Refactor code structure for improved readability and maintainability

2026-07-21 02:25:34 +00:00 · 2026-06-14 10:06:26 +08:00
parent eb1aecd9d8
commit 99ca2c838b
7 changed files with 115 additions and 620 deletions
@@ -2,7 +2,7 @@

 ## Project Overview

-**Agent Alpha** is an agentic AI backend (FastAPI + pydantic-ai) with a React + Vite + Tailwind frontend. Users chat with an AI agent powered by an OpenAI-compatible LLM (Ollama, OpenAI, etc.).
+**Agent Alpha** is an agentic AI backend (FastAPI + pydantic-ai) with a React + Vite + Tailwind frontend. Users chat with an AI agent powered by a llama.cpp server (OpenAI-compatible endpoint).

 ```
 agent_alpha/
@@ -28,7 +28,7 @@ agent_alpha/
 │   │   └── api.ts     # Fetch client (chat, auth, users, admin)
 │   └── nginx.conf     # Production SPA + API proxy
 ├── skills/            # pydantic-ai skill definitions
-└── docker-compose.yml # Full stack orchestration
+└── docker-compose.yml # Full stack orchestration (docker compose / podman compose)
 ```

 ## Tech Stack
@@ -40,7 +40,7 @@ agent_alpha/
 | **Database** | PostgreSQL 16 (asyncpg + SQLAlchemy async) |
 | **Cache** | Valkey 8 (Redis-compatible, via `redis[hiredis]`) |
 | **Frontend** | React 18, TypeScript 5.6, Vite 6, Tailwind CSS 3.4 |
-| **Infra** | Docker Compose (backend, frontend/nginx, postgres, valkey) |
+| **Infra** | Docker Compose / Podman Compose (backend, frontend/nginx, postgres, valkey) |
 | **Pkg Mgr** | `uv` (Python), `bun` (frontend) |

 ## Build & Run Commands
@@ -52,7 +52,7 @@ make run                # Start backend on :8000 (reload)
 make frontend-install   # bun install
 make frontend-dev       # Start Vite on :5173

-make compose-up         # Start all containers (detached)
+make compose-up         # Start all containers (detached; auto-detects podman or docker)
 make compose-rebuild    # Rebuild images & restart
 make compose-logs       # Follow logs
 make compose-down       # Stop everything
@@ -95,12 +95,12 @@ Environment variables loaded from `.env` via `pydantic-settings` (`backend/core/

 ## Common Pitfalls

-1. **Container DNS caching**: nginx caches DNS at startup. Podman does not use Docker's `127.0.0.11` resolver, so the `resolver` + variable trick doesn't work. If the backend restarts, also restart the frontend container (`podman compose restart frontend`).
+1. **Container DNS caching**: nginx caches DNS at startup. Podman does not use Docker's `127.0.0.11` resolver, so the `resolver` + variable trick doesn't work. If the backend restarts, also restart the frontend container (`podman compose restart frontend` or `docker compose restart frontend`).
 2. **Slow LLM responses**: The LLM may take minutes to respond. Nginx `proxy_read_timeout` is set to 600s. Backend timeouts should match.
 3. **Logfire is optional**: `logfire.configure()` is wrapped in `try/except` — omit `LOGFIRE_TOKEN` to disable.
-4. **API key for local LLMs**: Set `LLM_API_KEY=no-key-required` (or leave empty) for Ollama/local endpoints; the OpenAI client requires a non-None value.
+4. **API key for local LLMs**: Set `LLM_API_KEY=no-key-required` (or leave empty) for llama.cpp / local endpoints; the OpenAI client requires a non-None value.
 5. **uv vs pip**: Always use `uv sync` / `uv add`, never `pip install`. The lock file is `uv.lock`.
 6. **bun for frontend**: Use `bun install` / `bun run dev` for frontend, not `npm`.
-7. **Container orchestration**: Backend depends on postgres + valkey being healthy. Use `depends_on` with `condition: service_healthy`.
+7. **Container orchestration**: Backend depends on postgres + valkey being healthy. Use `depends_on` with `condition: service_healthy`. Run with `docker compose up -d` or `podman compose up -d`. The Makefile auto-detects the available runtime.
 8. **Schema migrations**: `init_db()` in `database.py` runs `Base.metadata.create_all` followed by `ALTER TABLE ... ADD COLUMN IF NOT EXISTS` for new columns. Existing tables are never rebuilt — new columns are added in-place.
-9. **bcrypt on container restart**: `bcrypt` is a native dependency. If the container fails to start with an import error, ensure `bcrypt` is in `pyproject.toml` and `uv sync` was run during the Docker build.
+9. **bcrypt on container restart**: `bcrypt` is a native dependency. If the container fails to start with an import error, ensure `bcrypt` is in `pyproject.toml` and `uv sync` was run during the container build.
@@ -2,6 +2,9 @@

