fix clippy

core/src/transformers/ollama.rs

@@ -1,27 +1,21 @@
 use anyhow::Result;
-use ollama_rs::{
-    generation::{completion::request::GenerationRequest, options::GenerationOptions},
-    Ollama,
-};
+use ollama_rs::{generation::completion::request::GenerationRequest, Ollama};
 use url::Url;
 
-use crate::transformers::openai::trim_inputs;
-use crate::transformers::types::{EmbeddingPayload, EmbeddingRequest, Inputs};
-use crate::types;
-
 pub struct OllamaInstance {
     pub model_name: String,
     pub instance: Ollama,
 }
 
 pub trait LLMFunctions {
     fn new(model_name: String, url: String) -> Self;
+    #[allow(async_fn_in_trait)]
     async fn generate_reponse(&self, prompt_text: String) -> Result<String, String>;
 }
 
 impl LLMFunctions for OllamaInstance {
     fn new(model_name: String, url: String) -> Self {
-        let parsed_url = Url::parse(&url).expect(format!("invalid url: {}", url).as_str());
+        let parsed_url = Url::parse(&url).unwrap_or_else(|_| panic!("invalid url: {}", url));
         let instance = Ollama::new(
             format!(
                 "{}://{}",
@@ -46,27 +40,6 @@ impl LLMFunctions for OllamaInstance {
     }
 }
 
-pub fn prepare_ollama_embedding_request(
-    vect_meta: types::VectorizeMeta,
-    inputs: &[Inputs],
-    model_url: String,
-) -> Result<EmbeddingRequest> {
-    let text_inputs = trim_inputs(inputs);
-    let payload = EmbeddingPayload {
-        input: text_inputs,
-        model: vect_meta.transformer.to_string(),
-    };
-
-    // TBD
-    let _job_params: types::JobParams = serde_json::from_value(vect_meta.params)?;
-
-    Ok(EmbeddingRequest {
-        url: model_url,
-        payload,
-        api_key: None,
-    })
-}
-
 pub fn ollama_embedding_dim(model_name: &str) -> i32 {
     match model_name {
         "llama2" => 5192,

core/src/worker/base.rs

@@ -1,4 +1,4 @@
-use crate::transformers::{generic, http_handler, ollama, openai};
+use crate::transformers::{generic, http_handler, openai};
 use crate::types::{JobMessage, JobParams, ModelSource};
 use crate::worker::ops;
 use anyhow::Result;

extension/src/chat/ops.rs

@@ -31,7 +31,7 @@ pub fn call_chat(
     let job_params = serde_json::from_value::<JobParams>(project_meta.params.clone())
         .unwrap_or_else(|e| error!("failed to deserialize job params: {}", e));
 
-        // for various token count estimations
+    // for various token count estimations
     let bpe = match chat_model.source {
         ModelSource::Ollama => {
             // Using gpt-3.5-turbo tokenizer for Ollama since the library does not support llama2