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| Author | SHA1 | Date | |
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2a4675a139 |
@@ -1,7 +1,6 @@
|
||||
.env
|
||||
.venv
|
||||
.vscode
|
||||
.venv
|
||||
build
|
||||
greeting.md
|
||||
node_modules
|
||||
|
||||
@@ -40,5 +40,3 @@ To run the proxy locally for development or testing, install Node.js >= 18.0.0 a
|
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4. Start the server in development mode with `npm run start:dev`.
|
||||
|
||||
You can also use `npm run start:dev:tsc` to enable project-wide type checking at the cost of slower startup times. `npm run type-check` can be used to run type checking without starting the server.
|
||||
|
||||
See the [Optional Dependencies](./docs/optional-dependencies.md) page for information on how to install the optional Claude tokenizer locally.
|
||||
|
||||
@@ -1,45 +0,0 @@
|
||||
# Switched to alpine both for smaller image size and because zeromq.js provides
|
||||
# a working prebuilt binary for alpine. On Debian, the prebuild was not working
|
||||
# and a bug in libzmq's makefile was causing the build from source to fail.
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||||
# https://github.com/zeromq/zeromq.js/issues/529#issuecomment-1370721089
|
||||
FROM node:18-alpine as builder
|
||||
|
||||
# Install general build dependencies
|
||||
RUN apk add --no-cache autoconf automake g++ libtool zeromq-dev python3 \
|
||||
py3-pip git curl cmake gcc musl-dev pkgconfig openssl-dev
|
||||
|
||||
# Install Rust (required to build huggingface/tokenizers)
|
||||
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
|
||||
ENV PATH="/root/.cargo/bin:${PATH}"
|
||||
|
||||
RUN git clone -b tokenize https://gitgud.io/khanon/oai-reverse-proxy.git /app
|
||||
WORKDIR /app
|
||||
|
||||
RUN npm ci
|
||||
|
||||
RUN npm run build && \
|
||||
npm prune --production
|
||||
|
||||
FROM node:18-alpine as runner
|
||||
|
||||
RUN apk add --no-cache \
|
||||
zeromq-dev \
|
||||
python3
|
||||
|
||||
COPY --from=builder /app/build /app/build
|
||||
COPY --from=builder /app/node_modules /app/node_modules
|
||||
COPY --from=builder /app/.venv /app/.venv
|
||||
COPY --from=builder /app/package.json /app/package.json
|
||||
|
||||
WORKDIR /app
|
||||
RUN . .venv/bin/activate
|
||||
|
||||
EXPOSE 7860
|
||||
|
||||
ENV NODE_ENV=production
|
||||
|
||||
# TODO: stamp with tag and git commit
|
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ENV RENDER=true
|
||||
ENV RENDER_GIT_COMMIT=ci-test
|
||||
|
||||
CMD [ "npm", "start" ]
|
||||
@@ -1,10 +1,9 @@
|
||||
FROM node:18-bullseye
|
||||
FROM node:18-bullseye-slim
|
||||
RUN apt-get update && \
|
||||
apt-get install -y git python3 python3-pip libzmq3-dev curl cmake g++ libsodium-dev pkg-config
|
||||
apt-get install -y git
|
||||
RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
|
||||
WORKDIR /app
|
||||
RUN pip3 install --no-cache-dir -r requirements.txt
|
||||
RUN npm ci --loglevel=verbose
|
||||
RUN npm install
|
||||
COPY Dockerfile greeting.md* .env* ./
|
||||
RUN npm run build
|
||||
EXPOSE 7860
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
# Warning
|
||||
**I strongly suggest against using this feature with a Google account that you care about.** Depending on the content of the prompts people submit, Google may flag the spreadsheet as containing inappropriate content. This seems to prevent you from sharing that spreadsheet _or any others on the account. This happened with my throwaway account during testing; the existing shared spreadsheet continues to work but even completely new spreadsheets are flagged and cannot be shared.
|
||||
# ⚠️ Warning ⚠️
|
||||
**I strongly suggest against using this feature with a Google account that you care about.** Depending on the content of the prompts people submit (which you obviously have no control over), Google may flag the spreadsheet as containing inappropriate content. If this happens, Google may suspend your ability to share the spreadsheet, block access to Google Sheets, or even suspend your entire Google account (this happened to my throwaway, though it may have been because it was very clearly a throwaway and used a burner SMS number).
|
||||
|
||||
I'll be looking into alternative storage backends but you should not use this implementation with a Google account you care about, or even one remotely connected to your main accounts (as Google has a history of linking accounts together via IPs/browser fingerprinting). Use a VPN and completely isolated VM to be safe.
|
||||
**Be aware that Google has been known to link accounts through device/browser fingerprinting, so even a VPN may not be sufficient; if you must use this feature, do so entirely from an isolated VM and VPN with no other Google accounts logged in.**
|
||||
|
||||
There are now other logging options available, so you should use those instead. I'm leaving this here for posterity, but I will not be providing any support for it.
|
||||
|
||||
# Configuring Google Sheets Prompt Logging
|
||||
This proxy can log incoming prompts and model responses to Google Sheets. Some configuration on the Google side is required to enable this feature. The APIs used are free, but you will need a Google account and a Google Cloud Platform project.
|
||||
@@ -10,7 +12,7 @@ NOTE: Concurrency is not supported. Don't connect two instances of the server to
|
||||
|
||||
## Prerequisites
|
||||
- A Google account
|
||||
- **USE A THROWAWAY ACCOUNT!**
|
||||
- **⚠️ USE A THROWAWAY ACCOUNT!**
|
||||
- A Google Cloud Platform project
|
||||
|
||||
### 0. Create a Google Cloud Platform Project
|
||||
|
||||
@@ -1,35 +0,0 @@
|
||||
# Optional Dependencies
|
||||
## Claude tokenizer
|
||||
As Anthropic does not ship a NodeJS tokenizer, the server includes a small Python script that runs alongside the proxy to tokenize Claude requests. It is automatically started when the server is launched, but requires additional dependencies to be installed. If these dependencies are not installed, the server will not be able to accurately count the number of tokens in Claude requests but will still function normally otherwise.
|
||||
|
||||
Note: On Windows, a Windows Firewall prompt may appear when the Claude tokenizer is started. This is normal and is caused by the Python process attempting to open a socket to communicate with the NodeJS server. You can safely allow the connection.
|
||||
|
||||
### Automatic installation (local development)
|
||||
This will create a venv and install the required dependencies. You still need to activate the venv when running the server, and you must have Python >= 3.8.0 installed.
|
||||
1. Install Python >= 3.8.0
|
||||
2. Run `npm install`, which should automatically create a venv and install the required dependencies.
|
||||
3. Activate the virtual environment with `source .venv/bin/activate` (Linux/Mac) or `.\.venv\Scripts\activate` (PowerShell/Windows)
|
||||
- **This step is required every time you start the server from a new terminal.**
|
||||
|
||||
### Manual installation (local development)
|
||||
1. Install Python >= 3.8.0
|
||||
2. Create a virtual environment using `python -m .venv venv`
|
||||
3. Activate the virtual environment with `source .venv/bin/activate` (Linux/Mac) or `.\.venv\Scripts\activate` (PowerShell/Windows)
|
||||
- **This step is required every time you start the server from a new terminal.**
|
||||
4. Install dependencies with `pip install -r requirements.txt`
|
||||
5. Provided you have the virtual environment activated, the server will automatically start the tokenizer when it is launched.
|
||||
|
||||
### Docker (production deployment)
|
||||
Refer to the reference Dockerfiles for examples on how to install the tokenizer. The Huggingface and Render Dockerfiles both include the tokenizer.
|
||||
|
||||
Generally, you will need libzmq3-dev, cmake, g++, and Python >= 3.8.0 installed. The postinstall script will automatically install the required Python dependencies.
|
||||
|
||||
### Troubleshooting
|
||||
Ensure that:
|
||||
- Python >= 3.8 is installed and in your PATH
|
||||
- Python dependencies are installed (re-run `npm install`)
|
||||
- Python venv is activated (see above)
|
||||
- zeromq optional dependency installed successfully
|
||||
- This should generally be installed automatically.
|
||||
- On Windows, you may need to install MS C++ Build Tools or set msvs_version (eg `npm config set msvs_version 2019`), then re-run npm install.
|
||||
- On Linux, ensure you have the appropriate build tools and headers installed for your distribution; refer to the reference Dockerfiles for examples.
|
||||
@@ -0,0 +1,41 @@
|
||||
# Prompt Logging
|
||||
|
||||
This proxy supports logging incoming prompts and model responses to different destinations. Currently, Airtable and Google Sheets (not recommended) are supported. You can enable prompt logging by setting the `PROMPT_LOGGING` environment variable to `true` and configuring the `PROMPT_LOGGING_BACKEND` environment variable to the desired logging backend.
|
||||
|
||||
The included backends are generally designed with the goal of working within the limitations of a service's free tier, such as strict API rate limits or maximum record limits. As a result, they may be a little clunky to use and may not be as performant as a dedicated logging solution, but they should be sufficient for low-volume use cases. You can implement your own backend by exporting a module that implements the `PromptLoggingBackend` interface and wiring it up to `src/prompt-logging/log-queue.ts`.
|
||||
|
||||
Refer to the list below for the required configuration for each backend.
|
||||
|
||||
## Airtable
|
||||
|
||||
1. Create an Airtable.com account
|
||||
2. Create a Personal Access Token
|
||||
1. Go to https://airtable.com/create/tokens/new and enter a name for your token
|
||||
2. Under **Scopes**, click **Add a scope** and assign the following scopes:
|
||||
- `data.records:read`
|
||||
- `data.records:write`
|
||||
- `schema.bases:read`
|
||||
- `schema.bases:write`
|
||||
3. Under **Access**, click **Add a base** and assign "All current and future bases in this workspace"
|
||||
- Create a new workspace for prompt logging if you don't want to give the script access to all your bases
|
||||
4. Click **Create token**
|
||||
5. A modal will appear with your token; copy it and set is as the `AIRTABLE_KEY` environment variable
|
||||
3. Find your workspace ID
|
||||
- You can find your workspace ID by going to https://airtable.com/workspaces and selecting **View Workspace** on the workspace you want to use
|
||||
- The ID is the text beginning with `wsp` in the URL, after `airtable.com/workspaces/`
|
||||
- Set this value as the `AIRTABLE_WORKSPACE_ID` environment variable
|
||||
4. Set the `PROMPT_LOGGING_BACKEND` environment variable to `airtable`
|
||||
|
||||
The proxy will handle creating and migrating bases for you. The following bases will be created in the workspace you select:
|
||||
|
||||
- `oai-proxy-index`
|
||||
- Stores metadata about the proxy and the bases it creates
|
||||
- `oai-proxy-logs-*`
|
||||
- Stores prompt logs
|
||||
- As free bases are limited in size, the proxy will create additional bases as needed
|
||||
|
||||
## Google Sheets (deprecated)
|
||||
|
||||
**⚠️ This implementation is strongly discouraged** due to the nature of content users may submit, which may be in violation of Google's policies. They seem to analyze the content of API requests and may suspend your account. Don't use this unless you know what you're doing.
|
||||
|
||||
Refer to the dedicated [Google Sheets docs](logging-sheets.md) for detailed instructions on how to set up Google Sheets logging.
