enhance(ai-help): record embedding duration and model separately

migrations/2024-03-29-101252_add_ai_help_metadata_embedding_duration/down.sql

@@ -0,0 +1,3 @@
+ALTER TABLE ai_help_message_meta
+DROP COLUMN embedding_duration,
+DROP COLUMN embedding_model;

migrations/2024-03-29-101252_add_ai_help_metadata_embedding_duration/up.sql

@@ -0,0 +1,3 @@
+ALTER TABLE ai_help_message_meta
+ADD COLUMN embedding_duration BIGINT DEFAULT NULL,
+ADD COLUMN embedding_model TEXT NOT NULL DEFAULT '';

src/ai/embeddings.rs

@@ -1,8 +1,10 @@
+use std::time::Instant;
+
 use async_openai::{config::OpenAIConfig, types::CreateEmbeddingRequestArgs, Client};
 use itertools::Itertools;
 
 use crate::{
-    ai::{constants::EMBEDDING_MODEL, error::AIError},
+    ai::{constants::EMBEDDING_MODEL, error::AIError, help::AIHelpRequestMeta},
     db::SupaPool,
 };
 
@@ -69,23 +71,30 @@ pub async fn get_related_macro_docs(
     client: &Client<OpenAIConfig>,
     pool: &SupaPool,
     prompt: String,
+    request_meta: &mut AIHelpRequestMeta,
 ) -> Result<Vec<RelatedDoc>, AIError> {
+    request_meta.embedding_model = Some(EMBEDDING_MODEL);
+
     let embedding_req = CreateEmbeddingRequestArgs::default()
         .model(EMBEDDING_MODEL)
         .input(prompt)
         .build()?;
+    let start = Instant::now();
     let embedding_res = client.embeddings().create(embedding_req).await?;
+    request_meta.embedding_duration = Some(start.elapsed());
 
     let embedding =
         pgvector::Vector::from(embedding_res.data.into_iter().next().unwrap().embedding);
 
+    let start = Instant::now();
     let mut docs: Vec<RelatedDoc> = sqlx::query_as(MACRO_DOCS_QUERY)
         .bind(embedding)
         .bind(MACRO_EMB_DISTANCE)
         .bind(MACRO_EMB_DOC_LIMIT)
         .bind(MACRO_EMB_SEC_MIN_LENGTH)
         .fetch_all(pool)
         .await?;
+    request_meta.search_duration = Some(start.elapsed());
 
     let duplicate_titles: Vec<String> = docs
         .iter()
@@ -108,44 +117,60 @@ pub async fn get_related_full_docs(
     client: &Client<OpenAIConfig>,
     pool: &SupaPool,
     prompt: String,
+    request_meta: &mut AIHelpRequestMeta,
 ) -> Result<Vec<RelatedDoc>, AIError> {
+    request_meta.embedding_model = Some(EMBEDDING_MODEL);
+
     let embedding_req = CreateEmbeddingRequestArgs::default()
         .model(EMBEDDING_MODEL)
         .input(prompt)
         .build()?;
+    let start = Instant::now();
     let embedding_res = client.embeddings().create(embedding_req).await?;
+    request_meta.embedding_duration = Some(start.elapsed());
 
     let embedding =
         pgvector::Vector::from(embedding_res.data.into_iter().next().unwrap().embedding);
+    let start = Instant::now();
     let docs: Vec<RelatedDoc> = sqlx::query_as(FULL_DOCS_QUERY)
         .bind(embedding)
         .bind(FULL_EMB_DISTANCE)
         .bind(FULL_EMB_DOC_LIMIT)
         .bind(FULL_EMB_SEC_MIN_LENGTH)
         .fetch_all(pool)
         .await?;
+    request_meta.search_duration = Some(start.elapsed());
+
     Ok(docs)
 }
 
 pub async fn get_related_docs(
     client: &Client<OpenAIConfig>,
     pool: &SupaPool,
     prompt: String,
+    request_meta: &mut AIHelpRequestMeta,
 ) -> Result<Vec<RelatedDoc>, AIError> {
+    request_meta.embedding_model = Some(EMBEDDING_MODEL);
+
     let embedding_req = CreateEmbeddingRequestArgs::default()
         .model(EMBEDDING_MODEL)
         .input(prompt)
         .build()?;
+    let start = Instant::now();
     let embedding_res = client.embeddings().create(embedding_req).await?;
+    request_meta.embedding_duration = Some(start.elapsed());
 
     let embedding =
         pgvector::Vector::from(embedding_res.data.into_iter().next().unwrap().embedding);
+    let start = Instant::now();
     let docs: Vec<RelatedDoc> = sqlx::query_as(DEFAULT_QUERY)
         .bind(embedding)
         .bind(DEFAULT_EMB_DISTANCE)
         .bind(DEFAULT_EMB_DOC_LIMIT)
         .bind(DEFAULT_EMB_SEC_MIN_LENGTH)
         .fetch_all(pool)
         .await?;
+    request_meta.search_duration = Some(start.elapsed());
+
     Ok(docs)
 }

src/ai/help.rs

@@ -1,4 +1,4 @@
-use std::time::{Duration, Instant};
+use std::time::Duration;
 
 use async_openai::{
     config::OpenAIConfig,
@@ -39,7 +39,9 @@ pub struct AIHelpRequest {
 pub struct AIHelpRequestMeta {
     pub query_len: Option<usize>,
     pub context_len: Option<usize>,
+    pub embedding_duration: Option<Duration>,
     pub search_duration: Option<Duration>,
+    pub embedding_model: Option<&'static str>,
     pub model: Option<&'static str>,
     pub sources: Option<Vec<RefDoc>>,
 }
@@ -95,13 +97,23 @@ pub async fn prepare_ai_help_req(
         .ok_or(AIError::NoUserPrompt)?;
     request_meta.query_len = Some(last_user_message.len());
 
