diff --git a/docker/.env.example b/docker/.env.example index a5358c6684..9051321c5e 100644 --- a/docker/.env.example +++ b/docker/.env.example @@ -15,7 +15,7 @@ GID='1000' # LLM_PROVIDER='gemini' # GEMINI_API_KEY= -# GEMINI_LLM_MODEL_PREF='gemini-pro' +# GEMINI_LLM_MODEL_PREF='gemini-2.0-flash-lite' # LLM_PROVIDER='azure' # AZURE_OPENAI_ENDPOINT= diff --git a/frontend/src/components/LLMSelection/GeminiLLMOptions/index.jsx b/frontend/src/components/LLMSelection/GeminiLLMOptions/index.jsx index dc2b248133..e733ec139a 100644 --- a/frontend/src/components/LLMSelection/GeminiLLMOptions/index.jsx +++ b/frontend/src/components/LLMSelection/GeminiLLMOptions/index.jsx @@ -29,6 +29,11 @@ export default function GeminiLLMOptions({ settings }) { {!settings?.credentialsOnly && ( <> + {/* + + Safety setting is not supported for Gemini yet due to the openai compatible Gemini API. + We are not using the generativeAPI endpoint and therefore cannot set the safety threshold. +
+ */} )} diff --git a/frontend/src/hooks/useGetProvidersModels.js b/frontend/src/hooks/useGetProvidersModels.js index 0b31434907..ad12da8bf2 100644 --- a/frontend/src/hooks/useGetProvidersModels.js +++ b/frontend/src/hooks/useGetProvidersModels.js @@ -10,21 +10,7 @@ export const DISABLED_PROVIDERS = [ ]; const PROVIDER_DEFAULT_MODELS = { openai: [], - gemini: [ - "gemini-pro", - "gemini-1.0-pro", - "gemini-1.5-pro-latest", - "gemini-1.5-flash-latest", - "gemini-1.5-pro-exp-0801", - "gemini-1.5-pro-exp-0827", - "gemini-1.5-flash-exp-0827", - "gemini-1.5-flash-8b-exp-0827", - "gemini-exp-1114", - "gemini-exp-1121", - "gemini-exp-1206", - "learnlm-1.5-pro-experimental", - "gemini-2.0-flash-exp", - ], + gemini: [], anthropic: [], azure: [], lmstudio: [], diff --git a/frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx b/frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx index 10d986830a..fa3acdb871 100644 --- a/frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx +++ b/frontend/src/pages/WorkspaceSettings/AgentConfig/AgentLLMSelection/index.jsx @@ -30,10 +30,10 @@ const ENABLED_PROVIDERS = [ "apipie", "xai", "nvidia-nim", + "gemini", // TODO: More agent support. // "cohere", // Has tool calling and will need to build explicit support // "huggingface" // Can be done but already has issues with no-chat templated. Needs to be tested. - // "gemini", // Too rate limited and broken in several ways to use for agents. ]; const WARN_PERFORMANCE = [ "lmstudio", diff --git a/server/.env.example b/server/.env.example index cfd17789cc..c8b81fef83 100644 --- a/server/.env.example +++ b/server/.env.example @@ -12,7 +12,7 @@ SIG_SALT='salt' # Please generate random string at least 32 chars long. # LLM_PROVIDER='gemini' # GEMINI_API_KEY= -# GEMINI_LLM_MODEL_PREF='gemini-pro' +# GEMINI_LLM_MODEL_PREF='gemini-2.0-flash-lite' # LLM_PROVIDER='azure' # AZURE_OPENAI_ENDPOINT= diff --git a/server/models/systemSettings.js b/server/models/systemSettings.js index bd811af1ca..242ee4e5aa 100644 --- a/server/models/systemSettings.js +++ b/server/models/systemSettings.js @@ -450,7 +450,8 @@ const SystemSettings = { // Gemini Keys GeminiLLMApiKey: !!process.env.GEMINI_API_KEY, - GeminiLLMModelPref: process.env.GEMINI_LLM_MODEL_PREF || "gemini-pro", + GeminiLLMModelPref: + process.env.GEMINI_LLM_MODEL_PREF || "gemini-2.0-flash-lite", GeminiSafetySetting: process.env.GEMINI_SAFETY_SETTING || "BLOCK_MEDIUM_AND_ABOVE", diff --git a/server/utils/AiProviders/gemini/index.js b/server/utils/AiProviders/gemini/index.js index 311d39d3b0..bd2268e517 100644 --- a/server/utils/AiProviders/gemini/index.js +++ b/server/utils/AiProviders/gemini/index.js @@ -5,9 +5,8 @@ const { LLMPerformanceMonitor, } = require("../../helpers/chat/LLMPerformanceMonitor"); const { - writeResponseChunk, - clientAbortedHandler, formatChatHistory, + handleDefaultStreamResponseV2, } = require("../../helpers/chat/responses"); const { MODEL_MAP } = require("../modelMap"); const { defaultGeminiModels, v1BetaModels } = require("./defaultModels"); @@ -18,22 +17,31 @@ const cacheFolder = path.resolve( : path.resolve(__dirname, `../../../storage/models/gemini`) ); +const NO_SYSTEM_PROMPT_MODELS = [ + "gemma-3-1b-it", + "gemma-3-4b-it", + "gemma-3-12b-it", + "gemma-3-27b-it", +]; + class GeminiLLM { constructor(embedder = null, modelPreference = null) { if (!process.env.GEMINI_API_KEY) throw new Error("No Gemini API key was set."); - // Docs: https://ai.google.dev/tutorials/node_quickstart - const { GoogleGenerativeAI } = require("@google/generative-ai"); - const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY); + const { OpenAI: OpenAIApi } = require("openai"); this.model = - modelPreference || process.env.GEMINI_LLM_MODEL_PREF || "gemini-pro"; + modelPreference || + process.env.GEMINI_LLM_MODEL_PREF || + "gemini-2.0-flash-lite"; const isExperimental = this.isExperimentalModel(this.model); - this.gemini = genAI.getGenerativeModel( - { model: this.model }, - { apiVersion: isExperimental ? "v1beta" : "v1" } - ); + this.openai = new OpenAIApi({ + apiKey: process.env.GEMINI_API_KEY, + // Even models that are v1 in gemini API can be used with v1beta/openai/ endpoint and nobody knows why. + baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", + }); + this.limits = { history: this.promptWindowLimit() * 0.15, system: this.promptWindowLimit() * 0.15, @@ -41,8 +49,7 @@ class GeminiLLM { }; this.embedder = embedder ?? new NativeEmbedder(); - this.defaultTemp = 0.7; // not used for Gemini - this.safetyThreshold = this.#fetchSafetyThreshold(); + this.defaultTemp = 0.7; if (!fs.existsSync(cacheFolder)) fs.mkdirSync(cacheFolder, { recursive: true }); @@ -53,6 +60,16 @@ class GeminiLLM { ); } + /** + * Checks if the model supports system prompts + * This is a static list of models that are known to not support system prompts + * since this information is not available in the API model response. + * @returns {boolean} + */ + get supportsSystemPrompt() { + return !NO_SYSTEM_PROMPT_MODELS.includes(this.model); + } + #log(text, ...args) { console.log(`\x1b[32m[GeminiLLM]\x1b[0m ${text}`, ...args); } @@ -82,41 +99,6 @@ class GeminiLLM { ); } - // BLOCK_NONE can be a special candidate for some fields - // https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes#how_to_remove_automated_response_blocking_for_select_safety_attributes - // so if you are wondering why BLOCK_NONE still failed, the link above will explain why. - #fetchSafetyThreshold() { - const threshold = - process.env.GEMINI_SAFETY_SETTING ?? "BLOCK_MEDIUM_AND_ABOVE"; - const safetyThresholds = [ - "BLOCK_NONE", - "BLOCK_ONLY_HIGH", - "BLOCK_MEDIUM_AND_ABOVE", - "BLOCK_LOW_AND_ABOVE", - ]; - return safetyThresholds.includes(threshold) - ? threshold - : "BLOCK_MEDIUM_AND_ABOVE"; - } - - #safetySettings() { - return [ - { - category: "HARM_CATEGORY_HATE_SPEECH", - threshold: this.safetyThreshold, - }, - { - category: "HARM_CATEGORY_SEXUALLY_EXPLICIT", - threshold: this.safetyThreshold, - }, - { category: "HARM_CATEGORY_HARASSMENT", threshold: this.safetyThreshold }, - { - category: "HARM_CATEGORY_DANGEROUS_CONTENT", - threshold: this.safetyThreshold, - }, - ]; - } - streamingEnabled() { return "streamGetChatCompletion" in this; } @@ -336,147 +318,114 @@ class GeminiLLM { * @returns {string|object[]} */ #generateContent({ userPrompt, attachments = [] }) { - if (!attachments.length) { - return userPrompt; - } + if (!attachments.length) return userPrompt; - const content = [{ text: userPrompt }]; + const content = [{ type: "text", text: userPrompt }]; for (let attachment of attachments) { content.push({ - inlineData: { - data: attachment.contentString.split("base64,")[1], - mimeType: attachment.mime, + type: "image_url", + image_url: { + url: attachment.contentString, + detail: "high", }, }); } return content.flat(); } + /** + * Construct the user prompt for this model. + * @param {{attachments: import("../../helpers").Attachment[]}} param0 + * @returns + */ constructPrompt({ systemPrompt = "", contextTexts = [], chatHistory = [], userPrompt = "", - attachments = [], + attachments = [], // This is the specific attachment for only this prompt }) { - const prompt = { - role: "system", - content: `${systemPrompt}${this.