gitdataai/libs/agent/chat/message_builder.rs

use models::projects::project_skill;
use sea_orm::*;

use super::context::RoomMessageContext;
use super::{AiRequest, Mention};
use crate::client::types::ChatRequestMessage;
use crate::compact::{CompactConfig, CompactService};
use crate::embed::EmbedService;
use crate::error::Result;
use crate::perception::{PerceptionService, SkillEntry};

pub struct MessageBuilder {
    pub compact_service: Option<CompactService>,
    pub embed_service: Option<EmbedService>,
    pub perception_service: PerceptionService,
}

impl MessageBuilder {
    pub fn new() -> Self {
        Self {
            compact_service: None,
            embed_service: None,
            perception_service: PerceptionService::default(),
        }
    }

    pub fn with_compact_service(mut self, cs: CompactService) -> Self {
        self.compact_service = Some(cs);
        self
    }

    pub fn with_embed_service(mut self, es: EmbedService) -> Self {
        self.embed_service = Some(es);
        self
    }

    pub fn with_perception_service(mut self, ps: PerceptionService) -> Self {
        self.perception_service = ps;
        self
    }

    pub async fn build_messages(&self, request: &AiRequest) -> Result<Vec<ChatRequestMessage>> {
        let mut messages = Vec::new();

        messages.push(ChatRequestMessage::system(
            "When receiving a question or problem, follow this reasoning process:\n\
             1. ANALYZE: Break down the question. Identify what is being asked, what context is available, and what information is missing.\n\
             2. GATHER: Use available tools (repository search, file reading, etc.) to collect relevant information before answering.\n\
             3. REASON: Synthesize the gathered information. Consider edge cases and potential issues.\n\
             4. ANSWER: Provide a clear, actionable answer based on your analysis.\n\
             \n\
             Do NOT guess or assume when tools can provide concrete answers. Always verify claims against actual code or documentation.".to_string()
        ));

        let mut processed_history = Vec::new();
        if let Some(compact_service) = &self.compact_service {
            let compact_cache_key = format!("ai:compact:{}", request.room.id);
            let compact_config = CompactConfig::default();
            let cached_summary: Option<String> = match request.cache.conn().await {
                Ok(mut conn) => redis::cmd("GET").arg(&compact_cache_key).query_async::<Option<String>>(&mut conn).await.unwrap_or(None),
                Err(e) => { tracing::warn!(error = %e, "compact cache: conn failed"); None }
            };

            if let Some(cached_json) = cached_summary {
                if let Ok(summary) = serde_json::from_str::<crate::compact::CompactSummary>(&cached_json) {
                    if !summary.summary.is_empty() {
                        messages.push(ChatRequestMessage::system(format!("Conversation summary:\n{}", summary.summary)));
                    }
                    processed_history = summary.retained;
                }
            }

            if processed_history.is_empty() {
                match compact_service.compact_room_auto(request.room.id, Some(request.user_names.clone()), compact_config).await {
                    Ok(compact_summary) => {
                        if !compact_summary.summary.is_empty() {
                            messages.push(ChatRequestMessage::system(format!("Conversation summary:\n{}", compact_summary.summary)));
                        }
                        if let Ok(json) = serde_json::to_string(&compact_summary) {
                            if let Ok(mut conn) = request.cache.conn().await {
                                let _ = redis::cmd("SETEX").arg(&compact_cache_key).arg(300u64).arg(&json).query_async::<()>(&mut conn).await
                                    .inspect_err(|e| { tracing::warn!(error = %e, "compact cache: SETEX failed"); });
                            }
                        }
                        processed_history = compact_summary.retained;
                    }
                    Err(e) => tracing::warn!(error = %e, "conversation compaction failed, using full history"),
                }
            }
        }

        if !processed_history.is_empty() {
            for msg_summary in processed_history {
                let ctx = RoomMessageContext::from(msg_summary);
                messages.push(ctx.to_message());
            }
        } else {
            for msg in &request.history {
                let ctx = RoomMessageContext::from_model_with_names(msg, &request.user_names);
                messages.push(ctx.to_message());
            }
        }

        for mention in &request.mention {
            match mention {
                Mention::Repo(repo) => {
                    let mut parts = vec![format!("Name: {}", repo.repo_name), format!("ID: {}", repo.id)];
                    if let Some(ref desc) = repo.description { parts.push(format!("Description: {}", desc)); }
                    parts.push(format!("Default branch: {}", repo.default_branch));
                    parts.push(format!("Private: {}", if repo.is_private { "yes" } else { "no" }));
                    parts.push(format!("Created: {}", repo.created_at.format("%Y-%m-%d")));
                    messages.push(ChatRequestMessage::system(format!("Mentioned repository:\n{}", parts.join("\n"))));

