ok(settings) };
let EmbeddingSettings { source, model, revision, api_key, dimensions, document_template } =
settings;
let EmbeddingSettings {
source,
model,
revision,
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
} = settings;
if let Some(0) = dimensions.set() {
return Err(crate::error::UserError::InvalidSettingsDimensions {
@ -1183,11 +1207,25 @@ pub fn validate_embedding_settings(
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
}));
};
match inferred_source {
EmbedderSource::OpenAi => {
check_unset(&revision, "revision", inferred_source, name)?;
check_unset(&url, "url", inferred_source, name)?;
check_unset(&query, "query", inferred_source, name)?;
check_unset(&input_field, "inputField", inferred_source, name)?;
check_unset(&path_to_embeddings, "pathToEmbeddings", inferred_source, name)?;
check_unset(&embedding_object, "embeddingObject", inferred_source, name)?;
check_unset(&input_type, "inputType", inferred_source, name)?;
if let Setting::Set(model) = &model {
let model = crate::vector::openai::EmbeddingModel::from_name(model.as_str())
.ok_or(crate::error::UserError::InvalidOpenAiModel {
@ -1224,10 +1262,24 @@ pub fn validate_embedding_settings(
check_set(&model, "model", inferred_source, name)?;
check_unset(&api_key, "apiKey", inferred_source, name)?;
check_unset(&revision, "revision", inferred_source, name)?;
check_unset(&url, "url", inferred_source, name)?;
check_unset(&query, "query", inferred_source, name)?;
check_unset(&input_field, "inputField", inferred_source, name)?;
check_unset(&path_to_embeddings, "pathToEmbeddings", inferred_source, name)?;
check_unset(&embedding_object, "embeddingObject", inferred_source, name)?;
check_unset(&input_type, "inputType", inferred_source, name)?;
}
EmbedderSource::HuggingFace => {
check_unset(&api_key, "apiKey", inferred_source, name)?;
check_unset(&dimensions, "dimensions", inferred_source, name)?;
check_unset(&url, "url", inferred_source, name)?;
check_unset(&query, "query", inferred_source, name)?;
check_unset(&input_field, "inputField", inferred_source, name)?;
check_unset(&path_to_embeddings, "pathToEmbeddings", inferred_source, name)?;
check_unset(&embedding_object, "embeddingObject", inferred_source, name)?;
check_unset(&input_type, "inputType", inferred_source, name)?;
}
EmbedderSource::UserProvided => {
check_unset(&model, "model", inferred_source, name)?;
@ -1235,6 +1287,18 @@ pub fn validate_embedding_settings(
check_unset(&api_key, "apiKey", inferred_source, name)?;
check_unset(&document_template, "documentTemplate", inferred_source, name)?;
check_set(&dimensions, "dimensions", inferred_source, name)?;
check_unset(&url, "url", inferred_source, name)?;
check_unset(&query, "query", inferred_source, name)?;
check_unset(&input_field, "inputField", inferred_source, name)?;
check_unset(&path_to_embeddings, "pathToEmbeddings", inferred_source, name)?;
check_unset(&embedding_object, "embeddingObject", inferred_source, name)?;
check_unset(&input_type, "inputType", inferred_source, name)?;
}
EmbedderSource::Rest => {
check_unset(&model, "model", inferred_source, name)?;
check_unset(&revision, "revision", inferred_source, name)?;
check_set(&url, "url", inferred_source, name)?;
}
}
Ok(Setting::Set(EmbeddingSettings {
@ -1244,6 +1308,12 @@ pub fn validate_embedding_settings(
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
}))
}

5
milli/src/vector/hf.rs
View File

@ -194,7 +194,10 @@ impl Embedder {
pub fn distribution(&self) -> Option<DistributionShift> {
if self.options.model == "BAAI/bge-base-en-v1.5" {
Some(DistributionShift { current_mean: 0.85, current_sigma: 0.1 })
Some(DistributionShift {
current_mean: ordered_float::OrderedFloat(0.85),
current_sigma: ordered_float::OrderedFloat(0.1),
})
} else {
None
}

