Merge #4815

4815: Rest embedder api mk2 r=ManyTheFish a=dureuill

# Pull Request

## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/4756

- [x] [REST API parameter names and behavior are unclear](https://github.com/meilisearch/documentation/pull/2824#issuecomment-2124073720)
  - unclear names are removed. There remain only two parameters: `request`, a template of what Meilisearch's request to the embedding server should be, and `response`, a template of what the embedding server's response to Meilisearch should look like
- [x] [Bad error message or bad default value when we don't specify the `query` parameter](85d8455c11/meilisearch/tests/vector/rest.rs (L105-L140))
  - The replacement for `query`, which is `request`, is now a mandatory parameter. Omitting it will result in the following error message : "`.embedders.rest`: Missing field `request` (note: this field is mandatory for source rest)", which is clear
- [x] [Bad error message when both `pathToEmbeddings` and `embeddingObject` are missing](2141cb3b69/meilisearch/tests/vector/rest.rs (L142-L178))
  - These parameters no longer exist. Now, the point of extraction is given directly by the location of an `{{embedding}}` placeholder in the `response` parameter.
- [x] [Unexpected error when we don't specify both `pathToEmbeddings` and `embeddingObject` (only once should be required)](2141cb3b69/meilisearch/tests/vector/rest.rs (L180-L260))
  - These parameters no longer exist. Now, the point of extraction is given directly by the location of an `{{embedding}}` placeholder in the `response` parameter.
- [x] [Should not panic when the dimensions specified do not work with the model](2141cb3b69/meilisearch/tests/vector/rest.rs (L262-L299))
  - This no longer panics, instead returns "While embedding documents for embedder `rest`: runtime error: was expecting embeddings of dimension `2`, got embeddings of dimensions `3`"
- [x] [Be more flexible on the type of data that is accepted](https://github.com/meilisearch/meilisearch/issues/4757#issuecomment-2201948531)
  - [x] Always accept arrays of embeddings even if `inputType` is set to `text`
    - This is controlled by the repeat placeholder `"{..}"`, an array of embeddings can be configured even if the input is not in an array.
  - [x] Accept arrays of result at the root level and texts/array of text at the root level.
    -  doable with `request: "{{text}}"` and `response: "{{embedding}}"` or `response: ["{{embedding}}"]` (see test `vector::rest::server_raw`)

## What does this PR do?
- [See public usage](https://meilisearch.notion.site/v1-10-AI-search-changes-737c9d7d010d4dd685582bf5dab579e2#8de842673ffa4a139210094a89c1ec3e)
- Add new `milli::vector::json_template` module to parse JSON templates with an injection placeholder and a repeat placeholder
- Change rest embedder to use two JSON templates
- Change ollama and openai embedders to use the new rest embedder
- Update settings
- Update and add tests

## Breaking change

> [!CAUTION]
> This PR is a breaking change to the REST embedder.
> Importing a dump containing a REST embedder configuration will fail in v1.10 with an error: "Error: unknown field `query`, expected one of `source`, `model`, `revision`, `apiKey`, `dimensions`, `documentTemplate`, `url`, `request`, `response`, `distribution` at line 1 column 752".

Upgrade procedure:

1. Remove any embedder with source "rest"
2. Create a dump
3. Import that dump in a v1.10
4. Re-add any removed embedder, using the new settings.

Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: Louis Dureuil <louis.dureuil@xinra.net>
Co-authored-by: Tamo <tamo@meilisearch.com>

Cargo.lock

@@ -55,7 +55,7 @@ dependencies = [
  "encoding_rs",
  "flate2",
  "futures-core",
- "h2",
+ "h2 0.3.26",
  "http 0.2.11",
  "httparse",
  "httpdate",
@@ -403,6 +403,16 @@ dependencies = [
  "thiserror",
 ]
 
+[[package]]
+name = "assert-json-diff"
+version = "2.0.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "47e4f2b81832e72834d7518d8487a0396a28cc408186a2e8854c0f98011faf12"
+dependencies = [
+ "serde",
+ "serde_json",
+]
+
 [[package]]
 name = "async-trait"
 version = "0.1.81"
@@ -414,6 +424,12 @@ dependencies = [
  "syn 2.0.60",
 ]
 
+[[package]]
+name = "atomic-waker"
+version = "1.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1505bd5d3d116872e7271a6d4e16d81d0c8570876c8de68093a09ac269d8aac0"
+
 [[package]]
 name = "autocfg"
 version = "1.2.0"
@@ -1377,6 +1393,24 @@ dependencies = [
  "syn 2.0.60",
 ]
 
+[[package]]
+name = "deadpool"
+version = "0.10.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fb84100978c1c7b37f09ed3ce3e5f843af02c2a2c431bae5b19230dad2c1b490"
+dependencies = [
+ "async-trait",
+ "deadpool-runtime",
+ "num_cpus",
+ "tokio",
+]
+
+[[package]]
+name = "deadpool-runtime"
+version = "0.1.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "092966b41edc516079bdf31ec78a2e0588d1d0c08f78b91d8307215928642b2b"
+
 [[package]]
 name = "debugid"
 version = "0.8.0"
@@ -2231,6 +2265,25 @@ dependencies = [
  "tracing",
 ]
 
+[[package]]
+name = "h2"
+version = "0.4.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fa82e28a107a8cc405f0839610bdc9b15f1e25ec7d696aa5cf173edbcb1486ab"
+dependencies = [
+ "atomic-waker",
+ "bytes",
+ "fnv",
+ "futures-core",
+ "futures-sink",
+ "http 1.1.0",
+ "indexmap",
+ "slab",
+ "tokio",
+ "tokio-util",
+ "tracing",
+]
+
 [[package]]
 name = "half"
 version = "1.8.2"
@@ -2441,9 +2494,11 @@ dependencies = [
  "bytes",
  "futures-channel",
  "futures-util",
+ "h2 0.4.5",
  "http 1.1.0",
  "http-body",
  "httparse",
+ "httpdate",
  "itoa",
  "pin-project-lite",
  "smallvec",
@@ -3423,6 +3478,7 @@ dependencies = [
  "url",
  "urlencoding",
  "uuid",
+ "wiremock",
  "yaup",
  "zip 2.1.3",
 ]
@@ -6281,6 +6337,30 @@ dependencies = [
  "windows-sys 0.48.0",
 ]
 
+[[package]]
+name = "wiremock"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ec874e1eef0df2dcac546057fe5e29186f09c378181cd7b635b4b7bcc98e9d81"
+dependencies = [
+ "assert-json-diff",
+ "async-trait",
+ "base64 0.21.7",
+ "deadpool",
+ "futures",
+ "http 1.1.0",
+ "http-body-util",
+ "hyper",
+ "hyper-util",
+ "log",
+ "once_cell",
+ "regex",
+ "serde",
+ "serde_json",
+ "tokio",
+ "url",
+]
+
 [[package]]
 name = "wyz"
 version = "0.5.1"

index-scheduler/src/lib.rs

@@ -3047,6 +3047,8 @@ mod tests {
             api_key: Setting::Set(S("My super secret")),
             url: Setting::Set(S("http://localhost:7777")),
             dimensions: Setting::Set(4),
+            request: Setting::Set(serde_json::json!("{{text}}")),
+            response: Setting::Set(serde_json::json!("{{embedding}}")),
             ..Default::default()
         };
         embedders.insert(S("default"), Setting::Set(embedding_settings));
@@ -5006,6 +5008,8 @@ mod tests {
             api_key: Setting::Set(S("My super secret")),
             url: Setting::Set(S("http://localhost:7777")),
             dimensions: Setting::Set(384),
+            request: Setting::Set(serde_json::json!("{{text}}")),
+            response: Setting::Set(serde_json::json!("{{embedding}}")),
             ..Default::default()
         };
         embedders.insert(S("A_fakerest"), Setting::Set(embedding_settings));

index-scheduler/src/snapshots/index_scheduler__tests__import_vectors-2.snap

@@ -8,7 +8,9 @@ expression: task.details
       "source": "rest",
       "apiKey": "MyXXXX...",
       "dimensions": 384,
-      "url": "http://localhost:7777"
+      "url": "http://localhost:7777",
+      "request": "{{text}}",
+      "response": "{{embedding}}"
     },
     "B_small_hf": {
       "source": "huggingFace",

index-scheduler/src/snapshots/index_scheduler__tests__import_vectors-5.snap

@@ -8,16 +8,7 @@ expression: fakerest_config.embedder_options
     "distribution": null,
     "dimensions": 384,
     "url": "http://localhost:7777",
-    "query": null,
+    "request": "{{text}}",
-    "input_field": [
+    "response": "{{embedding}}"
-      "input"
-    ],
-    "path_to_embeddings": [
-      "data"
-    ],
-    "embedding_object": [
-      "embedding"
-    ],
-    "input_type": "text"
   }
 }

index-scheduler/src/snapshots/index_scheduler__tests__import_vectors.snap

@@ -8,7 +8,9 @@ expression: task.details
       "source": "rest",
       "apiKey": "MyXXXX...",
       "dimensions": 384,
-      "url": "http://localhost:7777"
+      "url": "http://localhost:7777",
+      "request": "{{text}}",
+      "response": "{{embedding}}"
     },
     "B_small_hf": {
       "source": "huggingFace",

index-scheduler/src/snapshots/index_scheduler__tests__settings_update-2.snap

@@ -8,7 +8,9 @@ expression: task.details
       "source": "rest",
       "apiKey": "MyXXXX...",
       "dimensions": 4,
-      "url": "http://localhost:7777"
+      "url": "http://localhost:7777",
+      "request": "{{text}}",
+      "response": "{{embedding}}"
     }
   }
 }

index-scheduler/src/snapshots/index_scheduler__tests__settings_update-5.snap

@@ -1,6 +1,6 @@
 ---
 source: index-scheduler/src/lib.rs
-expression: embedding_config.embedder_options
+expression: config.embedder_options
 ---
 {
   "Rest": {
@@ -8,16 +8,7 @@ expression: embedding_config.embedder_options
     "distribution": null,
     "dimensions": 4,
     "url": "http://localhost:7777",
-    "query": null,
+    "request": "{{text}}",
-    "input_field": [
+    "response": "{{embedding}}"
-      "input"
-    ],
-    "path_to_embeddings": [
-      "data"
-    ],
-    "embedding_object": [
-      "embedding"
-    ],
-    "input_type": "text"
   }
 }

index-scheduler/src/snapshots/index_scheduler__tests__settings_update.snap

@@ -8,7 +8,9 @@ expression: task.details
       "source": "rest",
       "apiKey": "MyXXXX...",
       "dimensions": 4,
-      "url": "http://localhost:7777"
+      "url": "http://localhost:7777",
+      "request": "{{text}}",
+      "response": "{{embedding}}"
     }
   }
 }

index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir succeeds.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 2 {uid: 2, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
@@ -46,4 +46,3 @@ doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1
 ### File Store:
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/import_vectors/Intel to kefir.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 2 {uid: 2, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: None, method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000001, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
@@ -45,4 +45,3 @@ doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1
 00000000-0000-0000-0000-000000000001
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/import_vectors/adding Intel succeeds.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: succeeded, details: { received_documents: 1, indexed_documents: Some(1) }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
@@ -42,4 +42,3 @@ doggos: { number_of_documents: 1, field_distribution: {"_vectors": 1, "breed": 1
 ### File Store:
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/import_vectors/after adding Intel.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 1 {uid: 1, status: enqueued, details: { received_documents: 1, indexed_documents: None }, kind: DocumentAdditionOrUpdate { index_uid: "doggos", primary_key: Some("id"), method: UpdateDocuments, content_file: 00000000-0000-0000-0000-000000000000, documents_count: 1, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
@@ -41,4 +41,3 @@ doggos: { number_of_documents: 0, field_distribution: {} }
 00000000-0000-0000-0000-000000000000
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/import_vectors/after_registering_settings_task_vectors.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued [0,]
@@ -33,4 +33,3 @@ doggos [0,]
 ### File Store:
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/import_vectors/settings_update_processed_vectors.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"A_fakerest": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(384), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet }), "B_small_hf": Set(EmbeddingSettings { source: Set(HuggingFace), model: Set("sentence-transformers/all-MiniLM-L6-v2"), revision: Set("e4ce9877abf3edfe10b0d82785e83bdcb973e22e"), api_key: NotSet, dimensions: NotSet, document_template: Set("{{doc.doggo}} the {{doc.breed}} best doggo"), url: NotSet, request: NotSet, response: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued []
@@ -37,4 +37,3 @@ doggos: { number_of_documents: 0, field_distribution: {} }
 ### File Store:
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/test_settings_update/after_registering_settings_task.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: enqueued, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued [0,]
@@ -33,4 +33,3 @@ doggos [0,]
 ### File Store:
 
