feat: system prompt customisable

crates/composite-retrieval/src/answer_retrieval.rs

@@ -13,6 +13,7 @@ use common::{
         types::{
             knowledge_entity::KnowledgeEntity,
             message::{format_history, Message},
+            system_settings::SystemSettings,
         },
     },
 };
@@ -63,11 +64,12 @@ pub async fn get_answer_with_references(
     user_id: &str,
 ) -> Result<Answer, AppError> {
     let entities = retrieve_entities(surreal_db_client, openai_client, query, user_id).await?;
+    let settings = SystemSettings::get_current(surreal_db_client).await?;
 
     let entities_json = format_entities_json(&entities);
     let user_message = create_user_message(&entities_json, query);
 
-    let request = create_chat_request(user_message)?;
+    let request = create_chat_request(user_message, &settings)?;
     let response = openai_client.chat().create(request).await?;
 
     let llm_response = process_llm_response(response).await?;
@@ -139,6 +141,7 @@ pub fn create_user_message_with_history(
 
 pub fn create_chat_request(
     user_message: String,
+    settings: &SystemSettings,
 ) -> Result<CreateChatCompletionRequest, OpenAIError> {
     let response_format = ResponseFormat::JsonSchema {
         json_schema: ResponseFormatJsonSchema {
@@ -150,11 +153,11 @@ pub fn create_chat_request(
     };
 
     CreateChatCompletionRequestArgs::default()
-        .model("gpt-4o-mini")
+        .model(&settings.query_model)
         .temperature(0.2)
         .max_tokens(3048u32)
         .messages([
-            ChatCompletionRequestSystemMessage::from(QUERY_SYSTEM_PROMPT).into(),
+            ChatCompletionRequestSystemMessage::from(settings.query_system_prompt.clone()).into(),
             ChatCompletionRequestUserMessage::from(user_message).into(),
         ])
         .response_format(response_format)

crates/composite-retrieval/src/answer_retrieval_helper.rs

@@ -1,29 +1,7 @@
+use common::storage::types::system_prompts::DEFAULT_QUERY_SYSTEM_PROMPT;
 use serde_json::{json, Value};
 
-pub static QUERY_SYSTEM_PROMPT: &str = r#"
-      You are a knowledgeable assistant with access to a specialized knowledge base. You will be provided with relevant knowledge entities from the database as context. Each knowledge entity contains a name, description, and type, representing different concepts, ideas, and information.
-
-      Your task is to:
-      1. Carefully analyze the provided knowledge entities in the context
-      2. Answer user questions based on this information
-      3. Provide clear, concise, and accurate responses
-      4. When referencing information, briefly mention which knowledge entity it came from
-      5. If the provided context doesn't contain enough information to answer the question confidently, clearly state this
-      6. If only partial information is available, explain what you can answer and what information is missing
-      7. Avoid making assumptions or providing information not supported by the context
-      8. Output the references to the documents. Use the UUIDs and make sure they are correct!
-
-      Remember:
-      - Be direct and honest about the limitations of your knowledge
-      - Cite the relevant knowledge entities when providing information, but only provide the UUIDs in the reference array
-      - If you need to combine information from multiple entities, explain how they connect
-      - Don't speculate beyond what's provided in the context
-
-      Example response formats:
-      "Based on [Entity Name], [answer...]"
-      "I found relevant information in multiple entries: [explanation...]"
-      "I apologize, but the provided context doesn't contain information about [topic]"  
-    "#;
+pub static QUERY_SYSTEM_PROMPT: &str = DEFAULT_QUERY_SYSTEM_PROMPT;
 
 pub fn get_query_response_schema() -> Value {
     json!({

crates/composite-retrieval/src/lib.rs

@@ -13,6 +13,7 @@ use common::{
 use futures::future::{try_join, try_join_all};
 use graph::{find_entities_by_relationship_by_id, find_entities_by_source_ids};
 use std::collections::HashMap;
+use tracing::info;
 use vector::find_items_by_vector_similarity;
 
 /// Performs a comprehensive knowledge entity retrieval using multiple search strategies
@@ -42,8 +43,6 @@ pub async fn retrieve_entities(
     query: &str,
     user_id: &str,
 ) -> Result<Vec<KnowledgeEntity>, AppError> {
-    // info!("Received input: {:?}", query);
-
     let (items_from_knowledge_entity_similarity, closest_chunks) = try_join(
         find_items_by_vector_similarity(
             10,

crates/composite-retrieval/src/vector.rs

@@ -1,6 +1,7 @@
 use surrealdb::{engine::any::Any, Surreal};
 
 use common::{error::AppError, utils::embedding::generate_embedding};
+use tracing::info;
 
 /// Compares vectors and retrieves a number of items from the specified table.
 ///