ollama and openai use new EmbedderOptions

milli/src/vector/ollama.rs

@@ -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 {
-            api_key: options.api_key,
-            dimensions: None,
-            distribution: options.distribution,
-            url: options.url.unwrap_or_else(get_ollama_path),
-            query: serde_json::json!({
-                "model": model,
-            }),
-            input_field: vec!["prompt".to_owned()],
-            path_to_embeddings: Default::default(),
-            embedding_object: vec!["embedding".to_owned()],
-            input_type: super::rest::InputType::Text,
-        }) {
+        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),
+                request: serde_json::json!({
+                    "model": model,
+                    "prompt": super::rest::REQUEST_PLACEHOLDER,
+                }),
+                response: serde_json::json!({
+                    "embedding": super::rest::RESPONSE_PLACEHOLDER,
+                }),
+            },
+            super::rest::ConfigurationSource::Ollama,
+        ) {
             Ok(embedder) => embedder,
             Err(NewEmbedderError {
                 kind:

milli/src/vector/openai.rs

@@ -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 {
-            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()],
-        })?;
+        let rest_embedder = RestEmbedder::new(
+            RestEmbedderOptions {
+                api_key: Some(api_key.clone()),
+                distribution: None,
+                dimensions: Some(options.dimensions()),
+                url,
+                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);