 ----------------------------------------------------------------------
-

index-scheduler/src/snapshots/lib.rs/test_settings_update/settings_update_processed.snap

@@ -6,7 +6,7 @@ source: index-scheduler/src/lib.rs
 []
 ----------------------------------------------------------------------
 ### All Tasks:
-0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), query: NotSet, input_field: NotSet, path_to_embeddings: NotSet, embedding_object: NotSet, input_type: NotSet, distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
+0 {uid: 0, status: succeeded, details: { settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> } }, kind: SettingsUpdate { index_uid: "doggos", new_settings: Settings { displayed_attributes: WildcardSetting(NotSet), searchable_attributes: WildcardSetting(NotSet), filterable_attributes: NotSet, sortable_attributes: NotSet, ranking_rules: NotSet, stop_words: NotSet, non_separator_tokens: NotSet, separator_tokens: NotSet, dictionary: NotSet, synonyms: NotSet, distinct_attribute: NotSet, proximity_precision: NotSet, typo_tolerance: NotSet, faceting: NotSet, pagination: NotSet, embedders: Set({"default": Set(EmbeddingSettings { source: Set(Rest), model: NotSet, revision: NotSet, api_key: Set("My super secret"), dimensions: Set(4), document_template: NotSet, url: Set("http://localhost:7777"), request: Set(String("{{text}}")), response: Set(String("{{embedding}}")), distribution: NotSet })}), search_cutoff_ms: NotSet, _kind: PhantomData<meilisearch_types::settings::Unchecked> }, is_deletion: false, allow_index_creation: true }}
 ----------------------------------------------------------------------
 ### Status:
 enqueued []
@@ -37,4 +37,3 @@ doggos: { number_of_documents: 0, field_distribution: {} }
 ### File Store:
 
 ----------------------------------------------------------------------
-

meilisearch/Cargo.toml

@@ -114,6 +114,7 @@ maplit = "1.0.2"
 meili-snap = { path = "../meili-snap" }
 temp-env = "0.3.6"
 urlencoding = "2.1.3"
+wiremock = "0.6.0"
 yaup = "0.3.1"
 
 [build-dependencies]

meilisearch/tests/common/mod.rs

@@ -80,7 +80,14 @@ impl Display for Value {
         write!(
             f,
             "{}",
-            json_string!(self, { ".enqueuedAt" => "[date]", ".startedAt" => "[date]", ".finishedAt" => "[date]", ".duration" => "[duration]", ".processingTimeMs" => "[duration]" })
+            json_string!(self, {
+                ".enqueuedAt" => "[date]",
+                ".startedAt" => "[date]",
+                ".finishedAt" => "[date]",
+                ".duration" => "[duration]",
+                ".processingTimeMs" => "[duration]",
+                ".details.embedders.*.url" => "[url]"
+            })
         )
     }
 }

meilisearch/tests/search/hybrid.rs

@@ -497,7 +497,7 @@ async fn query_combination() {
     snapshot!(code, @"400 Bad Request");
     snapshot!(response, @r###"
     {
-      "message": "Error while generating embeddings: user error: attempt to embed the following text in a configuration where embeddings must be user provided: \"Captain\"",
+      "message": "Error while generating embeddings: user error: attempt to embed the following text in a configuration where embeddings must be user provided:\n  - `Captain`",
       "code": "vector_embedding_error",
       "type": "invalid_request",
       "link": "https://docs.meilisearch.com/errors#vector_embedding_error"

meilisearch/tests/settings/get_settings.rs

@@ -116,6 +116,8 @@ async fn secrets_are_hidden_in_settings() {
                     "url": "https://localhost:7777",
                     "apiKey": "My super secret value you will never guess",
                     "dimensions": 4,
+                    "request": "{{text}}",
+                    "response": "{{embedding}}"
                 }
             }
         }))
@@ -189,17 +191,8 @@ async fn secrets_are_hidden_in_settings() {
           "dimensions": 4,
           "documentTemplate": "{% for field in fields %} {{ field.name }}: {{ field.value }}\n{% endfor %}",
           "url": "https://localhost:7777",
-          "query": null,
+          "request": "{{text}}",
-          "inputField": [
+          "response": "{{embedding}}"
-            "input"
-          ],
-          "pathToEmbeddings": [
-            "data"
-          ],
-          "embeddingObject": [
-            "embedding"
-          ],
-          "inputType": "text"
         }
       },
       "searchCutoffMs": null
@@ -215,7 +208,9 @@ async fn secrets_are_hidden_in_settings() {
           "source": "rest",
           "apiKey": "My suXXXXXX...",
           "dimensions": 4,
-          "url": "https://localhost:7777"
+          "url": "https://localhost:7777",
+          "request": "{{text}}",
+          "response": "{{embedding}}"
         }
       }
     }

meilisearch/tests/vector/mod.rs

@@ -1,3 +1,4 @@
+mod rest;
 mod settings;
 
 use meili_snap::{json_string, snapshot};
@@ -505,7 +506,7 @@ async fn user_provided_vectors_error() {
         "indexedDocuments": 0
       },
       "error": {
-        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided: \" id: 42\\n name: kefir\\n _vectors: \\n _vectors.manual: \\n _vectors.manual.regenerate: \\n _vectors.manual.embeddings: \\n\"\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: opt-out for a document with `_vectors.manual: null`",
+        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided:\n  - ` id: 42\n name: kefir\n _vectors: \n _vectors.manual: \n _vectors.manual.regenerate: \n _vectors.manual.embeddings: \n`\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: opt-out for a document with `_vectors.manual: null`",
         "code": "vector_embedding_error",
         "type": "invalid_request",
         "link": "https://docs.meilisearch.com/errors#vector_embedding_error"
@@ -534,7 +535,7 @@ async fn user_provided_vectors_error() {
         "indexedDocuments": 0
       },
       "error": {
-        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided: \" id: 42\\n name: kefir\\n _vectors: \\n _vectors.manual: \\n _vectors.manual.regenerate: \\n _vectors.manual.embeddings: \\n _vector: manaul000\\n _vector.manaul: \\n\"\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vector` by `_vectors` in 1 document(s).",
+        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided:\n  - ` id: 42\n name: kefir\n _vectors: \n _vectors.manual: \n _vectors.manual.regenerate: \n _vectors.manual.embeddings: \n _vector: manaul000\n _vector.manaul: \n`\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vector` by `_vectors` in 1 document(s).",
         "code": "vector_embedding_error",
         "type": "invalid_request",
         "link": "https://docs.meilisearch.com/errors#vector_embedding_error"
@@ -563,7 +564,7 @@ async fn user_provided_vectors_error() {
         "indexedDocuments": 0
       },
       "error": {
-        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided: \" id: 42\\n name: kefir\\n _vectors: manaul000\\n _vectors.manual: \\n _vectors.manual.regenerate: \\n _vectors.manual.embeddings: \\n _vectors.manaul: \\n\"\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vectors.manaul` by `_vectors.manual` in 1 document(s).",
+        "message": "While embedding documents for embedder `manual`: user error: attempt to embed the following text in a configuration where embeddings must be user provided:\n  - ` id: 42\n name: kefir\n _vectors: manaul000\n _vectors.manual: \n _vectors.manual.regenerate: \n _vectors.manual.embeddings: \n _vectors.manaul: \n`\n- Note: `manual` has `source: userProvided`, so documents must provide embeddings as an array in `_vectors.manual`.\n- Hint: try replacing `_vectors.manaul` by `_vectors.manual` in 1 document(s).",
         "code": "vector_embedding_error",
         "type": "invalid_request",
         "link": "https://docs.meilisearch.com/errors#vector_embedding_error"

milli/src/update/index_documents/mod.rs

@@ -2741,11 +2741,8 @@ mod tests {
                         dimensions: Setting::Set(3),
                         document_template: Setting::NotSet,
                         url: Setting::NotSet,
-                        query: Setting::NotSet,
+                        request: Setting::NotSet,
-                        input_field: Setting::NotSet,
+                        response: Setting::NotSet,
-                        path_to_embeddings: Setting::NotSet,
-                        embedding_object: Setting::NotSet,
-                        input_type: Setting::NotSet,
                         distribution: Setting::NotSet,
                     }),
                 );

milli/src/update/settings.rs

@@ -1484,11 +1484,8 @@ fn validate_prompt(
             dimensions,
             document_template: Setting::Set(template),
             url,
-            query,
+            request,
-            input_field,
+            response,
-            path_to_embeddings,
-            embedding_object,
-            input_type,
             distribution,
         }) => {
             // validate
@@ -1504,11 +1501,8 @@ fn validate_prompt(
                 dimensions,
                 document_template: Setting::Set(template),
                 url,
-                query,
+                request,
-                input_field,
+                response,
-                path_to_embeddings,
-                embedding_object,
-                input_type,
                 distribution,
             }))
         }
@@ -1530,11 +1524,8 @@ pub fn validate_embedding_settings(
         dimensions,
         document_template,
         url,
-        query,
+        request,
-        input_field,
+        response,
-        path_to_embeddings,
-        embedding_object,
-        input_type,
         distribution,
     } = settings;
 
@@ -1553,6 +1544,15 @@ pub fn validate_embedding_settings(
         })?;
     }
 
+    if let Some(request) = request.as_ref().set() {
+        let request = crate::vector::rest::Request::new(request.to_owned())
+            .map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))?;
+        if let Some(response) = response.as_ref().set() {
+            crate::vector::rest::Response::new(response.to_owned(), &request)
+                .map_err(|error| crate::UserError::VectorEmbeddingError(error.into()))?;
+        }
+    }
+
     let Some(inferred_source) = source.set() else {
         return Ok(Setting::Set(EmbeddingSettings {
             source,
@@ -1562,11 +1562,8 @@ pub fn validate_embedding_settings(
             dimensions,
             document_template,
             url,
-            query,
+            request,
-            input_field,
+            response,
-            path_to_embeddings,
-            embedding_object,
-            input_type,
             distribution,
         }));
     };
@@ -1574,21 +1571,8 @@ pub fn validate_embedding_settings(
         EmbedderSource::OpenAi => {
             check_unset(&revision, EmbeddingSettings::REVISION, inferred_source, name)?;
 
