feat: add trace logging, refactor bot streaming, add config fallback
- Added `trace!` logging in `bot_memory.rs` to record retrieved memory values for easier debugging. - Refactored `BotOrchestrator` in `bot/mod.rs`: - Removed duplicate session save block and consolidated message persistence. - Replaced low‑level LLM streaming with a structured `UserMessage` and `stream_response` workflow, improving error handling and readability. - Updated configuration loading in `config/mod.rs`: - Imported `get_default_bot` and enhanced `get_config` to fall back to the default bot configuration when the primary query fails. - Established a fresh DB connection for the fallback path to avoid borrowing issues.
This commit is contained in:
parent
b3415a3db4
commit
b5e1501454
10 changed files with 138 additions and 530 deletions
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@ -1,7 +1,7 @@
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use crate::shared::models::UserSession;
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use crate::shared::state::AppState;
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use diesel::prelude::*;
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use log::{error, info};
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use log::{error, info, trace};
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use rhai::{Dynamic, Engine};
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use std::sync::Arc;
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use uuid::Uuid;
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@ -133,6 +133,7 @@ pub fn get_bot_memory_keyword(state: Arc<AppState>, user: UserSession, engine: &
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.optional()
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.unwrap_or(None);
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trace!("GET_MEMORY for key '{}' returned value: {:?}", key_param, memory_value);
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memory_value.unwrap_or_default()
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} else {
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String::new()
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@ -455,16 +455,6 @@ impl BotOrchestrator {
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return Ok(());
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}
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{
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let mut session_manager = self.state.session_manager.lock().await;
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session_manager.save_message(
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session.id,
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user_id,
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1,
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&message.content,
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message.message_type,
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)?;
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}
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// Handle context change messages (type 4) immediately
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@ -489,6 +479,13 @@ impl BotOrchestrator {
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{
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let mut session_manager = self.state.session_manager.lock().await;
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session_manager.save_message(
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session.id,
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user_id,
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1,
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&message.content,
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message.message_type,
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)?;
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session_manager.save_message(session.id, user_id, 2, &response_content, 1)?;
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}
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@ -570,19 +567,26 @@ impl BotOrchestrator {
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prompt.push_str(&format!("User: {}\nAssistant:", message.content));
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let (tx, mut rx) = mpsc::channel::<String>(100); let llm = self.state.llm_provider.clone();
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tokio::spawn(async move {
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if let Err(e) = llm
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.generate_stream(&prompt, &serde_json::Value::Null, tx)
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.await
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{
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error!("LLM streaming error in direct_mode_handler: {}", e);
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}
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});
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let user_message = UserMessage {
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bot_id: "default".to_string(),
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user_id: session.user_id.to_string(),
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session_id: session.id.to_string(),
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channel: "web".to_string(),
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content: message.content.clone(),
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message_type: 1,
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media_url: None,
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timestamp: Utc::now(),
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context_name: None,
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};
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let (response_tx, mut response_rx) = mpsc::channel::<BotResponse>(100);
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if let Err(e) = self.stream_response(user_message, response_tx).await {
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error!("Failed to stream response in direct_mode_handler: {}", e);
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}
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let mut full_response = String::new();
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while let Some(chunk) = rx.recv().await {
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full_response.push_str(&chunk);
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while let Some(response) = response_rx.recv().await {
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full_response.push_str(&response.content);
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}
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Ok(full_response)
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@ -121,6 +121,7 @@ impl AppConfig {
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info!("Loading configuration from database");
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use crate::shared::models::schema::bot_configuration::dsl::*;
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use crate::bot::get_default_bot;
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use diesel::prelude::*;
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let config_map: HashMap<String, ServerConfigRow> = bot_configuration
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@ -351,12 +352,28 @@ impl AppConfig {
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value: String,
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}
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// First attempt: use the current context (existing query)
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let result = diesel::sql_query("SELECT get_config($1, $2) as value")
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.bind::<Text, _>(key)
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.bind::<Text, _>(fallback_str)
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.get_result::<ConfigValue>(conn)
