Merge #4456

4456: Add Ollama as an embeddings provider r=dureuill a=jakobklemm

# Pull Request

## Related issue
[Related Discord Thread](https://discord.com/channels/1006923006964154428/1211977150316683305)

## What does this PR do?
- Adds Ollama as a provider of Embeddings besides HuggingFace and OpenAI under the name `ollama`
- Adds the environment variable `MEILI_OLLAMA_URL` to set the embeddings URL of an Ollama instance with a default value of `http://localhost:11434/api/embeddings` if no variable is set
- Changes some of the structs and functions in `openai.rs` to be public so that they can be shared.
- Added more error variants for Ollama specific errors
- It uses the model `nomic-embed-text` as default, but any string value is allowed, however it won't automatically check if the model actually exists or is an embedding model

Tested against Ollama version `v0.1.27` and the `nomic-embed-text` model.

## PR checklist
Please check if your PR fulfills the following requirements:
- [x] Does this PR fix an existing issue, or have you listed the changes applied in the PR description (and why they are needed)?
- [x] Have you read the contributing guidelines?
- [x] Have you made sure that the title is accurate and descriptive of the changes?

Co-authored-by: Jakob Klemm <jakob@jeykey.net>
Co-authored-by: Louis Dureuil <louis.dureuil@gmail.com>

meilisearch/src/routes/indexes/settings.rs

@@ -604,6 +604,7 @@ fn embedder_analytics(
                 EmbedderSource::OpenAi => sources.insert("openAi"),
                 EmbedderSource::HuggingFace => sources.insert("huggingFace"),
                 EmbedderSource::UserProvided => sources.insert("userProvided"),
+                EmbedderSource::Ollama => sources.insert("ollama"),
             };
         }
     };

milli/src/update/settings.rs

@@ -1178,6 +1178,13 @@ pub fn validate_embedding_settings(
                 }
             }
         }
+        EmbedderSource::Ollama => {
+            // Dimensions get inferred, only model name is required
+            check_unset(&dimensions, "dimensions", inferred_source, name)?;
+            check_set(&model, "model", inferred_source, name)?;
+            check_unset(&api_key, "apiKey", inferred_source, name)?;
+            check_unset(&revision, "revision", inferred_source, name)?;
+        }
         EmbedderSource::HuggingFace => {
             check_unset(&api_key, "apiKey", inferred_source, name)?;
             check_unset(&dimensions, "dimensions", inferred_source, name)?;

milli/src/vector/error.rs

@@ -2,6 +2,7 @@ use std::path::PathBuf;
 
 use hf_hub::api::sync::ApiError;
 
+use super::ollama::OllamaError;
 use crate::error::FaultSource;
 use crate::vector::openai::OpenAiError;
 
@@ -71,6 +72,17 @@ pub enum EmbedErrorKind {
     OpenAiRuntimeInit(std::io::Error),
     #[error("initializing web client for sending embedding requests failed: {0}")]
     InitWebClient(reqwest::Error),
+    // Dedicated Ollama error kinds, might have to merge them into one cohesive error type for all backends.
+    #[error("unexpected response from Ollama: {0}")]
+    OllamaUnexpected(reqwest::Error),
+    #[error("sent too many requests to Ollama: {0}")]
+    OllamaTooManyRequests(OllamaError),
+    #[error("received internal error from Ollama: {0}")]
+    OllamaInternalServerError(OllamaError),
+    #[error("model not found. Meilisearch will not automatically download models from the Ollama library, please pull the model manually: {0}")]
+    OllamaModelNotFoundError(OllamaError),
+    #[error("received unhandled HTTP status code {0} from Ollama")]
+    OllamaUnhandledStatusCode(u16),
 }
 