-            check_unset(&query, EmbeddingSettings::QUERY, inferred_source, name)?;
+            check_unset(&request, EmbeddingSettings::REQUEST, inferred_source, name)?;
-            check_unset(&input_field, EmbeddingSettings::INPUT_FIELD, inferred_source, name)?;
+            check_unset(&response, EmbeddingSettings::RESPONSE, inferred_source, name)?;
-            check_unset(
-                &path_to_embeddings,
-                EmbeddingSettings::PATH_TO_EMBEDDINGS,
-                inferred_source,
-                name,
-            )?;
-            check_unset(
-                &embedding_object,
-                EmbeddingSettings::EMBEDDING_OBJECT,
-                inferred_source,
-                name,
-            )?;
-            check_unset(&input_type, EmbeddingSettings::INPUT_TYPE, inferred_source, name)?;
 
             if let Setting::Set(model) = &model {
                 let model = crate::vector::openai::EmbeddingModel::from_name(model.as_str())
@@ -1626,42 +1610,16 @@ pub fn validate_embedding_settings(
             check_set(&model, EmbeddingSettings::MODEL, inferred_source, name)?;
             check_unset(&revision, EmbeddingSettings::REVISION, inferred_source, name)?;
 
-            check_unset(&query, EmbeddingSettings::QUERY, inferred_source, name)?;
+            check_unset(&request, EmbeddingSettings::REQUEST, inferred_source, name)?;
-            check_unset(&input_field, EmbeddingSettings::INPUT_FIELD, inferred_source, name)?;
+            check_unset(&response, EmbeddingSettings::RESPONSE, inferred_source, name)?;
-            check_unset(
-                &path_to_embeddings,
-                EmbeddingSettings::PATH_TO_EMBEDDINGS,
-                inferred_source,
-                name,
-            )?;
-            check_unset(
-                &embedding_object,
-                EmbeddingSettings::EMBEDDING_OBJECT,
-                inferred_source,
-                name,
-            )?;
-            check_unset(&input_type, EmbeddingSettings::INPUT_TYPE, inferred_source, name)?;
         }
         EmbedderSource::HuggingFace => {
             check_unset(&api_key, EmbeddingSettings::API_KEY, inferred_source, name)?;
             check_unset(&dimensions, EmbeddingSettings::DIMENSIONS, inferred_source, name)?;
 
             check_unset(&url, EmbeddingSettings::URL, inferred_source, name)?;
-            check_unset(&query, EmbeddingSettings::QUERY, inferred_source, name)?;
+            check_unset(&request, EmbeddingSettings::REQUEST, inferred_source, name)?;
-            check_unset(&input_field, EmbeddingSettings::INPUT_FIELD, inferred_source, name)?;
+            check_unset(&response, EmbeddingSettings::RESPONSE, inferred_source, name)?;
-            check_unset(
-                &path_to_embeddings,
-                EmbeddingSettings::PATH_TO_EMBEDDINGS,
-                inferred_source,
-                name,
-            )?;
-            check_unset(
-                &embedding_object,
-                EmbeddingSettings::EMBEDDING_OBJECT,
-                inferred_source,
-                name,
-            )?;
-            check_unset(&input_type, EmbeddingSettings::INPUT_TYPE, inferred_source, name)?;
         }
         EmbedderSource::UserProvided => {
             check_unset(&model, EmbeddingSettings::MODEL, inferred_source, name)?;
@@ -1676,26 +1634,15 @@ pub fn validate_embedding_settings(
             check_set(&dimensions, EmbeddingSettings::DIMENSIONS, inferred_source, name)?;
 
             check_unset(&url, EmbeddingSettings::URL, inferred_source, name)?;
-            check_unset(&query, EmbeddingSettings::QUERY, inferred_source, name)?;
+            check_unset(&request, EmbeddingSettings::REQUEST, inferred_source, name)?;
-            check_unset(&input_field, EmbeddingSettings::INPUT_FIELD, inferred_source, name)?;
+            check_unset(&response, EmbeddingSettings::RESPONSE, inferred_source, name)?;
-            check_unset(
-                &path_to_embeddings,
-                EmbeddingSettings::PATH_TO_EMBEDDINGS,
-                inferred_source,
-                name,
-            )?;
-            check_unset(
-                &embedding_object,
-                EmbeddingSettings::EMBEDDING_OBJECT,
-                inferred_source,
-                name,
-            )?;
-            check_unset(&input_type, EmbeddingSettings::INPUT_TYPE, inferred_source, name)?;
         }
         EmbedderSource::Rest => {
             check_unset(&model, EmbeddingSettings::MODEL, inferred_source, name)?;
             check_unset(&revision, EmbeddingSettings::REVISION, inferred_source, name)?;
             check_set(&url, EmbeddingSettings::URL, inferred_source, name)?;
+            check_set(&request, EmbeddingSettings::REQUEST, inferred_source, name)?;
+            check_set(&response, EmbeddingSettings::RESPONSE, inferred_source, name)?;
         }
     }
     Ok(Setting::Set(EmbeddingSettings {
@@ -1706,11 +1653,8 @@ pub fn validate_embedding_settings(
         dimensions,
         document_template,
         url,
-        query,
+        request,
-        input_field,
+        response,
-        path_to_embeddings,
-        embedding_object,
-        input_type,
         distribution,
     }))
 }