58
milli/src/vector/mod.rs
View File

@ -1,6 +1,9 @@
use std::collections::HashMap;
use std::sync::Arc;
use ordered_float::OrderedFloat;
use serde::{Deserialize, Serialize};
use self::error::{EmbedError, NewEmbedderError};
use crate::prompt::{Prompt, PromptData};
@ -104,7 +107,10 @@ pub enum Embedder {
OpenAi(openai::Embedder),
/// An embedder based on the user providing the embeddings in the documents and queries.
UserProvided(manual::Embedder),
/// An embedder based on making embedding queries against an <https://ollama.com> embedding server.
Ollama(ollama::Embedder),
/// An embedder based on making embedding queries against a generic JSON/REST embedding server.
Rest(rest::Embedder),
}
/// Configuration for an embedder.
@ -175,6 +181,7 @@ pub enum EmbedderOptions {
OpenAi(openai::EmbedderOptions),
Ollama(ollama::EmbedderOptions),
UserProvided(manual::EmbedderOptions),
Rest(rest::EmbedderOptions),
}
impl Default for EmbedderOptions {
@ -209,6 +216,7 @@ impl Embedder {
EmbedderOptions::UserProvided(options) => {
Self::UserProvided(manual::Embedder::new(options))
}
EmbedderOptions::Rest(options) => Self::Rest(rest::Embedder::new(options)?),
})
}
@ -224,6 +232,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.embed(texts),
Embedder::Ollama(embedder) => embedder.embed(texts),
Embedder::UserProvided(embedder) => embedder.embed(texts),
Embedder::Rest(embedder) => embedder.embed(texts),
}
}
@ -240,6 +249,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks, threads),
Embedder::Ollama(embedder) => embedder.embed_chunks(text_chunks, threads),
Embedder::UserProvided(embedder) => embedder.embed_chunks(text_chunks),
Embedder::Rest(embedder) => embedder.embed_chunks(text_chunks, threads),
}
}
@ -250,6 +260,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
Embedder::Ollama(embedder) => embedder.chunk_count_hint(),
Embedder::UserProvided(_) => 1,
Embedder::Rest(embedder) => embedder.chunk_count_hint(),
}
}
@ -260,6 +271,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.prompt_count_in_chunk_hint(),
Embedder::Ollama(embedder) => embedder.prompt_count_in_chunk_hint(),
Embedder::UserProvided(_) => 1,
Embedder::Rest(embedder) => embedder.prompt_count_in_chunk_hint(),
}
}
@ -270,6 +282,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.dimensions(),
Embedder::Ollama(embedder) => embedder.dimensions(),
Embedder::UserProvided(embedder) => embedder.dimensions(),
Embedder::Rest(embedder) => embedder.dimensions(),
}
}
@ -280,6 +293,7 @@ impl Embedder {
Embedder::OpenAi(embedder) => embedder.distribution(),
Embedder::Ollama(embedder) => embedder.distribution(),
Embedder::UserProvided(_embedder) => None,
Embedder::Rest(embedder) => embedder.distribution(),
}
}
}
@ -288,17 +302,47 @@ impl Embedder {
///
/// The intended use is to make the similarity score more comparable to the regular ranking score.
/// This allows to correct effects where results are too "packed" around a certain value.
#[derive(Debug, Clone, Copy)]
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Deserialize, Serialize)]
#[serde(from = "DistributionShiftSerializable")]
#[serde(into = "DistributionShiftSerializable")]
pub struct DistributionShift {
/// Value where the results are "packed".
///
/// Similarity scores are translated so that they are packed around 0.5 instead
pub current_mean: f32,
pub current_mean: OrderedFloat<f32>,
/// standard deviation of a similarity score.
///
/// Set below 0.4 to make the results less packed around the mean, and above 0.4 to make them more packed.
pub current_sigma: f32,
pub current_sigma: OrderedFloat<f32>,
}
#[derive(Serialize, Deserialize)]
struct DistributionShiftSerializable {
current_mean: f32,
current_sigma: f32,
}
impl From<DistributionShift> for DistributionShiftSerializable {
fn from(
DistributionShift {
current_mean: OrderedFloat(current_mean),
current_sigma: OrderedFloat(current_sigma),
}: DistributionShift,
) -> Self {
Self { current_mean, current_sigma }
}
}
impl From<DistributionShiftSerializable> for DistributionShift {
fn from(
DistributionShiftSerializable { current_mean, current_sigma }: DistributionShiftSerializable,
) -> Self {
Self {
current_mean: OrderedFloat(current_mean),
current_sigma: OrderedFloat(current_sigma),
}
}
}
impl DistributionShift {
@ -307,11 +351,13 @@ impl DistributionShift {
if sigma <= 0.0 {
None
} else {
Some(Self { current_mean: mean, current_sigma: sigma })
Some(Self { current_mean: OrderedFloat(mean), current_sigma: OrderedFloat(sigma) })
}
}
pub fn shift(&self, score: f32) -> f32 {
let current_mean = self.current_mean.0;
let current_sigma = self.current_sigma.0;
// <https://math.stackexchange.com/a/2894689>
// We're somewhat abusively mapping the distribution of distances to a gaussian.
// The parameters we're given is the mean and sigma of the native result distribution.
@ -321,9 +367,9 @@ impl DistributionShift {
let target_sigma = 0.4;
// a^2 sig1^2 = sig2^2 => a^2 = sig2^2 / sig1^2 => a = sig2 / sig1, assuming a, sig1, and sig2 positive.
let factor = target_sigma / self.current_sigma;
let factor = target_sigma / current_sigma;
// a*mu1 + b = mu2 => b = mu2 - a*mu1
let offset = target_mean - (factor * self.current_mean);
let offset = target_mean - (factor * current_mean);
let mut score = factor * score + offset;