                    if let Some(embed_service) = &self.embed_service {
                        let query = format!("{} {}", repo.repo_name, repo.description.as_deref().unwrap_or_default());
                        if let Ok(issues) = embed_service.search_issues(&query, 5).await {
                            if !issues.is_empty() {
                                let context = format!("Related issues for repo {}:\n{}", repo.repo_name, issues.iter().map(|i| format!("- {}", i.payload.text)).collect::<Vec<_>>().join("\n"));
                                messages.push(ChatRequestMessage::system(context));
                            }
                        }
                        if let Ok(repos) = embed_service.search_repos(&query, 3).await {
                            if !repos.is_empty() {
                                let context = format!("Similar repositories:\n{}", repos.iter().map(|r| format!("- {}", r.payload.text)).collect::<Vec<_>>().join("\n"));
                                messages.push(ChatRequestMessage::system(context));
                            }
                        }
                    }
                }
                Mention::User(user) => {
                    let mut profile_parts = vec![format!("Username: {}", user.username)];
                    if let Some(ref display_name) = user.display_name { profile_parts.push(format!("Display name: {}", display_name)); }
                    if let Some(ref org) = user.organization { profile_parts.push(format!("Organization: {}", org)); }
                    if let Some(ref website) = user.website_url { profile_parts.push(format!("Website: {}", website)); }
                    messages.push(ChatRequestMessage::system(format!("Mentioned user profile:\n{}", profile_parts.join("\n"))));
                }
            }
        }

        for ctx in self.build_skill_context(request).await { messages.push(ctx.to_system_message()); }
        for mem in self.build_memory_context(request).await { messages.push(mem.to_system_message()); }

        messages.push(ChatRequestMessage::system(format!(
            "Current Project:\n{}\nDescription: {}\nPublic: {}",
            request.project.display_name,
            request.project.description.as_deref().unwrap_or("(none)"),
            if request.project.is_public { "yes" } else { "no" }
        )));

        let mut sender_parts = vec![format!("**Sender:** {}", request.sender.username)];
        if let Some(ref display_name) = request.sender.display_name { sender_parts.push(display_name.clone()); }
        if let Some(ref org) = request.sender.organization { sender_parts.push(format!("({})", org)); }
        messages.push(ChatRequestMessage::system(format!("The person sending the next message:\n{}", sender_parts.join(" "))));
        messages.push(ChatRequestMessage::user(&request.input));
        Ok(messages)
    }

    async fn build_skill_context(&self, request: &AiRequest) -> Vec<crate::perception::SkillContext> {
        let db_skills: Vec<SkillEntry> = match project_skill::Entity::find()
            .filter(project_skill::Column::ProjectUuid.eq(request.project.id))
            .filter(project_skill::Column::Enabled.eq(true)).all(&request.db).await {
            Ok(models) => models.into_iter().map(|s| SkillEntry { slug: s.slug, name: s.name, description: s.description, content: s.content }).collect(),
            Err(_) => Vec::new(),
        };
        let mut all_skills: Vec<SkillEntry> = db_skills;
        for built_in in crate::skills::all_skills() {
            if !all_skills.iter().any(|s| s.slug == built_in.slug) {
                all_skills.push(SkillEntry { slug: built_in.slug.to_string(), name: built_in.name.to_string(), description: Some(built_in.description.to_string()), content: built_in.content.clone() });
            }
        }
        if all_skills.is_empty() { return Vec::new(); }

        let history_texts: Vec<String> = request.history.iter().rev().take(10).map(|msg| msg.content.clone()).collect();
        let keyword_skills = self.perception_service.inject_skills(&request.input, &history_texts, &[], &all_skills).await;
        let mut vector_skills = Vec::new();
        if let Some(es) = &self.embed_service {
            vector_skills = crate::perception::VectorActiveAwareness::default().detect(es, &request.input, &request.project.id.to_string()).await;
        }
        let mut seen = std::collections::HashSet::new();
        let mut result = Vec::new();
        for ctx in vector_skills { if seen.insert(ctx.label.clone()) { result.push(ctx); } }
        for ctx in keyword_skills { if seen.insert(ctx.label.clone()) { result.push(ctx); } }
        result
    }

    async fn build_memory_context(&self, request: &AiRequest) -> Vec<crate::perception::vector::MemoryContext> {
        match &self.embed_service {
            Some(es) => crate::perception::VectorPassiveAwareness::default().detect(es, &request.input, &request.project.display_name, &request.room.id.to_string()).await,
            None => Vec::new(),
        }
    }
}