milli/src/vector/error.rs

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

milli/src/vector/json_template.rs

@@ -0,0 +1,970 @@
+//! Module to manipulate JSON templates.
+//!
+//! This module allows two main operations:
+//! 1. Render JSON values from a template and a context value.
+//! 2. Retrieve data from a template and JSON values.
+
+#![warn(rustdoc::broken_intra_doc_links)]
+#![warn(missing_docs)]
+
+use serde::Deserialize;
+use serde_json::{Map, Value};
+
+type ValuePath = Vec<PathComponent>;
+
+/// Encapsulates a JSON template and allows injecting and extracting values from it.
+#[derive(Debug)]
+pub struct ValueTemplate {
+    template: Value,
+    value_kind: ValueKind,
+}
+
+#[derive(Debug)]
+enum ValueKind {
+    Single(ValuePath),
+    Array(ArrayPath),
+}
+
+#[derive(Debug)]
+struct ArrayPath {
+    repeated_value: Value,
+    path_to_array: ValuePath,
+    value_path_in_array: ValuePath,
+}
+
+/// Component of a path to a Value
+#[derive(Debug, Clone)]
+pub enum PathComponent {
+    /// A key inside of an object
+    MapKey(String),
+    /// An index inside of an array
+    ArrayIndex(usize),
+}
+
+impl PartialEq for PathComponent {
+    fn eq(&self, other: &Self) -> bool {
+        match (self, other) {
+            (Self::MapKey(l0), Self::MapKey(r0)) => l0 == r0,
+            (Self::ArrayIndex(l0), Self::ArrayIndex(r0)) => l0 == r0,
+            _ => false,
+        }
+    }
+}
+
+impl Eq for PathComponent {}
+
+/// Error that occurs when no few value was provided to a template for injection.
+#[derive(Debug)]
+pub struct MissingValue;
+
+/// Error that occurs when trying to parse a template in [`ValueTemplate::new`]
+#[derive(Debug)]
+pub enum TemplateParsingError {
+    /// A repeat string appears inside a repeated value
+    NestedRepeatString(ValuePath),
+    /// A repeat string appears outside of an array
+    RepeatStringNotInArray(ValuePath),
+    /// A repeat string appears in an array, but not in the second position
+    BadIndexForRepeatString(ValuePath, usize),
+    /// A repeated value lacks a placeholder
+    MissingPlaceholderInRepeatedValue(ValuePath),
+    /// Multiple repeat string appear in the template
+    MultipleRepeatString(ValuePath, ValuePath),
+    /// Multiple placeholder strings appear in the template
+    MultiplePlaceholderString(ValuePath, ValuePath),
+    /// No placeholder string appear in the template
+    MissingPlaceholderString,
+    /// A placeholder appears both inside a repeated value and outside of it
+    BothArrayAndSingle {
+        /// Path to the single value
+        single_path: ValuePath,
+        /// Path to the array of repeated values
+        path_to_array: ValuePath,
+        /// Path to placeholder inside each repeated value, starting from the array
+        array_to_placeholder: ValuePath,
+    },
+}
+
+impl TemplateParsingError {
+    /// Produce an error message from the error kind, the name of the root object, the placeholder string and the repeat string
+    pub fn error_message(&self, root: &str, placeholder: &str, repeat: &str) -> String {
+        match self {
+            TemplateParsingError::NestedRepeatString(path) => {
+                format!(
+                    r#"in {}: "{repeat}" appears nested inside of a value that is itself repeated"#,
+                    path_with_root(root, path)
+                )
+            }
+            TemplateParsingError::RepeatStringNotInArray(path) => format!(
+                r#"in {}: "{repeat}" appears outside of an array"#,
+                path_with_root(root, path)
+            ),
+            TemplateParsingError::BadIndexForRepeatString(path, index) => format!(
+                r#"in {}: "{repeat}" expected at position #1, but found at position #{index}"#,
+                path_with_root(root, path)
+            ),
+            TemplateParsingError::MissingPlaceholderInRepeatedValue(path) => format!(
+                r#"in {}: Expected "{placeholder}" inside of the repeated value"#,
+                path_with_root(root, path)
+            ),
+            TemplateParsingError::MultipleRepeatString(current, previous) => format!(
+                r#"in {}: Found "{repeat}", but it was already present in {}"#,
+                path_with_root(root, current),
+                path_with_root(root, previous)
+            ),
+            TemplateParsingError::MultiplePlaceholderString(current, previous) => format!(
+                r#"in {}: Found "{placeholder}", but it was already present in {}"#,
+                path_with_root(root, current),
+                path_with_root(root, previous)
+            ),
+            TemplateParsingError::MissingPlaceholderString => {
+                format!(r#"in `{root}`: "{placeholder}" not found"#)
+            }
+            TemplateParsingError::BothArrayAndSingle {
+                single_path,
+                path_to_array,
+                array_to_placeholder,
+            } => {
+                let path_to_first_repeated = path_to_array
+                    .iter()
+                    .chain(std::iter::once(&PathComponent::ArrayIndex(0)))
+                    .chain(array_to_placeholder.iter());
+                format!(
+                    r#"in {}: Found "{placeholder}", but it was already present in {} (repeated)"#,
+                    path_with_root(root, single_path),
+                    path_with_root(root, path_to_first_repeated)
+                )
+            }
+        }
+    }
+
+    fn prepend_path(self, mut prepended_path: ValuePath) -> Self {
+        match self {
+            TemplateParsingError::NestedRepeatString(mut path) => {
+                prepended_path.append(&mut path);
+                TemplateParsingError::NestedRepeatString(prepended_path)
+            }
+            TemplateParsingError::RepeatStringNotInArray(mut path) => {
+                prepended_path.append(&mut path);
+                TemplateParsingError::RepeatStringNotInArray(prepended_path)
+            }
+            TemplateParsingError::BadIndexForRepeatString(mut path, index) => {
+                prepended_path.append(&mut path);
+                TemplateParsingError::BadIndexForRepeatString(prepended_path, index)
+            }
+            TemplateParsingError::MissingPlaceholderInRepeatedValue(mut path) => {
+                prepended_path.append(&mut path);
+                TemplateParsingError::MissingPlaceholderInRepeatedValue(prepended_path)
+            }
+            TemplateParsingError::MultipleRepeatString(mut path, older_path) => {
+                let older_prepended_path =
+                    prepended_path.iter().cloned().chain(older_path).collect();
+                prepended_path.append(&mut path);
+                TemplateParsingError::MultipleRepeatString(prepended_path, older_prepended_path)
+            }
+            TemplateParsingError::MultiplePlaceholderString(mut path, older_path) => {
+                let older_prepended_path =
+                    prepended_path.iter().cloned().chain(older_path).collect();
+                prepended_path.append(&mut path);
+                TemplateParsingError::MultiplePlaceholderString(
+                    prepended_path,
+                    older_prepended_path,
+                )
+            }
+            TemplateParsingError::MissingPlaceholderString => {
+                TemplateParsingError::MissingPlaceholderString
+            }
+            TemplateParsingError::BothArrayAndSingle {
+                single_path,
+                mut path_to_array,
+                array_to_placeholder,
+            } => {
+                // note, this case is not super logical, but is also likely to be dead code
+                let single_prepended_path =
+                    prepended_path.iter().cloned().chain(single_path).collect();
+                prepended_path.append(&mut path_to_array);
+                // we don't prepend the array_to_placeholder path as it is the array path that is prepended
+                TemplateParsingError::BothArrayAndSingle {
+                    single_path: single_prepended_path,
+                    path_to_array: prepended_path,
+                    array_to_placeholder,
+                }
+            }
+        }
+    }
+}
+
+/// Error that occurs when [`ValueTemplate::extract`] fails.
+#[derive(Debug)]
+pub struct ExtractionError {
+    /// The cause of the failure
+    pub kind: ExtractionErrorKind,
+    /// The context where the failure happened: the operation that failed
+    pub context: ExtractionErrorContext,
+}
+
+impl ExtractionError {
+    /// Produce an error message from the error, the name of the root object, the placeholder string and the expected value type
+    pub fn error_message(
+        &self,
+        root: &str,
+        placeholder: &str,
+        expected_value_type: &str,
+    ) -> String {
+        let context = match &self.context {
+            ExtractionErrorContext::ExtractingSingleValue => {
+                format!(r#"extracting a single "{placeholder}""#)
+            }
+            ExtractionErrorContext::FindingPathToArray => {
+                format!(r#"extracting the array of "{placeholder}"s"#)
+            }
+            ExtractionErrorContext::ExtractingArrayItem(index) => {
+                format!(r#"extracting item #{index} from the array of "{placeholder}"s"#)
+            }
+        };
+        match &self.kind {
+            ExtractionErrorKind::MissingPathComponent { missing_index, path, key_suggestion } => {
+                let last_named_object = last_named_object(root, path.iter().take(*missing_index));
+                format!(
+                    "in {}, while {context}, configuration expects {}, which is missing in response{}",
+                    path_with_root(root, path.iter().take(*missing_index)),
+                    missing_component(path.get(*missing_index)),
+                    match key_suggestion {
+                        Some(key_suggestion) => format!("\n  - Hint: {last_named_object} has key `{key_suggestion}`, did you mean {} in embedder configuration?",
+                        path_with_root(root, path.iter().take(*missing_index).chain(std::iter::once(&PathComponent::MapKey(key_suggestion.to_owned()))))),
+                        None => "".to_owned(),
+                    }
+                )
+            }
+            ExtractionErrorKind::WrongPathComponent { wrong_component, index, path } => {
+                let last_named_object = last_named_object(root, path.iter().take(*index));
+                format!(
+                    "in {}, while {context}, configuration expects {last_named_object} to be {} but server sent {wrong_component}",
+                    path_with_root(root, path.iter().take(*index)),
+                    expected_component(path.get(*index))
+                )
+            }
+            ExtractionErrorKind::DeserializationError { error, path } => {
+                let last_named_object = last_named_object(root, path);
+                format!(
+                    "in {}, while {context}, expected {last_named_object} to be {expected_value_type}, but failed to parse server response:\n  - {error}",
+                    path_with_root(root, path)
+                )
+            }
+        }
+    }
+}
+
+fn missing_component(component: Option<&PathComponent>) -> String {
+    match component {
+        Some(PathComponent::ArrayIndex(index)) => {
+            format!(r#"item #{index}"#)
+        }
+        Some(PathComponent::MapKey(key)) => {
+            format!(r#"key "{key}""#)
+        }
+        None => "unknown".to_string(),
+    }
+}
+
+fn expected_component(component: Option<&PathComponent>) -> String {
+    match component {
+        Some(PathComponent::ArrayIndex(index)) => {
+            format!(r#"an array with at least {} item(s)"#, index.saturating_add(1))
+        }
+        Some(PathComponent::MapKey(key)) => {
+            format!("an object with key `{}`", key)
+        }
+        None => "unknown".to_string(),
+    }
+}
+
+fn last_named_object<'a>(
+    root: &'a str,
+    path: impl IntoIterator<Item = &'a PathComponent> + 'a,
+) -> LastNamedObject<'a> {
+    let mut last_named_object = LastNamedObject::Object { name: root };
+    for component in path.into_iter() {
+        last_named_object = match (component, last_named_object) {
+            (PathComponent::MapKey(name), _) => LastNamedObject::Object { name },
+            (PathComponent::ArrayIndex(index), LastNamedObject::Object { name }) => {
+                LastNamedObject::ArrayInsideObject { object_name: name, index: *index }
+            }
+            (
+                PathComponent::ArrayIndex(index),
+                LastNamedObject::ArrayInsideObject { object_name, index: _ },
+            ) => LastNamedObject::NestedArrayInsideObject {
+                object_name,
+                index: *index,
+                nesting_level: 0,
+            },
+            (
+                PathComponent::ArrayIndex(index),
+                LastNamedObject::NestedArrayInsideObject { object_name, index: _, nesting_level },
+            ) => LastNamedObject::NestedArrayInsideObject {
+                object_name,
+                index: *index,
+                nesting_level: nesting_level.saturating_add(1),
+            },
+        }
+    }
+    last_named_object
+}
+
+impl<'a> std::fmt::Display for LastNamedObject<'a> {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            LastNamedObject::Object { name } => write!(f, "`{name}`"),
+            LastNamedObject::ArrayInsideObject { object_name, index } => {
+                write!(f, "item #{index} inside `{object_name}`")
+            }
+            LastNamedObject::NestedArrayInsideObject { object_name, index, nesting_level } => {
+                if *nesting_level == 0 {
+                    write!(f, "item #{index} inside nested array in `{object_name}`")
+                } else {
+                    write!(f, "item #{index} inside nested array ({} levels of nesting) in `{object_name}`", nesting_level + 1)
+                }
+            }
+        }
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+enum LastNamedObject<'a> {
+    Object { name: &'a str },
+    ArrayInsideObject { object_name: &'a str, index: usize },
+    NestedArrayInsideObject { object_name: &'a str, index: usize, nesting_level: usize },
+}
+
+/// Builds a string representation of a path, preprending the name of the root value.
+pub fn path_with_root<'a>(
+    root: &str,
+    path: impl IntoIterator<Item = &'a PathComponent> + 'a,
+) -> String {
+    use std::fmt::Write as _;
+    let mut res = format!("`{root}");
+    for component in path.into_iter() {
+        match component {
+            PathComponent::MapKey(key) => {
+                let _ = write!(&mut res, ".{key}");
+            }
+            PathComponent::ArrayIndex(index) => {
+                let _ = write!(&mut res, "[{index}]");
+            }
+        }
+    }
+    res.push('`');
+    res
+}
+
+/// Context where an extraction failure happened
+///
+/// The operation that failed
+#[derive(Debug, Clone, Copy)]
+pub enum ExtractionErrorContext {
+    /// Failure happened while extracting a value at a single location
+    ExtractingSingleValue,
+    /// Failure happened while extracting an array of values
+    FindingPathToArray,
+    /// Failure happened while extracting a value inside of an array
+    ExtractingArrayItem(usize),
+}
+
+/// Kind of errors that can happen during extraction
+#[derive(Debug)]
+pub enum ExtractionErrorKind {
+    /// An expected path component is missing
+    MissingPathComponent {
+        /// Index of the missing component in the path
+        missing_index: usize,
+        /// Path where a component is missing
+        path: ValuePath,
+        /// Possible matching key in object
+        key_suggestion: Option<String>,
+    },
+    /// An expected path component cannot be found because its container is the wrong type
+    WrongPathComponent {
+        /// String representation of the wrong component
+        wrong_component: String,
+        /// Index of the wrong component in the path
+        index: usize,
+        /// Path where a component has the wrong type
+        path: ValuePath,
+    },
+    /// Could not deserialize an extracted value to its requested type
+    DeserializationError {
+        /// inner deserialization error
+        error: serde_json::Error,
+        /// path to extracted value
+        path: ValuePath,
+    },
+}
+
+enum ArrayParsingContext<'a> {
+    Nested,
+    NotNested(&'a mut Option<ArrayPath>),
+}
+
+impl ValueTemplate {
+    /// Prepare a template for injection or extraction.
+    ///
+    /// # Parameters
+    ///
+    /// - `template`: JSON value that acts a template. Its placeholder values will be replaced by actual values during injection,
+    ///   and actual values will be recovered from their location during extraction.
+    /// - `placeholder_string`: Value that a JSON string should assume to act as a placeholder value that can be injected into or
+    ///   extracted from.
+    /// - `repeat_string`: Sentinel value that can be placed as the second value in an array to indicate that the first value can be repeated
+    ///   any number of times. The first value should contain exactly one placeholder string.
+    ///
+    /// # Errors
+    ///
+    /// - [`TemplateParsingError`]: refer to the documentation of this type
+    pub fn new(
+        template: Value,
+        placeholder_string: &str,
+        repeat_string: &str,
+    ) -> Result<Self, TemplateParsingError> {
+        let mut value_path = None;
+        let mut array_path = None;
+        let mut current_path = Vec::new();
+        Self::parse_value(
+            &template,
+            placeholder_string,
+            repeat_string,
+            &mut value_path,
+            &mut ArrayParsingContext::NotNested(&mut array_path),
+            &mut current_path,
+        )?;
+
+        let value_kind = match (array_path, value_path) {
+            (None, None) => return Err(TemplateParsingError::MissingPlaceholderString),
+            (None, Some(value_path)) => ValueKind::Single(value_path),
+            (Some(array_path), None) => ValueKind::Array(array_path),
+            (Some(array_path), Some(value_path)) => {
+                return Err(TemplateParsingError::BothArrayAndSingle {
+                    single_path: value_path,
+                    path_to_array: array_path.path_to_array,
+                    array_to_placeholder: array_path.value_path_in_array,
+                })
+            }
+        };
+
+        Ok(Self { template, value_kind })
+    }
+
+    /// Whether there is a placeholder that can be repeated.
+    ///
+    /// - During injection, all values are injected in the array placeholder,
+    /// - During extraction, all repeatable placeholders are extracted from the array.
+    pub fn has_array_value(&self) -> bool {
+        matches!(self.value_kind, ValueKind::Array(_))
+    }
+
+    /// Render a value from the template and context values.
+    ///
+    /// # Error
+    ///
+    /// - [`MissingValue`]: if the number of injected values is 0.
+    pub fn inject(&self, values: impl IntoIterator<Item = Value>) -> Result<Value, MissingValue> {
