refactor: renamed instructions to context

ingestion-pipeline/src/enricher.rs

@@ -40,17 +40,17 @@ impl IngestionEnricher {
     pub async fn analyze_content(
         &self,
         category: &str,
-        instructions: &str,
+        context: Option<&str>,
         text: &str,
         user_id: &str,
     ) -> Result<LLMEnrichmentResult, AppError> {
         info!("getting similar entitities");
         let similar_entities = self
-            .find_similar_entities(category, instructions, text, user_id)
+            .find_similar_entities(category, context, text, user_id)
             .await?;
         info!("got similar entitities");
         let llm_request = self
-            .prepare_llm_request(category, instructions, text, &similar_entities)
+            .prepare_llm_request(category, context, text, &similar_entities)
             .await?;
         self.perform_analysis(llm_request).await
     }
@@ -58,13 +58,13 @@ impl IngestionEnricher {
     async fn find_similar_entities(
         &self,
         category: &str,
-        instructions: &str,
+        context: Option<&str>,
         text: &str,
         user_id: &str,
     ) -> Result<Vec<KnowledgeEntity>, AppError> {
         let input_text = format!(
-            "content: {}, category: {}, user_instructions: {}",
-            text, category, instructions
+            "content: {}, category: {}, user_context: {:?}",
+            text, category, context
         );
 
         retrieve_entities(&self.db_client, &self.openai_client, &input_text, user_id).await
@@ -73,7 +73,7 @@ impl IngestionEnricher {
     async fn prepare_llm_request(
         &self,
         category: &str,
-        instructions: &str,
+        context: Option<&str>,
         text: &str,
         similar_entities: &[KnowledgeEntity],
     ) -> Result<CreateChatCompletionRequest, AppError> {
@@ -93,8 +93,8 @@ impl IngestionEnricher {
             .collect::<Vec<_>>());
 
         let user_message = format!(
-            "Category:\n{}\nInstructions:\n{}\nContent:\n{}\nExisting KnowledgeEntities in database:\n{}",
-            category, instructions, text, entities_json
+            "Category:\n{}\ncontext:\n{:?}\nContent:\n{}\nExisting KnowledgeEntities in database:\n{}",
+            category, context, text, entities_json
         );
 
         debug!("Prepared LLM request message: {}", user_message);

ingestion-pipeline/src/pipeline.rs

@@ -113,7 +113,7 @@ impl IngestionPipeline {
         analyser
             .analyze_content(
                 &content.category,
-                &content.instructions,
+                content.context.as_deref(),
                 &content.text,
                 &content.user_id,
             )

ingestion-pipeline/src/types/mod.rs

@@ -16,7 +16,7 @@ use common::{
     },
 };
 use dom_smoothie::{Article, Readability, TextMode};
-use headless_chrome::{Browser, LaunchOptionsBuilder};
+use headless_chrome::Browser;
 use std::io::{Seek, SeekFrom};
 use tempfile::NamedTempFile;
 use tracing::{error, info};
@@ -28,14 +28,14 @@ pub async fn to_text_content(
     match ingestion_payload {
         IngestionPayload::Url {
             url,
-            instructions,
+            context,
             category,
             user_id,
         } => {
             let (article, file_info) = fetch_article_from_url(&url, db, &user_id).await?;
             Ok(TextContent::new(
                 article.text_content.into(),
-                instructions,
+                Some(context),
                 category,
                 None,
                 Some(UrlInfo {
@@ -48,12 +48,12 @@ pub async fn to_text_content(
         }
         IngestionPayload::Text {
             text,
-            instructions,
+            context,
             category,
             user_id,
         } => Ok(TextContent::new(
             text,
-            instructions,
+            Some(context),
             category,
             None,
             None,
@@ -61,14 +61,14 @@ pub async fn to_text_content(
         )),
         IngestionPayload::File {
             file_info,
-            instructions,
+            context,
             category,
             user_id,
         } => {
             let text = extract_text_from_file(&file_info).await?;
             Ok(TextContent::new(
                 text,
-                instructions,
+                Some(context),
                 category,
                 Some(file_info),
                 None,