|
||||
-47
@@ -1,47 +0,0 @@
|
||||
const esbuild = require("esbuild");
|
||||
const fs = require("fs");
|
||||
const { copy } = require("esbuild-plugin-copy");
|
||||
|
||||
const buildDir = "build";
|
||||
|
||||
const config = {
|
||||
entryPoints: ["src/server.ts"],
|
||||
bundle: true,
|
||||
outfile: `${buildDir}/server.js`,
|
||||
platform: "node",
|
||||
target: "es2020",
|
||||
format: "cjs",
|
||||
sourcemap: true,
|
||||
external: ["fs", "path", "zeromq", "tiktoken"],
|
||||
plugins: [
|
||||
copy({
|
||||
resolveFrom: "cwd",
|
||||
assets: {
|
||||
from: ["src/tokenization/*.py"],
|
||||
to: [`${buildDir}/tokenization`],
|
||||
},
|
||||
}),
|
||||
],
|
||||
};
|
||||
|
||||
function createBundler() {
|
||||
return {
|
||||
build: async () => esbuild.build(config),
|
||||
watch: async () => {
|
||||
const watchConfig = { ...config, logLevel: "info" };
|
||||
const ctx = await esbuild.context(watchConfig);
|
||||
ctx.watch();
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
(async () => {
|
||||
fs.rmSync(buildDir, { recursive: true, force: true });
|
||||
const isDev = process.argv.includes("--dev");
|
||||
const bundler = createBundler();
|
||||
if (isDev) {
|
||||
await bundler.watch();
|
||||
} else {
|
||||
await bundler.build();
|
||||
}
|
||||
})();
|
||||
Generated
+108
-678
File diff suppressed because it is too large
Load Diff
+7
-18
@@ -3,13 +3,12 @@
|
||||
"version": "1.0.0",
|
||||
"description": "Reverse proxy for the OpenAI API",
|
||||
"scripts": {
|
||||
"build:dev": "node esbuild.js --dev",
|
||||
"build": "node esbuild.js",
|
||||
"postinstall": "node scripts/install-python-deps.js",
|
||||
"start:dev:tsc": "nodemon --watch src --exec ts-node src/server.ts",
|
||||
"start:dev": "concurrently \"npm run build:dev\" \"npm run start:watch\"",
|
||||
"start:replit": "tsc && node build/server.js",
|
||||
"build:watch": "esbuild src/server.ts --outfile=build/server.js --platform=node --target=es2020 --format=cjs --bundle --sourcemap --watch",
|
||||
"build": "tsc",
|
||||
"start:dev": "concurrently \"npm run build:watch\" \"npm run start:watch\"",
|
||||
"start:dev:tsc": "nodemon --watch src --exec ts-node --transpile-only src/server.ts",
|
||||
"start:watch": "nodemon --require source-map-support/register build/server.js",
|
||||
"start:replit": "tsc && node build/server.js",
|
||||
"start": "node build/server.js",
|
||||
"type-check": "tsc --noEmit"
|
||||
},
|
||||
@@ -19,18 +18,18 @@
|
||||
"author": "",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"airtable": "^0.12.1",
|
||||
"axios": "^1.3.5",
|
||||
"cors": "^2.8.5",
|
||||
"dotenv": "^16.0.3",
|
||||
"express": "^4.18.2",
|
||||
"firebase-admin": "^11.9.0",
|
||||
"firebase-admin": "^11.8.0",
|
||||
"googleapis": "^117.0.0",
|
||||
"http-proxy-middleware": "^3.0.0-beta.1",
|
||||
"openai": "^3.2.1",
|
||||
"pino": "^8.11.0",
|
||||
"pino-http": "^8.3.3",
|
||||
"showdown": "^2.1.0",
|
||||
"tiktoken": "^1.0.7",
|
||||
"uuid": "^9.0.0",
|
||||
"zlib": "^1.0.5",
|
||||
"zod": "^3.21.4"
|
||||
@@ -40,22 +39,12 @@
|
||||
"@types/express": "^4.17.17",
|
||||
"@types/showdown": "^2.0.0",
|
||||
"@types/uuid": "^9.0.1",
|
||||
"@types/zeromq": "^5.2.2",
|
||||
"concurrently": "^8.0.1",
|
||||
"esbuild": "^0.17.16",
|
||||
"esbuild-node-externals": "^1.7.0",
|
||||
"esbuild-plugin-copy": "^2.1.1",
|
||||
"esbuild-register": "^3.4.2",
|
||||
"nodemon": "^2.0.22",
|
||||
"source-map-support": "^0.5.21",
|
||||
"ts-node": "^10.9.1",
|
||||
"typescript": "^5.0.4"
|
||||
},
|
||||
"overrides": {
|
||||
"optionator": "^0.9.3",
|
||||
"semver": "^7.5.3"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"zeromq": "^6.0.0-beta.16"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,2 +0,0 @@
|
||||
pyzmq==25.1.0
|
||||
anthropic==0.2.9
|
||||
@@ -1,68 +0,0 @@
|
||||
const fs = require("fs");
|
||||
const spawn = require("child_process").spawn;
|
||||
|
||||
const IS_WINDOWS = process.platform === "win32";
|
||||
const IS_DEV = process.env.NODE_ENV !== "production";
|
||||
|
||||
const installDeps = async () => {
|
||||
try {
|
||||
console.log("Installing additional optional dependencies...");
|
||||
console.log("Creating venv...");
|
||||
await maybeCreateVenv();
|
||||
console.log("Installing python dependencies...");
|
||||
await installPythonDependencies();
|
||||
} catch (error) {
|
||||
console.error("Error installing additional optional dependencies", error);
|
||||
process.exit(0); // don't fail the build
|
||||
}
|
||||
};
|
||||
|
||||
installDeps();
|
||||
|
||||
async function maybeCreateVenv() {
|
||||
if (!IS_DEV) {
|
||||
console.log("Skipping venv creation in production");
|
||||
return true;
|
||||
}
|
||||
if (fs.existsSync(".venv")) {
|
||||
console.log("Skipping venv creation, already exists");
|
||||
return true;
|
||||
}
|
||||
const python = IS_WINDOWS ? "python" : "python3";
|
||||
await runCommand(`${python} -m venv .venv`);
|
||||
return true;
|
||||
}
|
||||
|
||||
async function installPythonDependencies() {
|
||||
const commands = [];
|
||||
if (IS_DEV) {
|
||||
commands.push(
|
||||
IS_WINDOWS ? ".venv\\Scripts\\activate.bat" : "source .venv/bin/activate"
|
||||
);
|
||||
}
|
||||
const pip = IS_WINDOWS ? "pip" : "pip3";
|
||||
commands.push(`${pip} install -r requirements.txt`);
|
||||
|
||||
const command = commands.join(" && ");
|
||||
await runCommand(command);
|
||||
return true;
|
||||
}
|
||||
|
||||
async function runCommand(command) {
|
||||
return new Promise((resolve, reject) => {
|
||||
const child = spawn(command, [], { shell: true });
|
||||
child.stdout.on("data", (data) => {
|
||||
console.log(data.toString());
|
||||
});
|
||||
child.stderr.on("data", (data) => {
|
||||
console.error(data.toString());
|
||||
});
|
||||
child.on("close", (code) => {
|
||||
if (code === 0) {
|
||||
resolve();
|
||||
} else {
|
||||
reject();
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
+20
-11
@@ -8,7 +8,6 @@ const startupLogger = pino({ level: "debug" }).child({ module: "startup" });
|
||||
|
||||
const isDev = process.env.NODE_ENV !== "production";
|
||||
|
||||
type PromptLoggingBackend = "google_sheets";
|
||||
export type DequeueMode = "fair" | "random" | "none";
|
||||
|
||||
type Config = {
|
||||
@@ -75,12 +74,22 @@ type Config = {
|
||||
logLevel?: "debug" | "info" | "warn" | "error";
|
||||
/** Whether prompts and responses should be logged to persistent storage. */
|
||||
promptLogging?: boolean;
|
||||
/** Which prompt logging backend to use. */
|
||||
promptLoggingBackend?: PromptLoggingBackend;
|
||||
/** Which prompt logging backend to use.
|
||||
*
|
||||
* `google_sheets`: Logs prompts and responses to a Google Sheets spreadsheet.
|
||||
* This method is no longer recommended; see docs for more info.
|
||||
*
|
||||
* `airtable`: Logs prompts and responses to an Airtable table.
|
||||
*/
|
||||
promptLoggingBackend?: "google_sheets" | "airtable";
|
||||
/** Base64-encoded Google Sheets API key. */
|
||||
googleSheetsKey?: string;
|
||||
/** Google Sheets spreadsheet ID. */
|
||||
googleSheetsSpreadsheetId?: string;
|
||||
/** Airtable personal access token. */
|
||||
airtableKey?: string;
|
||||
/** Airtable workspace ID, under which bases will be automatically created. */
|
||||
airtableWorkspaceId?: string;
|
||||
/** Whether to periodically check keys for usage and validity. */
|
||||
checkKeys?: boolean;
|
||||
/**
|
||||
@@ -119,11 +128,6 @@ type Config = {
|
||||
* Desination URL to redirect blocked requests to, for non-JSON requests.
|
||||
*/
|
||||
blockRedirect?: string;
|
||||
/**
|
||||
* Whether the proxy should disallow requests for GPT-4 models in order to
|
||||
* prevent excessive spend. Applies only to OpenAI.
|
||||
*/
|
||||
turboOnly?: boolean;
|
||||
};
|
||||
|
||||
// To change configs, create a file called .env in the root directory.
|
||||
@@ -155,6 +159,8 @@ export const config: Config = {
|
||||
quotaDisplayMode: getEnvWithDefault("QUOTA_DISPLAY_MODE", "partial"),
|
||||
promptLogging: getEnvWithDefault("PROMPT_LOGGING", false),
|
||||
promptLoggingBackend: getEnvWithDefault("PROMPT_LOGGING_BACKEND", undefined),
|
||||
airtableKey: getEnvWithDefault("AIRTABLE_KEY", undefined),
|
||||
airtableWorkspaceId: getEnvWithDefault("AIRTABLE_WORKSPACE_ID", undefined),
|
||||
googleSheetsKey: getEnvWithDefault("GOOGLE_SHEETS_KEY", undefined),
|
||||
googleSheetsSpreadsheetId: getEnvWithDefault(
|
||||
"GOOGLE_SHEETS_SPREADSHEET_ID",
|
||||
@@ -167,7 +173,6 @@ export const config: Config = {
|
||||
"You must be over the age of majority in your country to use this service."
|
||||
),
|
||||
blockRedirect: getEnvWithDefault("BLOCK_REDIRECT", "https://www.9gag.com"),
|
||||
turboOnly: getEnvWithDefault("TURBO_ONLY", false),
|
||||
} as const;
|
||||
|
||||
function migrateConfigs() {
|
||||
@@ -240,7 +245,7 @@ export async function assertConfigIsValid() {
|
||||
// Ensure forks which add new secret-like config keys don't unwittingly expose
|
||||
// them to users.
|
||||
for (const key of getKeys(config)) {
|
||||
const maybeSensitive = ["key", "credentials", "secret", "password"].some(
|
||||
const maybeSensitive = ["key", "credential", "secret", "password"].some(
|
||||
(sensitive) => key.toLowerCase().includes(sensitive)
|
||||
);
|
||||
const secured = new Set([...SENSITIVE_KEYS, ...OMITTED_KEYS]);
|
||||
@@ -257,7 +262,10 @@ export async function assertConfigIsValid() {
|
||||
* Config keys that are masked on the info page, but not hidden as their
|
||||
* presence may be relevant to the user due to privacy implications.