 impl EmbedError {
@@ -129,6 +141,26 @@ impl EmbedError {
     pub fn openai_initialize_web_client(inner: reqwest::Error) -> Self {
         Self { kind: EmbedErrorKind::InitWebClient(inner), fault: FaultSource::Runtime }
     }
+
+    pub(crate) fn ollama_unexpected(inner: reqwest::Error) -> EmbedError {
+        Self { kind: EmbedErrorKind::OllamaUnexpected(inner), fault: FaultSource::Bug }
+    }
+
+    pub(crate) fn ollama_model_not_found(inner: OllamaError) -> EmbedError {
+        Self { kind: EmbedErrorKind::OllamaModelNotFoundError(inner), fault: FaultSource::User }
+    }
+
+    pub(crate) fn ollama_too_many_requests(inner: OllamaError) -> EmbedError {
+        Self { kind: EmbedErrorKind::OllamaTooManyRequests(inner), fault: FaultSource::Runtime }
+    }
+
+    pub(crate) fn ollama_internal_server_error(inner: OllamaError) -> EmbedError {
+        Self { kind: EmbedErrorKind::OllamaInternalServerError(inner), fault: FaultSource::Runtime }
+    }
+
+    pub(crate) fn ollama_unhandled_status_code(code: u16) -> EmbedError {
+        Self { kind: EmbedErrorKind::OllamaUnhandledStatusCode(code), fault: FaultSource::Bug }
+    }
 }
 
 #[derive(Debug, thiserror::Error)]
@@ -195,6 +227,13 @@ impl NewEmbedderError {
         }
     }
 
+    pub fn ollama_could_not_determine_dimension(inner: EmbedError) -> NewEmbedderError {
+        Self {
+            kind: NewEmbedderErrorKind::CouldNotDetermineDimension(inner),
+            fault: FaultSource::User,
+        }
+    }
+
     pub fn openai_invalid_api_key_format(inner: reqwest::header::InvalidHeaderValue) -> Self {
         Self { kind: NewEmbedderErrorKind::InvalidApiKeyFormat(inner), fault: FaultSource::User }
     }

milli/src/vector/mod.rs

@@ -10,6 +10,8 @@ pub mod manual;
 pub mod openai;
 pub mod settings;
 
+pub mod ollama;
+
 pub use self::error::Error;
 
 pub type Embedding = Vec<f32>;
@@ -76,6 +78,7 @@ pub enum Embedder {
     HuggingFace(hf::Embedder),
     OpenAi(openai::Embedder),
     UserProvided(manual::Embedder),
+    Ollama(ollama::Embedder),
 }
 
 #[derive(Debug, Clone, Default, serde::Deserialize, serde::Serialize)]
@@ -127,6 +130,7 @@ impl IntoIterator for EmbeddingConfigs {
 pub enum EmbedderOptions {
     HuggingFace(hf::EmbedderOptions),
     OpenAi(openai::EmbedderOptions),
+    Ollama(ollama::EmbedderOptions),
     UserProvided(manual::EmbedderOptions),
 }
 
@@ -144,6 +148,10 @@ impl EmbedderOptions {
     pub fn openai(api_key: Option<String>) -> Self {
         Self::OpenAi(openai::EmbedderOptions::with_default_model(api_key))
     }
+
+    pub fn ollama() -> Self {
+        Self::Ollama(ollama::EmbedderOptions::with_default_model())
+    }
 }
 
 impl Embedder {
@@ -151,6 +159,7 @@ impl Embedder {
         Ok(match options {
             EmbedderOptions::HuggingFace(options) => Self::HuggingFace(hf::Embedder::new(options)?),
             EmbedderOptions::OpenAi(options) => Self::OpenAi(openai::Embedder::new(options)?),
+            EmbedderOptions::Ollama(options) => Self::Ollama(ollama::Embedder::new(options)?),
             EmbedderOptions::UserProvided(options) => {
                 Self::UserProvided(manual::Embedder::new(options))
             }
@@ -167,6 +176,10 @@ impl Embedder {
                 let client = embedder.new_client()?;
                 embedder.embed(texts, &client).await
             }
+            Embedder::Ollama(embedder) => {
+                let client = embedder.new_client()?;
+                embedder.embed(texts, &client).await
+            }
             Embedder::UserProvided(embedder) => embedder.embed(texts),
         }
     }
@@ -181,6 +194,7 @@ impl Embedder {
         match self {
             Embedder::HuggingFace(embedder) => embedder.embed_chunks(text_chunks),
             Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks),
+            Embedder::Ollama(embedder) => embedder.embed_chunks(text_chunks),
             Embedder::UserProvided(embedder) => embedder.embed_chunks(text_chunks),
         }
     }
@@ -189,6 +203,7 @@ impl Embedder {
         match self {
             Embedder::HuggingFace(embedder) => embedder.chunk_count_hint(),
             Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
+            Embedder::Ollama(embedder) => embedder.chunk_count_hint(),
             Embedder::UserProvided(_) => 1,
         }
     }
@@ -197,6 +212,7 @@ impl Embedder {
         match self {
             Embedder::HuggingFace(embedder) => embedder.prompt_count_in_chunk_hint(),
             Embedder::OpenAi(embedder) => embedder.prompt_count_in_chunk_hint(),
+            Embedder::Ollama(embedder) => embedder.prompt_count_in_chunk_hint(),
             Embedder::UserProvided(_) => 1,
         }
     }
@@ -205,6 +221,7 @@ impl Embedder {
         match self {
             Embedder::HuggingFace(embedder) => embedder.dimensions(),
             Embedder::OpenAi(embedder) => embedder.dimensions(),
+            Embedder::Ollama(embedder) => embedder.dimensions(),
             Embedder::UserProvided(embedder) => embedder.dimensions(),
         }
     }
@@ -213,6 +230,7 @@ impl Embedder {
         match self {
             Embedder::HuggingFace(embedder) => embedder.distribution(),
             Embedder::OpenAi(embedder) => embedder.distribution(),
+            Embedder::Ollama(embedder) => embedder.distribution(),
             Embedder::UserProvided(_embedder) => None,
         }
     }