+        let mut rendered = self.template.clone();
+        let mut values = values.into_iter();
+
+        match &self.value_kind {
+            ValueKind::Single(injection_path) => {
+                let Some(injected_value) = values.next() else { return Err(MissingValue) };
+                inject_value(&mut rendered, injection_path, injected_value);
+            }
+            ValueKind::Array(ArrayPath { repeated_value, path_to_array, value_path_in_array }) => {
+                // 1. build the array of repeated values
+                let mut array = Vec::new();
+                for injected_value in values {
+                    let mut repeated_value = repeated_value.clone();
+                    inject_value(&mut repeated_value, value_path_in_array, injected_value);
+                    array.push(repeated_value);
+                }
+
+                if array.is_empty() {
+                    return Err(MissingValue);
+                }
+                // 2. inject at the injection point in the rendered value
+                inject_value(&mut rendered, path_to_array, Value::Array(array));
+            }
+        }
+
+        Ok(rendered)
+    }
+
+    /// Extract sub values from the template and a value.
+    ///
+    /// # Errors
+    ///
+    /// - if a single placeholder is missing.
+    /// - if there is no value corresponding to an array placeholder
+    /// - if the value corresponding to an array placeholder is not an array
+    pub fn extract<T>(&self, mut value: Value) -> Result<Vec<T>, ExtractionError>
+    where
+        T: for<'de> Deserialize<'de>,
+    {
+        Ok(match &self.value_kind {
+            ValueKind::Single(extraction_path) => {
+                let extracted_value =
+                    extract_value(extraction_path, &mut value).with_context(|kind| {
+                        ExtractionError {
+                            kind,
+                            context: ExtractionErrorContext::ExtractingSingleValue,
+                        }
+                    })?;
+                vec![extracted_value]
+            }
+            ValueKind::Array(ArrayPath {
+                repeated_value: _,
+                path_to_array,
+                value_path_in_array,
+            }) => {
+                // get the array
+                let array = extract_value(path_to_array, &mut value).with_context(|kind| {
+                    ExtractionError { kind, context: ExtractionErrorContext::FindingPathToArray }
+                })?;
+                let array = match array {
+                    Value::Array(array) => array,
+                    not_array => {
+                        let mut path = path_to_array.clone();
+                        path.push(PathComponent::ArrayIndex(0));
+                        return Err(ExtractionError {
+                            kind: ExtractionErrorKind::WrongPathComponent {
+                                wrong_component: format_value(&not_array),
+                                index: path_to_array.len(),
+                                path,
+                            },
+                            context: ExtractionErrorContext::FindingPathToArray,
+                        });
+                    }
+                };
+                let mut extracted_values = Vec::with_capacity(array.len());
+
+                for (index, mut item) in array.into_iter().enumerate() {
+                    let extracted_value = extract_value(value_path_in_array, &mut item)
+                        .with_context(|kind| ExtractionError {
+                            kind,
+                            context: ExtractionErrorContext::ExtractingArrayItem(index),
+                        })?;
+                    extracted_values.push(extracted_value);
+                }
+
+                extracted_values
+            }
+        })
+    }
+
+    fn parse_array(
+        array: &[Value],
+        placeholder_string: &str,
+        repeat_string: &str,
+        value_path: &mut Option<ValuePath>,
+        mut array_path: &mut ArrayParsingContext,
+        current_path: &mut ValuePath,
+    ) -> Result<(), TemplateParsingError> {
+        // two modes for parsing array.
+        match array {
+            // 1. array contains a repeat string in second position
+            [first, second, rest @ ..] if second == repeat_string => {
+                let ArrayParsingContext::NotNested(array_path) = &mut array_path else {
+                    return Err(TemplateParsingError::NestedRepeatString(current_path.clone()));
+                };
+                if let Some(array_path) = array_path {
+                    return Err(TemplateParsingError::MultipleRepeatString(
+                        current_path.clone(),
+                        array_path.path_to_array.clone(),
+                    ));
+                }
+                if first == repeat_string {
+                    return Err(TemplateParsingError::BadIndexForRepeatString(
+                        current_path.clone(),
+                        0,
+                    ));
+                }
+                if let Some(position) = rest.iter().position(|value| value == repeat_string) {
+                    let position = position + 2;
+                    return Err(TemplateParsingError::BadIndexForRepeatString(
+                        current_path.clone(),
+                        position,
+                    ));
+                }
+
+                let value_path_in_array = {
+                    let mut value_path = None;
+                    let mut current_path_in_array = Vec::new();
+
+                    Self::parse_value(
+                        first,
+                        placeholder_string,
+                        repeat_string,
+                        &mut value_path,
+                        &mut ArrayParsingContext::Nested,
+                        &mut current_path_in_array,
+                    )
+                    .map_err(|error| error.prepend_path(current_path.to_vec()))?;
+
+                    value_path.ok_or_else(|| {
+                        let mut repeated_value_path = current_path.clone();
+                        repeated_value_path.push(PathComponent::ArrayIndex(0));
+                        TemplateParsingError::MissingPlaceholderInRepeatedValue(repeated_value_path)
+                    })?
+                };
+                **array_path = Some(ArrayPath {
+                    repeated_value: first.to_owned(),
+                    path_to_array: current_path.clone(),
+                    value_path_in_array,
+                });
+            }
+            // 2. array does not contain a repeat string
+            array => {
+                if let Some(position) = array.iter().position(|value| value == repeat_string) {
+                    return Err(TemplateParsingError::BadIndexForRepeatString(
+                        current_path.clone(),
+                        position,
+                    ));
+                }
+                for (index, value) in array.iter().enumerate() {
+                    current_path.push(PathComponent::ArrayIndex(index));
+                    Self::parse_value(
+                        value,
+                        placeholder_string,
+                        repeat_string,
+                        value_path,
+                        array_path,
+                        current_path,
+                    )?;
+                    current_path.pop();
+                }
+            }
+        }
+        Ok(())
+    }
+
+    fn parse_object(
+        object: &Map<String, Value>,
+        placeholder_string: &str,
+        repeat_string: &str,
+        value_path: &mut Option<ValuePath>,
+        array_path: &mut ArrayParsingContext,
+        current_path: &mut ValuePath,
+    ) -> Result<(), TemplateParsingError> {
+        for (key, value) in object.iter() {
+            current_path.push(PathComponent::MapKey(key.to_owned()));
+            Self::parse_value(
+                value,
+                placeholder_string,
+                repeat_string,
+                value_path,
+                array_path,
+                current_path,
+            )?;
+            current_path.pop();
+        }
+        Ok(())
+    }
+
+    fn parse_value(
+        value: &Value,
+        placeholder_string: &str,
+        repeat_string: &str,
+        value_path: &mut Option<ValuePath>,
+        array_path: &mut ArrayParsingContext,
+        current_path: &mut ValuePath,
+    ) -> Result<(), TemplateParsingError> {
+        match value {
+            Value::String(str) => {
+                if placeholder_string == str {
+                    if let Some(value_path) = value_path {
+                        return Err(TemplateParsingError::MultiplePlaceholderString(
+                            current_path.clone(),
+                            value_path.clone(),
+                        ));
+                    }
+
+                    *value_path = Some(current_path.clone());
+                }
+                if repeat_string == str {
+                    return Err(TemplateParsingError::RepeatStringNotInArray(current_path.clone()));
+                }
+            }
+            Value::Null | Value::Bool(_) | Value::Number(_) => {}
+            Value::Array(array) => Self::parse_array(
+                array,
+                placeholder_string,
+                repeat_string,
+                value_path,
+                array_path,
+                current_path,
+            )?,
+            Value::Object(object) => Self::parse_object(
+                object,
+                placeholder_string,
+                repeat_string,
+                value_path,
+                array_path,
+                current_path,
+            )?,
+        }
+        Ok(())
+    }
+}
+
+fn inject_value(rendered: &mut Value, injection_path: &Vec<PathComponent>, injected_value: Value) {
+    let mut current_value = rendered;
+    for injection_component in injection_path {
+        current_value = match injection_component {
+            PathComponent::MapKey(key) => current_value.get_mut(key).unwrap(),
+            PathComponent::ArrayIndex(index) => current_value.get_mut(index).unwrap(),
+        }
+    }
+    *current_value = injected_value;
+}
+
+fn format_value(value: &Value) -> String {
+    match value {
+        Value::Array(array) => format!("an array of size {}", array.len()),
+        Value::Object(object) => {
+            format!("an object with {} field(s)", object.len())
+        }
+        value => value.to_string(),
+    }
+}
+
+fn extract_value<T>(
+    extraction_path: &[PathComponent],
+    initial_value: &mut Value,
+) -> Result<T, ExtractionErrorKind>
+where
+    T: for<'de> Deserialize<'de>,
+{
+    let mut current_value = initial_value;
+    for (path_index, extraction_component) in extraction_path.iter().enumerate() {
+        current_value = {
+            match extraction_component {
+                PathComponent::MapKey(key) => {
+                    if !current_value.is_object() {
+                        return Err(ExtractionErrorKind::WrongPathComponent {
+                            wrong_component: format_value(current_value),
+                            index: path_index,
+                            path: extraction_path.to_vec(),
+                        });
+                    }
+                    if let Some(object) = current_value.as_object_mut() {
+                        if !object.contains_key(key) {
+                            let typos =
+                                levenshtein_automata::LevenshteinAutomatonBuilder::new(2, true)
+                                    .build_dfa(key);
+                            let mut key_suggestion = None;
+                            'check_typos: for (key, _) in object.iter() {
+                                match typos.eval(key) {
+                                    levenshtein_automata::Distance::Exact(0) => { /* ??? */ }
+                                    levenshtein_automata::Distance::Exact(_) => {
+                                        key_suggestion = Some(key.to_owned());
+                                        break 'check_typos;
+                                    }
+                                    levenshtein_automata::Distance::AtLeast(_) => continue,
+                                }
+                            }
+                            return Err(ExtractionErrorKind::MissingPathComponent {
+                                missing_index: path_index,
+                                path: extraction_path.to_vec(),
+                                key_suggestion,
+                            });
+                        }
+                        if let Some(value) = object.get_mut(key) {
+                            value
+                        } else {
+                            // borrow checking limit: the borrow checker cannot be convinced that `object` is no longer mutably borrowed on the
+                            // `else` branch of the `if let`, so we cannot return MissingPathComponent here.
+                            // As a workaround, we checked that the object does not contain the key above, making this `else` unreachable.
+                            unreachable!()
+                        }
+                    } else {
+                        // borrow checking limit: the borrow checker cannot be convinced that `current_value` is no longer mutably borrowed
+                        // on the `else` branch of the `if let`, so we cannot return WrongPathComponent here.
+                        // As a workaround, we checked that the value was not a map above, making this `else` unreachable.
+                        unreachable!()
+                    }
+                }
+                PathComponent::ArrayIndex(index) => {
+                    if !current_value.is_array() {
+                        return Err(ExtractionErrorKind::WrongPathComponent {
+                            wrong_component: format_value(current_value),
+                            index: path_index,
+                            path: extraction_path.to_vec(),
+                        });
+                    }
+                    match current_value.get_mut(index) {
+                        Some(value) => value,
+                        None => {
+                            return Err(ExtractionErrorKind::MissingPathComponent {
+                                missing_index: path_index,
+                                path: extraction_path.to_vec(),
+                                key_suggestion: None,
+                            });
+                        }
+                    }
+                }
+            }
+        };
+    }
+    serde_json::from_value(current_value.take()).map_err(|error| {
+        ExtractionErrorKind::DeserializationError { error, path: extraction_path.to_vec() }
+    })
+}
+
+trait ExtractionResultErrorContext<T> {
+    fn with_context<F>(self, f: F) -> Result<T, ExtractionError>
+    where
+        F: FnOnce(ExtractionErrorKind) -> ExtractionError;
+}
+
+impl<T> ExtractionResultErrorContext<T> for Result<T, ExtractionErrorKind> {
+    fn with_context<F>(self, f: F) -> Result<T, ExtractionError>
+    where
+        F: FnOnce(ExtractionErrorKind) -> ExtractionError,
+    {
+        match self {
+            Ok(t) => Ok(t),
+            Err(kind) => Err(f(kind)),
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use serde_json::{json, Value};
+
+    use super::{PathComponent, TemplateParsingError, ValueTemplate};
+
+    fn new_template(template: Value) -> Result<ValueTemplate, TemplateParsingError> {
+        ValueTemplate::new(template, "{{text}}", "{{..}}")
+    }
+
+    #[test]
+    fn empty_template() {
+        let template = json!({
+            "toto": "no template at all",
+            "titi": ["this", "will", "not", "work"],
+            "tutu": null
+        });
+
+        let error = new_template(template.clone()).unwrap_err();
+        assert!(matches!(error, TemplateParsingError::MissingPlaceholderString))
+    }
+
+    #[test]
+    fn single_template() {
+        let template = json!({
+            "toto": "text",
+            "titi": ["this", "will", "still", "{{text}}"],
+            "tutu": null
+        });
+
+        let basic = new_template(template.clone()).unwrap();
+
+        assert!(!basic.has_array_value());
+
+        assert_eq!(
+            basic.inject(vec!["work".into(), Value::Null, "test".into()]).unwrap(),
+            json!({
+                "toto": "text",
+                "titi": ["this", "will", "still", "work"],
+                "tutu": null
+            })
+        );
+    }
+
+    #[test]
+    fn too_many_placeholders() {
+        let template = json!({
+            "toto": "{{text}}",
+            "titi": ["this", "will", "still", "{{text}}"],
+            "tutu": "text"
+        });
+
+        match new_template(template.clone()) {
+            Err(TemplateParsingError::MultiplePlaceholderString(left, right)) => {
+                assert_eq!(
+                    left,
+                    vec![PathComponent::MapKey("titi".into()), PathComponent::ArrayIndex(3)]
+                );
+
+                assert_eq!(right, vec![PathComponent::MapKey("toto".into())])
+            }
+            _ => panic!("should error"),
+        }
+    }
+
+    #[test]
+    fn dynamic_template() {
+        let template = json!({
+            "toto": "text",
+            "titi": [{
+                "type": "text",
+                "data": "{{text}}"
+            }, "{{..}}"],
+            "tutu": null
+        });
+
+        let basic = new_template(template.clone()).unwrap();
+
+        assert!(basic.has_array_value());
+
+        let injected_values = vec![
+            "work".into(),
+            Value::Null,
+            42.into(),
+            "test".into(),
+            "tata".into(),
+            "titi".into(),
+            "tutu".into(),
+        ];
+
+        let rendered = basic.inject(injected_values.clone()).unwrap();
+
+        assert_eq!(
+            rendered,
+            json!({
+                "toto": "text",
+                "titi": [
+                    {
+                        "type": "text",
+                        "data": "work"
+                    },
+                    {
+                        "type": "text",
+                        "data": Value::Null
+                    },
+                    {
+                        "type": "text",
+                        "data": 42
+                    },
+                    {
+                        "type": "text",
+                        "data": "test"
+                    },
+                    {
+                        "type": "text",
+                        "data": "tata"
+                    },
+                    {
+                        "type": "text",
+                        "data": "titi"
+                    },
+                    {
+                        "type": "text",
+                        "data": "tutu"
+                    }
+                ],
+                "tutu": null
+            })
+        );
+
+        let extracted_values: Vec<Value> = basic.extract(rendered).unwrap();
+        assert_eq!(extracted_values, injected_values);
+    }
+}