|
||||
*/
|
||||
export const SENSITIVE_KEYS: (keyof Config)[] = ["googleSheetsSpreadsheetId"];
|
||||
export const SENSITIVE_KEYS: (keyof Config)[] = [
|
||||
"googleSheetsSpreadsheetId",
|
||||
"airtableWorkspaceId",
|
||||
];
|
||||
|
||||
/**
|
||||
* Config keys that are not displayed on the info page at all, generally because
|
||||
@@ -273,6 +281,7 @@ export const OMITTED_KEYS: (keyof Config)[] = [
|
||||
"checkKeys",
|
||||
"quotaDisplayMode",
|
||||
"googleSheetsKey",
|
||||
"airtableKey",
|
||||
"firebaseKey",
|
||||
"firebaseRtdbUrl",
|
||||
"gatekeeperStore",
|
||||
|
||||
+4
-17
@@ -52,17 +52,7 @@ function cacheInfoPageHtml(baseUrl: string) {
|
||||
};
|
||||
|
||||
const title = getServerTitle();
|
||||
let headerHtml = buildInfoPageHeader(new showdown.Converter(), title);
|
||||
|
||||
if (process.env.MISSING_PYTHON_WARNING) {
|
||||
headerHtml +=
|
||||
`<p style="color: red;">Python is not installed; the Claude tokenizer ` +
|
||||
`cannot start. Your Dockerfile may be out of date; see <a ` +
|
||||
`href="https://gitgud.io/khanon/oai-reverse-proxy">the docs</a> for an ` +
|
||||
`updated Huggingface Dockerfile.</p><p>You can disable this warning by ` +
|
||||
`setting <code>DISABLE_MISSING_PYTHON_WARNING=true</code> in your ` +
|
||||
`environment.</p>`;
|
||||
}
|
||||
const headerHtml = buildInfoPageHeader(new showdown.Converter(), title);
|
||||
|
||||
const pageBody = `<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
@@ -99,7 +89,7 @@ type ServiceInfo = {
|
||||
function getOpenAIInfo() {
|
||||
const info: { [model: string]: Partial<ServiceInfo> } = {};
|
||||
const keys = keyPool.list().filter((k) => k.service === "openai");
|
||||
const hasGpt4 = keys.some((k) => k.isGpt4) && !config.turboOnly;
|
||||
const hasGpt4 = keys.some((k) => k.isGpt4);
|
||||
|
||||
if (keyPool.anyUnchecked()) {
|
||||
const uncheckedKeys = keys.filter((k) => !k.lastChecked);
|
||||
@@ -147,9 +137,6 @@ function getOpenAIInfo() {
|
||||
} else {
|
||||
info.status = "Key checking is disabled." as any;
|
||||
info.turbo = { activeKeys: keys.filter((k) => !k.isDisabled).length };
|
||||
info.gpt4 = {
|
||||
activeKeys: keys.filter((k) => !k.isDisabled && k.isGpt4).length,
|
||||
};
|
||||
}
|
||||
|
||||
if (config.queueMode !== "none") {
|
||||
@@ -203,14 +190,14 @@ Logs are anonymous and do not contain IP addresses or timestamps. [You can see t
|
||||
}
|
||||
|
||||
if (config.queueMode !== "none") {
|
||||
const waits: string[] = [];
|
||||
const waits = [];
|
||||
infoBody += `\n## Estimated Wait Times\nIf the AI is busy, your prompt will processed when a slot frees up.`;
|
||||
|
||||
if (config.openaiKey) {
|
||||
const turboWait = getQueueInformation("turbo").estimatedQueueTime;
|
||||
const gpt4Wait = getQueueInformation("gpt-4").estimatedQueueTime;
|
||||
waits.push(`**Turbo:** ${turboWait}`);
|
||||
if (keyPool.list().some((k) => k.isGpt4) && !config.turboOnly) {
|
||||
if (keyPool.list().some((k) => k.isGpt4)) {
|
||||
waits.push(`**GPT-4:** ${gpt4Wait}`);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,13 +3,11 @@ import { Key, KeyProvider } from "..";
|
||||
import { config } from "../../config";
|
||||
import { logger } from "../../logger";
|
||||
|
||||
// https://docs.anthropic.com/claude/reference/selecting-a-model
|
||||
export const ANTHROPIC_SUPPORTED_MODELS = [
|
||||
"claude-instant-v1",
|
||||
"claude-instant-v1-100k",
|
||||
"claude-v1",
|
||||
"claude-v1-100k",
|
||||
"claude-2",
|
||||
] as const;
|
||||
export type AnthropicModel = (typeof ANTHROPIC_SUPPORTED_MODELS)[number];
|
||||
|
||||
@@ -40,16 +38,10 @@ export interface AnthropicKey extends Key {
|
||||
}
|
||||
|
||||
/**
|
||||
* Upon being rate limited, a key will be locked out for this many milliseconds
|
||||
* while we wait for other concurrent requests to finish.
|
||||
* We don't get rate limit headers from Anthropic so if we get a 429, we just
|
||||
* lock out the key for a few seconds
|
||||
*/
|
||||
const RATE_LIMIT_LOCKOUT = 2000;
|
||||
/**
|
||||
* Upon assigning a key, we will wait this many milliseconds before allowing it
|
||||
* to be used again. This is to prevent the queue from flooding a key with too
|
||||
* many requests while we wait to learn whether previous ones succeeded.
|
||||
*/
|
||||
const KEY_REUSE_DELAY = 500;
|
||||
const RATE_LIMIT_LOCKOUT = 5000;
|
||||
|
||||
export class AnthropicKeyProvider implements KeyProvider<AnthropicKey> {
|
||||
readonly service = "anthropic";
|
||||
@@ -135,7 +127,7 @@ export class AnthropicKeyProvider implements KeyProvider<AnthropicKey> {
|
||||
// Intended to throttle the queue processor as otherwise it will just
|
||||
// flood the API with requests and we want to wait a sec to see if we're
|
||||
// going to get a rate limit error on this key.
|
||||
selectedKey.rateLimitedUntil = now + KEY_REUSE_DELAY;
|
||||
selectedKey.rateLimitedUntil = now + 1000;
|
||||
return { ...selectedKey };
|
||||
}
|
||||
|
||||
@@ -189,9 +181,15 @@ export class AnthropicKeyProvider implements KeyProvider<AnthropicKey> {
|
||||
/**
|
||||
* This is called when we receive a 429, which means there are already five
|
||||
* concurrent requests running on this key. We don't have any information on
|
||||
* when these requests will resolve, so all we can do is wait a bit and try
|
||||
* again. We will lock the key for 2 seconds after getting a 429 before
|
||||
* retrying in order to give the other requests a chance to finish.
|
||||
* when these requests will resolve so all we can do is wait a bit and try
|
||||
* again.
|
||||
* We will lock the key for 10 seconds, which should let a few of the other
|
||||
* generations finish. This is an arbitrary number but the goal is to balance
|
||||
* between not hammering the API with requests and not locking out a key that
|
||||
* is actually available.
|
||||
* TODO; Try to assign requests to slots on each key so we have an idea of how
|
||||
* long each slot has been running and can make a more informed decision on
|
||||
* how long to lock the key.
|
||||
*/
|
||||
public markRateLimited(keyHash: string) {
|
||||
this.log.warn({ key: keyHash }, "Key rate limited");
|
||||
|
||||
@@ -221,13 +221,6 @@ export class OpenAIKeyChecker {
|
||||
"Key is out of quota. Disabling key."
|
||||
);
|
||||
this.updateKey(key.hash, { isDisabled: true });
|
||||
}
|
||||
else if (status === 429 && data.error.type === "access_terminated") {
|
||||
this.log.warn(
|
||||
{ key: key.hash, isTrial: key.isTrial, error: data },
|
||||
"Key has been terminated due to policy violations. Disabling key."
|
||||
);
|
||||
this.updateKey(key.hash, { isDisabled: true });
|
||||
} else {
|
||||
this.log.error(
|
||||
{ key: key.hash, status, error: data },
|
||||
|
||||
@@ -77,7 +77,7 @@ export class OpenAIKeyProvider implements KeyProvider<OpenAIKey> {
|
||||
const newKey = {
|
||||
key: k,
|
||||
service: "openai" as const,
|
||||
isGpt4: true,
|
||||
isGpt4: false,
|
||||
isTrial: false,
|
||||
isDisabled: false,
|
||||
softLimit: 0,
|
||||
@@ -128,17 +128,11 @@ export class OpenAIKeyProvider implements KeyProvider<OpenAIKey> {
|
||||
);
|
||||
if (availableKeys.length === 0) {
|
||||
let message = needGpt4
|
||||
? "No GPT-4 keys available. Try selecting a Turbo model."
|
||||
: "No active OpenAI keys available.";
|
||||
? "No active OpenAI keys available."
|
||||
: "No GPT-4 keys available. Try selecting a non-GPT-4 model.";
|
||||
throw new Error(message);
|
||||
}
|
||||
|
||||
if (needGpt4 && config.turboOnly) {
|
||||
throw new Error(
|
||||
"Proxy operator has disabled GPT-4 to reduce quota usage. Try selecting a Turbo model."
|
||||
);
|
||||
}
|
||||
|
||||
// Select a key, from highest priority to lowest priority:
|
||||
// 1. Keys which are not rate limited
|
||||
// a. We ignore rate limits from over a minute ago
|
||||
|
||||
@@ -0,0 +1,226 @@
|
||||
import Airtable from "airtable";
|
||||
import axios, { AxiosError } from "axios";
|
||||
import { config } from "../../config";
|
||||
import { logger } from "../../logger";
|
||||
import { PromptLogBackend, PromptLogEntry } from "..";
|
||||
|
||||
type AirbaseFieldType =
|
||||
| "singleLineText"
|
||||
| "multilineText"
|
||||
| "number"
|
||||
| "dateTime";
|
||||
|
||||
type IndexRecord = {
|
||||
/** Name of the base */
|
||||
id: string;
|
||||
/** Schema version of the base */
|
||||
schema: 1;
|
||||
/** Last row number used */
|
||||
lastRow: number;
|
||||
/** When the base was created. ISO 8601 format. */
|
||||
created: string;
|
||||
};
|
||||
|
||||
const INDEX_BASE_NAME = "oai-proxy-index";
|
||||
|
||||
export class AirtableBackend implements PromptLogBackend {
|
||||
private log = logger.child({ module: "airtable" });
|
||||
private airtable: Airtable;
|
||||
private indexBase: Airtable.Base | null = null;
|
||||
private indexTable: Airtable.Table<IndexRecord> | null = null;
|
||||
private activeLogBase: Airtable.Base | null = null;
|
||||
private activeLogTable: Airtable.Table<PromptLogEntry> | null = null;
|
||||
|
||||
constructor() {
|
||||
this.airtable = new Airtable({
|
||||
apiKey: config.airtableKey,
|
||||
requestTimeout: 1000 * 60 * 1,
|
||||
});
|
||||
}
|
||||
|
||||
async init() {
|
||||
this.log.info("Initializing Airtable backend...");
|
||||
await this.ensureIndexBase();
|
||||
await this.ensureLogBase();
|
||||
}
|
||||
|
||||
private async ensureIndexBase() {
|
||||
const bases = await this.listBases();
|
||||
const indexBaseId = bases.find((b) => b.name === INDEX_BASE_NAME)?.id;
|
||||
if (!indexBaseId) {
|
||||
this.log.info("Creating index base.");
|
||||
const result = await this.createBase(INDEX_BASE_NAME, [
|
||||
{ name: "id", type: "singleLineText" },
|
||||
{ name: "schema", type: "number" },
|
||||
{ name: "lastRow", type: "number" },
|
||||
{ name: "created", type: "dateTime" },
|
||||
]);
|
||||
this.log.info("Index base created.");
|
||||
this.indexBase = this.airtable.base(result);
|
||||
this.indexTable = this.indexBase.table<IndexRecord>(INDEX_BASE_NAME);
|
||||
} else {
|
||||
this.log.info("Index base already exists.");
|
||||
this.indexBase = this.airtable.base(indexBaseId);
|
||||
this.indexTable = this.indexBase.table<IndexRecord>(INDEX_BASE_NAME);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the active log base to the newest one in the index, unless there are
|
||||
* no bases or the newest one is already full. Creates a new base if needed.