milli/src/vector/ollama.rs

@@ -0,0 +1,307 @@
+// Copied from "openai.rs" with the sections I actually understand changed for Ollama.
+// The common components of the Ollama and OpenAI interfaces might need to be extracted.
+
+use std::fmt::Display;
+
+use reqwest::StatusCode;
+
+use super::error::{EmbedError, NewEmbedderError};
+use super::openai::Retry;
+use super::{DistributionShift, Embedding, Embeddings};
+
+#[derive(Debug)]
+pub struct Embedder {
+    headers: reqwest::header::HeaderMap,
+    options: EmbedderOptions,
+}
+
+#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
+pub struct EmbedderOptions {
+    pub embedding_model: EmbeddingModel,
+}
+
+#[derive(
+    Debug, Clone, Hash, PartialEq, Eq, serde::Serialize, serde::Deserialize, deserr::Deserr,
+)]
+#[deserr(deny_unknown_fields)]
+pub struct EmbeddingModel {
+    name: String,
+    dimensions: usize,
+}
+
+#[derive(Debug, serde::Serialize)]
+struct OllamaRequest<'a> {
+    model: &'a str,
+    prompt: &'a str,
+}
+
+#[derive(Debug, serde::Deserialize)]
+struct OllamaResponse {
+    embedding: Embedding,
+}
+
+#[derive(Debug, serde::Deserialize)]
+pub struct OllamaError {
+    error: String,
+}
+
+impl EmbeddingModel {
+    pub fn max_token(&self) -> usize {
+        // this might not be the same for all models
+        8192
+    }
+
+    pub fn default_dimensions(&self) -> usize {
+        // Dimensions for nomic-embed-text
+        768
+    }
+
+    pub fn name(&self) -> String {
+        self.name.clone()
+    }
+
+    pub fn from_name(name: &str) -> Self {
+        Self { name: name.to_string(), dimensions: 0 }
+    }
+
+    pub fn supports_overriding_dimensions(&self) -> bool {
+        false
+    }
+}
+
+impl Default for EmbeddingModel {
+    fn default() -> Self {
+        Self { name: "nomic-embed-text".to_string(), dimensions: 0 }
+    }
+}
+
+impl EmbedderOptions {
+    pub fn with_default_model() -> Self {
+        Self { embedding_model: Default::default() }
+    }
+
+    pub fn with_embedding_model(embedding_model: EmbeddingModel) -> Self {
+        Self { embedding_model }
+    }
+}
+
+impl Embedder {
+    pub fn new_client(&self) -> Result<reqwest::Client, EmbedError> {
+        reqwest::ClientBuilder::new()
+            .default_headers(self.headers.clone())
+            .build()
+            .map_err(EmbedError::openai_initialize_web_client)
+    }
+
+    pub fn new(options: EmbedderOptions) -> Result<Self, NewEmbedderError> {
+        let mut headers = reqwest::header::HeaderMap::new();
+        headers.insert(
+            reqwest::header::CONTENT_TYPE,
+            reqwest::header::HeaderValue::from_static("application/json"),
+        );
+
+        let mut embedder = Self { options, headers };
+
+        let rt = tokio::runtime::Builder::new_current_thread()
+            .enable_io()
+            .enable_time()
+            .build()
+            .map_err(EmbedError::openai_runtime_init)
+            .map_err(NewEmbedderError::ollama_could_not_determine_dimension)?;
+
+        // Get dimensions from Ollama
+        let request =
+            OllamaRequest { model: &embedder.options.embedding_model.name(), prompt: "test" };
+        // TODO: Refactor into shared error type
+        let client = embedder
+            .new_client()
+            .map_err(NewEmbedderError::ollama_could_not_determine_dimension)?;
+
+        rt.block_on(async move {
+            let response = client
+                .post(get_ollama_path())
+                .json(&request)
+                .send()
+                .await
+                .map_err(EmbedError::ollama_unexpected)
+                .map_err(NewEmbedderError::ollama_could_not_determine_dimension)?;
+
+            // Process error in case model not found
+            let response = Self::check_response(response).await.map_err(|_err| {
+                let e = EmbedError::ollama_model_not_found(OllamaError {
+                    error: format!("model: {}", embedder.options.embedding_model.name()),
+                });
+                NewEmbedderError::ollama_could_not_determine_dimension(e)
+            })?;
+
+            let response: OllamaResponse = response
+                .json()
+                .await
+                .map_err(EmbedError::ollama_unexpected)
+                .map_err(NewEmbedderError::ollama_could_not_determine_dimension)?;
+
+            let embedding = Embeddings::from_single_embedding(response.embedding);
+
+            embedder.options.embedding_model.dimensions = embedding.dimension();
+
+            tracing::info!(
+                "ollama model {} with dimensionality {} added",
+                embedder.options.embedding_model.name(),
+                embedding.dimension()
+            );
+
+            Ok(embedder)
+        })
+    }
+
+    async fn check_response(response: reqwest::Response) -> Result<reqwest::Response, Retry> {
+        if !response.status().is_success() {
+            // Not the same number of possible error cases covered as with OpenAI.
+            match response.status() {
+                StatusCode::TOO_MANY_REQUESTS => {
