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refactor(automation, llm): simplify service init and prompt handling

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- Remove `scripts_dir` from `AutomationService` and its constructor, as it was unused.
- Drop LLM and embedding server readiness checks; the service now only schedules periodic tasks.
- Increase the health‑check interval from 5 seconds to 15 seconds for reduced load.
- Streamline the LLM keyword prompt to a concise `"User: {}"` format, removing verbose boilerplate.
- Remove unnecessary logging and LLM cache handling code from the bot orchestrator, cleaning up unused environment variable checks and cache queries.
This commit is contained in:
Rodrigo Rodriguez (Pragmatismo) 2025-11-03 08:23:03 -03:00
parent a5bfda4d09
commit b033be3e64
10 changed files with 37 additions and 154 deletions
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45
src/automation/mod.rs
View file

@ -12,19 +12,13 @@ use tokio::time::Duration;
use uuid::Uuid;
pub struct AutomationService {
state: Arc<AppState>,
scripts_dir: String,
state: Arc<AppState>
}
impl AutomationService {
pub fn new(state: Arc<AppState>, scripts_dir: &str) -> Self {
trace!(
"Creating AutomationService with scripts_dir='{}'",
scripts_dir
);
pub fn new(state: Arc<AppState>) -> Self {
Self {
state,
scripts_dir: scripts_dir.to_string(),
state
}
}
@ -34,39 +28,8 @@ impl AutomationService {
tokio::task::spawn_local({
let service = service.clone();
async move {
// Check if llama servers are ready before starting
let config_manager = ConfigManager::new(Arc::clone(&service.state.conn));
let default_bot_id = {
let mut conn = service.state.conn.lock().unwrap();
bots.filter(name.eq("default"))
.select(id)
.first::<uuid::Uuid>(&mut *conn)
.unwrap_or_else(|_| uuid::Uuid::nil())
};
let llm_url = match config_manager.get_config(&default_bot_id, "llm-url", None) {
Ok(url) => url,
Err(e) => {
error!("Failed to get llm-url config: {}", e);
return;
}
};
let embedding_url = match config_manager.get_config(&default_bot_id, "embedding-url", None) {
Ok(url) => url,
Err(e) => {
error!("Failed to get embedding-url config: {}", e);
return;
}
};
if !crate::llm::local::is_server_running(&llm_url).await ||
!crate::llm::local::is_server_running(&embedding_url).await {
trace!("LLM servers not ready - llm: {}, embedding: {}", llm_url, embedding_url);
return;
}
let mut interval = tokio::time::interval(Duration::from_secs(5));
let mut interval = tokio::time::interval(Duration::from_secs(15));
let mut last_check = Utc::now();
loop {
interval.tick().await;

10
src/basic/keywords/llm_keyword.rs
View file

@ -70,14 +70,7 @@ pub fn llm_keyword(state: Arc<AppState>, _user: UserSession, engine: &mut Engine
/// You can change the style/structure here to guide the model's behavior.
fn build_llm_prompt(user_text: &str) -> String {
format!(
"You are a AI assistant in form of KEYWORD called LLM
running inside a General Bots BASIC environment.
Task: Process and respond concisely to the following call to x = LLM 'prompt' syntax.
---
User Input:
{}
---
Respond clearly and accurately in the same language as the input.",
"User: {}",
user_text.trim()
)
}
@ -87,7 +80,6 @@ pub async fn execute_llm_generation(
state: Arc<AppState>,
prompt: String,
) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
info!("Starting LLM generation for prompt: '{}'", prompt);
state
.llm_provider