#appendContext(contextTexts)}`, - }; + let prompt = []; + if (this.supportsSystemPrompt) { + prompt.push({ + role: "system", + content: `${systemPrompt}${this.#appendContext(contextTexts)}`, + }); + } else { + this.#log( + `${this.model} - does not support system prompts - emulating...` + ); + prompt.push( + { + role: "user", + content: `${systemPrompt}${this.#appendContext(contextTexts)}`, + }, + { + role: "assistant", + content: "Okay.", + } + ); + } + return [ - prompt, - { role: "assistant", content: "Okay." }, + ...prompt, ...formatChatHistory(chatHistory, this.#generateContent), { - role: "USER_PROMPT", + role: "user", content: this.#generateContent({ userPrompt, attachments }), }, ]; } - // This will take an OpenAi format message array and only pluck valid roles from it. - formatMessages(messages = []) { - // Gemini roles are either user || model. - // and all "content" is relabeled to "parts" - const allMessages = messages - .map((message) => { - if (message.role === "system") - return { role: "user", parts: [{ text: message.content }] }; - - if (message.role === "user") { - // If the content is an array - then we have already formatted the context so return it directly. - if (Array.isArray(message.content)) - return { role: "user", parts: message.content }; - - // Otherwise, this was a regular user message with no attachments - // so we need to format it for Gemini - return { role: "user", parts: [{ text: message.content }] }; - } - - if (message.role === "assistant") - return { role: "model", parts: [{ text: message.content }] }; - return null; - }) - .filter((msg) => !!msg); + async getChatCompletion(messages = null, { temperature = 0.7 }) { + const result = await LLMPerformanceMonitor.measureAsyncFunction( + this.openai.chat.completions + .create({ + model: this.model, + messages, + temperature: temperature, + }) + .catch((e) => { + console.error(e); + throw new Error(e.message); + }) + ); - // Specifically, Google cannot have the last sent message be from a user with no assistant reply - // otherwise it will crash. So if the last item is from the user, it was not completed so pop it off - // the history. if ( - allMessages.length > 0 && - allMessages[allMessages.length - 1].role === "user" + !result.output.hasOwnProperty("choices") || + result.output.choices.length === 0 ) - allMessages.pop(); - - // Validate that after every user message, there is a model message - // sometimes when using gemini we try to compress messages in order to retain as - // much context as possible but this may mess up the order of the messages that the gemini model expects - // we do this check to work around the edge case where 2 user prompts may be next to each other, in the message array - for (let i = 0; i < allMessages.length; i++) { - if ( - allMessages[i].role === "user" && - i < allMessages.length - 1 && - allMessages[i + 1].role !== "model" - ) { - allMessages.splice(i + 1, 0, { - role: "model", - parts: [{ text: "Okay." }], - }); - } - } - - return allMessages; - } - - async getChatCompletion(messages = [], _opts = {}) { - const prompt = messages.find( - (chat) => chat.role === "USER_PROMPT" - )?.content; - const chatThread = this.gemini.startChat({ - history: this.formatMessages(messages), - safetySettings: this.#safetySettings(), - }); - - const { output: result, duration } = - await LLMPerformanceMonitor.measureAsyncFunction( - chatThread.sendMessage(prompt) - ); - const responseText = result.response.text(); - if (!responseText) throw new Error("Gemini: No response could be parsed."); - - const promptTokens = LLMPerformanceMonitor.countTokens(messages); - const completionTokens = LLMPerformanceMonitor.countTokens([ - { content: responseText }, - ]); + return null; return { - textResponse: responseText, + textResponse: result.output.choices[0].message.content, metrics: { - prompt_tokens: promptTokens, - completion_tokens: completionTokens, - total_tokens: promptTokens + completionTokens, - outputTps: (promptTokens + completionTokens) / duration, - duration, + prompt_tokens: result.output.usage.prompt_tokens || 0, + completion_tokens: result.output.usage.completion_tokens || 0, + total_tokens: result.output.usage.total_tokens || 0, + outputTps: result.output.usage.completion_tokens / result.duration, + duration: result.duration, }, }; } - async streamGetChatCompletion(messages = [], _opts = {}) { - const prompt = messages.find( - (chat) => chat.role === "USER_PROMPT" - )?.content; - const chatThread = this.gemini.startChat({ - history: this.formatMessages(messages), - safetySettings: this.