+                    let error_response: OllamaError = response
+                        .json()
+                        .await
+                        .map_err(EmbedError::ollama_unexpected)
+                        .map_err(Retry::retry_later)?;
+
+                    return Err(Retry::rate_limited(EmbedError::ollama_too_many_requests(
+                        OllamaError { error: error_response.error },
+                    )));
+                }
+                StatusCode::SERVICE_UNAVAILABLE => {
+                    let error_response: OllamaError = response
+                        .json()
+                        .await
+                        .map_err(EmbedError::ollama_unexpected)
+                        .map_err(Retry::retry_later)?;
+                    return Err(Retry::retry_later(EmbedError::ollama_internal_server_error(
+                        OllamaError { error: error_response.error },
+                    )));
+                }
+                StatusCode::NOT_FOUND => {
+                    let error_response: OllamaError = response
+                        .json()
+                        .await
+                        .map_err(EmbedError::ollama_unexpected)
+                        .map_err(Retry::give_up)?;
+
+                    return Err(Retry::give_up(EmbedError::ollama_model_not_found(OllamaError {
+                        error: error_response.error,
+                    })));
+                }
+                code => {
+                    return Err(Retry::give_up(EmbedError::ollama_unhandled_status_code(
+                        code.as_u16(),
+                    )));
+                }
+            }
+        }
+        Ok(response)
+    }
+
+    pub async fn embed(
+        &self,
+        texts: Vec<String>,
+        client: &reqwest::Client,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+        // Ollama only embedds one document at a time.
+        let mut results = Vec::with_capacity(texts.len());
+
+        // The retry loop is inside the texts loop, might have to switch that around
+        for text in texts {
+            // Retries copied from openai.rs
+            for attempt in 0..7 {
+                let retry_duration = match self.try_embed(&text, client).await {
+                    Ok(result) => {
+                        results.push(result);
+                        break;
+                    }
+                    Err(retry) => {
+                        tracing::warn!("Failed: {}", retry.error);
+                        retry.into_duration(attempt)
+                    }
+                }?;
+                tracing::warn!(
+                    "Attempt #{}, retrying after {}ms.",
+                    attempt,
+                    retry_duration.as_millis()
+                );
+                tokio::time::sleep(retry_duration).await;
+            }
+        }
+
+        Ok(results)
+    }
+
+    async fn try_embed(
+        &self,
+        text: &str,
+        client: &reqwest::Client,
+    ) -> Result<Embeddings<f32>, Retry> {
+        let request = OllamaRequest { model: &self.options.embedding_model.name(), prompt: text };
+        let response = client
+            .post(get_ollama_path())
+            .json(&request)
+            .send()
+            .await
+            .map_err(EmbedError::openai_network)
+            .map_err(Retry::retry_later)?;
+
+        let response = Self::check_response(response).await?;
+
+        let response: OllamaResponse = response
+            .json()
+            .await
+            .map_err(EmbedError::openai_unexpected)
+            .map_err(Retry::retry_later)?;
+
+        tracing::trace!("response: {:?}", response.embedding);
+
+        let embedding = Embeddings::from_single_embedding(response.embedding);
+        Ok(embedding)
+    }
+
+    pub fn embed_chunks(
+        &self,
+        text_chunks: Vec<Vec<String>>,
+    ) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
+        let rt = tokio::runtime::Builder::new_current_thread()
+            .enable_io()
+            .enable_time()
+            .build()
+            .map_err(EmbedError::openai_runtime_init)?;
+        let client = self.new_client()?;
+        rt.block_on(futures::future::try_join_all(
+            text_chunks.into_iter().map(|prompts| self.embed(prompts, &client)),
+        ))
+    }
+
+    // Defaults copied from openai.rs
+    pub fn chunk_count_hint(&self) -> usize {
+        10
+    }
+
+    pub fn prompt_count_in_chunk_hint(&self) -> usize {
+        10
+    }
+
+    pub fn dimensions(&self) -> usize {
+        self.options.embedding_model.dimensions
+    }
+
+    pub fn distribution(&self) -> Option<DistributionShift> {
+        None
+    }
+}
+
+impl Display for OllamaError {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{}", self.error)
+    }
+}
+
+fn get_ollama_path() -> String {
+    // Important: Hostname not enough, has to be entire path to embeddings endpoint
+    std::env::var("MEILI_OLLAMA_URL").unwrap_or("http://localhost:11434/api/embeddings".to_string())
+}