milli/src/vector/mod.rs

@@ -11,6 +11,7 @@ use crate::ThreadPoolNoAbort;
 
 pub mod error;
 pub mod hf;
+pub mod json_template;
 pub mod manual;
 pub mod openai;
 pub mod parsed_vectors;
@@ -227,7 +228,9 @@ impl Embedder {
             EmbedderOptions::UserProvided(options) => {
                 Self::UserProvided(manual::Embedder::new(options))
             }
-            EmbedderOptions::Rest(options) => Self::Rest(rest::Embedder::new(options)?),
+            EmbedderOptions::Rest(options) => {
+                Self::Rest(rest::Embedder::new(options, rest::ConfigurationSource::User)?)
+            }
         })
     }
 

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 {
+        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()],
+                response: serde_json::json!({
-            path_to_embeddings: Default::default(),
+                    "embedding": super::rest::RESPONSE_PLACEHOLDER,
-            embedding_object: vec!["embedding".to_owned()],
+                }),
-            input_type: super::rest::InputType::Text,
+            },
-        }) {
+            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 {
+        let rest_embedder = RestEmbedder::new(
+            RestEmbedderOptions {
                 api_key: Some(api_key.clone()),
                 distribution: None,
                 dimensions: Some(options.dimensions()),
                 url,
-            query: options.query(),
+                request: options.request(),
-            input_field: vec!["input".to_owned()],
+                response: serde_json::json!({
-            input_type: crate::vector::rest::InputType::TextArray,
+                    "data": [{
-            path_to_embeddings: vec!["data".to_owned()],
+                        "embedding": super::rest::RESPONSE_PLACEHOLDER
-            embedding_object: vec!["embedding".to_owned()],
+                    },
-        })?;
+                    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);

milli/src/vector/rest.rs

@@ -4,6 +4,7 @@ use rayon::iter::{IntoParallelIterator as _, ParallelIterator as _};
 use serde::{Deserialize, Serialize};
 
 use super::error::EmbedErrorKind;
+use super::json_template::ValueTemplate;
 use super::{
     DistributionShift, EmbedError, Embedding, Embeddings, NewEmbedderError, REQUEST_PARALLELISM,
 };
@@ -11,12 +12,18 @@ use crate::error::FaultSource;
 use crate::ThreadPoolNoAbort;
 
 // retrying in case of failure
-
 pub struct Retry {
     pub error: EmbedError,
     strategy: RetryStrategy,
 }
 
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum ConfigurationSource {
+    OpenAi,
+    Ollama,
+    User,
+}
+
 pub enum RetryStrategy {
     GiveUp,
     Retry,
@@ -63,10 +70,20 @@ impl Retry {
 
 #[derive(Debug)]
 pub struct Embedder {
-    client: ureq::Agent,
+    data: EmbedderData,
-    options: EmbedderOptions,
-    bearer: Option<String>,
     dimensions: usize,
+    distribution: Option<DistributionShift>,
+}
+
+/// All data needed to perform requests and parse responses
+#[derive(Debug)]
+struct EmbedderData {
+    client: ureq::Agent,
+    bearer: Option<String>,
+    url: String,
+    request: Request,
+    response: Response,
+    configuration_source: ConfigurationSource,
 }
 
 #[derive(Debug, Clone, PartialEq, Eq, Deserialize, Serialize)]
@@ -75,29 +92,8 @@ pub struct EmbedderOptions {
     pub distribution: Option<DistributionShift>,
     pub dimensions: Option<usize>,
     pub url: String,
-    pub query: serde_json::Value,
+    pub request: serde_json::Value,
-    pub input_field: Vec<String>,
+    pub response: serde_json::Value,
-    // path to the array of embeddings
-    pub path_to_embeddings: Vec<String>,
-    // shape of a single embedding
-    pub embedding_object: Vec<String>,
-    pub input_type: InputType,
-}
-
-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 {
@@ -106,26 +102,25 @@ impl std::hash::Hash for EmbedderOptions {
         self.distribution.hash(state);
         self.dimensions.hash(state);
         self.url.hash(state);
-        // skip hashing the query
+        // skip hashing the request and response
         // 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 {
+enum InputType {
     Text,
     TextArray,
 }
 
 impl Embedder {
-    pub fn new(options: EmbedderOptions) -> Result<Self, NewEmbedderError> {
+    pub fn new(
+        options: EmbedderOptions,
+        configuration_source: ConfigurationSource,
+    ) -> Result<Self, NewEmbedderError> {
         let bearer = options.api_key.as_deref().map(|api_key| format!("Bearer {api_key}"));
 
         let client = ureq::AgentBuilder::new()
@@ -133,28 +128,40 @@ impl Embedder {
             .max_idle_connections_per_host(REQUEST_PARALLELISM * 2)
             .build();
 
+        let request = Request::new(options.request)?;
+        let response = Response::new(options.response, &request)?;
+
+        let data = EmbedderData {
+            client,
+            bearer,
+            url: options.url,
+            request,
+            response,
+            configuration_source,
+        };
+
         let dimensions = if let Some(dimensions) = options.dimensions {
             dimensions
         } else {
-            infer_dimensions(&client, &options, bearer.as_deref())?
+            infer_dimensions(&data)?
         };
 
-        Ok(Self { client, dimensions, options, bearer })
+        Ok(Self { data, dimensions, distribution: options.distribution })
     }
 
     pub fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
-        embed(&self.client, &self.options, self.bearer.as_deref(), texts.as_slice(), texts.len())
+        embed(&self.data, texts.as_slice(), texts.len(), Some(self.dimensions))
     }
 
     pub fn embed_ref<S>(&self, texts: &[S]) -> Result<Vec<Embeddings<f32>>, EmbedError>
     where
         S: AsRef<str> + Serialize,
     {
-        embed(&self.client, &self.options, self.bearer.as_deref(), texts, texts.len())
+        embed(&self.data, texts, texts.len(), Some(self.dimensions))
     }
 
     pub fn embed_tokens(&self, tokens: &[usize]) -> Result<Embeddings<f32>, EmbedError> {
-        let mut embeddings = embed(&self.client, &self.options, self.bearer.as_deref(), tokens, 1)?;
+        let mut embeddings = embed(&self.data, tokens, 1, Some(self.dimensions))?;
         // unwrap: guaranteed that embeddings.len() == 1, otherwise the previous line terminated in error
         Ok(embeddings.pop().unwrap())
     }
@@ -179,7 +186,7 @@ impl Embedder {
     }
 
     pub fn prompt_count_in_chunk_hint(&self) -> usize {
-        match self.options.input_type {
+        match self.data.request.input_type() {
             InputType::Text => 1,
             InputType::TextArray => 10,
         }
@@ -190,87 +197,44 @@ impl Embedder {
     }
 
     pub fn distribution(&self) -> Option<DistributionShift> {
-        self.options.distribution
+        self.distribution
     }
 }
 
