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feat: init llm-proxy

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Praveen Thirumurugan
2025-10-18 01:22:02 +05:30
commit 6839ae6f6a
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# Dependencies
node_modules/
npm-debug.log*
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yarn-error.log*
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# Dockerfile
FROM node:22-slim
WORKDIR /app
# Copy package files first for caching
COPY package*.json ./
# Install ALL dependencies (including devDeps for TypeScript)
RUN npm install
# Copy all source code
COPY . .
# Compile TypeScript inside container
RUN npx tsc
ENV PORT=8080
EXPOSE 8080
# Run the compiled JS file
CMD ["node", "dist/proxy.js"]

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# LLM Proxy Server
A simple OpenAI-compatible proxy server that forwards requests to any LLM endpoint with logging and cost tracking. The logs are stored in a PostgreSQL database. It's not possible to ignore the logging at the moment.
## Configuration
| Environment Variable | Description | Default Value |
|----------------------|-------------|-----------------|
| `PORT` | Server port | `3007` |
| `UPSTREAM_URL` | Your LLM endpoint URL | `""` |
| `DATABASE_URL` | PostgreSQL connection string for logging | `""` |
| `DATABASE_TABLE` | Name of the table to store the logs | `"llm_proxy"` |
### Cost Calculation
The cost is calculated based on the model and the usage.
You'll need to add the cost configuration for your models in the `cost.ts` file. The default cost configuration in the project (with sample values from `z.ai` models) is:
```typescript
export const MODEL_COSTS: Record<string, CostConfig> = {
"glm-4.5-air": { input: 0.2, cached: 0.03, output: 1.1 }, // cost per million prompt tokens (USD)
"glm-4.6": { input: 0.6, cached: 0.11, output: 2.2 }, // cost per million cached tokens (USD)
"default": { input: 0, cached: 0, output: 0 }, // default cost configuration
};
```
You can add more models to the `MODEL_COSTS` object.
## PostgreSQL Table Schema
```sql
CREATE TABLE IF NOT EXISTS <DATABASE_TABLE> (
timestamp TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
request_method VARCHAR(10) NOT NULL,
request_path VARCHAR(255) NOT NULL,
model VARCHAR(20) NOT NULL,
completion_tokens INTEGER,
prompt_tokens INTEGER,
total_tokens INTEGER,
cached_tokens INTEGER,
total_cost NUMERIC,
response_time INTEGER,
request_body JSONB,
response_body JSONB,
response_status INTEGER,
provider_url VARCHAR(500)
);
CREATE INDEX IF NOT EXISTS idx_<DATABASE_TABLE>_timestamp ON <DATABASE_TABLE> (timestamp);
```
## Usage
The proxy works with any OpenAI-compatible endpoint. Just point your client to the proxy:
```bash
curl -X POST http://localhost:3007/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-api-key" \
-d '{
"model": "your-model",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
## Features
- ✅ OpenAI-compatible API
- ✅ Streaming support
- ✅ PostgreSQL logging
- ✅ Cost tracking
- ✅ CORS enabled
- ✅ Simple configuration - just set your endpoint URL

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// cost.ts
// Helper for calculating token-based LLM costs
export type Usage = {
prompt_tokens?: number;
completion_tokens?: number;
total_tokens?: number;
prompt_tokens_details?: {
cached_tokens?: number;
}
};
export type CostConfig = {
input: number; // cost per million prompt tokens (USD)
cached: number; // cost per million cached tokens (USD)
output: number; // cost per million completion tokens (USD)
};
// Cost table per model (USD per million tokens)
export const MODEL_COSTS: Record<string, CostConfig> = {
"glm-4.5-air": { input: 0.2, cached: 0.03, output: 1.1 },
"glm-4.6": { input: 0.6, cached: 0.11, output: 2.2 },
"default": { input: 0, cached: 0, output: 0 },
};
// Compute total cost (in USD)
export function calculateCost(model: string, usage?: Usage): number | null {
if (!usage) return null;
const { prompt_tokens = 0, completion_tokens = 0, prompt_tokens_details = { cached_tokens: 0 } } = usage;
const cost = MODEL_COSTS[model] || MODEL_COSTS["default"];
let inputCost = 0;
let cachedCost = 0;
let outputCost = 0;
if (prompt_tokens_details?.cached_tokens) {
cachedCost = ((prompt_tokens_details.cached_tokens) / 1_000_000) * cost.cached;
inputCost = ((prompt_tokens - prompt_tokens_details.cached_tokens) / 1_000_000) * cost.input;
} else {
inputCost = ((prompt_tokens) / 1_000_000) * cost.input;
}
outputCost = (completion_tokens / 1_000_000) * cost.output;
const total = inputCost + cachedCost + outputCost;
return total > 0 ? Number(total.toFixed(6)) : null;
}

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21
package.json Normal file
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{
"name": "llm-proxy-server",
"version": "1.0.0",
"main": "proxy.ts",
"scripts": {
"dev": "ts-node-dev --respawn --transpile-only proxy.ts",
"start": "node dist/proxy.js",
"build": "tsc"
},
"devDependencies": {
"@types/node": "^24.8.1",
"@types/pg": "^8.15.5",
"ts-node": "^10.9.2",
"ts-node-dev": "^2.0.0",
"typescript": "^5.9.3"
},
"dependencies": {
"dotenv": "^17.2.3",
"pg": "^8.16.3"
}
}

191
proxy.ts Normal file
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// proxy.ts
// Minimal OpenAI-compatible proxy with streaming & PostgreSQL logging.
// Node 18+ required.
import "dotenv/config";
import http, { IncomingMessage, ServerResponse } from "http";
import { TextDecoder } from "util";
import { Pool } from "pg";
import { calculateCost } from "./cost";
const PORT = Number(process.env.PORT || 3007);
const UPSTREAM_URL = (process.env.UPSTREAM_URL || "").replace(/\/+$/, "");
// --- PostgreSQL connection ---
const pool = new Pool({
connectionString: process.env.DATABASE_URL,
});
// --- Helper functions ---
function setCors(res: ServerResponse) {
res.setHeader("Access-Control-Allow-Origin", "*");
res.setHeader("Access-Control-Allow-Headers", "Authorization, Content-Type, X-Requested-With");
res.setHeader("Access-Control-Allow-Methods", "GET,POST,OPTIONS");
}
function readBody(req: IncomingMessage): Promise<Buffer> {
return new Promise((resolve, reject) => {
const chunks: Buffer[] = [];
req.on("data", (c: Buffer) => chunks.push(c));
req.on("end", () => resolve(Buffer.concat(chunks)));
req.on("error", reject);
});
}
function okPath(path: string) {
return path.startsWith("/chat/completions") ||
path.startsWith("/completions") ||
path.startsWith("/models");
}
// --- Logging to Postgres ---
async function logToPG(data: Record<string, any>) {
const keys = Object.keys(data);
const cols = keys.map(k => `"${k}"`).join(",");
const vals = keys.map((_, i) => `$${i + 1}`).join(",");
const values = Object.values(data);
await pool.query(`INSERT INTO ${process.env.DATABASE_TABLE || "llm_proxy"} (${cols}) VALUES (${vals})`, values);
}
// --- Main proxy server ---
const server = http.createServer(async (req, res) => {
const start = Date.now();
setCors(res);
if (req.method === "OPTIONS") {
res.statusCode = 204;
return res.end();
}
try {
const url = new URL(req.url || "/", `http://${req.headers.host}`);
const path = url.pathname;
const method = req.method || "GET";
if (!okPath(path)) {
res.statusCode = 404;
res.end(JSON.stringify({ error: "Not found" }));
return;
}
const auth = req.headers["authorization"];
if (!auth?.toString().startsWith("Bearer ")) {
res.statusCode = 401;
res.end(JSON.stringify({ error: "Missing or invalid Authorization header" }));
return;
}
const bodyBuf = method === "POST" ? await readBody(req) : Buffer.from("");
const requestJson = bodyBuf.length ? JSON.parse(bodyBuf.toString()) : null;
const upstreamRes = await fetch(UPSTREAM_URL + path + url.search, {
method,
headers: {
"Content-Type": (req.headers["content-type"] as string) || "application/json",
Authorization: auth.toString(),
},
// @ts-ignore
duplex: "half",
body: method === "POST" ? bodyBuf.toString() : undefined,
});
const contentType = upstreamRes.headers.get("content-type") || "application/json";
res.statusCode = upstreamRes.status;
res.setHeader("Content-Type", contentType);
// --- Streaming or non-streaming response handling ---
let responseBody: any = null;
if (contentType.includes("text/event-stream")) {
res.setHeader("Cache-Control", "no-cache");
res.setHeader("Connection", "keep-alive");
res.setHeader("Transfer-Encoding", "chunked");
const reader = upstreamRes.body?.getReader();
const decoder = new TextDecoder();
let rawText = "";
let usageFromStream: any = null;
if (reader) {
let buffer = "";
while (true) {
const { done, value } = await reader.read();
if (done) break;
if (value) {
const chunk = decoder.decode(value);
res.write(value);
rawText += chunk;
buffer += chunk;
const lines = buffer.split(/\r?\n/);
buffer = lines.pop() || "";
for (const line of lines) {
if (!line.startsWith("data:")) continue;
const jsonStr = line.slice(5).trim();
if (jsonStr === "[DONE]") continue;
try {
const obj = JSON.parse(jsonStr);
if (obj.usage) usageFromStream = obj.usage;
} catch {
/* ignore partial lines */
}
}
}
}
}
res.end();
// Use whatever we captured from the stream
responseBody = {
streamed: true,
preview: rawText.slice(0, 5000),
usage: usageFromStream,
};
}
else {
const text = await upstreamRes.text();
res.end(text);
try {
responseBody = JSON.parse(text);
} catch {
responseBody = text;
}
}
// --- Token usage and metadata ---
const usage = responseBody?.usage || {};
const totalCost = calculateCost(requestJson?.model || "default", usage);
const logData = {
timestamp: new Date(),
request_method: method,
request_path: path,
model: requestJson?.model || "default",
completion_tokens: usage.completion_tokens || null,
prompt_tokens: usage.prompt_tokens || null,
total_tokens: usage.total_tokens || null,
cached_tokens: usage.cached_tokens || null,
total_cost: totalCost,
response_time: Date.now() - start,
request_body: requestJson,
response_body: responseBody,
response_status: upstreamRes.status,
provider_url: UPSTREAM_URL,
provider: "Upstream",
};
logToPG(logData).catch(err => console.error("PG log error:", err));
} catch (err: any) {
console.error("Proxy error:", err);
res.statusCode = 502;
res.end(JSON.stringify({ error: "Proxy error", message: err?.message }));
}
});
server.listen(PORT, () => {
console.log(`✅ Proxy running at http://localhost:${PORT}`);
});

12
tsconfig.json Normal file
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{
"compilerOptions": {
"target": "ES2022",
"module": "CommonJS",
"rootDir": ".",
"outDir": "dist",
"esModuleInterop": true,
"strict": true,
"skipLibCheck": true
},
"include": ["*.ts"]
}