milli/src/vector/openai.rs

@@ -419,12 +419,12 @@ impl Embedder {
 
 // retrying in case of failure
 
-struct Retry {
+pub struct Retry {
-    error: EmbedError,
+    pub error: EmbedError,
     strategy: RetryStrategy,
 }
 
-enum RetryStrategy {
+pub enum RetryStrategy {
     GiveUp,
     Retry,
     RetryTokenized,
@@ -432,23 +432,23 @@ enum RetryStrategy {
 }
 
 impl Retry {
-    fn give_up(error: EmbedError) -> Self {
+    pub fn give_up(error: EmbedError) -> Self {
         Self { error, strategy: RetryStrategy::GiveUp }
     }
 
-    fn retry_later(error: EmbedError) -> Self {
+    pub fn retry_later(error: EmbedError) -> Self {
         Self { error, strategy: RetryStrategy::Retry }
     }
 
-    fn retry_tokenized(error: EmbedError) -> Self {
+    pub fn retry_tokenized(error: EmbedError) -> Self {
         Self { error, strategy: RetryStrategy::RetryTokenized }
     }
 
-    fn rate_limited(error: EmbedError) -> Self {
+    pub fn rate_limited(error: EmbedError) -> Self {
         Self { error, strategy: RetryStrategy::RetryAfterRateLimit }
     }
 
-    fn into_duration(self, attempt: u32) -> Result<tokio::time::Duration, EmbedError> {
+    pub fn into_duration(self, attempt: u32) -> Result<tokio::time::Duration, EmbedError> {
         match self.strategy {
             RetryStrategy::GiveUp => Err(self.error),
             RetryStrategy::Retry => Ok(tokio::time::Duration::from_millis((10u64).pow(attempt))),
@@ -459,11 +459,11 @@ impl Retry {
         }
     }
 
