use rig::client::EmbeddingsClient;
use rig::embeddings::EmbeddingModel;
use rig::providers::openai::Client as OpenAiClient;
use serde::{Deserialize, Serialize};

use crate::embed::qdrant::QdrantClient;

pub struct EmbedClient {
    openai: OpenAiClient,
    qdrant: QdrantClient,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbedVector {
    pub id: String,
    pub vector: Vec<f32>,
    pub payload: EmbedPayload,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmbedPayload {
    pub entity_type: String,
    pub entity_id: String,
    pub text: String,
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub extra: Option<serde_json::Value>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SearchResult {
    pub id: String,
    pub score: f32,
    pub payload: EmbedPayload,
}

impl EmbedClient {
    pub fn new(openai: OpenAiClient, qdrant: QdrantClient) -> Self {
        Self { openai, qdrant }
    }

    pub async fn embed_text(&self, text: &str, model: &str) -> crate::Result<Vec<f32>> {
        let model = self.openai.embedding_model(model);
        let embeddings = model
            .embed_texts(vec![text.to_string()])
            .await
            .map_err(|e| crate::AgentError::OpenAi(format!("embedding failed: {}", e)))?;

        embeddings
            .first()
            .map(|e| e.vec.iter().map(|v| *v as f32).collect())
            .ok_or_else(|| crate::AgentError::OpenAi("no embedding returned".into()))
    }

    pub async fn embed_batch(&self, texts: &[String], model: &str) -> crate::Result<Vec<Vec<f32>>> {
        let model = self.openai.embedding_model(model);
        let embeddings = model
            .embed_texts(texts.to_vec())
            .await
            .map_err(|e| crate::AgentError::OpenAi(format!("embedding batch failed: {}", e)))?;

        tracing::debug!(input_count = texts.len(), returned_count = embeddings.len(), "embed_batch: API returned");

        let mut result = vec![Vec::new(); texts.len()];
        for embedding in embeddings {
            // Find the original index by matching the document text
            if let Some(idx) = texts.iter().position(|t| t == &embedding.document) {
                result[idx] = embedding.vec.iter().map(|v| *v as f32).collect();
            } else {
                tracing::warn!(doc = %embedding.document, "embed_batch: document mismatch — text not found in input list");
            }
        }

        // Check for empty results
        let empty_count = result.iter().filter(|v| v.is_empty()).count();
        if empty_count > 0 {
            tracing::warn!(empty_count = empty_count, total = texts.len(), "embed_batch: some embeddings returned empty vectors");
        }

        Ok(result)
    }

    pub async fn upsert(&self, points: Vec<EmbedVector>) -> crate::Result<()> {
        self.qdrant.upsert_points(points).await
    }

    /// Upsert points into a named collection (bypasses entity_type routing).
    pub async fn upsert_to_collection(
        &self,
        collection_name: &str,
        points: Vec<EmbedVector>,
    ) -> crate::Result<()> {
        self.qdrant.upsert_to_collection(collection_name, points).await
    }

    pub async fn search(
        &self,
        query: &str,
        entity_type: &str,
        model: &str,
        limit: usize,
    ) -> crate::Result<Vec<SearchResult>> {
        let vector = self.embed_text(query, model).await?;
        self.qdrant.search(&vector, entity_type, limit).await
    }

    pub async fn search_with_filter(
        &self,
        query: &str,
        entity_type: &str,
        model: &str,
        limit: usize,
        filter: qdrant_client::qdrant::Filter,
    ) -> crate::Result<Vec<SearchResult>> {
        let vector = self.embed_text(query, model).await?;
        self.qdrant
            .search_with_filter(&vector, entity_type, limit, filter)
            .await
    }

    pub async fn delete_by_entity_id(
        &self,
        entity_type: &str,
        entity_id: &str,
    ) -> crate::Result<()> {
        self.qdrant.delete_by_filter(entity_type, entity_id).await
    }

    pub async fn ensure_collection(&self, entity_type: &str, dimensions: u64) -> crate::Result<()> {
        self.qdrant.ensure_collection(entity_type, dimensions).await
    }

    pub async fn ensure_skill_collection(&self, dimensions: u64) -> crate::Result<()> {
        self.qdrant.ensure_skill_collection(dimensions).await
    }

    /// Ensure a room-specific memory collection exists.
    pub async fn ensure_room_memory_collection(
        &self,
        project_name: &str,
        room_id: &str,
        dimensions: u64,
    ) -> crate::Result<()> {
        self.qdrant
            .ensure_room_memory_collection(project_name, room_id, dimensions)
            .await
    }

    /// Embed and store a conversation memory (message) in Qdrant.
    /// Uses per-room collection: `room:{project_name}:{room_id}`.
    pub async fn embed_memory(
        &self,
        id: &str,
        text: &str,
        project_name: &str,
        room_id: &str,
        user_id: Option<&str>,
        model: &str,
    ) -> crate::Result<()> {
        // Compute embedding first to know dimensions
        let vector = self.embed_text(text, model).await?;
        let collection = crate::embed::qdrant::QdrantClient::room_memory_collection_name(project_name, room_id);
        // Auto-create the room collection with correct dimensions
        self.qdrant
            .ensure_room_memory_collection(project_name, room_id, vector.len() as u64)
            .await?;
        let point = EmbedVector {
            id: id.to_string(),
            vector,
            payload: EmbedPayload {
                entity_type: "memory".to_string(),
                entity_id: room_id.to_string(),
                text: text.to_string(),
                extra: serde_json::json!({ "user_id": user_id }).into(),
            },
        };
        self.qdrant.upsert_to_collection(&collection, vec![point]).await
    }

    /// Search memory embeddings by semantic similarity within a room.
    /// Searches the per-room collection directly — no post-filtering needed.
    pub async fn search_memories(
        &self,
        query: &str,
        model: &str,
        project_name: &str,
        room_id: &str,
        limit: usize,
        dimensions: u64,
    ) -> crate::Result<Vec<SearchResult>> {
        let vector = self.embed_text(query, model).await?;
        let collection = crate::embed::qdrant::QdrantClient::room_memory_collection_name(project_name, room_id);
        // Ensure collection exists (will be no-op if already created)
        self.qdrant
            .ensure_room_memory_collection(project_name, room_id, dimensions)
            .await?;
        self.qdrant.search_collection(&collection, &vector, limit).await
    }

    /// Embed and store a skill in Qdrant.
    pub async fn embed_skill(
        &self,
        id: &str,
        name: &str,
        description: &str,
        content: &str,
        project_uuid: &str,
        model: &str,
    ) -> crate::Result<()> {
        let text = format!("{}: {} {}", name, description, content);
        let vector = self.embed_text(&text, model).await?;
        let point = EmbedVector {
            id: id.to_string(),
            vector,
            payload: EmbedPayload {
                entity_type: "skill".to_string(),
                entity_id: project_uuid.to_string(),
                text,
                extra: serde_json::json!({ "name": name, "description": description }).into(),
            },
        };
        self.qdrant.upsert_points(vec![point]).await
    }

    /// Search skill embeddings by semantic similarity within a project.
    pub async fn search_skills(
        &self,
        query: &str,
        model: &str,
        project_uuid: &str,
        limit: usize,
    ) -> crate::Result<Vec<SearchResult>> {
        let vector = self.embed_text(query, model).await?;
        let mut results = self.qdrant.search_skill(&vector, limit + 1).await?;
        results.retain(|r| r.payload.entity_id == project_uuid);
        results.truncate(limit);
        Ok(results)
    }
}