llmonitor
Version:
llmonitor is an open-source monitoring and analytics platform for AI apps.
205 lines (203 loc) • 6.26 kB
JavaScript
import {
src_default
} from "./chunk-6URYTMWA.js";
import {
__name,
cleanExtra
} from "./chunk-CDCVVLTO.js";
// src/openai.ts
var parseOpenaiMessage = /* @__PURE__ */ __name((message) => {
if (!message)
return void 0;
const { role, content, name, function_call, tool_calls, tool_call_id } = message;
return {
role,
text: content,
function_call,
tool_calls,
tool_call_id,
name
};
}, "parseOpenaiMessage");
var teeAsync = /* @__PURE__ */ __name((iterable) => {
const AsyncIteratorProto = Object.getPrototypeOf(
Object.getPrototypeOf(async function* () {
}.prototype)
);
const iterator = iterable[Symbol.asyncIterator]();
const buffers = [[], []];
function makeIterator(buffer, i) {
return Object.assign(Object.create(AsyncIteratorProto), {
next() {
if (!buffer)
return Promise.resolve({ done: true, value: void 0 });
if (buffer.length)
return buffer.shift();
const res = iterator.next();
if (buffers[i ^ 1])
buffers[i ^ 1].push(res);
return res;
},
async return() {
if (buffer) {
buffer = buffers[i] = null;
if (!buffers[i ^ 1])
await iterator.return();
}
return { done: true, value: void 0 };
}
});
}
__name(makeIterator, "makeIterator");
return buffers.map(makeIterator);
}, "teeAsync");
var PARAMS_TO_CAPTURE = [
"temperature",
"top_p",
"top_k",
"stop",
"presence_penalty",
"frequence_penalty",
"seed",
"function_call",
"functions",
"tools",
"tool_choice",
"response_format",
"max_tokens",
"logit_bias"
];
function openAIv3(openai, params = {}) {
const createChatCompletion = openai.createChatCompletion.bind(openai);
const wrapped = src_default.wrapModel(createChatCompletion, {
nameParser: (request) => request.model,
inputParser: (request) => request.messages.map(parseOpenaiMessage),
extraParser: (request) => {
const rawExtra = {
temperature: request.temperature,
maxTokens: request.max_tokens,
frequencyPenalty: request.frequency_penalty,
presencePenalty: request.presence_penalty,
stop: request.stop,
functionCall: request.function_call
};
return cleanExtra(rawExtra);
},
outputParser: ({ data }) => parseOpenaiMessage(data.choices[0].text || ""),
tokensUsageParser: async ({ data }) => ({
completion: data.usage?.completion_tokens,
prompt: data.usage?.prompt_tokens
}),
...params
});
openai.createChatCompletion = wrapped;
return openai;
}
__name(openAIv3, "openAIv3");
function monitorOpenAI(openai, params = {}) {
const createChatCompletion = openai.chat.completions.create;
const wrappedCreateChatCompletion = /* @__PURE__ */ __name((...args) => (
// @ts-ignore
createChatCompletion.apply(openai.chat.completions, args)
), "wrappedCreateChatCompletion");
async function handleStream(stream, onComplete, onError) {
try {
let tokens = 0;
let choices = [];
for await (const part of stream) {
tokens += 1;
const chunk = part.choices[0];
const { index, delta } = chunk;
const { content, function_call, role, tool_calls } = delta;
if (!choices[index]) {
choices.splice(index, 0, {
message: { role, content, function_call, tool_calls: [] }
});
}
if (content)
choices[index].message.content += content;
if (role)
choices[index].message.role = role;
if (function_call?.name)
choices[index].message.function_call.name = function_call.name;
if (function_call?.arguments)
choices[index].message.function_call.arguments += function_call.arguments;
if (tool_calls) {
for (const tool_call of tool_calls) {
const existingCallIndex = choices[index].message.tool_calls.findIndex((tc) => tc.index === tool_call.index);
if (existingCallIndex === -1) {
choices[index].message.tool_calls.push(tool_call);
} else {
const existingCall = choices[index].message.tool_calls[existingCallIndex];
if (tool_call.function?.arguments) {
existingCall.function.arguments += tool_call.function.arguments;
}
}
}
}
}
choices = choices.map((c) => {
if (c.message.tool_calls) {
c.message.tool_calls = c.message.tool_calls.map((tc) => {
const { index, ...rest } = tc;
return rest;
});
}
return c;
});
const res = {
choices,
usage: { completion_tokens: tokens, prompt_tokens: void 0 }
};
onComplete(res);
} catch (error) {
console.error(error);
onError(error);
}
}
__name(handleStream, "handleStream");
const wrapped = src_default.wrapModel(wrappedCreateChatCompletion, {
nameParser: (request) => request.model,
inputParser: (request) => request.messages.map(parseOpenaiMessage),
extraParser: (request) => {
const rawExtra = {};
for (const param of PARAMS_TO_CAPTURE) {
if (request[param])
rawExtra[param] = request[param];
}
return cleanExtra(rawExtra);
},
outputParser: (res) => parseOpenaiMessage(res.choices[0].message || ""),
tokensUsageParser: async (res) => {
return {
completion: res.usage?.completion_tokens,
prompt: res.usage?.prompt_tokens
};
},
tagsParser: (request) => {
const t = request.tags;
delete request.tags;
return t;
},
userIdParser: (request) => request.user,
userPropsParser: (request) => {
const props = request.userProps;
delete request.userProps;
return props;
},
enableWaitUntil: (request) => !!request.stream,
waitUntil: (stream, onComplete, onError) => {
const [og, copy] = teeAsync(stream);
handleStream(copy, onComplete, onError);
return og;
},
...params
});
openai.chat.completions.create = wrapped;
return openai;
}
__name(monitorOpenAI, "monitorOpenAI");
export {
monitorOpenAI,
openAIv3
};