-    fn must_tokenize(&self) -> bool {
+    pub fn must_tokenize(&self) -> bool {
         matches!(self.strategy, RetryStrategy::RetryTokenized)
     }
 
-    fn into_error(self) -> EmbedError {
+    pub fn into_error(self) -> EmbedError {
         self.error
     }
 }

milli/src/vector/settings.rs

@@ -1,7 +1,7 @@
 use deserr::Deserr;
 use serde::{Deserialize, Serialize};
 
-use super::openai;
+use super::{ollama, openai};
 use crate::prompt::PromptData;
 use crate::update::Setting;
 use crate::vector::EmbeddingConfig;
@@ -80,11 +80,15 @@ impl EmbeddingSettings {
             Self::SOURCE => {
                 &[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::UserProvided]
             }
-            Self::MODEL => &[EmbedderSource::HuggingFace, EmbedderSource::OpenAi],
+            Self::MODEL => {
+                &[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::Ollama]
+            }
             Self::REVISION => &[EmbedderSource::HuggingFace],
             Self::API_KEY => &[EmbedderSource::OpenAi],
             Self::DIMENSIONS => &[EmbedderSource::OpenAi, EmbedderSource::UserProvided],
-            Self::DOCUMENT_TEMPLATE => &[EmbedderSource::HuggingFace, EmbedderSource::OpenAi],
+            Self::DOCUMENT_TEMPLATE => {
+                &[EmbedderSource::HuggingFace, EmbedderSource::OpenAi, EmbedderSource::Ollama]
+            }
             _other => unreachable!("unknown field"),
         }
     }
@@ -101,6 +105,7 @@ impl EmbeddingSettings {
             EmbedderSource::HuggingFace => {
                 &[Self::SOURCE, Self::MODEL, Self::REVISION, Self::DOCUMENT_TEMPLATE]
             }
+            EmbedderSource::Ollama => &[Self::SOURCE, Self::MODEL, Self::DOCUMENT_TEMPLATE],
             EmbedderSource::UserProvided => &[Self::SOURCE, Self::DIMENSIONS],
         }
     }
@@ -134,6 +139,7 @@ pub enum EmbedderSource {
     #[default]
     OpenAi,
     HuggingFace,
+    Ollama,
     UserProvided,
 }
 
@@ -143,6 +149,7 @@ impl std::fmt::Display for EmbedderSource {
             EmbedderSource::OpenAi => "openAi",
             EmbedderSource::HuggingFace => "huggingFace",
             EmbedderSource::UserProvided => "userProvided",
+            EmbedderSource::Ollama => "ollama",
         };
         f.write_str(s)
     }
@@ -192,7 +199,15 @@ impl From<EmbeddingConfig> for EmbeddingSettings {
                 model: Setting::Set(options.embedding_model.name().to_owned()),
                 revision: Setting::NotSet,
                 api_key: options.api_key.map(Setting::Set).unwrap_or_default(),
-                dimensions: options.dimensions.map(Setting::Set).unwrap_or_default(),
+                dimensions: Setting::Set(options.dimensions.unwrap_or_default()),
+                document_template: Setting::Set(prompt.template),
+            },
+            super::EmbedderOptions::Ollama(options) => Self {
+                source: Setting::Set(EmbedderSource::Ollama),
+                model: Setting::Set(options.embedding_model.name().to_owned()),
+                revision: Setting::NotSet,
+                api_key: Setting::NotSet,
+                dimensions: Setting::NotSet,
                 document_template: Setting::Set(prompt.template),
             },
             super::EmbedderOptions::UserProvided(options) => Self {
@@ -229,6 +244,14 @@ impl From<EmbeddingSettings> for EmbeddingConfig {
                     }
                     this.embedder_options = super::EmbedderOptions::OpenAi(options);
                 }
+                EmbedderSource::Ollama => {
+                    let mut options: ollama::EmbedderOptions =
+                        super::ollama::EmbedderOptions::with_default_model();
+                    if let Some(model) = model.set() {
+                        options.embedding_model = super::ollama::EmbeddingModel::from_name(&model);
+                    }
+                    this.embedder_options = super::EmbedderOptions::Ollama(options);
+                }
                 EmbedderSource::HuggingFace => {
                     let mut options = super::hf::EmbedderOptions::default();
                     if let Some(model) = model.set() {