97
src/bot/mod.rs
View file

@ -569,94 +569,23 @@ impl BotOrchestrator {
prompt.push_str(&format!("User: {}\nAssistant:", message.content));
let use_langcache = std::env::var("LLM_CACHE")
.unwrap_or_else(|_| "false".to_string())
.eq_ignore_ascii_case("true");
if use_langcache {
ensure_collection_exists(&self.state, "semantic_cache").await?;
let langcache_client = get_langcache_client()?;
let isolated_question = message.content.trim().to_string();
let question_embeddings = generate_embeddings(vec![isolated_question.clone()]).await?;
let question_embedding = question_embeddings
.get(0)
.ok_or_else(|| "Failed to generate embedding for question")?
.clone();
let search_results = langcache_client
.search("semantic_cache", question_embedding.clone(), 1)
.await?;
if let Some(result) = search_results.first() {
let payload = &result.payload;
if let Some(resp) = payload.get("response").and_then(|v| v.as_str()) {
return Ok(resp.to_string());
let (tx, mut rx) = mpsc::channel::<String>(100); let llm = self.state.llm_provider.clone();
tokio::spawn(async move {
if let Err(e) = llm
.generate_stream(&prompt, &serde_json::Value::Null, tx)
.await
{
error!("LLM streaming error in direct_mode_handler: {}", e);
}
});
let mut full_response = String::new();
while let Some(chunk) = rx.recv().await {
full_response.push_str(&chunk);
}
let response = self
.state
.llm_provider
.generate(&prompt, &serde_json::Value::Null)
.await?;
let point = QdrantPoint {
id: uuid::Uuid::new_v4().to_string(),
vector: question_embedding,
payload: serde_json::json!({
"question": isolated_question,
"prompt": prompt,
"response": response
}),
};
langcache_client
.upsert_points("semantic_cache", vec![point])
.await?;
Ok(response)
} else {
ensure_collection_exists(&self.state, "semantic_cache").await?;
let qdrant_client = get_qdrant_client(&self.state)?;
let embeddings = generate_embeddings(vec![prompt.clone()]).await?;
let embedding = embeddings
.get(0)
.ok_or_else(|| "Failed to generate embedding")?
.clone();
let search_results = qdrant_client
.search("semantic_cache", embedding.clone(), 1)
.await?;
if let Some(result) = search_results.first() {
if let Some(payload) = &result.payload {
if let Some(resp) = payload.get("response").and_then(|v| v.as_str()) {
return Ok(resp.to_string());
}
}
}
let response = self
.state
.llm_provider
.generate(&prompt, &serde_json::Value::Null)
.await?;
let point = QdrantPoint {
id: uuid::Uuid::new_v4().to_string(),
vector: embedding,
payload: serde_json::json!({
"prompt": prompt,
"response": response
}),
};
qdrant_client
.upsert_points("semantic_cache", vec![point])
.await?;
Ok(response)
}
Ok(full_response)
}
pub async fn stream_response(

25
src/main.rs
View file

@ -244,19 +244,6 @@ async fn main() -> std::io::Result<()> {
.map(|n| n.get())
.unwrap_or(4);
let automation_state = app_state.clone();
std::thread::spawn(move || {
let rt = tokio::runtime::Builder::new_current_thread()
.enable_all()
.build()
.expect("Failed to create runtime for automation");
let local = tokio::task::LocalSet::new();
local.block_on(&rt, async move {
let scripts_dir = "work/default.gbai/.gbdialog".to_string();
let automation = AutomationService::new(automation_state, &scripts_dir);
automation.spawn().await.ok();
});
});
// Initialize bot orchestrator and mount all bots
let bot_orchestrator = BotOrchestrator::new(app_state.clone());
@ -271,6 +258,18 @@ async fn main() -> std::io::Result<()> {
.await
.expect("Failed to initialize LLM local server");
let automation_state = app_state.clone();
std::thread::spawn(move || {
let rt = tokio::runtime::Builder::new_current_thread()
.enable_all()
.build()
.expect("Failed to create runtime for automation");
let local = tokio::task::LocalSet::new();
local.block_on(&rt, async move {
let automation = AutomationService::new(automation_state);
automation.spawn().await.ok();
});
});
HttpServer::new(move || {

6
src/session/mod.rs
View file

@ -293,9 +293,9 @@ impl SessionManager {
let mut history: Vec<(String, String)> = Vec::new();
for (other_role, content) in messages {
let role_str = match other_role {
0 => "user".to_string(),
1 => "assistant".to_string(),
2 => "system".to_string(),
1 => "user".to_string(),
2 => "assistant".to_string(),
3 => "system".to_string(),
_ => "unknown".to_string(),
};
history.push((role_str, content));

4
templates/announcements.gbai/announcements.gbdialog/start.bas
View file

@ -1,4 +1,4 @@
let resume1 = GET_BOT_MEMORY("general");
let resume1 = GET_BOT_MEMORY("resume");
let resume2 = GET_BOT_MEMORY("auxiliom");
let resume3 = GET_BOT_MEMORY("toolbix");
@ -14,5 +14,5 @@ ADD_SUGGESTION "auxiliom" AS "What does Auxiliom do?"
ADD_SUGGESTION "toolbix" AS "Show me Toolbix features"
ADD_SUGGESTION "toolbix" AS "How can Toolbix help my business?"
TALK resume1
TALK "You can ask me about any of the announcements or circulars."

4
templates/announcements.gbai/announcements.gbdialog/update-summary.bas
View file

@ -1,8 +1,8 @@
SET_SCHEDULE "* * * * *"
SET_SCHEDULE "*/10 * * * *"
let text = GET "announcements.gbkb/news/news.pdf"
let resume = LLM "Resume this document, in a table (DO NOT THINK) no_think: " + text
let resume = LLM "Resume this document: " + text
SET_BOT_MEMORY "resume", resume

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templates/announcements.gbai/announcements.gbkb/auxiliom/auxiliom.pdf
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templates/announcements.gbai/announcements.gbkb/news/news.pdf
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templates/announcements.gbai/announcements.gbkb/toolbix/toolbix.pdf
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