|
||||
*/
|
||||
private async ensureLogBase() {
|
||||
const indexRecords = await this.indexTable!.select().all();
|
||||
if (indexRecords.length === 0) {
|
||||
this.log.info("No log bases found, creating a new one.");
|
||||
await this.createLogBase();
|
||||
} else {
|
||||
const newestBase = indexRecords.reduce((a, b) => {
|
||||
const aDate = new Date(a.get("created"));
|
||||
const bDate = new Date(b.get("created"));
|
||||
return aDate > bDate ? a : b;
|
||||
});
|
||||
const lastRow = newestBase.get("lastRow");
|
||||
if (lastRow >= 1000) {
|
||||
this.log.info(
|
||||
{ lastRow },
|
||||
"Last log base is full, creating a new one."
|
||||
);
|
||||
await this.createLogBase();
|
||||
} else if (this.activeLogBase === null) {
|
||||
const newestBaseId = newestBase.get("id");
|
||||
this.log.info(
|
||||
{ activeLogBase: newestBaseId },
|
||||
"Setting active log base."
|
||||
);
|
||||
this.activeLogBase = this.airtable.base(newestBaseId);
|
||||
this.activeLogTable =
|
||||
this.activeLogBase.table<PromptLogEntry>(newestBaseId);
|
||||
} else {
|
||||
this.log.debug("Active log base already set.");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private async createLogBase() {
|
||||
const indexRecords = await this.indexTable!.select().all();
|
||||
const baseCount = indexRecords.length;
|
||||
const baseName = `oai-proxy-log-${baseCount.toString().padStart(3, "0")}`;
|
||||
this.log.info({ baseName }, "Creating new log base.");
|
||||
|
||||
const newBaseId = await this.createBase(baseName, [
|
||||
{ name: "model", type: "singleLineText" },
|
||||
{ name: "endpoint", type: "singleLineText" },
|
||||
{ name: "promptRaw", type: "multilineText" },
|
||||
{ name: "prompt", type: "multilineText" },
|
||||
{ name: "response", type: "multilineText" },
|
||||
]);
|
||||
this.activeLogBase = this.airtable.base(newBaseId);
|
||||
this.activeLogTable = this.activeLogBase.table<PromptLogEntry>(baseName);
|
||||
this.log.info({ baseName }, "New log base created and activated.");
|
||||
await this.indexTable!.create([
|
||||
{
|
||||
fields: {
|
||||
id: newBaseId,
|
||||
schema: 1,
|
||||
lastRow: 0,
|
||||
created: new Date().toISOString(),
|
||||
},
|
||||
},
|
||||
]);
|
||||
this.log.info({ baseName }, "New log base added to index.");
|
||||
}
|
||||
|
||||
/**
|
||||
* Appends a batch of entries to the log and updates the index. If the log
|
||||
* has reached its maximum size, a new log base will be created.
|
||||
*/
|
||||
async appendBatch(entries: PromptLogEntry[]) {
|
||||
if (!this.activeLogBase || !this.activeLogTable) {
|
||||
throw new Error("No active log base.");
|
||||
}
|
||||
// Airtable can only create 10 rows at a time, so we have to chunk it.
|
||||
const chunkSize = 10;
|
||||
const chunks = [];
|
||||
for (let i = 0; i < entries.length; i += chunkSize) {
|
||||
chunks.push(entries.slice(i, i + chunkSize));
|
||||
}
|
||||
this.log.info(
|
||||
{ batchSize: entries.length, chunks: chunks.length },
|
||||
"Appending batch of log entries."
|
||||
);
|
||||
for (const chunk of chunks) {
|
||||
const records = chunk.map((entry) => ({
|
||||
fields: {
|
||||
model: entry.model,
|
||||
endpoint: entry.endpoint,
|
||||
promptRaw: entry.promptRaw,
|
||||
prompt: entry.promptFlattened,
|
||||
response: entry.response,
|
||||
},
|
||||
}));
|
||||
await this.activeLogTable.create(records);
|
||||
this.log.info(
|
||||
{ count: records.length },
|
||||
"Submitted chunk of log entries."
|
||||
);
|
||||
}
|
||||
await this.syncIndex();
|
||||
await this.ensureLogBase();
|
||||
}
|
||||
|
||||
async syncIndex() {
|
||||
if (!this.activeLogBase || !this.activeLogTable) {
|
||||
throw new Error("No active log base.");
|
||||
}
|
||||
const logRecords = await this.activeLogTable.select().all();
|
||||
const logCount = logRecords.length;
|
||||
// Update the index with the new row count, by the active log base ID.
|
||||
const indexRecords = await this.indexTable!.select({
|
||||
filterByFormula: `{id} = "${this.activeLogBase.getId()}"`,
|
||||
}).all();
|
||||
if (indexRecords.length !== 1) {
|
||||
throw new Error("Index record not found.");
|
||||
}
|
||||
const indexRecord = indexRecords[0];
|
||||
await this.indexTable!.update([
|
||||
{ id: indexRecord.id, fields: { lastRow: logCount } },
|
||||
]);
|
||||
}
|
||||
|
||||
// The airtable library doesn't support meta operations like listing or
|
||||
// creating bases, so we have to do that ourselves.
|
||||
|
||||
/**
|
||||
* Lists all bases in the workspace.
|
||||
* @returns Array of base objects with `id` and `name` properties.
|
||||
*/
|
||||
private async listBases(): Promise<{ id: string; name: string }[]> {
|
||||
// Maximum page size is 1000 but I'm not going to bother with that for now.
|
||||
const url = `https://api.airtable.com/v0/meta/bases`;
|
||||
const response = await axios.get(url, {
|
||||
headers: { Authorization: `Bearer ${config.airtableKey}` },
|
||||
});
|
||||
return response.data.bases;
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a new base with the given name and table schema. Table will be
|
||||
* created with the same name as the base.
|
||||
* Schema is a list of fields, each of which has a name and type. Only a
|
||||
* subset of field types are supported.
|
||||
* Returns the id of the new base.
|
||||
*/
|
||||
private async createBase(
|
||||
name: string,
|
||||
fields: { name: string; type: AirbaseFieldType }[]
|
||||
) {
|
||||
const url = `https://api.airtable.com/v0/meta/bases`;
|
||||
const response = await axios.post(
|
||||
url,
|
||||
{ name, tables: [{ name, fields }] },
|
||||
{ headers: { Authorization: `Bearer ${config.airtableKey}` } }
|
||||
);
|
||||
return response.data.id;
|
||||
}
|
||||
}
|
||||
@@ -1 +1,19 @@
|
||||
export * as sheets from "./sheets";
|
||||
import { config } from "../../config";
|
||||
import { PromptLogBackend } from "..";
|
||||
import { AirtableBackend } from "./airtable";
|
||||
import { sheets } from "./sheets";
|
||||
|
||||
export const createPromptLogBackend = (
|
||||
backend: NonNullable<typeof config.promptLoggingBackend>
|
||||
): PromptLogBackend => {
|
||||
switch (backend) {
|
||||
case "google_sheets":
|
||||
// Sheets backend is just a module, though it has a bunch of state so it
|
||||
// should probably be a class just like the Airtable backend.
|
||||
return sheets;
|
||||
case "airtable":
|
||||
return new AirtableBackend();
|
||||
default:
|
||||
throw new Error(`Unknown log backend: ${backend}`);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -10,7 +10,7 @@ import type { CredentialBody } from "google-auth-library";
|
||||
import type { GaxiosResponse } from "googleapis-common";
|
||||
import { config } from "../../config";
|
||||
import { logger } from "../../logger";
|
||||
import { PromptLogEntry } from "..";
|
||||
import { PromptLogBackend, PromptLogEntry } from "..";
|
||||
|
||||
// There is always a sheet called __index__ which contains a list of all the
|
||||
// other sheets. We use this rather than iterating over all the sheets in case
|
||||
@@ -240,7 +240,7 @@ const createLogSheet = async () => {
|
||||
activeLogSheet = { sheetName, rows: [] };
|
||||
};
|
||||
|
||||
export const appendBatch = async (batch: PromptLogEntry[]) => {
|
||||
const appendBatch = async (batch: PromptLogEntry[]) => {
|
||||
if (!activeLogSheet) {
|
||||
// Create a new log sheet if we don't have one yet.
|
||||
await createLogSheet();
|
||||
@@ -256,9 +256,9 @@ export const appendBatch = async (batch: PromptLogEntry[]) => {
|
||||
return [
|
||||
entry.model,
|
||||
entry.endpoint,
|
||||
entry.promptRaw.slice(0, 50000),
|
||||
entry.promptFlattened.slice(0, 50000),
|
||||
entry.response.slice(0, 50000),
|
||||
entry.promptRaw,
|
||||
entry.promptFlattened,
|
||||
entry.response,
|
||||
];
|
||||
});
|
||||
log.info({ sheetName, rowCount: newRows.length }, "Appending log batch.");
|
||||
@@ -310,40 +310,7 @@ const finalizeBatch = async () => {
|
||||
log.info({ sheetName, rowCount }, "Batch finalized.");
|
||||
};
|
||||
|
||||
type LoadLogSheetArgs = {
|
||||
sheetName: string;
|
||||
/** The starting row to load. If omitted, loads all rows (expensive). */
|
||||
fromRow?: number;
|
||||
};
|
||||
|
||||
/** Not currently used. */
|
||||
export const loadLogSheet = async ({
|
||||
sheetName,
|
||||
fromRow = 2, // omit header row
|
||||
}: LoadLogSheetArgs) => {
|
||||
const client = sheetsClient!;
|
||||
const spreadsheetId = config.googleSheetsSpreadsheetId!;
|
||||
|
||||
const range = `${sheetName}!A${fromRow}:E`;
|
||||
const res = await client.spreadsheets.values.get({
|
||||
spreadsheetId: spreadsheetId,
|
||||
range,
|
||||
});
|
||||
const data = assertData(res);
|
||||
const values = data.values || [];
|
||||
const rows = values.slice(1).map((row) => {
|
||||
return {
|
||||
model: row[0],
|
||||
endpoint: row[1],
|
||||
promptRaw: row[2],
|
||||
promptFlattened: row[3],
|
||||
response: row[4],
|
||||
};
|
||||
});
|
||||
activeLogSheet = { sheetName, rows };
|
||||
};
|
||||
|
||||
export const init = async (onStop: () => void) => {
|
||||
const init = async (onStop: () => void) => {
|
||||
if (sheetsClient) {
|
||||
return;
|
||||
}
|
||||
@@ -420,3 +387,5 @@ function assertData<T = sheets_v4.Schema$ValueRange>(res: GaxiosResponse<T>) {
|
||||
}
|
||||
return res.data!;
|
||||
}
|
||||
|
||||
export const sheets = { init, appendBatch };
|
||||
|
||||
@@ -6,7 +6,7 @@ database for now.
