mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-21 19:00:13 +01:00
* refactor: pass model in message edit payload, use encoder in standalone util function * feat: add summaryBuffer helper * refactor(api/messages): use new countTokens helper and add auth middleware at top * wip: ConversationSummaryBufferMemory * refactor: move pre-generation helpers to prompts dir * chore: remove console log * chore: remove test as payload will no longer carry tokenCount * chore: update getMessagesWithinTokenLimit JSDoc * refactor: optimize getMessagesForConversation and also break on summary, feat(ci): getMessagesForConversation tests * refactor(getMessagesForConvo): count '00000000-0000-0000-0000-000000000000' as root message * chore: add newer model to token map * fix: condition was point to prop of array instead of message prop * refactor(BaseClient): use object for refineMessages param, rename 'summary' to 'summaryMessage', add previous_summary refactor(getMessagesWithinTokenLimit): replace text and tokenCount if should summarize, summary, and summaryTokenCount are present fix/refactor(handleContextStrategy): use the right comparison length for context diff, and replace payload first message when a summary is present * chore: log previous_summary if debugging * refactor(formatMessage): assume if role is defined that it's a valid value * refactor(getMessagesWithinTokenLimit): remove summary logic refactor(handleContextStrategy): add usePrevSummary logic in case only summary was pruned refactor(loadHistory): initial message query will return all ordered messages but keep track of the latest summary refactor(getMessagesForConversation): use object for single param, edit jsdoc, edit all files using the method refactor(ChatGPTClient): order messages before buildPrompt is called, TODO: add convoSumBuffMemory logic * fix: undefined handling and summarizing only when shouldRefineContext is true * chore(BaseClient): fix test results omitting system role for summaries and test edge case * chore: export summaryBuffer from index file * refactor(OpenAIClient/BaseClient): move refineMessages to subclass, implement LLM initialization for summaryBuffer * feat: add OPENAI_SUMMARIZE to enable summarizing, refactor: rename client prop 'shouldRefineContext' to 'shouldSummarize', change contextStrategy value to 'summarize' from 'refine' * refactor: rename refineMessages method to summarizeMessages for clarity * chore: clarify summary future intent in .env.example * refactor(initializeLLM): handle case for either 'model' or 'modelName' being passed * feat(gptPlugins): enable summarization for plugins * refactor(gptPlugins): utilize new initializeLLM method and formatting methods for messages, use payload array for currentMessages and assign pastMessages sooner * refactor(agents): use ConversationSummaryBufferMemory for both agent types * refactor(formatMessage): optimize original method for langchain, add helper function for langchain messages, add JSDocs and tests * refactor(summaryBuffer): add helper to createSummaryBufferMemory, and use new formatting helpers * fix: forgot to spread formatMessages also took opportunity to pluralize filename * refactor: pass memory to tools, namely openapi specs. not used and may never be used by new method but added for testing * ci(formatMessages): add more exhaustive checks for langchain messages * feat: add debug env var for OpenAI * chore: delete unnecessary comments * chore: add extra note about summary feature * fix: remove tokenCount from payload instructions * fix: test fail * fix: only pass instructions to payload when defined or not empty object * refactor: fromPromptMessages is deprecated, use renamed method fromMessages * refactor: use 'includes' instead of 'startsWith' for extended OpenRouter compatibility * fix(PluginsClient.buildPromptBody): handle undefined message strings * chore: log langchain titling error * feat: getModelMaxTokens helper * feat: tokenSplit helper * feat: summary prompts updated * fix: optimize _CUT_OFF_SUMMARIZER prompt * refactor(summaryBuffer): use custom summary prompt, allow prompt to be passed, pass humanPrefix and aiPrefix to memory, along with any future variables, rename messagesToRefine to context * fix(summaryBuffer): handle edge case where messagesToRefine exceeds summary context, refactor(BaseClient): allow custom maxContextTokens to be passed to getMessagesWithinTokenLimit, add defined check before unshifting summaryMessage, update shouldSummarize based on this refactor(OpenAIClient): use getModelMaxTokens, use cut-off message method for summary if no messages were left after pruning * fix(handleContextStrategy): handle case where incoming prompt is bigger than model context * chore: rename refinedContent to splitText * chore: remove unnecessary debug log
174 lines
4.6 KiB
JavaScript
174 lines
4.6 KiB
JavaScript
require('dotenv').config();
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const { z } = require('zod');
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const fs = require('fs');
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const yaml = require('js-yaml');
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const path = require('path');
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const { DynamicStructuredTool } = require('langchain/tools');
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const { createOpenAPIChain } = require('langchain/chains');
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const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain/prompts');
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function addLinePrefix(text, prefix = '// ') {
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return text
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.split('\n')
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.map((line) => prefix + line)
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.join('\n');
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}
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function createPrompt(name, functions) {
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const prefix = `// The ${name} tool has the following functions. Determine the desired or most optimal function for the user's query:`;
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const functionDescriptions = functions
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.map((func) => `// - ${func.name}: ${func.description}`)
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.join('\n');
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return `${prefix}\n${functionDescriptions}
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// The user's message will be passed as the function's query.
