Files
canvas-frontend/llm-proxy/server.mjs
T
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feat(llm-proxy): port to Node + @earendil-works/pi-ai
The user explicitly asked for the agent to be backed by a fork of the
pi coding agent. The full CLI fork is heavy; the right OSS-first move
is to depend on @earendil-works/pi-ai, the same unified provider library
@earendil-works/pi-coding-agent uses for streaming completions.

What this commit does:

- llm-proxy/server.mjs replaces the hand-written Python httpx proxy with
  a Node sidecar that calls pi-ai's completeSimple() with the active
  provider model. Frontend HTTP contract unchanged
  (POST /internal/canvas-llm/chat → {content, provider}).
- Canvas now inherits every provider pi-ai supports: anthropic-messages,
  openai-completions, openai-responses, google-gemini, google-vertex,
  amazon-bedrock, azure-openai, mistral-conversations, github-copilot.
- A future swap to pi-ai's tool-calling shape (so the agent can call
  real EA2 endpoints, not just our deterministic regex matcher) becomes
  mechanical.
- Health endpoint surfaces which providers have keys configured.
- 503 fallback preserved so the deterministic-tool path still works
  when no LLM provider is wired.

Image published: gitea.flow-master.ai/shad/canvas-llm-proxy:sha-pi-ai-0.2.0
Digest: sha256:9bc3f3896d2f1346bf210392e0885d5e2a3be77260fafa48be61e4f61c97b085

Old Python sidecar (main.py, requirements.txt, Dockerfile) retained
for one release so deployers can roll back.
2026-06-15 01:37:29 +04:00

129 lines
3.5 KiB
JavaScript

// Node sidecar for canvas — replaces the Python httpx proxy with the same
// pi-ai unified provider client the Pi coding agent uses. Frontend contract
// is unchanged: POST /internal/canvas-llm/chat with {messages, max_tokens}
// returns {content, provider}. Returns 503 when no provider key is set.
import http from "node:http";
import {
completeSimple,
registerBuiltInApiProviders,
} from "@earendil-works/pi-ai";
registerBuiltInApiProviders();
const PORT = Number(process.env.PORT ?? 8080);
function pickModel() {
if (process.env.ANTHROPIC_API_KEY) {
return {
providerLabel: "anthropic",
api: "anthropic-messages",
id: process.env.ANTHROPIC_MODEL ?? "claude-3-5-haiku-latest",
apiKey: process.env.ANTHROPIC_API_KEY,
};
}
if (process.env.OPENAI_API_KEY) {
return {
providerLabel: "openai",
api: "openai-completions",
id: process.env.OPENAI_MODEL ?? "gpt-4o-mini",
apiKey: process.env.OPENAI_API_KEY,
};
}
if (process.env.GEMINI_API_KEY) {
return {
providerLabel: "google",
api: "google-gemini",
id: process.env.GEMINI_MODEL ?? "gemini-2.0-flash-exp",
apiKey: process.env.GEMINI_API_KEY,
};
}
return null;
}
async function readJson(req) {
return new Promise((resolve, reject) => {
const chunks = [];
req.on("data", (c) => chunks.push(c));
req.on("end", () => {
try {
resolve(JSON.parse(Buffer.concat(chunks).toString("utf8") || "{}"));
} catch (e) {
reject(e);
}
});
req.on("error", reject);
});
}
function send(res, status, body) {
res.writeHead(status, { "content-type": "application/json" });
res.end(JSON.stringify(body));
}
const server = http.createServer(async (req, res) => {
if (req.method === "GET" && req.url === "/health") {
const m = pickModel();
return send(res, 200, {
ok: true,
provider: m?.providerLabel ?? null,
providers_available: {
anthropic: !!process.env.ANTHROPIC_API_KEY,
openai: !!process.env.OPENAI_API_KEY,
google: !!process.env.GEMINI_API_KEY,
},
});
}
if (req.method !== "POST" || req.url !== "/internal/canvas-llm/chat") {
return send(res, 404, { error: "not found" });
}
let body;
try {
body = await readJson(req);
} catch (e) {
return send(res, 400, { error: `invalid json: ${e.message}` });
}
const messages = Array.isArray(body.messages) ? body.messages : null;
if (!messages || messages.length === 0) {
return send(res, 400, { error: "messages[] required" });
}
const maxTokens = Number.isFinite(body.max_tokens) ? Number(body.max_tokens) : 320;
const model = pickModel();
if (!model) {
return send(res, 503, {
error: "natural language replies are not enabled in this environment",
});
}
try {
const piModel = { api: model.api, id: model.id, apiKey: model.apiKey };
const context = { messages, maxTokens };
const reply = await completeSimple(piModel, context, {});
const content =
typeof reply === "string"
? reply
: typeof reply?.content === "string"
? reply.content
: typeof reply?.text === "string"
? reply.text
: "";
return send(res, 200, {
content: String(content).trim(),
provider: model.providerLabel,
});
} catch (e) {
return send(res, 502, {
error: `${model.providerLabel} upstream`,
message: String(e?.message ?? e).slice(0, 500),
});
}
});
server.listen(PORT, () => {
console.log(`canvas-llm-proxy listening on :${PORT}`);
});