-    let start = Instant::now();
     let related_docs = if config.full_doc {
-        get_related_macro_docs(client, pool, last_user_message.replace('\n', " ")).await?
+        get_related_macro_docs(
+            client,
+            pool,
+            last_user_message.replace('\n', " "),
+            request_meta,
+        )
+        .await?
     } else {
-        get_related_docs(client, pool, last_user_message.replace('\n', " ")).await?
+        get_related_docs(
+            client,
+            pool,
+            last_user_message.replace('\n', " "),
+            request_meta,
+        )
+        .await?
     };
-    request_meta.search_duration = Some(start.elapsed());
 
     let mut context = vec![];
     let mut refs = vec![];

src/api/ai_help.rs

@@ -1,4 +1,7 @@
-use std::{future, time::Instant};
+use std::{
+    future,
+    time::{Duration, Instant},
+};
 
 use actix_identity::Identity;
 use actix_web::{
@@ -503,15 +506,13 @@ pub async fn ai_help(
                                     message_id,
                                     parent_id,
                                     created_at: Some(created_at.naive_utc()),
-                                    search_duration: default_meta_big_int(
-                                        ai_help_req_meta.search_duration.map(|d| d.as_millis()),
-                                    ),
-                                    response_duration: default_meta_big_int(Some(
-                                        response_duration.as_millis(),
-                                    )),
+                                    embedding_duration: default_meta_duration(ai_help_req_meta.embedding_duration),
+                                    search_duration: default_meta_duration(ai_help_req_meta.search_duration),
+                                    response_duration: default_meta_duration(Some(response_duration)),
                                     query_len: default_meta_big_int(ai_help_req_meta.query_len),
                                     context_len: default_meta_big_int(ai_help_req_meta.context_len),
                                     response_len: default_meta_big_int(Some(context.len)),
+                                    embedding_model: ai_help_req_meta.embedding_model.unwrap_or_default(),
                                     model: ai_help_req_meta.model.unwrap_or(""),
                                     status,
                                     sources: ai_help_req_meta.sources.as_ref().map(|sources| {
@@ -537,11 +538,11 @@ pub async fn ai_help(
                     chat_id,
                     message_id,
                     parent_id,
-                    search_duration: default_meta_big_int(
-                        ai_help_req_meta.search_duration.map(|d| d.as_millis()),
-                    ),
+                    embedding_duration: default_meta_duration(ai_help_req_meta.embedding_duration),
+                    search_duration: default_meta_duration(ai_help_req_meta.search_duration),
                     query_len: default_meta_big_int(ai_help_req_meta.query_len),
                     context_len: default_meta_big_int(ai_help_req_meta.context_len),
+                    embedding_model: ai_help_req_meta.embedding_model.unwrap_or_default(),
                     model: ai_help_req_meta.model.unwrap_or(""),
                     status: (&e).into(),
                     sources: ai_help_req_meta
@@ -716,3 +717,7 @@ fn qa_check_for_error_trigger(
 fn default_meta_big_int(value: Option<impl TryInto<i64>>) -> Option<i64> {
     value.and_then(|v| v.try_into().ok())
 }
+
+fn default_meta_duration(duration: Option<Duration>) -> Option<i64> {
+    default_meta_big_int(duration.map(|d| d.as_millis()))
+}

src/db/model.rs

@@ -327,7 +327,9 @@ pub struct AiHelpMessageMetaInsert<'a> {
     pub parent_id: Option<Uuid>,
     /// Timestamp at which the message failed or finished.
     pub created_at: Option<NaiveDateTime>,
-    /// Time it took to search related content in milliseconds.
+    /// Time it took to generate the embedding in milliseconds.
+    pub embedding_duration: Option<i64>,
+    /// Time it took to search using the embedding in milliseconds.
     pub search_duration: Option<i64>,
     /// Time it took to generate the answer in milliseconds.
     pub response_duration: Option<i64>,
@@ -337,6 +339,8 @@ pub struct AiHelpMessageMetaInsert<'a> {
     pub context_len: Option<i64>,
     /// Length of LLM's reply in bytes.
     pub response_len: Option<i64>,
+    /// Model used to generate the embedding.
+    pub embedding_model: &'a str,
     /// Model used to generate the answer.
     pub model: &'a str,
     /// Status of the message.

src/db/schema.rs

@@ -126,6 +126,8 @@ diesel::table! {
         model -> Text,
         status -> AiHelpMessageStatus,
         sources -> Jsonb,
+        embedding_duration -> Nullable<Int8>,
+        embedding_model -> Text,
     }
 }