refactor: better separation of dependencies to crates

node stuff to html crate only

common/src/utils/embedding.rs

@@ -0,0 +1,48 @@
+use async_openai::types::CreateEmbeddingRequestArgs;
+
+use crate::error::AppError;
+/// Generates an embedding vector for the given input text using OpenAI's embedding model.
+///
+/// This function takes a text input and converts it into a numerical vector representation (embedding)
+/// using OpenAI's text-embedding-3-small model. These embeddings can be used for semantic similarity
+/// comparisons, vector search, and other natural language processing tasks.
+///
+/// # Arguments
+///
+/// * `client`: The OpenAI client instance used to make API requests.
+/// * `input`: The text string to generate embeddings for.
+///
+/// # Returns
+///
+/// Returns a `Result` containing either:
+/// * `Ok(Vec<f32>)`: A vector of 32-bit floating point numbers representing the text embedding
+/// * `Err(ProcessingError)`: An error if the embedding generation fails
+///
+/// # Errors
+///
+/// This function can return a `AppError` in the following cases:
+/// * If the OpenAI API request fails
+/// * If the request building fails
+/// * If no embedding data is received in the response
+pub async fn generate_embedding(
+    client: &async_openai::Client<async_openai::config::OpenAIConfig>,
+    input: &str,
+) -> Result<Vec<f32>, AppError> {
+    let request = CreateEmbeddingRequestArgs::default()
+        .model("text-embedding-3-small")
+        .input([input])
+        .build()?;
+
+    // Send the request to OpenAI
+    let response = client.embeddings().create(request).await?;
+
+    // Extract the embedding vector
+    let embedding: Vec<f32> = response
+        .data
+        .first()
+        .ok_or_else(|| AppError::LLMParsing("No embedding data received".into()))?
+        .embedding
+        .clone();
+
+    Ok(embedding)
+}