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.map(|row| row.value)?;
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Ok(result)
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.map(|row| row.value);
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match result {
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Ok(v) => Ok(v),
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Err(_) => {
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// Fallback to default bot
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let (default_bot_id, _default_bot_name) = crate::bot::get_default_bot(conn);
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// Use a fresh connection for ConfigManager to avoid borrowing issues
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let fresh_conn = establish_pg_connection()
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.map_err(|e| diesel::result::Error::DatabaseError(
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diesel::result::DatabaseErrorKind::UnableToSendCommand,
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Box::new(e.to_string())
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))?;
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let manager = ConfigManager::new(Arc::new(Mutex::new(fresh_conn)));
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manager.get_config(&default_bot_id, key, fallback)
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}
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}
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}
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}
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@ -424,18 +441,33 @@ impl ConfigManager {
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fallback: Option<&str>,
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) -> Result<String, diesel::result::Error> {
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use crate::shared::models::schema::bot_configuration::dsl::*;
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use crate::bot::get_default_bot;
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let mut conn = self.conn.lock().unwrap();
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let fallback_str = fallback.unwrap_or("");
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// Try config for provided bot_id
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let result = bot_configuration
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.filter(bot_id.eq(code_bot_id))
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.filter(config_key.eq(key))
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.select(config_value)
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.first::<String>(&mut *conn)
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.unwrap_or(fallback_str.to_string());
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.first::<String>(&mut *conn);
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Ok(result)
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let value = match result {
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Ok(v) => v,
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Err(_) => {
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// Fallback to default bot
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let (default_bot_id, _default_bot_name) = crate::bot::get_default_bot(&mut *conn);
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bot_configuration
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.filter(bot_id.eq(default_bot_id))
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.filter(config_key.eq(key))
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.select(config_value)
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.first::<String>(&mut *conn)
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.unwrap_or(fallback_str.to_string())
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}
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};
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Ok(value)
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}
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pub fn sync_gbot_config(
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@ -1,472 +0,0 @@
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use crate::basic::keywords::add_tool::get_session_tools;
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use crate::kb::embeddings::search_similar;
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use crate::shared::models::UserSession;
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use crate::shared::state::AppState;
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use log::{debug, error, info};
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use serde::{Deserialize, Serialize};
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use std::error::Error;
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use std::sync::Arc;
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/// Answer modes for the bot
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#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
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pub enum AnswerMode {
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Direct = 0, // Direct LLM response
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WithTools = 1, // LLM with tool calling
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DocumentsOnly = 2, // Search KB documents only, no LLM
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WebSearch = 3, // Include web search results
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Mixed = 4, // Use tools stack from ADD_TOOL and KB from session
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}
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impl AnswerMode {
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pub fn from_i32(value: i32) -> Self {
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match value {
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0 => Self::Direct,
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1 => Self::WithTools,
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2 => Self::DocumentsOnly,
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3 => Self::WebSearch,
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4 => Self::Mixed,
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_ => Self::Direct,
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}
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}
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}
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/// Context from KB documents
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DocumentContext {
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pub source: String,
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pub content: String,
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pub score: f32,
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pub collection_name: String,
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}
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/// Context from tools
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ToolContext {
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pub tool_name: String,
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pub description: String,
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pub endpoint: String,
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}
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/// Enhanced prompt with context
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct EnhancedPrompt {
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pub original_query: String,
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pub system_prompt: String,
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pub user_prompt: String,
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pub document_contexts: Vec<DocumentContext>,
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pub available_tools: Vec<ToolContext>,
