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

/// Embeddings client for Ollama
#[derive(Clone)]
pub struct EmbeddingsClient {
    base_url: String,
    model: String,
    #[allow(dead_code)]
    http: Client,
}

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

#[derive(Debug, Deserialize)]
struct EmbeddingResponse {
    embeddings: Vec<Vec<f32>>,
    model: String,
}

impl EmbeddingsClient {
    /// Create from environment
    /// Uses api.riotpiao.com gateway (nomic-ai/nomic-embed-text-v2-moe model)
    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 = "nomic-ai/nomic-embed-text-v2-moe".to_string();

        Ok(Self {
            base_url,
            model,
            http: Client::new(),
        })
    }

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

    /// Embed multiple texts in a batch using api.riotpiao.com gateway
    pub async fn embed_batch(&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 resp: EmbeddingResponse = self.http.post(&url).json(&req).send().await?.json().await?;

        Ok(resp
            .embeddings
            .into_iter()
            .map(Vector::from)
            .collect())
    }
}