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// Always provide the function name as such: {{"func": "function_name"}}`;
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}
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const AuthBearer = z
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.object({
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type: z.string().includes('service_http'),
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authorization_type: z.string().includes('bearer'),
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verification_tokens: z.object({
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openai: z.string(),
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}),
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})
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.catch(() => false);
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const AuthDefinition = z
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.object({
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type: z.string(),
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authorization_type: z.string(),
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verification_tokens: z.object({
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openai: z.string(),
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}),
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})
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.catch(() => false);
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async function readSpecFile(filePath) {
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try {
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const fileContents = await fs.promises.readFile(filePath, 'utf8');
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if (path.extname(filePath) === '.json') {
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return JSON.parse(fileContents);
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}
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return yaml.load(fileContents);
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} catch (e) {
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console.error(e);
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return false;
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}
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}
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async function getSpec(url) {
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const RegularUrl = z
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.string()
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.url()
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.catch(() => false);
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if (RegularUrl.parse(url) && path.extname(url) === '.json') {
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const response = await fetch(url);
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return await response.json();
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}
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const ValidSpecPath = z
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.string()
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.url()
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.catch(async () => {
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const spec = path.join(__dirname, '..', '.well-known', 'openapi', url);
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if (!fs.existsSync(spec)) {
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return false;
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}
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return await readSpecFile(spec);
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});
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return ValidSpecPath.parse(url);
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}
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async function createOpenAPIPlugin({ data, llm, user, message, memory, verbose = false }) {
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let spec;
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try {
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spec = await getSpec(data.api.url, verbose);
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} catch (error) {
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verbose && console.debug('getSpec error', error);
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return null;
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}
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if (!spec) {
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verbose && console.debug('No spec found');
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return null;
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}
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const headers = {};
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const { auth, name_for_model, description_for_model, description_for_human } = data;
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if (auth && AuthDefinition.parse(auth)) {
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verbose && console.debug('auth detected', auth);
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const { openai } = auth.verification_tokens;
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if (AuthBearer.parse(auth)) {
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headers.authorization = `Bearer ${openai}`;
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verbose && console.debug('added auth bearer', headers);
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}
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}
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const chainOptions = {
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llm,
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verbose,
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};
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if (memory) {
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verbose && console.debug('openAPI chain: memory detected', memory);
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chainOptions.memory = memory;
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}
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if (data.headers && data.headers['librechat_user_id']) {
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verbose && console.debug('id detected', headers);
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headers[data.headers['librechat_user_id']] = user;
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}
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if (Object.keys(headers).length > 0) {
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verbose && console.debug('headers detected', headers);
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chainOptions.headers = headers;
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}
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if (data.params) {
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verbose && console.debug('params detected', data.params);
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chainOptions.params = data.params;
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}
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chainOptions.prompt = ChatPromptTemplate.fromMessages([
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HumanMessagePromptTemplate.fromTemplate(
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`# Use the provided API's to respond to this query:\n\n{query}\n\n## Instructions:\n${addLinePrefix(
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description_for_model,
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)}`,
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),
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]);
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const chain = await createOpenAPIChain(spec, chainOptions);
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const { functions } = chain.chains[0].lc_kwargs.llmKwargs;
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return new DynamicStructuredTool({
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name: name_for_model,
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description_for_model: `${addLinePrefix(description_for_human)}${createPrompt(
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name_for_model,
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functions,
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)}`,
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description: `${description_for_human}`,
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schema: z.object({
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func: z
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.string()
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.describe(
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`The function to invoke. The functions available are: ${functions
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.map((func) => func.name)
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.join(', ')}`,
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),
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}),
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func: async ({ func = '' }) => {
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const result = await chain.run(`${message}${func?.length > 0 ? `\nUse ${func}` : ''}`);
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return result;
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},
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});
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}
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module.exports = {
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getSpec,
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readSpecFile,
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createOpenAPIPlugin,
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};
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