|
||||
Due to the limitations of Google Sheets, we'll queue up log entries and flush
|
||||
them to the API periodically. */
|
||||
|
||||
export interface PromptLogEntry {
|
||||
export type PromptLogEntry = {
|
||||
model: string;
|
||||
endpoint: string;
|
||||
/** JSON prompt passed to the model */
|
||||
@@ -15,6 +15,11 @@ export interface PromptLogEntry {
|
||||
promptFlattened: string;
|
||||
response: string;
|
||||
// TODO: temperature, top_p, top_k, etc.
|
||||
};
|
||||
|
||||
export interface PromptLogBackend {
|
||||
init(onStop: () => void): Promise<void>;
|
||||
appendBatch(entries: PromptLogEntry[]): Promise<void>;
|
||||
}
|
||||
|
||||
export * as logQueue from "./log-queue";
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
/* Queues incoming prompts/responses and periodically flushes them to configured
|
||||
* logging backend. */
|
||||
|
||||
import { config } from "../config";
|
||||
import { logger } from "../logger";
|
||||
import { PromptLogEntry } from ".";
|
||||
import { sheets } from "./backends";
|
||||
import { PromptLogBackend, PromptLogEntry } from ".";
|
||||
import { createPromptLogBackend } from "./backends";
|
||||
|
||||
const FLUSH_INTERVAL = 1000 * 10;
|
||||
const MAX_BATCH_SIZE = 25;
|
||||
@@ -11,11 +11,19 @@ const MAX_BATCH_SIZE = 25;
|
||||
const queue: PromptLogEntry[] = [];
|
||||
const log = logger.child({ module: "log-queue" });
|
||||
|
||||
let activeBackend: PromptLogBackend | null = null;
|
||||
let started = false;
|
||||
let timeoutId: NodeJS.Timeout | null = null;
|
||||
let retrying = false;
|
||||
let consecutiveFailedBatches = 0;
|
||||
|
||||
const getBackend = () => {
|
||||
if (!activeBackend) {
|
||||
throw new Error("Log queue not initialized.");
|
||||
}
|
||||
return activeBackend;
|
||||
};
|
||||
|
||||
export const enqueue = (payload: PromptLogEntry) => {
|
||||
if (!started) {
|
||||
log.warn("Log queue not started, discarding incoming log entry.");
|
||||
@@ -34,7 +42,7 @@ export const flush = async () => {
|
||||
const nextBatch = queue.splice(0, batchSize);
|
||||
log.info({ size: nextBatch.length }, "Submitting new batch.");
|
||||
try {
|
||||
await sheets.appendBatch(nextBatch);
|
||||
await getBackend().appendBatch(nextBatch);
|
||||
retrying = false;
|
||||
consecutiveFailedBatches = 0;
|
||||
} catch (e: any) {
|
||||
@@ -65,7 +73,13 @@ export const flush = async () => {
|
||||
|
||||
export const start = async () => {
|
||||
try {
|
||||
await sheets.init(() => stop());
|
||||
const selectedBackend = config.promptLoggingBackend;
|
||||
if (!selectedBackend) {
|
||||
throw new Error("No logging backend configured.");
|
||||
}
|
||||
|
||||
activeBackend = createPromptLogBackend(selectedBackend);
|
||||
await getBackend().init(() => stop());
|
||||
log.info("Logging backend initialized.");
|
||||
started = true;
|
||||
} catch (e) {
|
||||
|
||||
+1
-13
@@ -9,12 +9,10 @@ import { handleProxyError } from "./middleware/common";
|
||||
import {
|
||||
addKey,
|
||||
addAnthropicPreamble,
|
||||
blockZoomerOrigins,
|
||||
createPreprocessorMiddleware,
|
||||
finalizeBody,
|
||||
languageFilter,
|
||||
limitOutputTokens,
|
||||
removeOriginHeaders,
|
||||
} from "./middleware/request";
|
||||
import {
|
||||
ProxyResHandlerWithBody,
|
||||
@@ -43,8 +41,6 @@ const getModelsResponse = () => {
|
||||
"claude-instant-v1.1",
|
||||
"claude-instant-v1.1-100k",
|
||||
"claude-instant-v1.0",
|
||||
"claude-2", // claude-2 is 100k by default it seems
|
||||
"claude-2.0",
|
||||
];
|
||||
|
||||
const models = claudeVariants.map((id) => ({
|
||||
@@ -77,8 +73,6 @@ const rewriteAnthropicRequest = (
|
||||
addAnthropicPreamble,
|
||||
languageFilter,
|
||||
limitOutputTokens,
|
||||
blockZoomerOrigins,
|
||||
removeOriginHeaders,
|
||||
finalizeBody,
|
||||
];
|
||||
|
||||
@@ -108,16 +102,10 @@ const anthropicResponseHandler: ProxyResHandlerWithBody = async (
|
||||
body.proxy_note = `Prompts are logged on this proxy instance. See ${host} for more information.`;
|
||||
}
|
||||
|
||||
if (req.inboundApi === "openai") {
|
||||
if (!req.originalUrl.includes("/v1/complete")) {
|
||||
req.log.info("Transforming Anthropic response to OpenAI format");
|
||||
body = transformAnthropicResponse(body);
|
||||
}
|
||||
|
||||
// TODO: Remove once tokenization is stable
|
||||
if (req.debug) {
|
||||
body.proxy_tokenizer_debug_info = req.debug;
|
||||
}
|
||||
|
||||
res.status(200).json(body);
|
||||
};
|
||||
|
||||
|
||||
@@ -33,7 +33,7 @@ export const gatekeeper: RequestHandler = (req, res, next) => {
|
||||
// TODO: Generate anonymous users based on IP address for public or proxy_key
|
||||
// modes so that all middleware can assume a user of some sort is present.
|
||||
|
||||
if (ADMIN_KEY && token === ADMIN_KEY) {
|
||||
if (token === ADMIN_KEY) {
|
||||
return next();
|
||||
}
|
||||
|
||||
|
||||
@@ -2,6 +2,7 @@ import { Request, Response } from "express";
|
||||
import httpProxy from "http-proxy";
|
||||
import { ZodError } from "zod";
|
||||
|
||||
|
||||
const OPENAI_CHAT_COMPLETION_ENDPOINT = "/v1/chat/completions";
|
||||
const ANTHROPIC_COMPLETION_ENDPOINT = "/v1/complete";
|
||||
|
||||
@@ -31,23 +32,15 @@ export function writeErrorResponse(
|
||||
res.headersSent ||
|
||||
res.getHeader("content-type") === "text/event-stream"
|
||||
) {
|
||||
const errorContent =
|
||||
statusCode === 403
|
||||
? JSON.stringify(errorPayload)
|
||||
: JSON.stringify(errorPayload, null, 2);
|
||||
|
||||
const msg = buildFakeSseMessage(
|
||||
`${errorSource} error (${statusCode})`,
|
||||
errorContent,
|
||||
JSON.stringify(errorPayload, null, 2),
|
||||
req
|
||||
);
|
||||
res.write(msg);
|
||||
res.write(`data: [DONE]\n\n`);
|
||||
res.end();
|
||||
} else {
|
||||
if (req.debug) {
|
||||
errorPayload.error.proxy_tokenizer_debug_info = req.debug;
|
||||
}
|
||||
res.status(statusCode).json(errorPayload);
|
||||
}
|
||||
}
|
||||
@@ -60,12 +53,10 @@ export const handleProxyError: httpProxy.ErrorCallback = (err, req, res) => {
|
||||
export const handleInternalError = (
|
||||
err: Error,
|
||||
req: Request,
|
||||
res: Response,
|
||||
errorType: string = "proxy_internal_error"
|
||||
res: Response
|
||||
) => {
|
||||
try {
|
||||
const isZod = err instanceof ZodError;
|
||||
const isForbidden = err.name === "ForbiddenError";
|
||||
if (isZod) {
|
||||
writeErrorResponse(req, res, 400, {
|
||||
error: {
|
||||
@@ -76,21 +67,10 @@ export const handleInternalError = (
|
||||
message: err.message,
|
||||
},
|
||||
});
|
||||
} else if (isForbidden) {
|
||||
// Spoofs a vaguely threatening OpenAI error message. Only invoked by the
|
||||
// block-zoomers rewriter to scare off tiktokers.
|
||||
writeErrorResponse(req, res, 403, {
|
||||
error: {
|
||||
type: "organization_account_disabled",
|
||||
code: "policy_violation",
|
||||
param: null,
|
||||
message: err.message,
|
||||
},
|
||||
});
|
||||
} else {
|
||||
writeErrorResponse(req, res, 500, {
|
||||
error: {
|
||||
type: errorType,
|
||||
type: "proxy_rewriter_error",
|
||||
proxy_note: `Reverse proxy encountered an error before it could reach the upstream API.`,
|
||||
message: err.message,
|
||||
stack: err.stack,
|
||||
@@ -111,14 +91,10 @@ export function buildFakeSseMessage(
|
||||
req: Request
|
||||
) {
|
||||
let fakeEvent;
|
||||
const useBackticks = !type.includes("403");
|
||||
const msgContent = useBackticks
|
||||
? `\`\`\`\n[${type}: ${string}]\n\`\`\`\n`
|
||||
: `[${type}: ${string}]`;
|
||||
|
||||
if (req.inboundApi === "anthropic") {
|
||||
fakeEvent = {
|
||||
completion: msgContent,
|
||||
completion: `\`\`\`\n[${type}: ${string}]\n\`\`\`\n`,
|
||||
stop_reason: type,
|
||||
truncated: false, // I've never seen this be true
|
||||
stop: null,
|
||||
@@ -133,7 +109,7 @@ export function buildFakeSseMessage(
|
||||
model: req.body?.model,
|
||||
choices: [
|
||||
{
|
||||
delta: { content: msgContent },
|
||||
delta: { content: `\`\`\`\n[${type}: ${string}]\n\`\`\`\n` },
|
||||
index: 0,
|
||||
finish_reason: type,
|
||||
},
|
||||
|
||||
@@ -41,6 +41,8 @@ export const addKey: ProxyRequestMiddleware = (proxyReq, req) => {
|
||||
// For such cases, ignore the requested model entirely.
|
||||
if (req.inboundApi === "openai" && req.outboundApi === "anthropic") {
|
||||
req.log.debug("Using an Anthropic key for an OpenAI-compatible request");
|
||||
// We don't assign the model here, that will happen when transforming the
|
||||
// request body.
|
||||
assignedKey = keyPool.get("claude-v1");
|
||||
} else {
|
||||
assignedKey = keyPool.get(req.body.model);
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
import { isCompletionRequest } from "../common";
|
||||
import { ProxyRequestMiddleware } from ".";
|
||||
|
||||
const DISALLOWED_ORIGIN_SUBSTRINGS = "janitorai.com,janitor.ai".split(",");
|
||||
|
||||
class ForbiddenError extends Error {
|
||||
constructor(message: string) {
|
||||
super(message);
|
||||
this.name = "ForbiddenError";
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Blocks requests from Janitor AI users with a fake, scary error message so I
|
||||
* stop getting emails asking for tech support.
|
||||
*/
|
||||
export const blockZoomerOrigins: ProxyRequestMiddleware = (_proxyReq, req) => {
|
||||
if (!isCompletionRequest(req)) {
|
||||
return;
|
||||
}
|
||||
|
||||
const origin = req.headers.origin || req.headers.referer;
|
||||
if (origin && DISALLOWED_ORIGIN_SUBSTRINGS.some((s) => origin.includes(s))) {
|
||||
// Venus-derivatives send a test prompt to check if the proxy is working.