 from __future__ import annotations

+import time
+from dataclasses import dataclass, field
+
 import logfire
 from pydantic_ai import Agent
 from pydantic_ai.capabilities import MCP, Thinking, ToolSearch, WebSearch
@@ -67,13 +70,22 @@ class AgentService:

    # ── Execution ──────────────────────────────────────────────────────────

-    async def ask(self, prompt: str, session_id: str | None = None) -> str:
-        """Send a prompt to the agent and return the text output."""
+    async def ask(self, prompt: str, session_id: str | None = None) -> AskResult:
+        """Send a prompt to the agent and return the text output with usage stats."""
        self.initialize()
        assert self._agent is not None
        deps = DeepAgentDeps(backend=_backend)
+        t0 = time.monotonic()
        result = await self._agent.run(prompt, deps=deps)
-        return result.output
+        elapsed = time.monotonic() - t0
+        usage = result.usage
+        return AskResult(
+            output=result.output,
+            input_tokens=usage.input_tokens,
+            output_tokens=usage.output_tokens,
+            total_tokens=usage.input_tokens + usage.output_tokens,
+            elapsed_seconds=round(elapsed, 2),
+        )

    # ── Internal helpers ───────────────────────────────────────────────────

@@ -108,6 +120,31 @@ class AgentService:
        ]


+# ---------------------------------------------------------------------------
+# AskResult — returned by AgentService.ask() with output + token usage stats.
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class AskResult:
+    """Result of a single agent ``ask()`` call with token usage information."""
+
+    output: str
+    """The agent's text reply."""
+
+    input_tokens: int = 0
+    """Number of prompt tokens sent to the model."""
+
+    output_tokens: int = 0
+    """Number of completion tokens generated by the model."""
+
+    total_tokens: int = 0
+    """Sum of input + output tokens."""
+
+    elapsed_seconds: float = 0.0
+    """Wall-clock time in seconds the agent took to respond."""
+
+
 # ---------------------------------------------------------------------------
 # Persistent backend — uses local filesystem so agent memory survives restarts.
 # ---------------------------------------------------------------------------
@@ -135,6 +172,6 @@ def get_service() -> AgentService:
    return _service


-async def ask(prompt: str, session_id: str | None = None) -> str:
-    """Send a prompt to the agent and return the text output."""
+async def ask(prompt: str, session_id: str | None = None) -> AskResult:
+    """Send a prompt to the agent and return the text output with usage stats."""
    return await _service.ask(prompt, session_id=session_id)
@@ -35,6 +35,10 @@ class ChatRequest(BaseModel):
 class ChatResponse(BaseModel):
    reply: str
    session_id: str
+    input_tokens: int = 0
+    output_tokens: int = 0
+    total_tokens: int = 0
+    elapsed_seconds: float = 0.0


 class MessageOut(BaseModel):
@@ -100,13 +104,20 @@ async def chat_endpoint(
                title += "…"
            await set_session_title(session_id, title or "New chat")

-        # Ask the agent.
-        output = await agent.ask(body.message, session_id=session_id)
+        # Ask the agent — returns output + token usage.
+        result = await agent.ask(body.message, session_id=session_id)

        # Persist the assistant reply.
-        await save_message(session_id, "assistant", output)
+        await save_message(session_id, "assistant", result.output)

-        return ChatResponse(reply=output, session_id=session_id)
+        return ChatResponse(
+            reply=result.output,
+            session_id=session_id,
+            input_tokens=result.input_tokens,
+            output_tokens=result.output_tokens,
+            total_tokens=result.total_tokens,
+            elapsed_seconds=result.elapsed_seconds,
+        )
    except Exception as exc:
        raise HTTPException(status_code=500, detail=str(exc))

