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Code Issues Releases Wiki Activity

ollama and openai use new EmbedderOptions

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This commit is contained in:
Louis Dureuil 2024-07-16 15:17:49 +02:00
parent a1beddd5d9
commit d731fa661b
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2 changed files with 40 additions and 30 deletions
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17
milli/src/vector/ollama.rs
View File

@ -28,19 +28,22 @@ impl EmbedderOptions {
impl Embedder {
pub fn new(options: EmbedderOptions) -> Result<Self, NewEmbedderError> {
let model = options.embedding_model.as_str();
let rest_embedder = match RestEmbedder::new(RestEmbedderOptions {
let rest_embedder = match RestEmbedder::new(
RestEmbedderOptions {
api_key: options.api_key,
dimensions: None,
distribution: options.distribution,
url: options.url.unwrap_or_else(get_ollama_path),
query: serde_json::json!({
request: serde_json::json!({
"model": model,
"prompt": super::rest::REQUEST_PLACEHOLDER,
}),
input_field: vec!["prompt".to_owned()],
path_to_embeddings: Default::default(),
embedding_object: vec!["embedding".to_owned()],
input_type: super::rest::InputType::Text,
}) {
response: serde_json::json!({
"embedding": super::rest::RESPONSE_PLACEHOLDER,
}),
},
super::rest::ConfigurationSource::Ollama,
) {
Ok(embedder) => embedder,
Err(NewEmbedderError {
kind:

33
milli/src/vector/openai.rs
View File

@ -26,20 +26,21 @@ impl EmbedderOptions {
}
}
pub fn query(&self) -> serde_json::Value {
pub fn request(&self) -> serde_json::Value {
let model = self.embedding_model.name();
let mut query = serde_json::json!({
let mut request = serde_json::json!({
"model": model,
"input": [super::rest::REQUEST_PLACEHOLDER, super::rest::REPEAT_PLACEHOLDER]
});
if self.embedding_model.supports_overriding_dimensions() {
if let Some(dimensions) = self.dimensions {
query["dimensions"] = dimensions.into();
request["dimensions"] = dimensions.into();
}
}
query
request
}
pub fn distribution(&self) -> Option<DistributionShift> {
@ -180,17 +181,23 @@ impl Embedder {
let url = options.url.as_deref().unwrap_or(OPENAI_EMBEDDINGS_URL).to_owned();
let rest_embedder = RestEmbedder::new(RestEmbedderOptions {
let rest_embedder = RestEmbedder::new(
RestEmbedderOptions {
api_key: Some(api_key.clone()),
distribution: None,
dimensions: Some(options.dimensions()),
url,
query: options.query(),
input_field: vec!["input".to_owned()],
input_type: crate::vector::rest::InputType::TextArray,
path_to_embeddings: vec!["data".to_owned()],
embedding_object: vec!["embedding".to_owned()],
})?;
request: options.request(),
response: serde_json::json!({
"data": [{
"embedding": super::rest::RESPONSE_PLACEHOLDER
},
super::rest::REPEAT_PLACEHOLDER
]
}),
},
super::rest::ConfigurationSource::OpenAi,
)?;
// looking at the code it is very unclear that this can actually fail.
let tokenizer = tiktoken_rs::cl100k_base().unwrap();
@ -201,7 +208,7 @@ impl Embedder {
pub fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
match self.rest_embedder.embed_ref(&texts) {
Ok(embeddings) => Ok(embeddings),
Err(EmbedError { kind: EmbedErrorKind::RestBadRequest(error), fault: _ }) => {
Err(EmbedError { kind: EmbedErrorKind::RestBadRequest(error, _), fault: _ }) => {
tracing::warn!(error=?error, "OpenAI: received `BAD_REQUEST`. Input was maybe too long, retrying on tokenized version. For best performance, limit the size of your document template.");
self.try_embed_tokenized(&texts)
}
@ -225,7 +232,7 @@ impl Embedder {
let embedding = self.rest_embedder.embed_tokens(tokens)?;
embeddings_for_prompt.append(embedding.into_inner()).map_err(|got| {
EmbedError::openai_unexpected_dimension(self.dimensions(), got.len())
EmbedError::rest_unexpected_dimension(self.dimensions(), got.len())
})?;
all_embeddings.push(embeddings_for_prompt);