22
milli/src/vector/openai.rs
View File

@ -1,3 +1,4 @@
use ordered_float::OrderedFloat;
use rayon::iter::{IntoParallelIterator, ParallelIterator as _};
use super::error::{EmbedError, NewEmbedderError};
@ -110,15 +111,18 @@ impl EmbeddingModel {
fn distribution(&self) -> Option<DistributionShift> {
match self {
EmbeddingModel::TextEmbeddingAda002 => {
Some(DistributionShift { current_mean: 0.90, current_sigma: 0.08 })
}
EmbeddingModel::TextEmbedding3Large => {
Some(DistributionShift { current_mean: 0.70, current_sigma: 0.1 })
}
EmbeddingModel::TextEmbedding3Small => {
Some(DistributionShift { current_mean: 0.75, current_sigma: 0.1 })
}
EmbeddingModel::TextEmbeddingAda002 => Some(DistributionShift {
current_mean: OrderedFloat(0.90),
current_sigma: OrderedFloat(0.08),
}),
EmbeddingModel::TextEmbedding3Large => Some(DistributionShift {
current_mean: OrderedFloat(0.70),
current_sigma: OrderedFloat(0.1),
}),
EmbeddingModel::TextEmbedding3Small => Some(DistributionShift {
current_mean: OrderedFloat(0.75),
current_sigma: OrderedFloat(0.1),
}),
}
}

41
milli/src/vector/rest.rs
View File

@ -1,5 +1,6 @@
use deserr::Deserr;
use rayon::iter::{IntoParallelIterator as _, ParallelIterator as _};
use serde::Serialize;
use serde::{Deserialize, Serialize};
use super::{
DistributionShift, EmbedError, Embedding, Embeddings, NewEmbedderError, REQUEST_PARALLELISM,
@ -64,7 +65,7 @@ pub struct Embedder {
dimensions: usize,
}
#[derive(Debug)]
#[derive(Debug, Clone, PartialEq, Eq, Deserialize, Serialize)]
pub struct EmbedderOptions {
pub api_key: Option<String>,
pub distribution: Option<DistributionShift>,
@ -79,7 +80,41 @@ pub struct EmbedderOptions {
pub input_type: InputType,
}
#[derive(Debug)]
impl Default for EmbedderOptions {
fn default() -> Self {
Self {
url: Default::default(),
query: Default::default(),
input_field: vec!["input".into()],
path_to_embeddings: vec!["data".into()],
embedding_object: vec!["embedding".into()],
input_type: InputType::Text,
api_key: None,
distribution: None,
dimensions: None,
}
}
}
impl std::hash::Hash for EmbedderOptions {
fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
self.api_key.hash(state);
self.distribution.hash(state);
self.dimensions.hash(state);
self.url.hash(state);
// skip hashing the query
// collisions in regular usage should be minimal,
// and the list is limited to 256 values anyway
self.input_field.hash(state);
self.path_to_embeddings.hash(state);
self.embedding_object.hash(state);
self.input_type.hash(state);
}
}
#[derive(Debug, Clone, Copy, Deserialize, Serialize, PartialEq, Eq, Hash, Deserr)]
#[serde(rename_all = "camelCase")]
#[deserr(rename_all = camelCase, deny_unknown_fields)]
pub enum InputType {
Text,
TextArray,