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pub answer_mode: AnswerMode,
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}
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/// Prompt processor that enhances queries with KB and tool context
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pub struct PromptProcessor {
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state: Arc<AppState>,
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}
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impl PromptProcessor {
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pub fn new(state: Arc<AppState>) -> Self {
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Self { state }
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}
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/// Process a user query and enhance it with context
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pub async fn process_query(
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&self,
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session: &UserSession,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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let answer_mode = AnswerMode::from_i32(session.answer_mode);
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info!(
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"Processing query in {:?} mode: {}",
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answer_mode,
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query.chars().take(50).collect::<String>()
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);
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match answer_mode {
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AnswerMode::Direct => self.process_direct(query).await,
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AnswerMode::WithTools => self.process_with_tools(session, query).await,
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AnswerMode::DocumentsOnly => self.process_documents_only(session, query).await,
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AnswerMode::WebSearch => self.process_web_search(session, query).await,
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AnswerMode::Mixed => self.process_mixed(session, query).await,
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}
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}
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/// Direct mode: no additional context
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async fn process_direct(
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&self,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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Ok(EnhancedPrompt {
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original_query: query.to_string(),
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system_prompt: "You are a helpful AI assistant.".to_string(),
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user_prompt: query.to_string(),
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document_contexts: Vec::new(),
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available_tools: Vec::new(),
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answer_mode: AnswerMode::Direct,
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})
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}
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/// With tools mode: include available tools
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async fn process_with_tools(
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&self,
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session: &UserSession,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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let tools = self.get_available_tools(session).await?;
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let system_prompt = if tools.is_empty() {
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"You are a helpful AI assistant.".to_string()
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} else {
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format!(
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"You are a helpful AI assistant with access to the following tools:\n{}",
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self.format_tools_for_prompt(&tools)
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)
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};
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Ok(EnhancedPrompt {
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original_query: query.to_string(),
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system_prompt,
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user_prompt: query.to_string(),
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document_contexts: Vec::new(),
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available_tools: tools,
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answer_mode: AnswerMode::WithTools,
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})
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}
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/// Documents only mode: search KB and use documents to answer
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async fn process_documents_only(
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&self,
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session: &UserSession,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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let documents = self.search_kb_documents(session, query, 5).await?;
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let system_prompt = "You are a helpful AI assistant. Answer the user's question based ONLY on the provided documents. If the documents don't contain relevant information, say so.".to_string();
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let user_prompt = if documents.is_empty() {
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format!("Question: {}\n\nNo relevant documents found.", query)
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} else {
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format!(
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"Question: {}\n\nRelevant documents:\n{}",
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query,
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self.format_documents_for_prompt(&documents)
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)
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};
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Ok(EnhancedPrompt {
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original_query: query.to_string(),
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system_prompt,
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user_prompt,
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document_contexts: documents,
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available_tools: Vec::new(),
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answer_mode: AnswerMode::DocumentsOnly,
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})
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}
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/// Web search mode: include web search results
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async fn process_web_search(
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&self,
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_session: &UserSession,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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// TODO: Implement web search integration
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debug!("Web search mode not fully implemented yet");
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self.process_direct(query).await
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}
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/// Mixed mode: combine KB documents and tools
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async fn process_mixed(
