feat: implement full pipeline (pgvector, embeddings, ingest, query, HTTP)

- Add database schema with pgvector extension (L0/L1/L2 memories)
- Implement pgvector-backed vector store with similarity search
- Add Ollama embeddings client for 768-dim nomic embeddings
- Implement ingest worker to process records into L0/L1 memory
- Implement query worker with semantic search across memory tiers
- Rewrite HTTP server with database connection pooling
- Wire all endpoints to actual backend (ingest, query, projects, skills)
- Update main.rs to use DATABASE_URL from environment
- All code compiles, ready for Docker build and deployment

crates/mem-cli/src/main.rs

@@ -90,13 +90,20 @@ enum Commands {
     Serve {
         #[arg(long, default_value = "8080")]
         port: u16,
-        #[arg(long, default_value = "test-key")]
-        api_key: String,
+        #[arg(long)]
+        api_key: Option<String>,
+        #[arg(long)]
+        database_url: Option<String>,
     },
 }
 
 #[tokio::main]
 async fn main() -> anyhow::Result<()> {
+    // Initialize logging
+    tracing_subscriber::fmt()
+        .with_max_level(tracing::Level::INFO)
+        .init();
+    
     let cli = Cli::parse();
 
     match cli.command {
@@ -127,8 +134,10 @@ async fn main() -> anyhow::Result<()> {
             floor,
         } => lessons_cmd::cmd_lookup(tool.as_deref(), cmd.as_deref(), file.as_deref(), floor)?,
         Commands::Materialize => lessons_cmd::cmd_materialize()?,
-        Commands::Serve { port, api_key } => {
-            http_server::start_server(port, api_key).await?
+        Commands::Serve { port, api_key, database_url } => {
+            let api_key = api_key.unwrap_or_else(|| std::env::var("MEM_API_KEY").unwrap_or_else(|_| "test-key".to_string()));
+            let database_url = database_url.unwrap_or_else(|| std::env::var("DATABASE_URL").unwrap_or_else(|_| "postgresql://app:poimen@localhost:5432/memory".to_string()));
+            http_server::start_server(port, api_key, &database_url).await?
         }
     }