LibreChat/api/server/controllers/agents/client.js
Danny Avila ad74350036
🚧 chore: merge latest dev build (#4288)
* fix: agent initialization, add `collectedUsage` handling

* style: improve side panel styling

* refactor(loadAgent): Optimize order agent project ID retrieval

* feat: code execution

* fix: typing issues

* feat: ExecuteCode content part

* refactor: use local state for default collapsed state of analysis content parts

* fix: code parsing in ExecuteCode component

* chore: bump agents package, export loadAuthValues

* refactor: Update handleTools.js to use EnvVar for code execution tool authentication

* WIP

* feat: download code outputs

* fix(useEventHandlers): type issues

* feat: backend handling for code outputs

* Refactor: Remove console.log statement in Part.tsx

* refactor: add attachments to TMessage/messageSchema

* WIP: prelim handling for code outputs

* feat: attachments rendering

* refactor: improve attachments rendering

* fix: attachments, nullish edge case, handle attachments from event stream, bump agents package

* fix filename download

* fix: tool assignment for 'run code' on agent creation

* fix: image handling by adding attachments

* refactor: prevent agent creation without provider/model

* refactor: remove unnecessary space in agent creation success message

* refactor: select first model if selecting provider from empty on form

* fix: Agent avatar bug

* fix: `defaultAgentFormValues` causing boolean typing issue and typeerror

* fix: capabilities counting as tools, causing duplication of them

* fix: formatted messages edge case where consecutive content text type parts with the latter having tool_call_ids would cause consecutive AI messages to be created. furthermore, content could not be an array for tool_use messages (anthropic limitation)

* chore: bump @librechat/agents dependency to version 1.6.9

* feat: bedrock agents

* feat: new Agents icon

* feat: agent titling

* feat: agent landing

* refactor: allow sharing agent globally only if user is admin or author

* feat: initial AgentPanelSkeleton

* feat: AgentPanelSkeleton

* feat: collaborative agents

* chore: add potential authorName as part of schema

* chore: Remove unnecessary console.log statement

* WIP: agent model parameters

* chore: ToolsDialog typing and tool related localization chnages

* refactor: update tool instance type (latest langchain class), and rename google tool to 'google' proper

* chore: add back tools

* feat: Agent knowledge files upload

* refactor: better verbiage for disabled knowledge

* chore: debug logs for file deletions

* chore: debug logs for file deletions

* feat: upload/delete agent knowledge/file-search files

* feat: file search UI for agents

* feat: first pass, file search tool

* chore: update default agent capabilities and info
2024-09-30 17:17:57 -04:00

611 lines
18 KiB
JavaScript

// const { HttpsProxyAgent } = require('https-proxy-agent');
// const {
// Constants,
// ImageDetail,
// EModelEndpoint,
// resolveHeaders,
// validateVisionModel,
// mapModelToAzureConfig,
// } = require('librechat-data-provider');
const { Callback, createMetadataAggregator } = require('@librechat/agents');
const {
Constants,
openAISchema,
EModelEndpoint,
anthropicSchema,
bedrockOutputParser,
providerEndpointMap,
removeNullishValues,
} = require('librechat-data-provider');
const {
extractBaseURL,
// constructAzureURL,
// genAzureChatCompletion,
} = require('~/utils');
const {
formatMessage,
formatAgentMessages,
createContextHandlers,
} = require('~/app/clients/prompts');
const { encodeAndFormat } = require('~/server/services/Files/images/encode');
const Tokenizer = require('~/server/services/Tokenizer');
const { spendTokens } = require('~/models/spendTokens');
const BaseClient = require('~/app/clients/BaseClient');
// const { sleep } = require('~/server/utils');
const { createRun } = require('./run');
const { logger } = require('~/config');
/** @typedef {import('@librechat/agents').MessageContentComplex} MessageContentComplex */
const providerParsers = {
[EModelEndpoint.openAI]: openAISchema,
[EModelEndpoint.azureOpenAI]: openAISchema,
[EModelEndpoint.anthropic]: anthropicSchema,
[EModelEndpoint.bedrock]: bedrockOutputParser,
};
class AgentClient extends BaseClient {
constructor(options = {}) {
super(null, options);
/** @type {'discard' | 'summarize'} */
this.contextStrategy = 'discard';
/** @deprecated @type {true} - Is a Chat Completion Request */
this.isChatCompletion = true;
/** @type {AgentRun} */
this.run;
const {
contentParts,
collectedUsage,
artifactPromises,
maxContextTokens,
modelOptions = {},
...clientOptions
} = options;
this.modelOptions = modelOptions;
this.maxContextTokens = maxContextTokens;
/** @type {MessageContentComplex[]} */
this.contentParts = contentParts;
/** @type {Array<UsageMetadata>} */
this.collectedUsage = collectedUsage;
/** @type {ArtifactPromises} */
this.artifactPromises = artifactPromises;
this.options = Object.assign({ endpoint: options.endpoint }, clientOptions);
}
/**
* Returns the aggregated content parts for the current run.
* @returns {MessageContentComplex[]} */
getContentParts() {
return this.contentParts;
}
setOptions(options) {
logger.info('[api/server/controllers/agents/client.js] setOptions', options);
}
/**
*
* Checks if the model is a vision model based on request attachments and sets the appropriate options:
* - Sets `this.modelOptions.model` to `gpt-4-vision-preview` if the request is a vision request.
* - Sets `this.isVisionModel` to `true` if vision request.
* - Deletes `this.modelOptions.stop` if vision request.
* @param {MongoFile[]} attachments
*/
checkVisionRequest(attachments) {
logger.info(
'[api/server/controllers/agents/client.js #checkVisionRequest] not implemented',
attachments,
);
// if (!attachments) {
// return;
// }
// const availableModels = this.options.modelsConfig?.[this.options.endpoint];
// if (!availableModels) {
// return;
// }
// let visionRequestDetected = false;
// for (const file of attachments) {
// if (file?.type?.includes('image')) {
// visionRequestDetected = true;
// break;
// }
// }
// if (!visionRequestDetected) {
// return;
// }
// this.isVisionModel = validateVisionModel({ model: this.modelOptions.model, availableModels });
// if (this.isVisionModel) {
// delete this.modelOptions.stop;
// return;
// }
// for (const model of availableModels) {
// if (!validateVisionModel({ model, availableModels })) {
// continue;
// }
// this.modelOptions.model = model;
// this.isVisionModel = true;
// delete this.modelOptions.stop;
// return;
// }
// if (!availableModels.includes(this.defaultVisionModel)) {
// return;
// }
// if (!validateVisionModel({ model: this.defaultVisionModel, availableModels })) {
// return;
// }
// this.modelOptions.model = this.defaultVisionModel;
// this.isVisionModel = true;
// delete this.modelOptions.stop;
}
getSaveOptions() {
const parseOptions = providerParsers[this.options.endpoint];
let runOptions =
this.options.endpoint === EModelEndpoint.agents
? {
model: undefined,
// TODO:
// would need to be override settings; otherwise, model needs to be undefined
// model: this.override.model,
// instructions: this.override.instructions,
// additional_instructions: this.override.additional_instructions,
}
: {};
if (parseOptions) {
runOptions = parseOptions(this.modelOptions);
}
return removeNullishValues(
Object.assign(
{
endpoint: this.options.endpoint,
agent_id: this.options.agent.id,
modelLabel: this.options.modelLabel,
maxContextTokens: this.options.maxContextTokens,
resendFiles: this.options.resendFiles,
imageDetail: this.options.imageDetail,
spec: this.options.spec,
},
// TODO: PARSE OPTIONS BY PROVIDER, MAY CONTAIN SENSITIVE DATA
runOptions,
),
);
}
getBuildMessagesOptions(opts) {
return {
instructions: opts.instructions,
additional_instructions: opts.additional_instructions,
};
}
async addImageURLs(message, attachments) {
const { files, image_urls } = await encodeAndFormat(
this.options.req,
attachments,
this.options.agent.provider,
);
message.image_urls = image_urls.length ? image_urls : undefined;
return files;
}
async buildMessages(
messages,
parentMessageId,
{ instructions = null, additional_instructions = null },
opts,
) {
let orderedMessages = this.constructor.getMessagesForConversation({
messages,
parentMessageId,
summary: this.shouldSummarize,
});
let payload;
/** @type {{ role: string; name: string; content: string } | undefined} */
let systemMessage;
/** @type {number | undefined} */
let promptTokens;
/** @type {string} */
let systemContent = `${instructions ?? ''}${additional_instructions ?? ''}`;
if (this.options.attachments) {
const attachments = await this.options.attachments;
if (this.message_file_map) {
this.message_file_map[orderedMessages[orderedMessages.length - 1].messageId] = attachments;
} else {
this.message_file_map = {
[orderedMessages[orderedMessages.length - 1].messageId]: attachments,
};
}
const files = await this.addImageURLs(
orderedMessages[orderedMessages.length - 1],
attachments,
);
this.options.attachments = files;
}
if (this.message_file_map) {
this.contextHandlers = createContextHandlers(
this.options.req,
orderedMessages[orderedMessages.length - 1].text,
);
}
const formattedMessages = orderedMessages.map((message, i) => {
const formattedMessage = formatMessage({
message,
userName: this.options?.name,
assistantName: this.options?.modelLabel,
});
const needsTokenCount = this.contextStrategy && !orderedMessages[i].tokenCount;
/* If tokens were never counted, or, is a Vision request and the message has files, count again */
if (needsTokenCount || (this.isVisionModel && (message.image_urls || message.files))) {
orderedMessages[i].tokenCount = this.getTokenCountForMessage(formattedMessage);
}
/* If message has files, calculate image token cost */
// if (this.message_file_map && this.message_file_map[message.messageId]) {
// const attachments = this.message_file_map[message.messageId];
// for (const file of attachments) {
// if (file.embedded) {
// this.contextHandlers?.processFile(file);
// continue;
// }
// orderedMessages[i].tokenCount += this.calculateImageTokenCost({
// width: file.width,
// height: file.height,
// detail: this.options.imageDetail ?? ImageDetail.auto,
// });
// }
// }
return formattedMessage;
});
if (this.contextHandlers) {
this.augmentedPrompt = await this.contextHandlers.createContext();
systemContent = this.augmentedPrompt + systemContent;
}
if (systemContent) {
systemContent = `${systemContent.trim()}`;
systemMessage = {
role: 'system',
name: 'instructions',
content: systemContent,
};
if (this.contextStrategy) {
const instructionTokens = this.getTokenCountForMessage(systemMessage);
if (instructionTokens >= 0) {
const firstMessageTokens = orderedMessages[0].tokenCount ?? 0;
orderedMessages[0].tokenCount = firstMessageTokens + instructionTokens;
}
}
}
if (this.contextStrategy) {
({ payload, promptTokens, messages } = await this.handleContextStrategy({
orderedMessages,
formattedMessages,
/* prefer usage_metadata from final message */
buildTokenMap: false,
}));
}
const result = {
prompt: payload,
promptTokens,
messages,
};
if (promptTokens >= 0 && typeof opts?.getReqData === 'function') {
opts.getReqData({ promptTokens });
}
return result;
}
/** @type {sendCompletion} */
async sendCompletion(payload, opts = {}) {
this.modelOptions.user = this.user;
await this.chatCompletion({
payload,
onProgress: opts.onProgress,
abortController: opts.abortController,
});
return this.contentParts;
}
/**
* @param {Object} params
* @param {string} [params.model]
* @param {string} [params.context='message']
* @param {UsageMetadata[]} [params.collectedUsage=this.collectedUsage]
*/
async recordCollectedUsage({ model, context = 'message', collectedUsage = this.collectedUsage }) {
for (const usage of collectedUsage) {
await spendTokens(
{
context,
model: model ?? this.modelOptions.model,
conversationId: this.conversationId,
user: this.user ?? this.options.req.user?.id,
endpointTokenConfig: this.options.endpointTokenConfig,
},
{ promptTokens: usage.input_tokens, completionTokens: usage.output_tokens },
);
}
}
async chatCompletion({ payload, abortController = null }) {
try {
if (!abortController) {
abortController = new AbortController();
}
const baseURL = extractBaseURL(this.completionsUrl);
logger.debug('[api/server/controllers/agents/client.js] chatCompletion', {
baseURL,
payload,
});
// if (this.useOpenRouter) {
// opts.defaultHeaders = {
// 'HTTP-Referer': 'https://librechat.ai',
// 'X-Title': 'LibreChat',
// };
// }
// if (this.options.headers) {
// opts.defaultHeaders = { ...opts.defaultHeaders, ...this.options.headers };
// }
// if (this.options.proxy) {
// opts.httpAgent = new HttpsProxyAgent(this.options.proxy);
// }
// if (this.isVisionModel) {
// modelOptions.max_tokens = 4000;
// }
// /** @type {TAzureConfig | undefined} */
// const azureConfig = this.options?.req?.app?.locals?.[EModelEndpoint.azureOpenAI];
// if (
// (this.azure && this.isVisionModel && azureConfig) ||
// (azureConfig && this.isVisionModel && this.options.endpoint === EModelEndpoint.azureOpenAI)
// ) {
// const { modelGroupMap, groupMap } = azureConfig;
// const {
// azureOptions,
// baseURL,
// headers = {},
// serverless,
// } = mapModelToAzureConfig({
// modelName: modelOptions.model,
// modelGroupMap,
// groupMap,
// });
// opts.defaultHeaders = resolveHeaders(headers);
// this.langchainProxy = extractBaseURL(baseURL);
// this.apiKey = azureOptions.azureOpenAIApiKey;
// const groupName = modelGroupMap[modelOptions.model].group;
// this.options.addParams = azureConfig.groupMap[groupName].addParams;
// this.options.dropParams = azureConfig.groupMap[groupName].dropParams;
// // Note: `forcePrompt` not re-assigned as only chat models are vision models
// this.azure = !serverless && azureOptions;
// this.azureEndpoint =
// !serverless && genAzureChatCompletion(this.azure, modelOptions.model, this);
// }
// if (this.azure || this.options.azure) {
// /* Azure Bug, extremely short default `max_tokens` response */
// if (!modelOptions.max_tokens && modelOptions.model === 'gpt-4-vision-preview') {
// modelOptions.max_tokens = 4000;
// }
// /* Azure does not accept `model` in the body, so we need to remove it. */
// delete modelOptions.model;
// opts.baseURL = this.langchainProxy
// ? constructAzureURL({
// baseURL: this.langchainProxy,
// azureOptions: this.azure,
// })
// : this.azureEndpoint.split(/(?<!\/)\/(chat|completion)\//)[0];
// opts.defaultQuery = { 'api-version': this.azure.azureOpenAIApiVersion };
// opts.defaultHeaders = { ...opts.defaultHeaders, 'api-key': this.apiKey };
// }
// if (process.env.OPENAI_ORGANIZATION) {
// opts.organization = process.env.OPENAI_ORGANIZATION;
// }
// if (this.options.addParams && typeof this.options.addParams === 'object') {
// modelOptions = {
// ...modelOptions,
// ...this.options.addParams,
// };
// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] added params', {
// addParams: this.options.addParams,
// modelOptions,
// });
// }
// if (this.options.dropParams && Array.isArray(this.options.dropParams)) {
// this.options.dropParams.forEach((param) => {
// delete modelOptions[param];
// });
// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] dropped params', {
// dropParams: this.options.dropParams,
// modelOptions,
// });
// }
const run = await createRun({
req: this.options.req,
agent: this.options.agent,
tools: this.options.tools,
toolMap: this.options.toolMap,
runId: this.responseMessageId,
modelOptions: this.modelOptions,
customHandlers: this.options.eventHandlers,
});
const config = {
configurable: {
provider: providerEndpointMap[this.options.agent.provider],
thread_id: this.conversationId,
},
signal: abortController.signal,
streamMode: 'values',
version: 'v2',
};
if (!run) {
throw new Error('Failed to create run');
}
this.run = run;
const messages = formatAgentMessages(payload);
await run.processStream({ messages }, config, {
[Callback.TOOL_ERROR]: (graph, error, toolId) => {
logger.error(
'[api/server/controllers/agents/client.js #chatCompletion] Tool Error',
error,
toolId,
);
},
});
this.recordCollectedUsage({ context: 'message' }).catch((err) => {
logger.error(
'[api/server/controllers/agents/client.js #chatCompletion] Error recording collected usage',
err,
);
});
} catch (err) {
if (!abortController.signal.aborted) {
logger.error(
'[api/server/controllers/agents/client.js #sendCompletion] Unhandled error type',
err,
);
throw err;
}
logger.warn(
'[api/server/controllers/agents/client.js #sendCompletion] Operation aborted',
err,
);
}
}
/**
*
* @param {Object} params
* @param {string} params.text
* @param {string} params.conversationId
*/
async titleConvo({ text }) {
if (!this.run) {
throw new Error('Run not initialized');
}
const { handleLLMEnd, collected: collectedMetadata } = createMetadataAggregator();
const clientOptions = {};
const providerConfig = this.options.req.app.locals[this.options.agent.provider];
if (
providerConfig &&
providerConfig.titleModel &&
providerConfig.titleModel !== Constants.CURRENT_MODEL
) {
clientOptions.model = providerConfig.titleModel;
}
try {
const titleResult = await this.run.generateTitle({
inputText: text,
contentParts: this.contentParts,
clientOptions,
chainOptions: {
callbacks: [
{
handleLLMEnd,
},
],
},
});
const collectedUsage = collectedMetadata.map((item) => {
let input_tokens, output_tokens;
if (item.usage) {
input_tokens = item.usage.input_tokens || item.usage.inputTokens;
output_tokens = item.usage.output_tokens || item.usage.outputTokens;
} else if (item.tokenUsage) {
input_tokens = item.tokenUsage.promptTokens;
output_tokens = item.tokenUsage.completionTokens;
}
return {
input_tokens: input_tokens,
output_tokens: output_tokens,
};
});
this.recordCollectedUsage({
model: clientOptions.model,
context: 'title',
collectedUsage,
}).catch((err) => {
logger.error(
'[api/server/controllers/agents/client.js #titleConvo] Error recording collected usage',
err,
);
});
return titleResult.title;
} catch (err) {
logger.error('[api/server/controllers/agents/client.js #titleConvo] Error', err);
return;
}
}
getEncoding() {
return this.modelOptions.model?.includes('gpt-4o') ? 'o200k_base' : 'cl100k_base';
}
/**
* Returns the token count of a given text. It also checks and resets the tokenizers if necessary.
* @param {string} text - The text to get the token count for.
* @returns {number} The token count of the given text.
*/
getTokenCount(text) {
const encoding = this.getEncoding();
return Tokenizer.getTokenCount(text, encoding);
}
}
module.exports = AgentClient;