* WIP: gemini-1.5 support
* feat: extended vertex ai support
* fix: handle possibly undefined modelName
* fix: gpt-4-turbo-preview invalid vision model
* feat: specify `fileConfig.imageOutputType` and make PNG default image conversion type
* feat: better truncation for errors including base64 strings
* fix: gemini inlineData formatting
* feat: RAG augmented prompt for gemini-1.5
* feat: gemini-1.5 rates and token window
* chore: adjust tokens, update docs, update vision Models
* chore: add back `ChatGoogleVertexAI` for chat models via vertex ai
* refactor: ask/edit controllers to not use `unfinished` field for google endpoint
* chore: remove comment
* chore(ci): fix AppService test
* chore: remove comment
* refactor(GoogleSearch): use `GOOGLE_SEARCH_API_KEY` instead, issue warning for old variable
* chore: bump data-provider to 0.5.4
* chore: update docs
* fix: condition for gemini-1.5 using generative ai lib
* chore: update docs
* ci: add additional AppService test for `imageOutputType`
* refactor: optimize new config value `imageOutputType`
* chore: bump CONFIG_VERSION
* fix(assistants): avatar upload
* refactor: add gemini-pro to google Models list; use defaultModels for central model listing
* refactor(SetKeyDialog): create useMultipleKeys hook to use for Azure, export `isJson` from utils, use EModelEndpoint
* refactor(useUserKey): change variable names to make keyName setting more clear
* refactor(FileUpload): allow passing container className string
* feat(GoogleClient): Gemini support
* refactor(GoogleClient): alternate stream speed for Gemini models
* feat(Gemini): styling/settings configuration for Gemini
* refactor(GoogleClient): substract max response tokens from max context tokens if context is above 32k (I/O max is combined between the two)
* refactor(tokens): correct google max token counts and subtract max response tokens when input/output count are combined towards max context count
* feat(google/initializeClient): handle both local and user_provided credentials and write tests
* fix(GoogleClient): catch if credentials are undefined, handle if serviceKey is string or object correctly, handle no examples passed, throw error if not a Generative Language model and no service account JSON key is provided, throw error if it is a Generative m
odel, but not google API key was provided
* refactor(loadAsyncEndpoints/google): activate Google endpoint if either the service key JSON file is provided in /api/data, or a GOOGLE_KEY is defined.
* docs: updated Google configuration
* fix(ci): Mock import of Service Account Key JSON file (auth.json)
* Update apis_and_tokens.md
* feat: increase max output tokens slider for gemini pro
* refactor(GoogleSettings): handle max and default maxOutputTokens on model change
* chore: add sensitive redact regex
* docs: add warning about data privacy
* Update apis_and_tokens.md
* chore: bump vite, vitejs/plugin-react, mark client package as esm, move react-query as a peer dep in data-provider
* chore: import changes due to new data-provider export strategy, also fix type imports where applicable
* chore: export react-query services as separate to avoid react dependencies in /api/
* chore: suppress sourcemap warnings and polyfill node:path which is used by filenamify
TODO: replace filenamify with an alternative and REMOVE polyfill
* chore: /api/ changes to support `librechat-data-provider`
* refactor: rewrite Dockerfile.multi in light of /api/ changes to support `librechat-data-provider`
* chore: remove volume mapping to node_modules directories in default compose file
* chore: remove schemas from /api/ as is no longer needed with use of `librechat-data-provider`
* fix(ci): jest `librechat-data-provider/react-query` module resolution
* feat: update PaLM icons
* feat: add additional google models
* POC: formatting inputs for Vertex AI streaming
* refactor: move endpoints services outside of /routes dir to /services/Endpoints
* refactor: shorten schemas import
* refactor: rename PALM to GOOGLE
* feat: make Google editable endpoint
* feat: reusable Ask and Edit controllers based off Anthropic
* chore: organize imports/logic
* fix(parseConvo): include examples in googleSchema
* fix: google only allows odd number of messages to be sent
* fix: pass proxy to AnthropicClient
* refactor: change `google` altName to `Google`
* refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values
* refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases)
* feat: google support for maxTokensMap
* feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient
* feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain
* chore(GoogleClient): remove comments, left before for reference in git history
* docs: update google instructions (WIP)
* docs(apis_and_tokens.md): add images to google instructions
* docs: remove typo apis_and_tokens.md
* Update apis_and_tokens.md
* feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models
* chore: update more PaLM references to Google
* chore: move playwright out of workflows to avoid failing tests
* fix: correct preset title for Anthropic endpoint
* fix(Settings/Anthropic): show correct default value for LLM temperature
* fix(AnthropicClient): use `getModelMaxTokens` to get the correct LLM max context tokens, correctly set default temperature to 1, use only 2 params for class constructor, use `getResponseSender` to add correct sender to response message
* refactor(/api/ask|edit/anthropic): save messages to database after the final response is sent to the client, and do not save conversation from route controller
* fix(initializeClient/anthropic): correctly pass client options (endpointOption) to class initialization
* feat(ModelService/Anthropic): add claude-1.2
* chore: update model rates with 11/06 rates
* chore: add new models to env.example for OPENAI_MODELS
* chore: reference actual maxTokensMap in ci tests
* refactor(Chains/llms): allow passing callbacks
* refactor(BaseClient): accurately count completion tokens as generation only
* refactor(OpenAIClient): remove unused getTokenCountForResponse, pass streaming var and callbacks in initializeLLM
* wip: summary prompt tokens
* refactor(summarizeMessages): new cut-off strategy that generates a better summary by adding context from beginning, truncating the middle, and providing the end
wip: draft out relevant providers and variables for token tracing
* refactor(createLLM): make streaming prop false by default
* chore: remove use of getTokenCountForResponse
* refactor(agents): use BufferMemory as ConversationSummaryBufferMemory token usage not easy to trace
* chore: remove passing of streaming prop, also console log useful vars for tracing
* feat: formatFromLangChain helper function to count tokens for ChatModelStart
* refactor(initializeLLM): add role for LLM tracing
* chore(formatFromLangChain): update JSDoc
* feat(formatMessages): formats langChain messages into OpenAI payload format
* chore: install openai-chat-tokens
* refactor(formatMessage): optimize conditional langChain logic
fix(formatFromLangChain): fix destructuring
* feat: accurate prompt tokens for ChatModelStart before generation
* refactor(handleChatModelStart): move to callbacks dir, use factory function
* refactor(initializeLLM): rename 'role' to 'context'
* feat(Balance/Transaction): new schema/models for tracking token spend
refactor(Key): factor out model export to separate file
* refactor(initializeClient): add req,res objects to client options
* feat: add-balance script to add to an existing users' token balance
refactor(Transaction): use multiplier map/function, return balance update
* refactor(Tx): update enum for tokenType, return 1 for multiplier if no map match
* refactor(Tx): add fair fallback value multiplier incase the config result is undefined
* refactor(Balance): rename 'tokens' to 'tokenCredits'
* feat: balance check, add tx.js for new tx-related methods and tests
* chore(summaryPrompts): update prompt token count
* refactor(callbacks): pass req, res
wip: check balance
* refactor(Tx): make convoId a String type, fix(calculateTokenValue)
* refactor(BaseClient): add conversationId as client prop when assigned
* feat(RunManager): track LLM runs with manager, track token spend from LLM,
refactor(OpenAIClient): use RunManager to create callbacks, pass user prop to langchain api calls
* feat(spendTokens): helper to spend prompt/completion tokens
* feat(checkBalance): add helper to check, log, deny request if balance doesn't have enough funds
refactor(Balance): static check method to return object instead of boolean now
wip(OpenAIClient): implement use of checkBalance
* refactor(initializeLLM): add token buffer to assure summary isn't generated when subsequent payload is too large
