Method process_file

server/src/processor.rs:30–37

process_file splits the text into chunks so to generate the embeddings for proper context length and saves them to the db

(&self, file_name: &str)

    // process_file splits the text into chunks so to generate the embeddings
    // for proper context length and saves them to the db
    pub async fn process_file(&self, file_name: &str) -> Result<()> {
        let chunks = self.process_chunks(file_name)?;
        let embeddings = self.process_embeddings(chunks.to_owned()).await.unwrap();
        let coll_name = file_name.split_once(".pdf").unwrap().0;
        self.save_embeddings(coll_name, embeddings.to_owned())
            .await?;
        Ok(())
    }

    // process_prompt gets the similar cosine embeddings for the user prompt
    // and sets the context for LLM to get the result generated as per the context