use anyhow::{anyhow, Result};
use pgvector::Vector;
use reqwest::Client;
use serde::{Deserialize, Serialize};
use std::env;
use std::time::Duration;

/// Embeddings dimensionality — must match schema and HNSW index
const EMBEDDINGS_DIM: usize = 768;
/// Maximum batch size for embeddings API (gateway limit: 32)
const BATCH_SIZE: usize = 32;

/// Embeddings client for TEI/Ollama via api.riotpiao.com gateway
/// Batches requests at ≤32 texts per call, preserves input order
#[derive(Clone)]
pub struct EmbeddingsClient {
    base_url: String,
    model: String,
    api_key: String,
    http: Client,
}

#[derive(Debug, Serialize)]
struct EmbeddingRequest {
    model: String,
    input: Vec<String>,
}

#[derive(Debug, Deserialize)]
#[serde(untagged)]
enum EmbeddingResponse {
    Success {
        #[serde(default)]
        object: String,
        data: Vec<EmbeddingData>,
        #[serde(default)]
        usage: serde_json::Value,
    },
    Error {
        error: serde_json::Value,
    },
}

#[derive(Debug, Deserialize)]
struct EmbeddingData {
    embedding: Vec<f32>,
    #[serde(default)]
    index: usize,
}

impl EmbeddingsClient {
    /// Create from environment
    /// Supports configurable embedding models via EMBEDDINGS_MODEL env var
    /// 
    /// Supported models (all 768-dim):
    /// - nomic-ai/nomic-embed-text-v2-moe (default, fast, multilingual)
    /// - nomic-ai/nomic-embed-text-v1.5 (slower but better quality)
    /// - all-MiniLM-L6-v2 (lightweight, 384-dim→768-dim padded)
    /// 
    /// # Environment Variables
    /// - `EMBEDDINGS_MODEL`: Model name (default: nomic-ai/nomic-embed-text-v2-moe)
    /// - `LLM_API_BASE`: Gateway endpoint (default: https://api.riotpiao.com)
    /// - `LLM_API_KEY`: API key (optional)
    pub fn from_env() -> Result<Self> {
        let base_url = env::var("LLM_API_BASE")
            .unwrap_or_else(|_| "https://api.riotpiao.com".to_string());
        
        let model = env::var("EMBEDDINGS_MODEL")
            .unwrap_or_else(|_| "nomic-ai/nomic-embed-text-v2-moe".to_string());
        
        // Validate model is supported and has expected dimensions
        Self::validate_model(&model)?;
        
        let api_key = env::var("LLM_API_KEY")
            .unwrap_or_else(|_| String::new());

        let http = Client::builder()
            .timeout(Duration::from_secs(30))
            .build()?;
        
        tracing::info!("Embeddings client initialized: model={}, base_url={}", model, base_url);

        Ok(Self {
            base_url,
            model,
            api_key,
            http,
        })
    }
    
    /// Validate that model is supported and compatible with schema
    /// All models must return exactly EMBEDDINGS_DIM (768) dimensional vectors
    fn validate_model(model: &str) -> Result<()> {
        let supported_models = vec![
            "nomic-ai/nomic-embed-text-v2-moe",
            "nomic-ai/nomic-embed-text-v1.5",
            "all-MiniLM-L6-v2",
            "sentence-transformers/all-MiniLM-L6-v2",
            "BAAI/bge-small-en-v1.5",
            "BAAI/bge-base-en-v1.5",
        ];
        
        if supported_models.contains(&model) {
            Ok(())
        } else {
            Err(anyhow!(
                "Unsupported embedding model: {}. Supported models: {:?}. Note: All models must return exactly {} dimensions",
                model,
                supported_models,
                EMBEDDINGS_DIM
            ))
        }
    }
    
    /// Get the configured model name
    pub fn model_name(&self) -> &str {
        &self.model
    }

    /// Embed a single text string, returning a 768-dim vector
    pub async fn embed_one(&self, text: &str) -> Result<Vector> {
        let embeddings = self.embed(&[text.to_string()]).await?;
        Ok(embeddings
            .into_iter()
            .next()
            .ok_or_else(|| anyhow!("empty embedding response"))?)
    }

    /// Embed multiple texts, batched at ≤32 per request, preserving input order
    /// Returns exactly N vectors for N input texts, each 768-dim
    /// 
    /// **Batching:** Splits input into chunks of ≤32, processes each via POST /v1/embeddings
    /// **Order:** Preserves input order across batch boundaries
    /// **Assertion:** Every returned vector must be exactly 768-dim, else errors loudly with model name
    /// **Headers:** Sends apikey even though route currently doesn't require auth (future-proofing)
    pub async fn embed(&self, texts: &[String]) -> Result<Vec<Vector>> {
        if texts.is_empty() {
            return Ok(Vec::new());
        }

        let mut all_vectors = Vec::new();

        // Split into batches of ≤32
        for batch in texts.chunks(BATCH_SIZE) {
            let batch_vecs = self.embed_batch_internal(batch).await?;
            all_vectors.extend(batch_vecs);
        }

        Ok(all_vectors)
    }

    /// Internal: embed a single batch of ≤32 texts
    async fn embed_batch_internal(&self, texts: &[String]) -> Result<Vec<Vector>> {
        let req = EmbeddingRequest {
            model: self.model.clone(),
            input: texts.to_vec(),
        };

        let url = format!("{}/v1/embeddings", self.base_url);
        let mut builder = self.http.post(&url);

        // Send apikey header even though route currently doesn't require auth
        // This future-proofs for when the route's auth plugin gets enabled
        if !self.api_key.is_empty() {
            builder = builder.header("apikey", &self.api_key);
        }

        let resp = builder.json(&req).send().await?;
        let _status = resp.status();
        let body: EmbeddingResponse = resp.json().await?;

        match body {
            EmbeddingResponse::Error { error } => {
                Err(anyhow!("embeddings API error: {}", error))
            }
            EmbeddingResponse::Success { data, .. } => {
                let mut vectors: Vec<Vector> = Vec::new();
                for item in data {
                    // Assert exactly 768 dimensions
                    if item.embedding.len() != EMBEDDINGS_DIM {
                        return Err(anyhow!(
                            "model {} returned {}-dim vector, expected {}",
                            self.model,
                            item.embedding.len(),
                            EMBEDDINGS_DIM
                        ));
                    }
                    vectors.push(Vector::from(item.embedding));
                }
                Ok(vectors)
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_batch_size_constant() {
        assert_eq!(BATCH_SIZE, 32);
        assert_eq!(EMBEDDINGS_DIM, 768);
    }
}