refactor(OpenAIClient): add checkBalance
refactor(createStartHandler): add checkBalance
* chore: remove prompt and completion token logging from route handler
* chore(spendTokens): add JSDoc
* feat(logTokenCost): record transactions for basic api calls
* chore(ask/edit): invoke getResponseSender only once per API call
* refactor(ask/edit): pass promptTokens to getIds and include in abort data
* refactor(getIds -> getReqData): rename function
* refactor(Tx): increase value if incomplete message
* feat: record tokenUsage when message is aborted
* refactor: subtract tokens when payload includes function_call
* refactor: add namespace for token_balance
* fix(spendTokens): only execute if corresponding token type amounts are defined
* refactor(checkBalance): throws Error if not enough token credits
* refactor(runTitleChain): pass and use signal, spread object props in create helpers, and use 'call' instead of 'run'
* fix(abortMiddleware): circular dependency, and default to empty string for completionTokens
* fix: properly cancel title requests when there isn't enough tokens to generate
* feat(predictNewSummary): custom chain for summaries to allow signal passing
refactor(summaryBuffer): use new custom chain
* feat(RunManager): add getRunByConversationId method, refactor: remove run and throw llm error on handleLLMError
* refactor(createStartHandler): if summary, add error details to runs
* fix(OpenAIClient): support aborting from summarization & showing error to user
refactor(summarizeMessages): remove unnecessary operations counting summaryPromptTokens and note for alternative, pass signal to summaryBuffer
* refactor(logTokenCost -> recordTokenUsage): rename
* refactor(checkBalance): include promptTokens in errorMessage
* refactor(checkBalance/spendTokens): move to models dir
* fix(createLanguageChain): correctly pass config
* refactor(initializeLLM/title): add tokenBuffer of 150 for balance check
* refactor(openAPIPlugin): pass signal and memory, filter functions by the one being called
* refactor(createStartHandler): add error to run if context is plugins as well
* refactor(RunManager/handleLLMError): throw error immediately if plugins, don't remove run
* refactor(PluginsClient): pass memory and signal to tools, cleanup error handling logic
* chore: use absolute equality for addTitle condition
* refactor(checkBalance): move checkBalance to execute after userMessage and tokenCounts are saved, also make conditional
* style: icon changes to match official
* fix(BaseClient): getTokenCountForResponse -> getTokenCount
* fix(formatLangChainMessages): add kwargs as fallback prop from lc_kwargs, update JSDoc
* refactor(Tx.create): does not update balance if CHECK_BALANCE is not enabled
* fix(e2e/cleanUp): cleanup new collections, import all model methods from index
* fix(config/add-balance): add uncaughtException listener
* fix: circular dependency
* refactor(initializeLLM/checkBalance): append new generations to errorMessage if cost exceeds balance
* fix(handleResponseMessage): only record token usage in this method if not error and completion is not skipped
* fix(createStartHandler): correct condition for generations
* chore: bump postcss due to moderate severity vulnerability
* chore: bump zod due to low severity vulnerability
* chore: bump openai & data-provider version
* feat(types): OpenAI Message types
* chore: update bun lockfile
* refactor(CodeBlock): add error block formatting
* refactor(utils/Plugin): factor out formatJSON and cn to separate files (json.ts and cn.ts), add extractJSON
* chore(logViolation): delete user_id after error is logged
* refactor(getMessageError -> Error): change to React.FC, add token_balance handling, use extractJSON to determine JSON instead of regex
* fix(DALL-E): use latest openai SDK
* chore: reorganize imports, fix type issue
* feat(server): add balance route
* fix(api/models): add auth
* feat(data-provider): /api/balance query
* feat: show balance if checking is enabled, refetch on final message or error
* chore: update docs, .env.example with token_usage info, add balance script command
* fix(Balance): fallback to empty obj for balance query
* style: slight adjustment of balance element
* docs(token_usage): add PR notes
* 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