-fn infer_dimensions(
+fn infer_dimensions(data: &EmbedderData) -> Result<usize, NewEmbedderError> {
-    client: &ureq::Agent,
+    let v = embed(data, ["test"].as_slice(), 1, None)
-    options: &EmbedderOptions,
-    bearer: Option<&str>,
-) -> Result<usize, NewEmbedderError> {
-    let v = embed(client, options, bearer, ["test"].as_slice(), 1)
         .map_err(NewEmbedderError::could_not_determine_dimension)?;
     // unwrap: guaranteed that v.len() == 1, otherwise the previous line terminated in error
     Ok(v.first().unwrap().dimension())
 }
 
 fn embed<S>(
-    client: &ureq::Agent,
+    data: &EmbedderData,
-    options: &EmbedderOptions,
-    bearer: Option<&str>,
     inputs: &[S],
     expected_count: usize,
+    expected_dimension: Option<usize>,
 ) -> Result<Vec<Embeddings<f32>>, EmbedError>
 where
     S: Serialize,
 {
-    let request = client.post(&options.url);
+    let request = data.client.post(&data.url);
-    let request =
+    let request = if let Some(bearer) = &data.bearer {
-        if let Some(bearer) = bearer { request.set("Authorization", bearer) } else { request };
+        request.set("Authorization", bearer)
+    } else {
+        request
+    };
     let request = request.set("Content-Type", "application/json");
 
-    let input_value = match options.input_type {
+    let body = data.request.inject_texts(inputs);
-        InputType::Text => serde_json::json!(inputs.first()),
-        InputType::TextArray => serde_json::json!(inputs),
-    };
-
-    let body = match options.input_field.as_slice() {
-        [] => {
-            // inject input in body
-            input_value
-        }
-        [input] => {
-            let mut body = options.query.clone();
-
-            body.as_object_mut()
-                .ok_or_else(|| {
-                    EmbedError::rest_not_an_object(
-                        options.query.clone(),
-                        options.input_field.clone(),
-                    )
-                })?
-                .insert(input.clone(), input_value);
-            body
-        }
-        [path @ .., input] => {
-            let mut body = options.query.clone();
-
-            let mut current_value = &mut body;
-            for component in path {
-                current_value = current_value
-                    .as_object_mut()
-                    .ok_or_else(|| {
-                        EmbedError::rest_not_an_object(
-                            options.query.clone(),
-                            options.input_field.clone(),
-                        )
-                    })?
-                    .entry(component.clone())
-                    .or_insert(serde_json::json!({}));
-            }
-
-            current_value.as_object_mut().unwrap().insert(input.clone(), input_value);
-            body
-        }
-    };
 
     for attempt in 0..10 {
         let response = request.clone().send_json(&body);
-        let result = check_response(response);
+        let result = check_response(response, data.configuration_source);
 
         let retry_duration = match result {
-            Ok(response) => return response_to_embedding(response, options, expected_count),
+            Ok(response) => {
+                return response_to_embedding(response, data, expected_count, expected_dimension)
+            }
             Err(retry) => {
                 tracing::warn!("Failed: {}", retry.error);
                 retry.into_duration(attempt)
@@ -288,13 +252,16 @@ where
     }
 
     let response = request.send_json(&body);
-    let result = check_response(response);
+    let result = check_response(response, data.configuration_source);
-    result
+    result.map_err(Retry::into_error).and_then(|response| {
-        .map_err(Retry::into_error)
+        response_to_embedding(response, data, expected_count, expected_dimension)
-        .and_then(|response| response_to_embedding(response, options, expected_count))
+    })
 }
 
-fn check_response(response: Result<ureq::Response, ureq::Error>) -> Result<ureq::Response, Retry> {
+fn check_response(
+    response: Result<ureq::Response, ureq::Error>,
+    configuration_source: ConfigurationSource,
+) -> Result<ureq::Response, Retry> {
     match response {
         Ok(response) => Ok(response),
         Err(ureq::Error::Status(code, response)) => {
@@ -302,7 +269,10 @@ fn check_response(response: Result<ureq::Response, ureq::Error>) -> Result<ureq:
             Err(match code {
                 401 => Retry::give_up(EmbedError::rest_unauthorized(error_response)),
                 429 => Retry::rate_limited(EmbedError::rest_too_many_requests(error_response)),
-                400 => Retry::give_up(EmbedError::rest_bad_request(error_response)),
+                400 => Retry::give_up(EmbedError::rest_bad_request(
+                    error_response,
+                    configuration_source,
+                )),
                 500..=599 => {
                     Retry::retry_later(EmbedError::rest_internal_server_error(code, error_response))
                 }
@@ -320,68 +290,111 @@ fn check_response(response: Result<ureq::Response, ureq::Error>) -> Result<ureq:
 
 fn response_to_embedding(
     response: ureq::Response,
-    options: &EmbedderOptions,
+    data: &EmbedderData,
     expected_count: usize,
+    expected_dimensions: Option<usize>,
 ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
     let response: serde_json::Value =
         response.into_json().map_err(EmbedError::rest_response_deserialization)?;
 
-    let mut current_value = &response;
+    let embeddings = data.response.extract_embeddings(response)?;
-    for component in &options.path_to_embeddings {
-        let component = component.as_ref();
-        current_value = current_value.get(component).ok_or_else(|| {
-            EmbedError::rest_response_missing_embeddings(
-                response.clone(),
-                component,
-                &options.path_to_embeddings,
-            )
-        })?;
-    }
-
-    let embeddings = match options.input_type {
-        InputType::Text => {
-            for component in &options.embedding_object {
-                current_value = current_value.get(component).ok_or_else(|| {
-                    EmbedError::rest_response_missing_embeddings(
-                        response.clone(),
-                        component,
-                        &options.embedding_object,
-                    )
-                })?;
-            }
-            let embeddings = current_value.to_owned();
-            let embeddings: Embedding =
-                serde_json::from_value(embeddings).map_err(EmbedError::rest_response_format)?;
-
-            vec![Embeddings::from_single_embedding(embeddings)]
-        }
-        InputType::TextArray => {
-            let empty = vec![];
-            let values = current_value.as_array().unwrap_or(&empty);
-            let mut embeddings: Vec<Embeddings<f32>> = Vec::with_capacity(expected_count);
-            for value in values {
-                let mut current_value = value;
-                for component in &options.embedding_object {
-                    current_value = current_value.get(component).ok_or_else(|| {
-                        EmbedError::rest_response_missing_embeddings(
-                            response.clone(),
-                            component,
-                            &options.embedding_object,
-                        )
-                    })?;
-                }
-                let embedding = current_value.to_owned();
-                let embedding: Embedding =
-                    serde_json::from_value(embedding).map_err(EmbedError::rest_response_format)?;
-                embeddings.push(Embeddings::from_single_embedding(embedding));
-            }
-            embeddings
-        }
-    };
 
     if embeddings.len() != expected_count {
         return Err(EmbedError::rest_response_embedding_count(expected_count, embeddings.len()));
     }
 
+    if let Some(dimensions) = expected_dimensions {
+        for embedding in &embeddings {
+            if embedding.dimension() != dimensions {
+                return Err(EmbedError::rest_unexpected_dimension(
+                    dimensions,
+                    embedding.dimension(),
+                ));
+            }
+        }
+    }
+
     Ok(embeddings)
 }
+
+pub(super) const REQUEST_PLACEHOLDER: &str = "{{text}}";
+pub(super) const RESPONSE_PLACEHOLDER: &str = "{{embedding}}";
+pub(super) const REPEAT_PLACEHOLDER: &str = "{{..}}";
+
+#[derive(Debug)]
+pub struct Request {
+    template: ValueTemplate,
+}
+
+impl Request {
+    pub fn new(template: serde_json::Value) -> Result<Self, NewEmbedderError> {
+        let template = match ValueTemplate::new(template, REQUEST_PLACEHOLDER, REPEAT_PLACEHOLDER) {
+            Ok(template) => template,
+            Err(error) => {
+                let message =
+                    error.error_message("request", REQUEST_PLACEHOLDER, REPEAT_PLACEHOLDER);
+                return Err(NewEmbedderError::rest_could_not_parse_template(message));
+            }
+        };
+
+        Ok(Self { template })
+    }
+
+    fn input_type(&self) -> InputType {
+        if self.template.has_array_value() {
+            InputType::TextArray
+        } else {
+            InputType::Text
+        }
+    }
+
+    pub fn inject_texts<S: Serialize>(
+        &self,
+        texts: impl IntoIterator<Item = S>,
+    ) -> serde_json::Value {
+        self.template.inject(texts.into_iter().map(|s| serde_json::json!(s))).unwrap()
+    }
+}
+
+#[derive(Debug)]
+pub struct Response {
+    template: ValueTemplate,
+}
+
+impl Response {
+    pub fn new(template: serde_json::Value, request: &Request) -> Result<Self, NewEmbedderError> {
+        let template = match ValueTemplate::new(template, RESPONSE_PLACEHOLDER, REPEAT_PLACEHOLDER)
+        {
+            Ok(template) => template,
+            Err(error) => {
+                let message =
+                    error.error_message("response", RESPONSE_PLACEHOLDER, REPEAT_PLACEHOLDER);
+                return Err(NewEmbedderError::rest_could_not_parse_template(message));
+            }
+        };
+
+        match (template.has_array_value(), request.template.has_array_value()) {
+            (true, true) | (false, false) => Ok(Self {template}),
+            (true, false) => Err(NewEmbedderError::rest_could_not_parse_template("in `response`: `response` has multiple embeddings, but `request` has only one text to embed".to_string())),
+            (false, true) => Err(NewEmbedderError::rest_could_not_parse_template("in `response`: `response` has a single embedding, but `request` has multiple texts to embed".to_string())),
+        }
+    }
+
+    pub fn extract_embeddings(
+        &self,
+        response: serde_json::Value,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+        let extracted_values: Vec<Embedding> = match self.template.extract(response) {
+            Ok(extracted_values) => extracted_values,
+            Err(error) => {
+                let error_message =
+                    error.error_message("response", "{{embedding}}", "an array of numbers");
+                return Err(EmbedError::rest_extraction_error(error_message));
+            }
+        };
+        let embeddings: Vec<Embeddings<f32>> =
+            extracted_values.into_iter().map(Embeddings::from_single_embedding).collect();
+
+        Ok(embeddings)
+    }
+}

milli/src/vector/settings.rs

@@ -2,7 +2,6 @@ use deserr::Deserr;
 use roaring::RoaringBitmap;
 use serde::{Deserialize, Serialize};
 
-use super::rest::InputType;
 use super::{ollama, openai, DistributionShift};
 use crate::prompt::PromptData;
 use crate::update::Setting;
@@ -36,19 +35,10 @@ pub struct EmbeddingSettings {
     pub url: Setting<String>,
     #[serde(default, skip_serializing_if = "Setting::is_not_set")]
     #[deserr(default)]
-    pub query: Setting<serde_json::Value>,
+    pub request: Setting<serde_json::Value>,
     #[serde(default, skip_serializing_if = "Setting::is_not_set")]
     #[deserr(default)]
-    pub input_field: Setting<Vec<String>>,
+    pub response: Setting<serde_json::Value>,
-    #[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>,
     #[serde(default, skip_serializing_if = "Setting::is_not_set")]
     #[deserr(default)]
     pub distribution: Setting<DistributionShift>,
@@ -112,11 +102,8 @@ impl SettingsDiff {
                     mut dimensions,
                     mut document_template,
                     mut url,
-                    mut query,
+                    mut request,
-                    mut input_field,
+                    mut response,
-                    mut path_to_embeddings,
-                    mut embedding_object,
-                    mut input_type,
                     mut distribution,
                 } = old;
 