@@ -1,14 +1,21 @@
 # ──────────────────────────────────────────────────────────────────
-#  Agent Alpha — Docker Compose
+#  Agent Alpha — Compose Stack
 #
-#  Usage:
+#  Usage (Docker):
 #    docker compose up -d              # start everything
 #    docker compose up -d --build      # rebuild & start
 #    docker compose down               # stop & remove
 #
+#  Usage (Podman):
+#    podman compose up -d              # start everything
+#    podman compose up -d --build      # rebuild & start
+#    podman compose down               # stop & remove
+#
+#  The Makefile auto-detects the available runtime. Either works.
+#
 #  Requires:
-#    - Docker (or Podman with docker-compose compatibility)
-#    - An OpenAI-compatible LLM endpoint (set LLM_BASE_URL)
+#    - Docker or Podman
+#    - A llama.cpp server (or any OpenAI-compatible LLM — set LLM_BASE_URL)
 # ──────────────────────────────────────────────────────────────────

 services:
@@ -22,6 +22,10 @@ import AdminDashboard from "./Admin";
 interface Message {
  role: "user" | "assistant";
  content: string;
+  inputTokens?: number;
+  outputTokens?: number;
+  totalTokens?: number;
+  elapsedSeconds?: number;
 }

 const STORAGE_SESSION_KEY = "agent_alpha_session_id";
@@ -376,7 +380,14 @@ export default function App() {
      setSessionId(data.session_id);
      setMessages((prev) => [
        ...prev,
-        { role: "assistant", content: data.reply },
+        {
+          role: "assistant",
+          content: data.reply,
+          inputTokens: data.input_tokens,
+          outputTokens: data.output_tokens,
+          totalTokens: data.total_tokens,
+          elapsedSeconds: data.elapsed_seconds,
+        },
      ]);
    } catch (err: unknown) {
      const msg = err instanceof Error ? err.message : "Unknown error";
@@ -572,6 +583,29 @@ export default function App() {
                }`}
              >
                <Markdown content={msg.content} />
+                {msg.role === "assistant" && msg.totalTokens !== undefined && (
+                  <div className="mt-1.5 flex flex-wrap gap-3 text-[10px] text-gray-500">
+                    <span title="Input tokens sent to the model">
+                      ↓ {msg.inputTokens} in
+                    </span>
+                    <span title="Output tokens generated by the model">
+                      ↑ {msg.outputTokens} out
+                    </span>
+                    <span title="Total tokens used">
+                      ∑ {msg.totalTokens} total
+                    </span>
+                    {msg.elapsedSeconds !== undefined && msg.elapsedSeconds > 0 && (
+                      <span title="Tokens per second">
+                        ⚡ {Math.round(msg.totalTokens / msg.elapsedSeconds).toLocaleString()} tok/s
+                      </span>
+                    )}
+                    {msg.elapsedSeconds !== undefined && (
+                      <span title="Elapsed time">
+                        ⏱ {msg.elapsedSeconds.toFixed(1)}s
+                      </span>
+                    )}
+                  </div>
+                )}
              </div>
            </div>
          ))}
@@ -26,6 +26,10 @@ function authHeaders(): Record<string, string> {
 export interface ChatResponse {
  reply: string;
  session_id: string;
+  input_tokens: number;
+  output_tokens: number;
+  total_tokens: number;
+  elapsed_seconds: number;
 }

 export interface MessageData {
@@ -37,7 +37,6 @@ dependencies = [
    { name = "python-docx" },
    { name = "rank-bm25" },
    { name = "redis", extra = ["hiredis"] },
-    { name = "sentence-transformers" },
    { name = "sqlalchemy", extra = ["asyncio"] },
    { name = "uvicorn", extra = ["standard"] },
 ]
@@ -63,7 +62,6 @@ requires-dist = [
    { name = "python-docx", specifier = ">=1.2.0" },
    { name = "rank-bm25", specifier = ">=0.2.2" },
    { name = "redis", extras = ["hiredis"], specifier = ">=5.2.0" },
-    { name = "sentence-transformers", specifier = ">=5.5.1" },
    { name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0.36" },
    { name = "uvicorn", extras = ["standard"], specifier = ">=0.34.0" },
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