fix: wire all LLM clients to api.riotpiao.com gateway

- EmbeddingsClient: use /v1/embeddings from gateway (nomic-ai/nomic-embed-text-v2-moe)
- RerankClient: use /v1/rerank from gateway (BAAI/bge-reranker-base)
- ChatClient: support gateway without auth headers (future: Bearer token)
- All clients use LLM_API_BASE env var (default: https://api.riotpiao.com)
- Ready for Kubernetes deployment with proper API routing

crates/mem-cli/src/http_server.rs

@@ -1,6 +1,6 @@
 use actix_web::{web, App, HttpServer, HttpResponse, HttpRequest, middleware::Logger};
 use anyhow::Result;
-use mem_llm::{ChatClient, EmbeddingsClient, RerankClient};
+use mem_llm::{EmbeddingsClient, RerankClient};
 use mem_store::{init_schema, VectorStore};
 use serde_json::json;
 use sqlx::PgPool;
@@ -49,9 +49,7 @@ pub async fn start_server(port: u16, api_key: String, database_url: &str) -> Res
     let vector_store = Arc::new(VectorStore::new(pool.clone()));
     let embeddings = Arc::new(EmbeddingsClient::from_env()?);
     let ingest_worker = Arc::new(IngestWorker::new(pool.clone(), (*embeddings).clone()));
-    
-    // Create a placeholder reranker (TODO: implement from_env)
-    let reranker = RerankClient::new("http://localhost:8000", "test", "cross-encoder")?;
+    let reranker = RerankClient::from_env()?;
     let query_worker = Arc::new(QueryWorker::new(VectorStore::new(pool.clone()), (*embeddings).clone(), reranker));
 
     let state = web::Data::new(AppState {

crates/mem-llm/src/chat.rs

@@ -144,10 +144,13 @@ impl ChatClient {
         let mut last_error: Option<anyhow::Error> = None;
 
         for attempt in 0..self.max_retries {
-            let response = self
-                .http
-                .post(&url)
-                .header("apikey", &self.api_key)
+            let mut req = self.http.post(&url);
+            // Only add apikey header if it's not empty (for backward compatibility)
+            if !self.api_key.is_empty() && !self.api_key.starts_with("http") {
+                req = req.header("apikey", &self.api_key);
+            }
+            
+            let response = req
                 .header("Content-Type", "application/json")
                 .body(body.clone())
                 .timeout(self.timeout)

crates/mem-llm/src/embeddings.rs

@@ -26,10 +26,11 @@ struct EmbeddingResponse {
 }
 
 impl EmbeddingsClient {
-    /// Create from environment (OLLAMA_BASE_URL, EMBEDDINGS_MODEL)
+    /// 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("OLLAMA_BASE_URL").unwrap_or_else(|_| "http://ollama:11434".to_string());
-        let model = env::var("EMBEDDINGS_MODEL").unwrap_or_else(|_| "nomic-embed-text-v2-moe".to_string());
+        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,
@@ -44,14 +45,14 @@ impl EmbeddingsClient {
         Ok(embeddings.into_iter().next().ok_or_else(|| anyhow::anyhow!("empty embedding response"))?)
     }
 
-    /// Embed multiple texts in a batch
+    /// 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!("{}/api/embed", self.base_url);
+        let url = format!("{}/v1/embeddings", self.base_url);
         let resp: EmbeddingResponse = self.http.post(&url).json(&req).send().await?.json().await?;
 
         Ok(resp

crates/mem-llm/src/rerank.rs

@@ -1,33 +1,51 @@
 use anyhow::Result;
 use reqwest::Client;
-use serde_json::json;
+use serde::{Deserialize, Serialize};
+use std::time::Duration;
 
-/// Rerank response item (bare array, not OpenAI envelope).
-#[derive(serde::Deserialize, Debug)]
+/// Rerank score result
+#[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct RerankScore {
     pub index: usize,
     pub score: f32,
 }
 
-/// Rerank client (BAAI/bge-reranker-base via TEI).
+/// Rerank response from gateway
+#[derive(Deserialize)]
+struct RerankResponse {
+    results: Vec<RerankScore>,
+}
+
+/// Rerank client using api.riotpiao.com gateway (BAAI/bge-reranker-base model)
 pub struct RerankClient {
     base_url: String,
-    api_key: String,
     model: String,
     timeout_secs: u64,
 }
 
 impl RerankClient {
-    /// Create rerank client.
-    pub fn new(base_url: &str, api_key: &str, model: &str) -> Result<Self> {
+    /// Create rerank client pointing to gateway
+    pub fn new(base_url: &str, _api_key: &str, model: &str) -> Result<Self> {
         Ok(Self {
             base_url: base_url.to_string(),
-            api_key: api_key.to_string(),
             model: model.to_string(),
             timeout_secs: 300,
         })
     }
 
+    /// Create from environment (uses api.riotpiao.com)
+    pub fn from_env() -> Result<Self> {
+        let base_url = std::env::var("LLM_API_BASE")
+            .unwrap_or_else(|_| "https://api.riotpiao.com".to_string());
+        let model = "BAAI/bge-reranker-base".to_string();
+        
+        Ok(Self {
+            base_url,
+            model,
+            timeout_secs: 300,
+        })
+    }
+
     /// Rerank query against texts, return scored items in score order.
     /// Returns Vec<(index, score)> mapping back to input positions.
     pub async fn rerank(&self, query: &str, texts: &[&str]) -> Result<Vec<(usize, f32)>> {
@@ -36,39 +54,41 @@ impl RerankClient {
             return Ok(vec![]);
         }
 
-        let url = format!("{}/rerank", self.base_url);
+        let url = format!("{}/v1/rerank", self.base_url);
 
         let client = Client::builder()
-            .timeout(std::time::Duration::from_secs(self.timeout_secs))
+            .timeout(Duration::from_secs(self.timeout_secs))
             .build()?;
 
-        let payload = json!({
+        let payload = serde_json::json!({
+            "model": self.model,
             "query": query,
             "texts": texts,
+            "top_k": texts.len(),
         });
 
         let response = client
             .post(&url)
-            .header("apikey", &self.api_key)
             .header("Content-Type", "application/json")
             .json(&payload)
             .send()
             .await?;
 
         if !response.status().is_success() {
-            return Err(anyhow::anyhow!("Rerank failed: {}", response.status()));
+            let error_text = response.text().await.unwrap_or_default();
+            return Err(anyhow::anyhow!("Rerank failed: {}", error_text));
         }
 
-        // Parse bare array (not OpenAI envelope)
-        let scores: Vec<RerankScore> = response.json().await?;
+        // Parse gateway response (OpenAI format with results field)
+        let resp: RerankResponse = response.json().await?;
 
-        // Map back to input positions and scores
-        let mut results: Vec<(usize, f32)> = scores
+        // Map to (index, score) and sort by score descending
+        let mut results: Vec<(usize, f32)> = resp
+            .results
             .into_iter()
             .map(|s| (s.index, s.score))
             .collect();
 
-        // Sort by score descending (highest first)
         results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
 
         Ok(results)