@@ -128,11 +115,8 @@ impl SettingsDiff {
                     dimensions: new_dimensions,
                     document_template: new_document_template,
                     url: new_url,
-                    query: new_query,
+                    request: new_request,
-                    input_field: new_input_field,
+                    response: new_response,
-                    path_to_embeddings: new_path_to_embeddings,
-                    embedding_object: new_embedding_object,
-                    input_type: new_input_type,
                     distribution: new_distribution,
                 } = new;
 
@@ -148,11 +132,8 @@ impl SettingsDiff {
                         &mut revision,
                         &mut dimensions,
                         &mut url,
-                        &mut query,
+                        &mut request,
-                        &mut input_field,
+                        &mut response,
-                        &mut path_to_embeddings,
-                        &mut embedding_object,
-                        &mut input_type,
                         &mut document_template,
                     )
                 }
@@ -177,19 +158,10 @@ impl SettingsDiff {
                         }
                     }
                 }
-                if query.apply(new_query) {
+                if request.apply(new_request) {
                     ReindexAction::push_action(&mut reindex_action, ReindexAction::FullReindex);
                 }
-                if input_field.apply(new_input_field) {
+                if response.apply(new_response) {
-                    ReindexAction::push_action(&mut reindex_action, ReindexAction::FullReindex);
-                }
-                if path_to_embeddings.apply(new_path_to_embeddings) {
-                    ReindexAction::push_action(&mut reindex_action, ReindexAction::FullReindex);
-                }
-                if embedding_object.apply(new_embedding_object) {
-                    ReindexAction::push_action(&mut reindex_action, ReindexAction::FullReindex);
-                }
-                if input_type.apply(new_input_type) {
                     ReindexAction::push_action(&mut reindex_action, ReindexAction::FullReindex);
                 }
                 if document_template.apply(new_document_template) {
@@ -210,11 +182,8 @@ impl SettingsDiff {
                     dimensions,
                     document_template,
                     url,
-                    query,
+                    request,
-                    input_field,
+                    response,
-                    path_to_embeddings,
-                    embedding_object,
-                    input_type,
                     distribution,
                 };
 
@@ -246,11 +215,8 @@ fn apply_default_for_source(
     revision: &mut Setting<String>,
     dimensions: &mut Setting<usize>,
     url: &mut Setting<String>,
-    query: &mut Setting<serde_json::Value>,
+    request: &mut Setting<serde_json::Value>,
-    input_field: &mut Setting<Vec<String>>,
+    response: &mut Setting<serde_json::Value>,
-    path_to_embeddings: &mut Setting<Vec<String>>,
-    embedding_object: &mut Setting<Vec<String>>,
-    input_type: &mut Setting<InputType>,
     document_template: &mut Setting<String>,
 ) {
     match source {
@@ -259,55 +225,40 @@ fn apply_default_for_source(
             *revision = Setting::Reset;
             *dimensions = Setting::NotSet;
             *url = Setting::NotSet;
-            *query = Setting::NotSet;
+            *request = Setting::NotSet;
-            *input_field = Setting::NotSet;
+            *response = Setting::NotSet;
-            *path_to_embeddings = Setting::NotSet;
-            *embedding_object = Setting::NotSet;
-            *input_type = Setting::NotSet;
         }
         Setting::Set(EmbedderSource::Ollama) => {
             *model = Setting::Reset;
             *revision = Setting::NotSet;
             *dimensions = Setting::Reset;
             *url = Setting::NotSet;
-            *query = Setting::NotSet;
+            *request = Setting::NotSet;
-            *input_field = Setting::NotSet;
+            *response = Setting::NotSet;
-            *path_to_embeddings = Setting::NotSet;
-            *embedding_object = Setting::NotSet;
-            *input_type = Setting::NotSet;
         }
         Setting::Set(EmbedderSource::OpenAi) | Setting::Reset => {
             *model = Setting::Reset;
             *revision = Setting::NotSet;
             *dimensions = Setting::NotSet;
             *url = Setting::Reset;
-            *query = Setting::NotSet;
+            *request = Setting::NotSet;
-            *input_field = Setting::NotSet;
+            *response = Setting::NotSet;
-            *path_to_embeddings = Setting::NotSet;
-            *embedding_object = Setting::NotSet;
-            *input_type = Setting::NotSet;
         }
         Setting::Set(EmbedderSource::Rest) => {
             *model = Setting::NotSet;
             *revision = Setting::NotSet;
             *dimensions = Setting::Reset;
             *url = Setting::Reset;
-            *query = Setting::Reset;
+            *request = Setting::Reset;
-            *input_field = Setting::Reset;
+            *response = Setting::Reset;
-            *path_to_embeddings = Setting::Reset;
-            *embedding_object = Setting::Reset;
-            *input_type = Setting::Reset;
         }
         Setting::Set(EmbedderSource::UserProvided) => {
             *model = Setting::NotSet;
             *revision = Setting::NotSet;
             *dimensions = Setting::Reset;
             *url = Setting::NotSet;
-            *query = Setting::NotSet;
+            *request = Setting::NotSet;
-            *input_field = Setting::NotSet;
+            *response = Setting::NotSet;
-            *path_to_embeddings = Setting::NotSet;
-            *embedding_object = Setting::NotSet;
-            *input_type = Setting::NotSet;
             *document_template = Setting::NotSet;
         }
         Setting::NotSet => {}
@@ -340,11 +291,8 @@ impl EmbeddingSettings {
     pub const DOCUMENT_TEMPLATE: &'static str = "documentTemplate";
 
     pub const URL: &'static str = "url";
-    pub const QUERY: &'static str = "query";
+    pub const REQUEST: &'static str = "request";
-    pub const INPUT_FIELD: &'static str = "inputField";
+    pub const RESPONSE: &'static str = "response";
-    pub const PATH_TO_EMBEDDINGS: &'static str = "pathToEmbeddings";
-    pub const EMBEDDING_OBJECT: &'static str = "embeddingObject";
-    pub const INPUT_TYPE: &'static str = "inputType";
 
     pub const DISTRIBUTION: &'static str = "distribution";
 
@@ -374,11 +322,8 @@ impl EmbeddingSettings {
                 EmbedderSource::Rest,
             ],
             Self::URL => &[EmbedderSource::Ollama, EmbedderSource::Rest, EmbedderSource::OpenAi],
-            Self::QUERY => &[EmbedderSource::Rest],
+            Self::REQUEST => &[EmbedderSource::Rest],
-            Self::INPUT_FIELD => &[EmbedderSource::Rest],
+            Self::RESPONSE => &[EmbedderSource::Rest],
-            Self::PATH_TO_EMBEDDINGS => &[EmbedderSource::Rest],
-            Self::EMBEDDING_OBJECT => &[EmbedderSource::Rest],
-            Self::INPUT_TYPE => &[EmbedderSource::Rest],
             Self::DISTRIBUTION => &[
                 EmbedderSource::HuggingFace,
                 EmbedderSource::Ollama,
@@ -423,11 +368,8 @@ impl EmbeddingSettings {
                 Self::DIMENSIONS,
                 Self::DOCUMENT_TEMPLATE,
                 Self::URL,
-                Self::QUERY,
+                Self::REQUEST,
-                Self::INPUT_FIELD,
+                Self::RESPONSE,
-                Self::PATH_TO_EMBEDDINGS,
-                Self::EMBEDDING_OBJECT,
-                Self::INPUT_TYPE,
                 Self::DISTRIBUTION,
             ],
         }
@@ -496,11 +438,8 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 dimensions: Setting::NotSet,
                 document_template: Setting::Set(prompt.template),
                 url: Setting::NotSet,
-                query: Setting::NotSet,
+                request: Setting::NotSet,
-                input_field: Setting::NotSet,
+                response: Setting::NotSet,
-                path_to_embeddings: Setting::NotSet,
-                embedding_object: Setting::NotSet,
-                input_type: Setting::NotSet,
                 distribution: distribution.map(Setting::Set).unwrap_or_default(),
             },
             super::EmbedderOptions::OpenAi(super::openai::EmbedderOptions {
@@ -517,11 +456,8 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 dimensions: dimensions.map(Setting::Set).unwrap_or_default(),
                 document_template: Setting::Set(prompt.template),
                 url: url.map(Setting::Set).unwrap_or_default(),
-                query: Setting::NotSet,
+                request: Setting::NotSet,
-                input_field: Setting::NotSet,
+                response: Setting::NotSet,
-                path_to_embeddings: Setting::NotSet,
-                embedding_object: Setting::NotSet,
-                input_type: Setting::NotSet,
                 distribution: distribution.map(Setting::Set).unwrap_or_default(),
             },
             super::EmbedderOptions::Ollama(super::ollama::EmbedderOptions {
@@ -537,11 +473,8 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 dimensions: Setting::NotSet,
                 document_template: Setting::Set(prompt.template),
                 url: url.map(Setting::Set).unwrap_or_default(),
-                query: Setting::NotSet,
+                request: Setting::NotSet,
-                input_field: Setting::NotSet,
+                response: Setting::NotSet,
-                path_to_embeddings: Setting::NotSet,
-                embedding_object: Setting::NotSet,
-                input_type: Setting::NotSet,
                 distribution: distribution.map(Setting::Set).unwrap_or_default(),
             },
             super::EmbedderOptions::UserProvided(super::manual::EmbedderOptions {
@@ -555,22 +488,16 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 dimensions: Setting::Set(dimensions),
                 document_template: Setting::NotSet,
                 url: Setting::NotSet,
-                query: Setting::NotSet,
+                request: Setting::NotSet,
-                input_field: Setting::NotSet,
+                response: Setting::NotSet,
-                path_to_embeddings: Setting::NotSet,
-                embedding_object: Setting::NotSet,
-                input_type: Setting::NotSet,
                 distribution: distribution.map(Setting::Set).unwrap_or_default(),
             },
             super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
                 api_key,
                 dimensions,
                 url,
-                query,
+                request,
-                input_field,
+                response,
-                path_to_embeddings,
-                embedding_object,
-                input_type,
                 distribution,
             }) => Self {
                 source: Setting::Set(EmbedderSource::Rest),
@@ -580,11 +507,8 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 dimensions: dimensions.map(Setting::Set).unwrap_or_default(),
                 document_template: Setting::Set(prompt.template),
                 url: Setting::Set(url),
-                query: Setting::Set(query),
+                request: Setting::Set(request),
-                input_field: Setting::Set(input_field),
+                response: Setting::Set(response),
-                path_to_embeddings: Setting::Set(path_to_embeddings),
-                embedding_object: Setting::Set(embedding_object),
-                input_type: Setting::Set(input_type),
                 distribution: distribution.map(Setting::Set).unwrap_or_default(),
             },
         }
@@ -602,11 +526,8 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
             dimensions,
             document_template,
             url,
-            query,
+            request,
-            input_field,
+            response,
-            path_to_embeddings,
-            embedding_object,
-            input_type,
             distribution,
         } = value;
 
@@ -669,22 +590,13 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
                         });
                 }
                 EmbedderSource::Rest => {
-                    let embedder_options = super::rest::EmbedderOptions::default();
-
                     this.embedder_options =
                         super::EmbedderOptions::Rest(super::rest::EmbedderOptions {
                             api_key: api_key.set(),
                             dimensions: dimensions.set(),
                             url: url.set().unwrap(),
-                            query: query.set().unwrap_or(embedder_options.query),
+                            request: request.set().unwrap(),
-                            input_field: input_field.set().unwrap_or(embedder_options.input_field),
+                            response: response.set().unwrap(),
-                            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),
                             distribution: distribution.set(),
                         })
                 }