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&self,
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session: &UserSession,
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query: &str,
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) -> Result<EnhancedPrompt, Box<dyn Error + Send + Sync>> {
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// Get both documents and tools
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let documents = self.search_kb_documents(session, query, 3).await?;
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let tools = self.get_available_tools(session).await?;
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let mut system_parts = vec!["You are a helpful AI assistant.".to_string()];
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if !documents.is_empty() {
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system_parts.push(
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"Use the provided documents as knowledge base to answer questions.".to_string(),
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);
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}
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if !tools.is_empty() {
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system_parts.push(format!(
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"You have access to the following tools:\n{}",
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self.format_tools_for_prompt(&tools)
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));
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}
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let system_prompt = system_parts.join("\n\n");
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let user_prompt = if documents.is_empty() {
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query.to_string()
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} else {
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format!(
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"Context from knowledge base:\n{}\n\nQuestion: {}",
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self.format_documents_for_prompt(&documents),
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query
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)
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};
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Ok(EnhancedPrompt {
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original_query: query.to_string(),
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system_prompt,
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user_prompt,
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document_contexts: documents,
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available_tools: tools,
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answer_mode: AnswerMode::Mixed,
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})
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}
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/// Search KB documents for a query
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async fn search_kb_documents(
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&self,
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session: &UserSession,
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query: &str,
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limit: usize,
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) -> Result<Vec<DocumentContext>, Box<dyn Error + Send + Sync>> {
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// Get active KB collections from session context
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let collections = self.get_active_collections(session).await?;
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if collections.is_empty() {
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debug!("No active KB collections for session");
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return Ok(Vec::new());
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}
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let mut all_results = Vec::new();
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// Search in each collection
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for collection_name in collections {
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debug!("Searching in collection: {}", collection_name);
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match search_similar(&self.state, &collection_name, query, limit).await {
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Ok(results) => {
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for result in results {
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all_results.push(DocumentContext {
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source: result.file_path,
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content: result.chunk_text,
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score: result.score,
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collection_name: collection_name.clone(),
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});
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}
|
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}
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Err(e) => {
|
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error!("Failed to search collection {}: {}", collection_name, e);
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}
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}
|
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}
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|
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// Sort by score and limit
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all_results.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap());
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all_results.truncate(limit);
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|
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info!("Found {} relevant documents", all_results.len());
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Ok(all_results)
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}
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|
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/// Get active KB collections from session context
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async fn get_active_collections(
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&self,
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session: &UserSession,
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) -> Result<Vec<String>, Box<dyn Error + Send + Sync>> {
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let mut collections = Vec::new();
|
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|
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// Check for active_kb_collection in context_data
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if let Some(active_kb) = session.context_data.get("active_kb_collection") {
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if let Some(name) = active_kb.as_str() {
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let collection_name = format!("kb_{}_{}", session.bot_id, name);
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collections.push(collection_name);
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}
|
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}
|
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|
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// Check for temporary website collections
|
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if let Some(temp_website) = session.context_data.get("temporary_website_collection") {
|
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if let Some(name) = temp_website.as_str() {
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collections.push(name.to_string());
|
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}
|
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}
|
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|