183
milli/src/vector/settings.rs
View File

@ -1,6 +1,7 @@
use deserr::Deserr;
use serde::{Deserialize, Serialize};
use super::rest::InputType;
use super::{ollama, openai};
use crate::prompt::PromptData;
use crate::update::Setting;
@ -29,6 +30,24 @@ pub struct EmbeddingSettings {
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub document_template: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub url: Setting<String>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub query: Setting<serde_json::Value>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub input_field: Setting<Vec<String>>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub path_to_embeddings: Setting<Vec<String>>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub embedding_object: Setting<Vec<String>>,
#[serde(default, skip_serializing_if = "Setting::is_not_set")]
#[deserr(default)]
pub input_type: Setting<InputType>,
}
pub fn check_unset<T>(
@ -75,20 +94,42 @@ impl EmbeddingSettings {
pub const DIMENSIONS: &'static str = "dimensions";
pub const DOCUMENT_TEMPLATE: &'static str = "documentTemplate";
pub const URL: &'static str = "url";
pub const QUERY: &'static str = "query";
pub const INPUT_FIELD: &'static str = "inputField";
pub const PATH_TO_EMBEDDINGS: &'static str = "pathToEmbeddings";
pub const EMBEDDING_OBJECT: &'static str = "embeddingObject";
pub const INPUT_TYPE: &'static str = "inputType";
pub fn allowed_sources_for_field(field: &'static str) -> &'static [EmbedderSource] {
match field {
Self::SOURCE => {
&[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::UserProvided]
}
Self::SOURCE => &[
EmbedderSource::HuggingFace,
EmbedderSource::OpenAi,
EmbedderSource::UserProvided,
EmbedderSource::Rest,
EmbedderSource::Ollama,
],
Self::MODEL => {
&[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::Ollama]
}
Self::REVISION => &[EmbedderSource::HuggingFace],
Self::API_KEY => &[EmbedderSource::OpenAi],
Self::DIMENSIONS => &[EmbedderSource::OpenAi, EmbedderSource::UserProvided],
Self::DOCUMENT_TEMPLATE => {
&[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::Ollama]
Self::API_KEY => &[EmbedderSource::OpenAi, EmbedderSource::Rest],
Self::DIMENSIONS => {
&[EmbedderSource::OpenAi, EmbedderSource::UserProvided, EmbedderSource::Rest]
}
Self::DOCUMENT_TEMPLATE => &[
EmbedderSource::HuggingFace,
EmbedderSource::OpenAi,
EmbedderSource::Ollama,
EmbedderSource::Rest,
],
Self::URL => &[EmbedderSource::Rest],
Self::QUERY => &[EmbedderSource::Rest],
Self::INPUT_FIELD => &[EmbedderSource::Rest],
Self::PATH_TO_EMBEDDINGS => &[EmbedderSource::Rest],
Self::EMBEDDING_OBJECT => &[EmbedderSource::Rest],
Self::INPUT_TYPE => &[EmbedderSource::Rest],
_other => unreachable!("unknown field"),
}
}
@ -107,6 +148,18 @@ impl EmbeddingSettings {
}
EmbedderSource::Ollama => &[Self::SOURCE, Self::MODEL, Self::DOCUMENT_TEMPLATE],
EmbedderSource::UserProvided => &[Self::SOURCE, Self::DIMENSIONS],
EmbedderSource::Rest => &[
Self::SOURCE,
Self::API_KEY,
Self::DIMENSIONS,
Self::DOCUMENT_TEMPLATE,
Self::URL,
Self::QUERY,
Self::INPUT_FIELD,
Self::PATH_TO_EMBEDDINGS,
Self::EMBEDDING_OBJECT,
Self::INPUT_TYPE,
],
}
}
@ -141,6 +194,7 @@ pub enum EmbedderSource {
HuggingFace,
Ollama,
UserProvided,
Rest,
}
impl std::fmt::Display for EmbedderSource {
@ -150,6 +204,7 @@ impl std::fmt::Display for EmbedderSource {
EmbedderSource::HuggingFace => "huggingFace",
EmbedderSource::UserProvided => "userProvided",
EmbedderSource::Ollama => "ollama",
EmbedderSource::Rest => "rest",
};
f.write_str(s)
}
@ -157,8 +212,20 @@ impl std::fmt::Display for EmbedderSource {
impl EmbeddingSettings {
pub fn apply(&mut self, new: Self) {
let EmbeddingSettings { source, model, revision, api_key, dimensions, document_template } =
new;
let EmbeddingSettings {
source,
model,
revision,
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
} = new;
let old_source = self.source;
self.source.apply(source);
// Reinitialize the whole setting object on a source change
@ -170,6 +237,12 @@ impl EmbeddingSettings {
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
};
return;
}
@ -179,6 +252,13 @@ impl EmbeddingSettings {
self.api_key.apply(api_key);