|
||||
// We don't want to block that just yet.
|
||||
if (req.body.messages[0]?.content === "Just say TEST") {
|
||||
return;
|
||||
}
|
||||
|
||||
throw new ForbiddenError(
|
||||
`Your access was terminated due to violation of our policies, please check your email for more information. If you believe this is in error and would like to appeal, please contact us through our help center at help.openai.com.`
|
||||
);
|
||||
}
|
||||
};
|
||||
@@ -1,36 +0,0 @@
|
||||
import { countTokens } from "../../../tokenization";
|
||||
import { RequestPreprocessor } from ".";
|
||||
import { openAIMessagesToClaudePrompt } from "./transform-outbound-payload";
|
||||
|
||||
export const checkPromptSize: RequestPreprocessor = async (req) => {
|
||||
const prompt =
|
||||
req.inboundApi === "openai" ? req.body.messages : req.body.prompt;
|
||||
|
||||
let result;
|
||||
if (req.outboundApi === "openai") {
|
||||
result = await countTokens({ req, prompt, service: "openai" });
|
||||
} else {
|
||||
// If we're doing OpenAI-to-Anthropic, we need to convert the messages to a
|
||||
// prompt first before counting tokens, as that process affects the token
|
||||
// count.
|
||||
let promptStr =
|
||||
req.inboundApi === "anthropic"
|
||||
? prompt
|
||||
: openAIMessagesToClaudePrompt(prompt);
|
||||
result = await countTokens({
|
||||
req,
|
||||
prompt: promptStr,
|
||||
service: "anthropic",
|
||||
});
|
||||
}
|
||||
|
||||
req.promptTokens = result.token_count;
|
||||
|
||||
// TODO: Remove once token counting is stable
|
||||
req.log.debug({ result }, "Counted prompt tokens");
|
||||
req.debug = req.debug ?? {};
|
||||
req.debug = {
|
||||
...req.debug,
|
||||
...result,
|
||||
};
|
||||
};
|
||||
@@ -4,19 +4,16 @@ import type { ProxyReqCallback } from "http-proxy";
|
||||
|
||||
// Express middleware (runs before http-proxy-middleware, can be async)
|
||||
export { createPreprocessorMiddleware } from "./preprocess";
|
||||
export { checkPromptSize } from "./check-prompt-size";
|
||||
export { setApiFormat } from "./set-api-format";
|
||||
export { transformOutboundPayload } from "./transform-outbound-payload";
|
||||
|
||||
// HPM middleware (runs on onProxyReq, cannot be async)
|
||||
export { addKey } from "./add-key";
|
||||
export { addAnthropicPreamble } from "./add-anthropic-preamble";
|
||||
export { blockZoomerOrigins } from "./block-zoomer-origins";
|
||||
export { finalizeBody } from "./finalize-body";
|
||||
export { languageFilter } from "./language-filter";
|
||||
export { limitCompletions } from "./limit-completions";
|
||||
export { limitOutputTokens } from "./limit-output-tokens";
|
||||
export { removeOriginHeaders } from "./remove-origin-headers";
|
||||
export { transformKoboldPayload } from "./transform-kobold-payload";
|
||||
|
||||
/**
|
||||
|
||||
@@ -1,11 +1,6 @@
|
||||
import { RequestHandler } from "express";
|
||||
import { handleInternalError } from "../common";
|
||||
import {
|
||||
RequestPreprocessor,
|
||||
checkPromptSize,
|
||||
setApiFormat,
|
||||
transformOutboundPayload,
|
||||
} from ".";
|
||||
import { RequestPreprocessor, setApiFormat, transformOutboundPayload } from ".";
|
||||
|
||||
/**
|
||||
* Returns a middleware function that processes the request body into the given
|
||||
@@ -17,7 +12,6 @@ export const createPreprocessorMiddleware = (
|
||||
): RequestHandler => {
|
||||
const preprocessors: RequestPreprocessor[] = [
|
||||
setApiFormat(apiFormat),
|
||||
checkPromptSize,
|
||||
transformOutboundPayload,
|
||||
...(additionalPreprocessors ?? []),
|
||||
];
|
||||
@@ -30,7 +24,7 @@ export const createPreprocessorMiddleware = (
|
||||
next();
|
||||
} catch (error) {
|
||||
req.log.error(error, "Error while executing request preprocessor");
|
||||
handleInternalError(error as Error, req, res, "proxy_preprocessor_error");
|
||||
handleInternalError(error as Error, req, res);
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
@@ -1,10 +0,0 @@
|
||||
import { ProxyRequestMiddleware } from ".";
|
||||
|
||||
/**
|
||||
* Removes origin and referer headers before sending the request to the API for
|
||||
* privacy reasons.
|
||||
**/
|
||||
export const removeOriginHeaders: ProxyRequestMiddleware = (proxyReq) => {
|
||||
proxyReq.setHeader("origin", "");
|
||||
proxyReq.setHeader("referer", "");
|
||||
};
|
||||
@@ -2,13 +2,7 @@ import { Request } from "express";
|
||||
import { z } from "zod";
|
||||
import { isCompletionRequest } from "../common";
|
||||
import { RequestPreprocessor } from ".";
|
||||
import { OpenAIPromptMessage } from "../../../tokenization/openai";
|
||||
|
||||
/**
|
||||
* The maximum number of tokens an Anthropic prompt can have before we switch to
|
||||
* the larger claude-100k context model.
|
||||
*/
|
||||
const CLAUDE_100K_TOKEN_THRESHOLD = 8200;
|
||||
// import { countTokens } from "../../../tokenization";
|
||||
|
||||
// https://console.anthropic.com/docs/api/reference#-v1-complete
|
||||
const AnthropicV1CompleteSchema = z.object({
|
||||
@@ -61,9 +55,10 @@ const OpenAIV1ChatCompletionSchema = z.object({
|
||||
/** Transforms an incoming request body to one that matches the target API. */
|
||||
export const transformOutboundPayload: RequestPreprocessor = async (req) => {
|
||||
const sameService = req.inboundApi === req.outboundApi;
|
||||
const alreadyTransformed = req.retryCount > 0;
|
||||
const notTransformable = !isCompletionRequest(req);
|
||||
|
||||
if (notTransformable) {
|
||||
if (alreadyTransformed || notTransformable) {
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -74,7 +69,6 @@ export const transformOutboundPayload: RequestPreprocessor = async (req) => {
|
||||
? OpenAIV1ChatCompletionSchema
|
||||
: AnthropicV1CompleteSchema;
|
||||
const result = validator.safeParse(req.body);
|
||||
|
||||
if (!result.success) {
|
||||
req.log.error(
|
||||
{ issues: result.error.issues, body: req.body },
|
||||
@@ -82,14 +76,11 @@ export const transformOutboundPayload: RequestPreprocessor = async (req) => {
|
||||
);
|
||||
throw result.error;
|
||||
}
|
||||
|
||||
validatePromptSize(req);
|
||||
return;
|
||||
}
|
||||
|
||||
if (req.inboundApi === "openai" && req.outboundApi === "anthropic") {
|
||||
req.body = openaiToAnthropic(req.body, req);
|
||||
validatePromptSize(req);
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -108,35 +99,46 @@ function openaiToAnthropic(body: any, req: Request) {
|
||||
throw result.error;
|
||||
}
|
||||
|
||||
// Anthropic has started versioning their API, indicated by an HTTP header
|
||||
// `anthropic-version`. The new June 2023 version is not backwards compatible
|
||||
// with our OpenAI-to-Anthropic transformations so we need to explicitly
|
||||
// request the older version for now. 2023-01-01 will be removed in September.
|
||||
// https://docs.anthropic.com/claude/reference/versioning
|
||||
req.headers["anthropic-version"] = "2023-01-01";
|
||||
|
||||
const { messages, ...rest } = result.data;
|
||||
const prompt = openAIMessagesToClaudePrompt(messages);
|
||||
const prompt =
|
||||
result.data.messages
|
||||
.map((m) => {
|
||||
let role: string = m.role;
|
||||
if (role === "assistant") {
|
||||
role = "Assistant";
|
||||
} else if (role === "system") {
|
||||
role = "System";
|
||||
} else if (role === "user") {
|
||||
role = "Human";
|
||||
}
|
||||
// https://console.anthropic.com/docs/prompt-design
|
||||
// `name` isn't supported by Anthropic but we can still try to use it.
|
||||
return `\n\n${role}: ${m.name?.trim() ? `(as ${m.name}) ` : ""}${
|
||||
m.content
|
||||
}`;
|
||||
})
|
||||
.join("") + "\n\nAssistant: ";
|
||||
|
||||
// No longer defaulting to `claude-v1.2` because it seems to be in the process
|
||||
// of being deprecated. `claude-v1` is the new default.
|
||||
// If you have keys that can still use `claude-v1.2`, you can set the
|
||||
// CLAUDE_BIG_MODEL and CLAUDE_SMALL_MODEL environment variables in your .env
|
||||
// file.
|
||||
// Claude 1.2 has been selected as the default for smaller prompts because it
|
||||
// is said to be less pozzed than the newer 1.3 model. But this is not based
|
||||
// on any empirical testing, just speculation based on Anthropic stating that
|
||||
// 1.3 is "safer and less susceptible to adversarial attacks" than 1.2.
|
||||
// From my own interactions, both are pretty easy to jailbreak so I don't
|
||||
// think there's much of a difference, honestly.
|
||||
|
||||
// If you want to override the model selection, you can set the
|
||||
// CLAUDE_BIG_MODEL and CLAUDE_SMALL_MODEL environment variables in your
|
||||
// .env file.
|
||||
|
||||
// Using "v1" of a model will automatically select the latest version of that
|
||||
// model on the Anthropic side.
|
||||
|
||||
const CLAUDE_BIG = process.env.CLAUDE_BIG_MODEL || "claude-v1-100k";
|
||||
const CLAUDE_SMALL = process.env.CLAUDE_SMALL_MODEL || "claude-v1";
|
||||
const CLAUDE_SMALL = process.env.CLAUDE_SMALL_MODEL || "claude-v1.2";
|
||||
|
||||
const contextTokens = Number(req.promptTokens ?? 0) + Number(rest.max_tokens);
|
||||
const model =
|
||||
(contextTokens ?? 0) > CLAUDE_100K_TOKEN_THRESHOLD
|
||||
? CLAUDE_BIG
|
||||
: CLAUDE_SMALL;
|
||||
|
||||
req.log.debug(
|
||||
{ contextTokens, model, CLAUDE_100K_TOKEN_THRESHOLD },
|
||||
"Selected Claude model"
|
||||
);
|
||||
// TODO: Finish implementing tokenizer for more accurate model selection.
|
||||
// This currently uses _character count_, not token count.
|
||||
const model = prompt.length > 25000 ? CLAUDE_BIG : CLAUDE_SMALL;
|
||||
|
||||
let stops = rest.stop
|
||||
? Array.isArray(rest.stop)
|
||||
@@ -159,63 +161,3 @@ function openaiToAnthropic(body: any, req: Request) {
|
||||
stop_sequences: stops,
|
||||
};
|
||||
}
|
||||
|
||||
export function openAIMessagesToClaudePrompt(messages: OpenAIPromptMessage[]) {
|
||||
return (
|
||||
messages
|
||||
.map((m) => {
|
||||
let role: string = m.role;
|
||||
if (role === "assistant") {
|
||||
role = "Assistant";
|
||||
} else if (role === "system") {
|
||||
role = "System";
|
||||
} else if (role === "user") {
|
||||
role = "Human";
|
||||
}
|
||||
// https://console.anthropic.com/docs/prompt-design
|
||||
// `name` isn't supported by Anthropic but we can still try to use it.