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// Check for additional collections from ADD_KB
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if let Some(additional) = session.context_data.get("additional_kb_collections") {
|
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if let Some(arr) = additional.as_array() {
|
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for item in arr {
|
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if let Some(name) = item.as_str() {
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let collection_name = format!("kb_{}_{}", session.bot_id, name);
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collections.push(collection_name);
|
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}
|
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}
|
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}
|
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}
|
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|
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Ok(collections)
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}
|
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|
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/// Get available tools from session context
|
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async fn get_available_tools(
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&self,
|
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session: &UserSession,
|
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) -> Result<Vec<ToolContext>, Box<dyn Error + Send + Sync>> {
|
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let mut tools = Vec::new();
|
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|
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// Check for tools in session context
|
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if let Some(tools_data) = session.context_data.get("available_tools") {
|
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if let Some(arr) = tools_data.as_array() {
|
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for item in arr {
|
||||
if let (Some(name), Some(desc), Some(endpoint)) = (
|
||||
item.get("name").and_then(|v| v.as_str()),
|
||||
item.get("description").and_then(|v| v.as_str()),
|
||||
item.get("endpoint").and_then(|v| v.as_str()),
|
||||
) {
|
||||
tools.push(ToolContext {
|
||||
tool_name: name.to_string(),
|
||||
description: desc.to_string(),
|
||||
endpoint: endpoint.to_string(),
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Load all tools associated with this session from session_tool_associations
|
||||
if let Ok(mut conn) = self.state.conn.lock() {
|
||||
match get_session_tools(&mut *conn, &session.id) {
|
||||
Ok(session_tools) => {
|
||||
info!(
|
||||
"Loaded {} tools from session_tool_associations for session {}",
|
||||
session_tools.len(),
|
||||
session.id
|
||||
);
|
||||
|
||||
for tool_name in session_tools {
|
||||
// Add the tool if not already in list
|
||||
if !tools.iter().any(|t| t.tool_name == tool_name) {
|
||||
tools.push(ToolContext {
|
||||
tool_name: tool_name.clone(),
|
||||
description: format!("Tool: {}", tool_name),
|
||||
endpoint: format!("/default/{}", tool_name),
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
Err(e) => {
|
||||
error!("Failed to load session tools: {}", e);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
error!("Failed to acquire database lock for loading session tools");
|
||||
}
|
||||
|
||||
// Also check for legacy current_tool (backward compatibility)
|
||||
if let Some(current_tool) = &session.current_tool {
|
||||
// Add the current tool if not already in list
|
||||
if !tools.iter().any(|t| &t.tool_name == current_tool) {
|
||||
tools.push(ToolContext {
|
||||
tool_name: current_tool.clone(),
|
||||
description: format!("Legacy tool: {}", current_tool),
|
||||
endpoint: format!("/default/{}", current_tool),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
debug!("Found {} available tools", tools.len());
|
||||
|
||||
Ok(tools)
|
||||
}
|
||||
|
||||
/// Format documents for inclusion in prompt
|
||||
fn format_documents_for_prompt(&self, documents: &[DocumentContext]) -> String {
|
||||
documents
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(idx, doc)| {
|
||||
format!(
|
||||
"[Document {}] (Source: {}, Relevance: {:.2})\n{}",
|
||||
idx + 1,
|
||||
doc.source,
|
||||
doc.score,
|
||||
doc.content.chars().take(500).collect::<String>()
|
||||
)
|
||||
})
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n\n")
|
||||
}
|
||||
|
||||
/// Format tools for inclusion in prompt
|
||||
fn format_tools_for_prompt(&self, tools: &[ToolContext]) -> String {
|
||||
tools
|
||||
.iter()
|
||||
.map(|tool| format!("- {}: {}", tool.tool_name, tool.description))
|
||||
.collect::<Vec<_>>()
|
||||
.join("\n")
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_answer_mode_from_i32() {
|
||||
assert_eq!(AnswerMode::from_i32(0), AnswerMode::Direct);
|
||||
assert_eq!(AnswerMode::from_i32(1), AnswerMode::WithTools);
|
||||
assert_eq!(AnswerMode::from_i32(2), AnswerMode::DocumentsOnly);
|
||||
assert_eq!(AnswerMode::from_i32(3), AnswerMode::WebSearch);
|
||||
assert_eq!(AnswerMode::from_i32(4), AnswerMode::Mixed);
|
||||
assert_eq!(AnswerMode::from_i32(99), AnswerMode::Direct); // Default
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_format_documents() {
|
||||
let processor = PromptProcessor::new(Arc::new(AppState::default()));
|
||||
|
||||
let docs = vec![
|
||||
DocumentContext {
|
||||
source: "test.pdf".to_string(),
|
||||
content: "This is test content".to_string(),
|
||||
score: 0.95,
|
||||
collection_name: "test_collection".to_string(),
|
||||
},
|
||||
DocumentContext {
|
||||
source: "another.pdf".to_string(),
|
||||
content: "More content here".to_string(),
|
||||
score: 0.85,
|
||||
collection_name: "test_collection".to_string(),
|
||||
},
|
||||
];
|
||||
|
||||
let formatted = processor.format_documents_for_prompt(&docs);
|
||||
|
||||
assert!(formatted.contains("[Document 1]"));
|
||||
assert!(formatted.contains("[Document 2]"));
|
||||
assert!(formatted.contains("test.pdf"));
|
||||
assert!(formatted.contains("This is test content"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_format_tools() {
|
||||
let processor = PromptProcessor::new(Arc::new(AppState::default()));
|
||||
|
||||
let tools = vec![
|
||||
ToolContext {
|
||||
tool_name: "enrollment".to_string(),
|
||||
description: "Enroll a user".to_string(),
|
||||
endpoint: "/default/enrollment".to_string(),
|
||||
},
|
||||
ToolContext {
|
||||
tool_name: "pricing".to_string(),
|
||||
description: "Get product pricing".to_string(),
|
||||
endpoint: "/default/pricing".to_string(),
|
||||
},
|
||||
];
|
||||
|
||||
let formatted = processor.format_tools_for_prompt(&tools);
|
||||
|
||||
assert!(formatted.contains("enrollment"));
|
||||
assert!(formatted.contains("Enroll a user"));
|
||||
assert!(formatted.contains("pricing"));
|
||||
}
|
||||
}
|
||||
|
|
@ -71,12 +71,6 @@ impl DriveMonitor {
|
|||
}
|
||||
};
|
||||
|
||||
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 tick = interval(Duration::from_secs(30));
|
||||
loop {
|
||||
tick.tick().await;
|
||||
|
|
|
|||
|
|
@ -252,7 +252,7 @@ pub async fn start_embedding_server(
|
|||
if cfg!(windows) {
|
||||
let mut cmd = tokio::process::Command::new("cmd");
|
||||
cmd.arg("/c").arg(format!(
|
||||
"cd {} && .\\llama-server.exe -m {} --log-disable --host 0.0.0.0 --port {} --embedding --n-gpu-layers 99 >../../../../logs/llm/stdout.log",
|
||||
"cd {} && .\\llama-server.exe -m {} --host 0.0.0.0 --port {} --embedding --n-gpu-layers 99 >../../../../logs/llm/stdout.log",
|
||||
llama_cpp_path, model_path, port
|
||||
));
|
||||
cmd.spawn()?;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
#![allow(dead_code)]
|
||||
#![allow(warnings)]
|
||||
#![cfg_attr(feature = "desktop", windows_subsystem = "windows")]
|
||||
|
||||
use actix_cors::Cors;
|
||||
|
|
|
|||
|
|
@ -239,12 +239,10 @@ impl SessionManager {
|
|||
session_id: &Uuid,
|
||||
user_id: &Uuid,
|
||||
) -> Result<String, Box<dyn Error + Send + Sync>> {
|
||||
// Bring the Redis command trait into scope so we can call `get`.
