2024-11-12 18:51:32 -05:00
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const { TokenTextSplitter } = require('@langchain/textsplitters');
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feat: ConversationSummaryBufferMemory (#973)
* 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
2023-09-26 21:02:28 -04:00
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/**
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* Splits a given text by token chunks, based on the provided parameters for the TokenTextSplitter.
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* Note: limit or memoize use of this function as its calculation is expensive.
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*
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* @param {Object} obj - Configuration object for the text splitting operation.
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* @param {string} obj.text - The text to be split.
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* @param {string} [obj.encodingName='cl100k_base'] - Encoding name. Defaults to 'cl100k_base'.
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* @param {number} [obj.chunkSize=1] - The token size of each chunk. Defaults to 1.
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* @param {number} [obj.chunkOverlap=0] - The number of chunk elements to be overlapped between adjacent chunks. Defaults to 0.
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* @param {number} [obj.returnSize] - If specified and not 0, slices the return array from the end by this amount.
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*
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* @returns {Promise<Array>} Returns a promise that resolves to an array of text chunks.
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* If no text is provided, an empty array is returned.
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* If returnSize is specified and not 0, slices the return array from the end by returnSize.
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*
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* @async
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* @function tokenSplit
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*/
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async function tokenSplit({
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text,
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encodingName = 'cl100k_base',
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chunkSize = 1,
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chunkOverlap = 0,
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returnSize,
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}) {
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if (!text) {
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return [];
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}
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const splitter = new TokenTextSplitter({
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encodingName,
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chunkSize,
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chunkOverlap,
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});
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if (!returnSize) {
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return await splitter.splitText(text);
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}
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const splitText = await splitter.splitText(text);
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if (returnSize && returnSize > 0 && splitText.length > 0) {
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return splitText.slice(-Math.abs(returnSize));
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}
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return splitText;
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}
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module.exports = tokenSplit;
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