|
||||
return `\n\n${role}: ${m.name?.trim() ? `(as ${m.name}) ` : ""}${
|
||||
m.content
|
||||
}`;
|
||||
})
|
||||
.join("") + "\n\nAssistant:"
|
||||
);
|
||||
}
|
||||
|
||||
function validatePromptSize(req: Request) {
|
||||
const promptTokens = req.promptTokens || 0;
|
||||
const model = req.body.model;
|
||||
let maxTokensForModel = 0;
|
||||
|
||||
if (model.match(/gpt-3.5/)) {
|
||||
maxTokensForModel = 4096;
|
||||
} else if (model.match(/gpt-4/)) {
|
||||
maxTokensForModel = 8192;
|
||||
} else if (model.match(/gpt-4-32k/)) {
|
||||
maxTokensForModel = 32768;
|
||||
} else if (model.match(/claude-(?:instant-)?v1(?:\.\d)?(?:-100k)/)) {
|
||||
// Claude models don't throw an error if you exceed the token limit and
|
||||
// instead just become extremely slow and give schizo results, so we will be
|
||||
// more conservative with the token limit for them.
|
||||
maxTokensForModel = 100000 * 0.98;
|
||||
} else if (model.match(/claude-(?:instant-)?v1(?:\.\d)?$/)) {
|
||||
maxTokensForModel = 9000 * 0.98;
|
||||
} else {
|
||||
// I don't trust my regular expressions enough to throw an error here so
|
||||
// we just log a warning and allow 100k tokens.
|
||||
req.log.warn({ model }, "Unknown model, using 100k token limit.");
|
||||
maxTokensForModel = 100000;
|
||||
}
|
||||
|
||||
if (req.debug) {
|
||||
req.debug.calculated_max_tokens = maxTokensForModel;
|
||||
}
|
||||
|
||||
z.number()
|
||||
.max(
|
||||
maxTokensForModel,
|
||||
`Prompt is too long for model ${model} (${promptTokens} tokens, max ${maxTokensForModel})`
|
||||
)
|
||||
.parse(promptTokens);
|
||||
req.log.debug({ promptTokens, maxTokensForModel }, "Prompt size validated");
|
||||
}
|
||||
|
||||
@@ -377,10 +377,6 @@ function handleOpenAIRateLimitError(
|
||||
// Billing quota exceeded (key is dead, disable it)
|
||||
keyPool.disable(req.key!);
|
||||
errorPayload.proxy_note = `Assigned key's quota has been exceeded. ${tryAgainMessage}`;
|
||||
} else if (type === "access_terminated") {
|
||||
// Account banned (key is dead, disable it)
|
||||
keyPool.disable(req.key!);
|
||||
errorPayload.proxy_note = `Assigned key has been banned by OpenAI for policy violations. ${tryAgainMessage}`;
|
||||
} else if (type === "billing_not_active") {
|
||||
// Billing is not active (key is dead, disable it)
|
||||
keyPool.disable(req.key!);
|
||||
|
||||
+3
-18
@@ -9,13 +9,11 @@ import { ipLimiter } from "./rate-limit";
|
||||
import { handleProxyError } from "./middleware/common";
|
||||
import {
|
||||
addKey,
|
||||
blockZoomerOrigins,
|
||||
createPreprocessorMiddleware,
|
||||
finalizeBody,
|
||||
languageFilter,
|
||||
limitCompletions,
|
||||
limitOutputTokens,
|
||||
removeOriginHeaders,
|
||||
} from "./middleware/request";
|
||||
import {
|
||||
createOnProxyResHandler,
|
||||
@@ -30,19 +28,13 @@ function getModelsResponse() {
|
||||
return modelsCache;
|
||||
}
|
||||
|
||||
// https://platform.openai.com/docs/models/overview
|
||||
const gptVariants = [
|
||||
"gpt-4",
|
||||
"gpt-4-0613",
|
||||
"gpt-4-0314", // EOL 2023-09-13
|
||||
"gpt-4-0314",
|
||||
"gpt-4-32k",
|
||||
"gpt-4-32k-0613",
|
||||
"gpt-4-32k-0314", // EOL 2023-09-13
|
||||
"gpt-4-32k-0314",
|
||||
"gpt-3.5-turbo",
|
||||
"gpt-3.5-turbo-0301", // EOL 2023-09-13
|
||||
"gpt-3.5-turbo-0613",
|
||||
"gpt-3.5-turbo-16k",
|
||||
"gpt-3.5-turbo-16k-0613",
|
||||
"gpt-3.5-turbo-0301",
|
||||
];
|
||||
|
||||
const gpt4Available = keyPool.list().filter((key) => {
|
||||
@@ -95,8 +87,6 @@ const rewriteRequest = (
|
||||
languageFilter,
|
||||
limitOutputTokens,
|
||||
limitCompletions,
|
||||
blockZoomerOrigins,
|
||||
removeOriginHeaders,
|
||||
finalizeBody,
|
||||
];
|
||||
|
||||
@@ -125,11 +115,6 @@ const openaiResponseHandler: ProxyResHandlerWithBody = async (
|
||||
body.proxy_note = `Prompts are logged on this proxy instance. See ${host} for more information.`;
|
||||
}
|
||||
|
||||
// TODO: Remove once tokenization is stable
|
||||
if (req.debug) {
|
||||
body.proxy_tokenizer_debug_info = req.debug;
|
||||
}
|
||||
|
||||
res.status(200).json(body);
|
||||
};
|
||||
|
||||
|
||||
+2
-5
@@ -12,7 +12,6 @@ import { handleInfoPage } from "./info-page";
|
||||
import { logQueue } from "./prompt-logging";
|
||||
import { start as startRequestQueue } from "./proxy/queue";
|
||||
import { init as initUserStore } from "./proxy/auth/user-store";
|
||||
import { init as initTokenizers } from "./tokenization";
|
||||
import { checkOrigin } from "./proxy/check-origin";
|
||||
|
||||
const PORT = config.port;
|
||||
@@ -100,8 +99,6 @@ async function start() {
|
||||
|
||||
keyPool.init();
|
||||
|
||||
await initTokenizers();
|
||||
|
||||
if (config.gatekeeper === "user_token") {
|
||||
await initUserStore();
|
||||
}
|
||||
@@ -200,8 +197,8 @@ async function setBuildInfo() {
|
||||
logger.error(
|
||||
{
|
||||
error,
|
||||
stdout: error.stdout?.toString(),
|
||||
stderr: error.stderr?.toString(),
|
||||
stdout: error.stdout.toString(),
|
||||
stderr: error.stderr.toString(),
|
||||
},
|
||||
"Failed to get commit SHA.",
|
||||
error
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
import { spawn, ChildProcess } from "child_process";
|
||||
import { join } from "path";
|
||||
import { logger } from "../logger";
|
||||
|
||||
const TOKENIZER_SOCKET = "tcp://localhost:5555";
|
||||
const log = logger.child({ module: "claude-ipc" });
|
||||
const pythonLog = logger.child({ module: "claude-python" });
|
||||
|
||||
let tokenizer: ChildProcess;
|
||||
let initialized = false;
|
||||
let socket: any; // zeromq.Dealer, not sure how to import it safely as it is optional
|
||||
|
||||
export async function init() {
|
||||
log.info("Initializing Claude tokenizer IPC");
|
||||
try {
|
||||
tokenizer = await launchTokenizer();
|
||||
const zmq = await import("zeromq");
|
||||
socket = new zmq.Dealer({ sendTimeout: 500 });
|
||||
socket.connect(TOKENIZER_SOCKET);
|
||||
|
||||
await socket.send(["init"]);
|
||||
const response = await socket.receive();
|
||||
if (response.toString() !== "ok") {
|
||||
throw new Error("Unexpected init response");
|
||||
}
|
||||
|
||||
// Start message pump
|
||||
processMessages();
|
||||
|
||||
// Test tokenizer
|
||||
const result = await requestTokenCount({
|
||||
requestId: "init-test",
|
||||
prompt: "test prompt",
|
||||
});
|
||||
if (result !== 2) {
|
||||
log.error({ result }, "Unexpected test token count");
|
||||
throw new Error("Unexpected test token count");
|
||||
}
|
||||
|
||||
initialized = true;
|
||||
} catch (err) {
|
||||
log.error({ err: err.message }, "Failed to initialize Claude tokenizer");
|
||||
if (process.env.NODE_ENV !== "production") {
|
||||
console.error(
|
||||
`\nClaude tokenizer failed to initialize.\nIf you want to use the tokenizer, see the Optional Dependencies documentation.\n`
|
||||
);
|
||||
}
|
||||
return false;
|
||||
}
|
||||
log.info("Claude tokenizer IPC ready");
|
||||
return true;
|
||||
}
|
||||
|
||||
const pendingRequests = new Map<
|
||||
string,
|
||||
{ resolve: (tokens: number) => void }
|
||||
>();
|
||||
|
||||
export async function requestTokenCount({
|
||||
requestId,
|
||||
prompt,
|
||||
}: {
|
||||
requestId: string;
|
||||
prompt: string;
|
||||
}) {
|
||||
if (!socket) {
|
||||
throw new Error("Claude tokenizer is not initialized");
|
||||
}
|
||||
|
||||
log.debug({ requestId, chars: prompt.length }, "Requesting token count");
|
||||
await socket.send(["tokenize", requestId, prompt]);
|
||||
|
||||
log.debug({ requestId }, "Waiting for socket response");
|
||||
return new Promise<number>(async (resolve, reject) => {
|
||||
const resolveFn = (tokens: number) => {
|
||||
log.debug({ requestId, tokens }, "Received token count");
|
||||
pendingRequests.delete(requestId);
|
||||
resolve(tokens);
|
||||
};
|
||||
|
||||
pendingRequests.set(requestId, { resolve: resolveFn });
|
||||
|
||||
const timeout = initialized ? 500 : 10000;
|
||||
setTimeout(() => {
|
||||
if (pendingRequests.has(requestId)) {
|
||||
pendingRequests.delete(requestId);
|
||||
const err = "Tokenizer deadline exceeded";
|
||||
log.warn({ requestId }, err);
|
||||
reject(new Error(err));
|
||||
}
|
||||
}, timeout);
|
||||
});
|
||||
}
|
||||
|
||||
async function processMessages() {
|
||||
if (!socket) {
|
||||
throw new Error("Claude tokenizer is not initialized");
|
||||
}
|
||||
log.debug("Starting message loop");
|
||||
for await (const [requestId, tokens] of socket) {
|
||||
const request = pendingRequests.get(requestId.toString());
|
||||
if (!request) {
|
||||
log.error({ requestId }, "No pending request found for incoming message");
|
||||
continue;
|
||||
}
|
||||
request.resolve(Number(tokens.toString()));
|
||||
}
|
||||
}
|
||||
|
||||
async function launchTokenizer() {
|
||||
return new Promise<ChildProcess>((resolve, reject) => {
|
||||
let resolved = false;
|
||||
|
||||
const python = process.platform === "win32" ? "python" : "python3";
|
||||
const proc = spawn(python, [
|
||||
"-u",
|
||||
join(__dirname, "tokenization", "claude-tokenizer.py"),
|
||||
]);
|
||||
if (!proc) {
|
||||
reject(new Error("Failed to spawn Claude tokenizer"));
|
||||
}
|
||||
|
||||
function cleanup() {
|
||||
socket?.close();
|
||||
socket = undefined!;
|
||||
tokenizer = undefined!;
|
||||
}
|
||||
|
||||
proc.stdout!.on("data", (data) => {
|
||||
pythonLog.info(data.toString().trim());
|
||||
});
|
||||
proc.stderr!.on("data", (data) => {
|
||||
pythonLog.error(data.toString().trim());
|
||||
});
|
||||
proc.on("error", (err) => {
|
||||
pythonLog.error({ err }, "Claude tokenizer error");
|
||||
cleanup();
|
||||
if (!resolved) {
|
||||
resolved = true;
|
||||
reject(err);
|
||||
}
|
||||
});
|
||||
proc.on("close", (code) => {
|
||||
pythonLog.info(`Claude tokenizer exited with code ${code}`);
|
||||
cleanup();
|
||||
if (code !== 0 && !resolved) {
|
||||
resolved = true;
|
||||
reject(new Error("Claude tokenizer exited immediately"));
|
||||
}
|
||||
});
|
||||
|
||||
// Wait a moment to catch any immediate errors (missing imports, etc)
|
||||
setTimeout(() => {
|
||||
if (!resolved) {
|
||||
resolved = true;
|
||||
resolve(proc);
|
||||
}
|
||||
}, 200);
|
||||
});
|
||||
}
|
||||
@@ -1,54 +0,0 @@
|
||||
"""
|
||||
This is a small process running alongside the main NodeJS server intended to
|
||||
tokenize prompts for Claude, as currently Anthropic only ships a Python
|
||||
implemetnation for their tokenizer.