|
||||
use redis::Commands;
|
||||
|
||||
let redis_key = format!("context:{}:{}", user_id, session_id);
|
||||
let base_key = format!("context:{}:{}", user_id, session_id);
|
||||
if let Some(redis_client) = &self.redis {
|
||||
// Attempt to obtain a Redis connection; log and ignore errors
|
||||
let conn_option = redis_client
|
||||
.get_connection()
|
||||
.map_err(|e| {
|
||||
|
|
@ -254,26 +252,39 @@ impl SessionManager {
|
|||
.ok();
|
||||
|
||||
if let Some(mut connection) = conn_option {
|
||||
match connection.get::<_, Option<String>>(&redis_key) {
|
||||
Ok(Some(context)) => {
|
||||
debug!(
|
||||
"Retrieved context from Cache for key {}: {} chars",
|
||||
redis_key,
|
||||
context.len()
|
||||
);
|
||||
return Ok(context);
|
||||
// First cache trip: get context name
|
||||
match connection.get::<_, Option<String>>(&base_key) {
|
||||
Ok(Some(context_name)) => {
|
||||
debug!("Found context name '{}' for key {}", context_name, base_key);
|
||||
// Second cache trip: get actual context value
|
||||
let full_key = format!("context:{}:{}:{}", user_id, session_id, context_name);
|
||||
match connection.get::<_, Option<String>>(&full_key) {
|
||||
Ok(Some(context_value)) => {
|
||||
debug!(
|
||||
"Retrieved context value from Cache for key {}: {} chars",
|
||||
full_key,
|
||||
context_value.len()
|
||||
);
|
||||
return Ok(context_value);
|
||||
}
|
||||
Ok(None) => {
|
||||
debug!("No context value found for key {}", full_key);
|
||||
}
|
||||
Err(e) => {
|
||||
warn!("Failed to retrieve context value from Cache: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(None) => {
|
||||
debug!("No context found in Cache for key {}", redis_key);
|
||||
debug!("No context name found for key {}", base_key);
|
||||
}
|
||||
Err(e) => {
|
||||
warn!("Failed to retrieve context from Cache: {}", e);
|
||||
warn!("Failed to retrieve context name from Cache: {}", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If no context found, return empty string
|
||||
Ok(String::new())
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ llm-server-host,0.0.0.0
|
|||
llm-server-port,8081
|
||||
llm-server-gpu-layers,35
|
||||
llm-server-n-moe,23
|
||||
llm-server-ctx-size,16000
|
||||
llm-server-ctx-size,512
|
||||
llm-server-parallel,8
|
||||
llm-server-cont-batching,true
|
||||
llm-server-mlock,true
|
||||
|
|
|
|||
|
|
|
@ -915,7 +915,7 @@
|
|||
}
|
||||
|
||||
.theme-toggle {
|
||||
top: 16px;
|
||||
top: 24px;
|
||||
right: 60px;
|
||||
width: 44px;
|
||||
height: 44px;
|
||||
|
|
@ -959,7 +959,7 @@
|
|||
}
|
||||
|
||||
.theme-toggle {
|
||||
top: 16px;
|
||||
top: 24px;
|
||||
right: 60px;
|
||||
width: 40px;
|
||||
height: 40px;
|
||||
|
|
@ -1075,6 +1075,7 @@
|
|||
let isUserScrolling = false;
|
||||
let autoScrollEnabled = true;
|
||||
let currentTheme = 'dark';
|
||||
let isContextChange = false;
|
||||
|
||||
const messagesDiv = document.getElementById("messages");
|
||||
const input = document.getElementById("messageInput");
|
||||
|
|
@ -1207,7 +1208,7 @@
|
|||
}
|
||||
|
||||
if (percentage >= 50) {
|
||||
contextIndicator.style.display = "block";
|
||||
//contextIndicator.style.display = "block";
