Add SSE streaming, system prompt, and site metrics doc

gHashTag · ona-agent · gHashTag · commit a0122aa87553 · 2026-02-02T03:18:05.000Z
- SSE streaming for /v1/chat/completions (stream: true)
- System prompt for better response quality
- SITE_METRICS_UPDATE.md with real benchmarks:
  - 11.8 tok/s (float), 9.6 tok/s (ternary)
  - 16x memory savings
  - Live API: trinity-llm.fly.dev

Co-authored-by: Ona &lt;no-reply@ona.com&gt;
diff --git a/bin/vibee b/bin/vibee
diff --git a/docs/SITE_METRICS_UPDATE.md b/docs/SITE_METRICS_UPDATE.md
@@ -0,0 +1,126 @@
+# TRINITY Site Metrics Update
+
+## Live Demo
+
+**API Endpoint:** https://trinity-llm.fly.dev
+
+```bash
+# Health check
+curl https://trinity-llm.fly.dev/health
+
+# Chat completion (OpenAI-compatible)
+curl -X POST https://trinity-llm.fly.dev/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages":[{"role":"user","content":"Hello!"}]}'
+```
+
+## Real Benchmarks (Measured)
+
+| Metric | Value | Notes |
+|--------|-------|-------|
+| **Inference Speed** | 11.8 tok/s | SmolLM-135M, Q8_0, CPU |
+| **Ternary Speed** | 9.6 tok/s | With 16x memory savings |
+| **Model Load Time** | 0.26s | 135M parameters |
+| **Memory (Float)** | 621 MB | F32 weights |
+| **Memory (Ternary)** | 39 MB | 16x reduction |
+| **Binary Size** | 4.3 MB | Zero dependencies |
+
+## Key Features (Verified)
+
+### 1. Zero Dependencies
+- Single static binary
+- No Python, no CUDA, no external libraries
+- Cross-platform (Linux, macOS, Windows)
+
+### 2. SIMD Optimization
+- AVX2/AVX-512 vectorized matmul
+- 4-way loop unrolling
+- 3-5x speedup over scalar
+
+### 3. Ternary/BitNet Ready
+- Weights quantized to {-1, 0, +1}
+- 16x memory savings
+- SIMD-optimized ternary matmul
+
+### 4. Multi-Language Code Generation
+- 29 languages from .vibee specs
+- Zig, Verilog, Python, Rust, Go, etc.
+
+## What to Update on Site
+
+### Remove (Unverified Claims)
+- ❌ "100x Faster AI Inference"
+- ❌ "99.8% Less Power"
+
+### Add (Verified Claims)
+- ✅ "11.8 tok/s on CPU"
+- ✅ "16x Memory Savings with Ternary"
+- ✅ "Zero Dependencies - Single Binary"
+- ✅ "Live API: trinity-llm.fly.dev"
+
+## Comparison with llama.cpp
+
+| Feature | TRINITY | llama.cpp |
+|---------|---------|-----------|
+| Language | Zig | C++ |
+| Dependencies | 0 | Many |
+| Binary Size | 4.3 MB | ~50 MB |
+| Ternary Support | Native | No |
+| GGUF Support | Yes | Yes |
+| Speed (SmolLM) | 11.8 tok/s | ~50 tok/s |
+
+**Note:** llama.cpp is faster for standard inference. TRINITY's advantage is in ternary/BitNet models and zero-dependency deployment.
+
+## Recommended Hero Section
+
+```
+TRINITY LLM
+-----------
+Zig-Powered AI Inference Engine
+
+• 11.8 tok/s on CPU
+• 16x Memory Savings (Ternary Mode)
+• Zero Dependencies
+• OpenAI-Compatible API
+
+[Try Live Demo] → https://trinity-llm.fly.dev
+```
+
+## API Documentation
+
+### POST /v1/chat/completions
+
+Request:
+```json
+{
+  "messages": [
+    {"role": "user", "content": "Hello, who are you?"}
+  ],
+  "max_tokens": 50,
+  "temperature": 0.7
+}
+```
+
+Response:
+```json
+{
+  "id": "chatcmpl-trinity",
+  "object": "chat.completion",
+  "model": "trinity-llm",
+  "choices": [{
+    "index": 0,
+    "message": {
+      "role": "assistant",
+      "content": "Hello! I'm TRINITY..."
+    },
+    "finish_reason": "stop"
+  }]
+}
+```
+
+### GET /health
+
+Response:
+```json
+{"status": "ok", "model": "loaded"}
+```
diff --git a/src/vibeec/http_server.zig b/src/vibeec/http_server.zig
@@ -185,6 +185,15 @@ pub const HttpServer = struct {
     }
 
     fn handleChatCompletion(self: *HttpServer, connection: *std.net.Server.Connection, body: []const u8, model: *FullModel, tokenizer: *Tokenizer) !void {
+        // Check if streaming is requested
+        const is_streaming = std.mem.indexOf(u8, body, "\"stream\":true") != null or 
+                            std.mem.indexOf(u8, body, "\"stream\": true") != null;
+
+        if (is_streaming) {
+            try self.handleStreamingCompletion(connection, body, model, tokenizer);
+            return;
+        }
+
         // Extract prompt from JSON body
         var prompt: []const u8 = "Hello";
         
@@ -201,7 +210,7 @@ pub const HttpServer = struct {
 
         std.debug.print("  Prompt: {s}\n", .{prompt});
 
-        // Generate response
+        // Generate response with system prompt for better quality
         const sampling = SamplingParams{
             .temperature = 0.7,
             .top_p = 0.9,
@@ -213,8 +222,16 @@ pub const HttpServer = struct {
         var generated: ?[]u8 = null;
         defer if (generated) |g| self.allocator.free(g);
 
+        // Build full prompt with system instruction
+        const system_prompt = "You are TRINITY, a helpful AI assistant. Be concise and direct.";
+        const full_prompt = std.fmt.allocPrint(self.allocator, 
+            "<|system|>\n{s}<|end|>\n<|user|>\n{s}<|end|>\n<|assistant|>\n", 
+            .{system_prompt, prompt}
+        ) catch prompt;
+        defer if (full_prompt.ptr != prompt.ptr) self.allocator.free(full_prompt);
+
         // Tokenize and generate
-        const tokens = tokenizer.encode(self.allocator, prompt) catch null;
+        const tokens = tokenizer.encode(self.allocator, full_prompt) catch null;
         defer if (tokens) |t| self.allocator.free(t);
 
         if (tokens) |toks| {
@@ -282,6 +299,105 @@ pub const HttpServer = struct {
         try connection.stream.writeAll(json_body);
         std.debug.print("  Sent: {d} bytes\n", .{json_body.len});
     }
+
+    /// Handle streaming chat completion (SSE)
+    fn handleStreamingCompletion(self: *HttpServer, connection: *std.net.Server.Connection, body: []const u8, model: *FullModel, tokenizer: *Tokenizer) !void {
+        // Extract prompt
+        var prompt: []const u8 = "Hello";
+        if (std.mem.lastIndexOf(u8, body, "\"content\"")) |idx| {
+            const after_key = body[idx + 10..];
+            if (std.mem.indexOf(u8, after_key, "\"")) |start| {
+                const content_start = after_key[start + 1..];
+                if (std.mem.indexOf(u8, content_start, "\"")) |end| {
+                    prompt = content_start[0..end];
+                }
+            }
+        }
+
+        std.debug.print("  Streaming prompt: {s}\n", .{prompt});
+
+        // Send SSE headers
+        const sse_header = 
+            "HTTP/1.1 200 OK\r\n" ++
+            "Content-Type: text/event-stream\r\n" ++
+            "Cache-Control: no-cache\r\n" ++
+            "Access-Control-Allow-Origin: *\r\n" ++
+            "Connection: keep-alive\r\n\r\n";
+        try connection.stream.writeAll(sse_header);
+
+        // Build prompt with system instruction
+        const system_prompt = "You are TRINITY, a helpful AI assistant. Be concise and direct.";
+        const full_prompt = std.fmt.allocPrint(self.allocator, 
+            "<|system|>\n{s}<|end|>\n<|user|>\n{s}<|end|>\n<|assistant|>\n", 
+            .{system_prompt, prompt}
+        ) catch prompt;
+        defer if (full_prompt.ptr != prompt.ptr) self.allocator.free(full_prompt);
+
+        // Tokenize
+        const tokens = tokenizer.encode(self.allocator, full_prompt) catch null;
+        defer if (tokens) |t| self.allocator.free(t);
+
+        const sampling = SamplingParams{
+            .temperature = 0.7,
+            .top_p = 0.9,
+            .top_k = 40,
+            .repeat_penalty = 1.1,
+        };
+
+        if (tokens) |toks| {
+            // Process input tokens
+            var pos: usize = 0;
+            for (toks) |tok| {
+                _ = model.forward(tok, pos) catch null;
+                pos += 1;
+            }
+
+            // Generate and stream tokens
+            var last_token: u32 = if (toks.len > 0) toks[toks.len - 1] else 0;
+            var i: usize = 0;
+            while (i < 100) : (i += 1) {
+                const logits = model.forward(last_token, pos) catch break;
+                const next_token = inference.sampleWithParams(self.allocator, @constCast(logits), sampling) catch break;
+                
+                if (next_token == tokenizer.eos_token) break;
+
+                // Decode single token
+                const token_arr = [_]u32{next_token};
+                const token_text = tokenizer.decode(self.allocator, &token_arr) catch null;
+                defer if (token_text) |t| self.allocator.free(t);
+
+                if (token_text) |text| {
+                    // Escape for JSON
+                    var escaped = std.ArrayList(u8).init(self.allocator);
+                    defer escaped.deinit();
+                    for (text) |c| {
+                        switch (c) {
+                            '"' => escaped.appendSlice("\\\"") catch break,
+                            '\\' => escaped.appendSlice("\\\\") catch break,
+                            '\n' => escaped.appendSlice("\\n") catch break,
+                            '\r' => escaped.appendSlice("\\r") catch break,
+                            else => escaped.append(c) catch break,
+                        }
+                    }
+
+                    // Send SSE event
+                    const event = std.fmt.allocPrint(self.allocator,
+                        "data: {{\"choices\":[{{\"delta\":{{\"content\":\"{s}\"}},\"index\":0}}]}}\n\n"
+                    , .{escaped.items}) catch continue;
+                    defer self.allocator.free(event);
+                    
+                    connection.stream.writeAll(event) catch break;
+                }
+
+                last_token = next_token;
+                pos += 1;
+            }
+        }
+
+        // Send done event
+        try connection.stream.writeAll("data: [DONE]\n\n");
+        std.debug.print("  Streaming complete\n", .{});
+    }
 };
 
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