|
||||
ZeroMQ is used for IPC between the NodeJS server and this process.
|
||||
"""
|
||||
import zmq
|
||||
import anthropic
|
||||
|
||||
def create_socket():
|
||||
context = zmq.Context()
|
||||
socket = context.socket(zmq.ROUTER)
|
||||
socket.bind("tcp://*:5555")
|
||||
return context, socket
|
||||
|
||||
def init(socket):
|
||||
print("claude-tokenizer.py: starting")
|
||||
try:
|
||||
while True:
|
||||
message = socket.recv_multipart()
|
||||
routing_id, command = message
|
||||
if command == b"init":
|
||||
print("claude-tokenizer.py: initialized")
|
||||
socket.send_multipart([routing_id, b"ok"])
|
||||
break
|
||||
except Exception as e:
|
||||
print("claude-tokenizer.py: failed to initialize")
|
||||
return
|
||||
|
||||
message_processor(socket)
|
||||
|
||||
def message_processor(socket):
|
||||
while True:
|
||||
try:
|
||||
message = socket.recv_multipart()
|
||||
routing_id, command, request_id, payload = message
|
||||
payload = payload.decode("utf-8")
|
||||
if command == b"exit":
|
||||
print("claude-tokenizer.py: exiting")
|
||||
break
|
||||
elif command == b"tokenize":
|
||||
token_count = anthropic.count_tokens(payload)
|
||||
socket.send_multipart([routing_id, request_id, str(token_count).encode("utf-8")])
|
||||
else:
|
||||
print("claude-tokenizer.py: unknown message type")
|
||||
except Exception as e:
|
||||
print(f"claude-tokenizer.py: failed to process message ({e})")
|
||||
break
|
||||
|
||||
if __name__ == "__main__":
|
||||
context, socket = create_socket()
|
||||
init(socket)
|
||||
socket.close()
|
||||
context.term()
|
||||
@@ -1 +0,0 @@
|
||||
export { init, countTokens } from "./tokenizer";
|
||||
@@ -1,57 +0,0 @@
|
||||
import { Tiktoken } from "tiktoken/lite";
|
||||
import cl100k_base from "tiktoken/encoders/cl100k_base.json";
|
||||
|
||||
let encoder: Tiktoken;
|
||||
|
||||
export function init() {
|
||||
encoder = new Tiktoken(
|
||||
cl100k_base.bpe_ranks,
|
||||
cl100k_base.special_tokens,
|
||||
cl100k_base.pat_str
|
||||
);
|
||||
return true;
|
||||
}
|
||||
|
||||
// Implmentation based and tested against:
|
||||
// https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
|
||||
|
||||
export function getTokenCount(messages: any[], model: string) {
|
||||
const gpt4 = model.startsWith("gpt-4");
|
||||
|
||||
const tokensPerMessage = gpt4 ? 3 : 4;
|
||||
const tokensPerName = gpt4 ? 1 : -1; // turbo omits role if name is present
|
||||
|
||||
let numTokens = 0;
|
||||
|
||||
for (const message of messages) {
|
||||
numTokens += tokensPerMessage;
|
||||
for (const key of Object.keys(message)) {
|
||||
{
|
||||
const value = message[key];
|
||||
// Break if we get a huge message or exceed the token limit to prevent DoS
|
||||
// 100k tokens allows for future 100k GPT-4 models and 250k characters is
|
||||
// just a sanity check
|
||||
if (value.length > 250000 || numTokens > 100000) {
|
||||
numTokens = 100000;
|
||||
return {
|
||||
tokenizer: "tiktoken (prompt length limit exceeded)",
|
||||
token_count: numTokens,
|
||||
};
|
||||
}
|
||||
|
||||
numTokens += encoder.encode(message[key]).length;
|
||||
if (key === "name") {
|
||||
numTokens += tokensPerName;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
numTokens += 3; // every reply is primed with <|start|>assistant<|message|>
|
||||
return { tokenizer: "tiktoken", token_count: numTokens };
|
||||
}
|
||||
|
||||
export type OpenAIPromptMessage = {
|
||||
name?: string;
|
||||
content: string;
|
||||
role: string;
|
||||
};
|
||||
@@ -1,129 +0,0 @@
|
||||
import { Request } from "express";
|
||||
import childProcess from "child_process";
|
||||
import { config } from "../config";
|
||||
import { logger } from "../logger";
|
||||
import {
|
||||
init as initIpc,
|
||||
requestTokenCount as requestClaudeTokenCount,
|
||||
} from "./claude-ipc";
|
||||
import {
|
||||
init as initEncoder,
|
||||
getTokenCount as getOpenAITokenCount,
|
||||
OpenAIPromptMessage,
|
||||
} from "./openai";
|
||||
|
||||
let canTokenizeClaude = false;
|
||||
|
||||
export async function init() {
|
||||
if (config.anthropicKey) {
|
||||
if (!isPythonInstalled()) {
|
||||
const skipWarning = !!process.env.DISABLE_MISSING_PYTHON_WARNING;
|
||||
process.env.MISSING_PYTHON_WARNING = skipWarning ? "" : "true";
|
||||
} else {
|
||||
canTokenizeClaude = await initIpc();
|
||||
if (!canTokenizeClaude) {
|
||||
logger.warn(
|
||||
"Anthropic key is set, but tokenizer is not available. Claude prompts will use a naive estimate for token count."
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
if (config.openaiKey) {
|
||||
initEncoder();
|
||||
}
|
||||
}
|
||||
|
||||
type TokenCountResult = {
|
||||
token_count: number;
|
||||
tokenizer: string;
|
||||
tokenization_duration_ms: number;
|
||||
};
|
||||
type TokenCountRequest = {
|
||||
req: Request;
|
||||
} & (
|
||||
| { prompt: string; service: "anthropic" }
|
||||
| { prompt: OpenAIPromptMessage[]; service: "openai" }
|
||||
);
|
||||
export async function countTokens({
|
||||
req,
|
||||
service,
|
||||
prompt,
|
||||
}: TokenCountRequest): Promise<TokenCountResult> {
|
||||
const time = process.hrtime();
|
||||
|
||||
switch (service) {
|
||||
case "anthropic":
|
||||
if (!canTokenizeClaude) {
|
||||
const result = guesstimateTokens(prompt);
|
||||
return {
|
||||
token_count: result,
|
||||
tokenizer: "guesstimate (claude-ipc disabled)",
|
||||
tokenization_duration_ms: getElapsedMs(time),
|
||||
};
|
||||
}
|
||||
|
||||
// If the prompt is absolutely massive (possibly malicious) don't even try
|
||||
if (prompt.length > 500000) {
|
||||
return {
|
||||
token_count: guesstimateTokens(JSON.stringify(prompt)),
|
||||
tokenizer: "guesstimate (prompt too long)",
|
||||
tokenization_duration_ms: getElapsedMs(time),
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const result = await requestClaudeTokenCount({
|
||||
requestId: String(req.id),
|
||||
prompt,
|
||||
});
|
||||
return {
|
||||
token_count: result,
|
||||
tokenizer: "claude-ipc",
|
||||
tokenization_duration_ms: getElapsedMs(time),
|
||||
};
|
||||
} catch (e: any) {
|
||||
req.log.error("Failed to tokenize with claude_tokenizer", e);
|
||||
const result = guesstimateTokens(prompt);
|
||||
return {
|
||||
token_count: result,
|
||||
tokenizer: `guesstimate (claude-ipc failed: ${e.message})`,
|
||||
tokenization_duration_ms: getElapsedMs(time),
|
||||
};
|
||||
}
|
||||
|
||||
case "openai":
|
||||
const result = getOpenAITokenCount(prompt, req.body.model);
|
||||
return {
|
||||
...result,
|
||||
tokenization_duration_ms: getElapsedMs(time),
|
||||
};
|
||||
default:
|
||||
throw new Error(`Unknown service: ${service}`);
|
||||
}
|
||||
}
|
||||
|
||||
function getElapsedMs(time: [number, number]) {
|
||||
const diff = process.hrtime(time);
|
||||
return diff[0] * 1000 + diff[1] / 1e6;
|
||||
}
|
||||
|
||||
function guesstimateTokens(prompt: string) {
|
||||
// From Anthropic's docs:
|
||||
// The maximum length of prompt that Claude can see is its context window.
|
||||
// Claude's context window is currently ~6500 words / ~8000 tokens /
|
||||
// ~28000 Unicode characters.
|
||||
// This suggests 0.28 tokens per character but in practice this seems to be
|
||||
// a substantial underestimate in some cases.
|
||||
return Math.ceil(prompt.length * 0.325);
|
||||
}
|
||||
|
||||
function isPythonInstalled() {
|
||||
try {
|
||||
const python = process.platform === "win32" ? "python" : "python3";
|
||||
childProcess.execSync(`${python} --version`, { stdio: "ignore" });
|
||||
return true;
|
||||
} catch (err) {
|
||||
logger.debug({ err: err.message }, "Python not installed.");
|
||||
return false;
|
||||
}
|
||||
}
|
||||
Vendored
-3
@@ -18,9 +18,6 @@ declare global {
|
||||
onAborted?: () => void;
|
||||
proceed: () => void;
|
||||
heartbeatInterval?: NodeJS.Timeout;
|
||||
promptTokens?: number;
|
||||
// TODO: remove later
|
||||
debug: Record<string, any>;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
+1
-3
@@ -9,9 +9,7 @@
|
||||
"skipLibCheck": true,
|
||||
"skipDefaultLibCheck": true,
|
||||
"outDir": "build",
|
||||
"sourceMap": true,
|
||||
"resolveJsonModule": true,
|
||||
"useUnknownInCatchVariables": false
|
||||
"sourceMap": true
|
||||
},
|
||||
"include": ["src"],
|
||||
"exclude": ["node_modules"],
|
||||
|
||||
Reference in New Issue
Block a user