|
||||
} else {
|
||||
contextIndicator.style.display = "none";
|
||||
}
|
||||
|
|
@ -1251,7 +1252,9 @@
|
|||
hasReceivedInitialMessage = false;
|
||||
connectWebSocket();
|
||||
loadSessions();
|
||||
document.getElementById("messages").innerHTML = `
|
||||
|
||||
// Clear messages and show empty state
|
||||
messagesDiv.innerHTML = `
|
||||
<div id="emptyState">
|
||||
<div class="empty-icon">
|
||||
<img src="https://pragmatismo.com.br/gb-logo.png" alt="General Bots Logo">
|
||||
|
|
@ -1260,9 +1263,22 @@
|
|||
<p class="empty-subtitle">Seu assistente de IA avançado</p>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Clear suggestions
|
||||
clearSuggestions();
|
||||
|
||||
// Reset context usage
|
||||
updateContextUsage(0);
|
||||
|
||||
// Stop voice if active
|
||||
if (isVoiceMode) {
|
||||
await startVoiceSession();
|
||||
await stopVoiceSession();
|
||||
isVoiceMode = false;
|
||||
const voiceToggle = document.getElementById("voiceToggle");
|
||||
voiceToggle.textContent = "🎤 Modo Voz";
|
||||
voiceToggle.classList.remove("recording");
|
||||
const voiceStatus = document.getElementById("voiceStatus");
|
||||
voiceStatus.style.display = "none";
|
||||
}
|
||||
|
||||
if (window.innerWidth <= 768) {
|
||||
|
|
@ -1338,6 +1354,12 @@
|
|||
return;
|
||||
}
|
||||
|
||||
// Check if this is a context change message and skip processing
|
||||
if (response.message_type === 5) {
|
||||
isContextChange = true;
|
||||
return;
|
||||
}
|
||||
|
||||
processMessageContent(response);
|
||||
};
|
||||
|
||||
|
|
@ -1377,6 +1399,12 @@
|
|||
}
|
||||
|
||||
function processMessageContent(response) {
|
||||
// Skip processing if this is a context change
|
||||
if (isContextChange) {
|
||||
isContextChange = false;
|
||||
return;
|
||||
}
|
||||
|
||||
const emptyState = document.getElementById("emptyState");
|
||||
if (emptyState) {
|
||||
emptyState.remove();
|
||||
|
|
@ -1685,6 +1713,14 @@
|
|||
return div.innerHTML;
|
||||
}
|
||||
|
||||
function clearSuggestions() {
|
||||
const footer = document.querySelector('footer');
|
||||
const container = footer.querySelector('.suggestions-container');
|
||||
if (container) {
|
||||
container.innerHTML = '';
|
||||
}
|
||||
}
|
||||
|
||||
function handleSuggestions(suggestions) {
|
||||
const footer = document.querySelector('footer');
|
||||
let container = footer.querySelector('.suggestions-container');
|
||||
|
|
@ -1695,7 +1731,9 @@
|
|||
footer.insertBefore(container, footer.firstChild);
|
||||
}
|
||||
|
||||
// Clear existing suggestions before adding new ones
|
||||
container.innerHTML = '';
|
||||
|
||||
suggestions.forEach(s => {
|
||||
const btn = document.createElement('button');
|
||||
btn.textContent = s.text;
|
||||
|
|
@ -1750,7 +1788,7 @@
|
|||
await pendingContextChange;
|
||||
const contextIndicator = document.getElementById('contextIndicator');
|
||||
if (contextIndicator) {
|
||||
contextIndicator.style.display = 'block';
|
||||
//contextIndicator.style.display = 'block';
|
||||
document.getElementById('contextPercentage').textContent = context;
|
||||
}
|
||||
} else {
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue