LibreChat/api/app/clients/chatgpt-client.js
Danny Avila 36a524a630
feat(OpenAI, PaLM): Add model support for new OpenAI models and codechat-bison (#516)
* feat(OpenAI, PaLM): add new models
refactor(chatgpt-client.js): use object to map max tokens for each model
refactor(askChatGPTBrowser.js, askGPTPlugins.js, askOpenAI.js): comment out unused function calls and error handling
feat(askGoogle.js): add support for codechat-bison model
refactor(endpoints.js): add gpt-4-0613 and gpt-3.5-turbo-16k to available models for OpenAI and GPT plugins
refactor(EditPresetDialog.jsx): hide examples for codechat-bison model in google endpoint

style(EndpointOptionsPopover.jsx): add cn utility function import and use it to set additionalButton className

refactor(Google/Settings.jsx): conditionally render custom name and prompt prefix fields based on model type

The code has been refactored to conditionally render the custom name and prompt prefix fields based on the type of model selected. If the model starts with 'codechat-', the fields will not be rendered.

refactor(Settings.jsx): remove duplicated code and wrap a section in a conditional statement based on a variable

style(Input): add z-index to Input component to fix overlapping issue
feat(GoogleOptions): disable Examples button when model starts with 'codechat-' prefix

* feat(.env.example, endpoints.js): add PLUGIN_MODELS environment variable and use it to get plugin models in endpoints.js
2023-06-13 16:42:01 -04:00

106 lines
2.9 KiB
JavaScript

require('dotenv').config();
const { KeyvFile } = require('keyv-file');
const { genAzureChatCompletion } = require('../../utils/genAzureEndpoints');
const tiktoken = require('@dqbd/tiktoken');
const tiktokenModels = require('../../utils/tiktokenModels');
const encoding_for_model = tiktoken.encoding_for_model;
const askClient = async ({
text,
parentMessageId,
conversationId,
model,
oaiApiKey,
chatGptLabel,
promptPrefix,
temperature,
top_p,
presence_penalty,
frequency_penalty,
onProgress,
abortController,
userId
}) => {
const { ChatGPTClient } = await import('@waylaidwanderer/chatgpt-api');
const store = {
store: new KeyvFile({ filename: './data/cache.json' })
};
const azure = process.env.AZURE_OPENAI_API_KEY ? true : false;
let promptText = 'You are ChatGPT, a large language model trained by OpenAI.';
if (promptPrefix) {
promptText = promptPrefix;
}
const maxTokensMap = {
'gpt-4': 8191,
'gpt-4-0613': 8191,
'gpt-4-32k': 32767,
'gpt-4-32k-0613': 32767,
'gpt-3.5-turbo': 4095,
'gpt-3.5-turbo-0613': 4095,
'gpt-3.5-turbo-0301': 4095,
'gpt-3.5-turbo-16k': 15999,
};
const maxContextTokens = maxTokensMap[model] ?? 4095; // 1 less than maximum
const clientOptions = {
reverseProxyUrl: process.env.OPENAI_REVERSE_PROXY || null,
azure,
maxContextTokens,
modelOptions: {
model,
temperature,
top_p,
presence_penalty,
frequency_penalty
},
chatGptLabel,
promptPrefix,
proxy: process.env.PROXY || null
// debug: true
};
let apiKey = oaiApiKey ? oaiApiKey : process.env.OPENAI_API_KEY || null;
if (azure) {
apiKey = oaiApiKey ? oaiApiKey : process.env.AZURE_OPENAI_API_KEY || null;
clientOptions.reverseProxyUrl = genAzureChatCompletion({
azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME,
azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME,
azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION
});
}
const client = new ChatGPTClient(apiKey, clientOptions, store);
const options = {
onProgress,
abortController,
...(parentMessageId && conversationId ? { parentMessageId, conversationId } : {})
};
let usage = {};
let enc = null;
try {
enc = encoding_for_model(tiktokenModels.has(model) ? model : 'gpt-3.5-turbo');
usage.prompt_tokens = (enc.encode(promptText)).length + (enc.encode(text)).length;
} catch (e) {
console.log('Error encoding prompt text', e);
}
const res = await client.sendMessage(text, { ...options, userId });
try {
usage.completion_tokens = (enc.encode(res.response)).length;
enc.free();
usage.total_tokens = usage.prompt_tokens + usage.completion_tokens;
res.usage = usage;
} catch (e) {
console.log('Error encoding response text', e);
}
return res;
};
module.exports = { askClient };