self.dimensions.apply(dimensions);
self.document_template.apply(document_template);
self.url.apply(url);
self.query.apply(query);
self.input_field.apply(input_field);
self.path_to_embeddings.apply(path_to_embeddings);
self.embedding_object.apply(embedding_object);
self.input_type.apply(input_type);
}
}
@ -193,6 +273,12 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
api_key: Setting::NotSet,
dimensions: Setting::NotSet,
document_template: Setting::Set(prompt.template),
url: Setting::NotSet,
query: Setting::NotSet,
input_field: Setting::NotSet,
path_to_embeddings: Setting::NotSet,
embedding_object: Setting::NotSet,
input_type: Setting::NotSet,
},
super::EmbedderOptions::OpenAi(options) => Self {
source: Setting::Set(EmbedderSource::OpenAi),
@ -201,6 +287,12 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
api_key: options.api_key.map(Setting::Set).unwrap_or_default(),
dimensions: options.dimensions.map(Setting::Set).unwrap_or_default(),
document_template: Setting::Set(prompt.template),
url: Setting::NotSet,
query: Setting::NotSet,
input_field: Setting::NotSet,
path_to_embeddings: Setting::NotSet,
embedding_object: Setting::NotSet,
input_type: Setting::NotSet,
},
super::EmbedderOptions::Ollama(options) => Self {
source: Setting::Set(EmbedderSource::Ollama),
@ -209,6 +301,12 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
api_key: Setting::NotSet,
dimensions: Setting::NotSet,
document_template: Setting::Set(prompt.template),
url: Setting::NotSet,
query: Setting::NotSet,
input_field: Setting::NotSet,
path_to_embeddings: Setting::NotSet,
embedding_object: Setting::NotSet,
input_type: Setting::NotSet,
},
super::EmbedderOptions::UserProvided(options) => Self {
source: Setting::Set(EmbedderSource::UserProvided),
@ -217,6 +315,37 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
api_key: Setting::NotSet,
dimensions: Setting::Set(options.dimensions),
document_template: Setting::NotSet,
url: Setting::NotSet,
query: Setting::NotSet,
input_field: Setting::NotSet,
path_to_embeddings: Setting::NotSet,
embedding_object: Setting::NotSet,
input_type: Setting::NotSet,
},
super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
api_key,
// TODO: support distribution
distribution: _,
dimensions,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
}) => Self {
source: Setting::Set(EmbedderSource::Rest),
model: Setting::NotSet,
revision: Setting::NotSet,
api_key: api_key.map(Setting::Set).unwrap_or_default(),
dimensions: dimensions.map(Setting::Set).unwrap_or_default(),
document_template: Setting::Set(prompt.template),
url: Setting::Set(url),
query: Setting::Set(query),
input_field: Setting::Set(input_field),
path_to_embeddings: Setting::Set(path_to_embeddings),
embedding_object: Setting::Set(embedding_object),
input_type: Setting::Set(input_type),
},
}
}
@ -225,8 +354,20 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
impl From<EmbeddingSettings> for EmbeddingConfig {
fn from(value: EmbeddingSettings) -> Self {
let mut this = Self::default();
let EmbeddingSettings { source, model, revision, api_key, dimensions, document_template } =
value;
let EmbeddingSettings {
source,
model,
revision,
api_key,
dimensions,
document_template,
url,
query,
input_field,
path_to_embeddings,
embedding_object,
input_type,
} = value;
if let Some(source) = source.set() {
match source {
EmbedderSource::OpenAi => {
@ -274,6 +415,26 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
dimensions: dimensions.set().unwrap(),
});
}
EmbedderSource::Rest => {
let embedder_options = super::rest::EmbedderOptions::default();
this.embedder_options =
super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
api_key: api_key.set(),
distribution: None,
dimensions: dimensions.set(),
url: url.set().unwrap(),
query: query.set().unwrap_or(embedder_options.query),
input_field: input_field.set().unwrap_or(embedder_options.input_field),
path_to_embeddings: path_to_embeddings
.set()
.unwrap_or(embedder_options.path_to_embeddings),
embedding_object: embedding_object
.set()
.unwrap_or(embedder_options.embedding_object),
input_type: input_type.set().unwrap_or(embedder_options.input_type),
})
}
}
}