#safetySettings(), - }); - const responseStream = await LLMPerformanceMonitor.measureStream( - (await chatThread.sendMessageStream(prompt)).stream, - messages + async streamGetChatCompletion(messages = null, { temperature = 0.7 }) { + const measuredStreamRequest = await LLMPerformanceMonitor.measureStream( + this.openai.chat.completions.create({ + model: this.model, + stream: true, + messages, + temperature: temperature, + }), + messages, + true ); - if (!responseStream) - throw new Error("Could not stream response stream from Gemini."); - return responseStream; + return measuredStreamRequest; + } + + handleStream(response, stream, responseProps) { + return handleDefaultStreamResponseV2(response, stream, responseProps); } async compressMessages(promptArgs = {}, rawHistory = []) { @@ -485,81 +434,6 @@ class GeminiLLM { return await messageArrayCompressor(this, messageArray, rawHistory); } - handleStream(response, stream, responseProps) { - const { uuid = uuidv4(), sources = [] } = responseProps; - // Usage is not available for Gemini streams - // so we need to calculate the completion tokens manually - // because 1 chunk != 1 token in gemini responses and it buffers - // many tokens before sending them to the client as a "chunk" - - return new Promise(async (resolve) => { - let fullText = ""; - - // Establish listener to early-abort a streaming response - // in case things go sideways or the user does not like the response. - // We preserve the generated text but continue as if chat was completed - // to preserve previously generated content. - const handleAbort = () => { - stream?.endMeasurement({ - completion_tokens: LLMPerformanceMonitor.countTokens([ - { content: fullText }, - ]), - }); - clientAbortedHandler(resolve, fullText); - }; - response.on("close", handleAbort); - - for await (const chunk of stream) { - let chunkText; - try { - // Due to content sensitivity we cannot always get the function .text(); - // https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes#gemini-TASK-samples-nodejs - // and it is not possible to unblock or disable this safety protocol without being allowlisted by Google. - chunkText = chunk.text(); - } catch (e) { - chunkText = e.message; - writeResponseChunk(response, { - uuid, - sources: [], - type: "abort", - textResponse: null, - close: true, - error: e.message, - }); - stream?.endMeasurement({ completion_tokens: 0 }); - resolve(e.message); - return; - } - - fullText += chunkText; - writeResponseChunk(response, { - uuid, - sources: [], - type: "textResponseChunk", - textResponse: chunk.text(), - close: false, - error: false, - }); - } - - writeResponseChunk(response, { - uuid, - sources, - type: "textResponseChunk", - textResponse: "", - close: true, - error: false, - }); - response.removeListener("close", handleAbort); - stream?.endMeasurement({ - completion_tokens: LLMPerformanceMonitor.countTokens([ - { content: fullText }, - ]), - }); - resolve(fullText); - }); - } - // Simple wrapper for dynamic embedder & normalize interface for all LLM implementations async embedTextInput(textInput) { return await this.embedder.embedTextInput(textInput); @@ -571,4 +445,5 @@ class GeminiLLM { module.exports = { GeminiLLM, + NO_SYSTEM_PROMPT_MODELS, }; diff --git a/server/utils/EmbeddingEngines/gemini/index.js b/server/utils/EmbeddingEngines/gemini/index.js index 49d80c3f53..4c60501a88 100644 --- a/server/utils/EmbeddingEngines/gemini/index.js +++ b/server/utils/EmbeddingEngines/gemini/index.js @@ -2,6 +2,8 @@ class GeminiEmbedder { constructor() { if (!process.env.GEMINI_EMBEDDING_API_KEY) throw new Error("No Gemini API key was set."); + + // TODO: Deprecate this and use OpenAI interface instead - after which, remove the @google/generative-ai dependency const { GoogleGenerativeAI } = require("@google/generative-ai"); const genAI = new GoogleGenerativeAI(process.env.GEMINI_EMBEDDING_API_KEY); this.model = process.env.EMBEDDING_MODEL_PREF || "text-embedding-004"; diff --git a/server/utils/agents/aibitat/index.js b/server/utils/agents/aibitat/index.js index 83bc736ea6..a479ba38a2 100644 --- a/server/utils/agents/aibitat/index.js +++ b/server/utils/agents/aibitat/index.js @@ -491,9 +491,7 @@ Only return the role. // and remove the @ from the response const { result } = await provider.complete(messages); const name = result?.replace(/^@/g, ""); - if (this.agents.get(name)) { - return name; - } + if (this.agents.get(name)) return name; // if the name is not in the nodes, return a random node return availableNodes[Math.floor(Math.random() * availableNodes.length)]; @@ -797,6 +795,8 @@ ${this.getHistory({ to: route.to }) return new Providers.NovitaProvider({ model: config.model }); case "ppio": return new Providers.PPIOProvider({ model: config.model }); + case "gemini": + return new Providers.GeminiProvider({ model: config.model }); default: throw new Error( `Unknown provider: ${config.provider}. Please use a valid provider.` diff --git a/server/utils/agents/aibitat/providers/ai-provider.js b/server/utils/agents/aibitat/providers/ai-provider.js index 1d7a37fac1..54581643ca 100644 --- a/server/utils/agents/aibitat/providers/ai-provider.js +++ b/server/utils/agents/aibitat/providers/ai-provider.js @@ -171,6 +171,14 @@ class Provider { apiKey: process.env.PPIO_API_KEY ?? null, ...config, }); + case "gemini": + return new ChatOpenAI({ + configuration: { + baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", + }, + apiKey: process.env.GEMINI_API_KEY ?? null, + ...config, + }); // OSS Model Runners // case "anythingllm_ollama": diff --git a/server/utils/agents/aibitat/providers/gemini.js b/server/utils/agents/aibitat/providers/gemini.js new file mode 100644 index 0000000000..c62357bb6d --- /dev/null +++ b/server/utils/agents/aibitat/providers/gemini.js @@ -0,0 +1,154 @@ +const OpenAI = require("openai"); +const Provider = require("./ai-provider.js"); +const InheritMultiple = require("./helpers/classes.js"); +const UnTooled = require("./helpers/untooled.js"); +const { + NO_SYSTEM_PROMPT_MODELS, +} = require("../../../AiProviders/gemini/index.js"); +const { APIError } = require("../error.js"); + +/** + * The agent provider for the Gemini provider. + * We wrap Gemini in UnTooled because its tool-calling is not supported via the dedicated OpenAI API. + */ +class GeminiProvider extends InheritMultiple([Provider, UnTooled]) { + model; + + constructor(config = {}) { + const { model = "gemini-2.0-flash-lite" } = config; + super(); + const client = new OpenAI({ + baseURL: "https://generativelanguage.googleapis.com/v1beta/openai/", + apiKey: process.env.GEMINI_API_KEY, + maxRetries: 0, + }); + + this._client = client; + this.model = model; + this.verbose = true; + } + + get client() { + return this._client; + } + + /** + * Format the messages to the format required by the Gemini API since some models do not support system prompts. + * @see {NO_SYSTEM_PROMPT_MODELS} + * @param {import("openai").OpenAI.ChatCompletionMessage[]} messages + * @returns {import("openai").OpenAI.ChatCompletionMessage[]} + */ + formatMessages(messages) { + if (!NO_SYSTEM_PROMPT_MODELS.includes(this.model)) return messages; + + // Replace the system message with a user/assistant message pair + const formattedMessages = []; + for (const message of messages) { + if (message.role === "system") { + formattedMessages.push({ + role: "user", + content: message.content, + }); + formattedMessages.push({ + role: "assistant", + content: "Okay, I'll follow your instructions.", + }); + continue; + } + formattedMessages.push(message); + } + return formattedMessages; + } + + async #handleFunctionCallChat({ messages = [] }) { + return await this.client.chat.completions + .create({ + model: this.model, + temperature: 0, + messages: this.cleanMsgs(this.formatMessages(messages)), + }) + .then((result) => { + if (!result.hasOwnProperty("choices")) + throw new Error("Gemini chat: No results!"); + if (result.choices.length === 0) + throw new Error("Gemini chat: No results length!"); + return result.choices[0].message.content; + }) + .catch((_) => { + return null; + }); + } + + /** + * Create a completion based on the received messages. + * + * @param messages A list of messages to send to the API. + * @param functions + * @returns The completion. + */ + async complete(messages, functions = []) { + try { + let completion; + + if (functions.length > 0) { + const { toolCall, text } = await this.functionCall( + this.cleanMsgs(this.formatMessages(messages)), + functions, + this.#handleFunctionCallChat.bind(this) + ); + + if (toolCall !== null) { + this.providerLog(`Valid tool call found - running ${toolCall.name}.`); + this.deduplicator.trackRun(toolCall.name, toolCall.arguments); + return { + result: null, + functionCall: { + name: toolCall.name, + arguments: toolCall.arguments, + }, + cost: 0, + }; + } + completion = { content: text }; + } + + if (!completion?.content) { + this.providerLog( + "Will assume chat completion without tool call inputs." + ); + const response = await this.client.chat.completions.create({ + model: this.model, + messages: this.cleanMsgs(this.formatMessages(messages)), + }); + completion = response.choices[0].message; + } + + // The UnTooled class inherited Deduplicator is mostly useful to prevent the agent + // from calling the exact same function over and over in a loop within a single chat exchange + // _but_ we should enable it to call previously used tools in a new chat interaction. + this.deduplicator.reset("runs"); + return { + result: completion.content, + cost: 0, + }; + } catch (error) { + throw new APIError( + error?.message + ? `${this.constructor.name} encountered an error while executing the request: ${error.message}` + : "There was an error with the Gemini provider executing the request" + ); + } + } + + /** + * Get the cost of the completion. + * + * @param _usage The completion to get the cost for. + * @returns The cost of the completion. + */ + getCost(_usage) { + return 0; + } +} + +module.exports = GeminiProvider; diff --git a/server/utils/agents/aibitat/providers/index.js b/server/utils/agents/aibitat/providers/index.js index 3b24ed2043..d98e49c34a 100644 --- a/server/utils/agents/aibitat/providers/index.js +++ b/server/utils/agents/aibitat/providers/index.js @@ -21,6 +21,7 @@ const XAIProvider = require("./xai.js"); const NovitaProvider = require("./novita.js"); const NvidiaNimProvider = require("./nvidiaNim.js"); const PPIOProvider = require("./ppio.js"); +const GeminiProvider = require("./gemini.js"); module.exports = { OpenAIProvider, @@ -46,4 +47,5 @@ module.exports = { NovitaProvider, NvidiaNimProvider, PPIOProvider, + GeminiProvider, }; diff --git a/server/utils/agents/index.js b/server/utils/agents/index.js index 595f233f00..c2765b2279 100644 --- a/server/utils/agents/index.js +++ b/server/utils/agents/index.js @@ -115,10 +115,6 @@ class AgentHandler { "LocalAI must have a valid base path to use for the api." ); break; - case "gemini": - if (!process.env.GEMINI_API_KEY) - throw new Error("Gemini API key must be provided to use agents."); - break; case "openrouter": if (!process.env.OPENROUTER_API_KEY) throw new Error("OpenRouter API key must be provided to use agents."); @@ -189,6 +185,10 @@ class AgentHandler { if (!process.env.PPIO_API_KEY) throw new Error("PPIO API Key must be provided to use agents."); break; + case "gemini": + if (!process.env.GEMINI_API_KEY) + throw new Error("Gemini API key must be provided to use agents."); + break; default: throw new Error( @@ -224,8 +224,6 @@ class AgentHandler { return null; case "koboldcpp": return process.env.KOBOLD_CPP_MODEL_PREF ?? null; - case "gemini": - return process.env.GEMINI_MODEL_PREF ?? "gemini-pro"; case "localai": return process.env.LOCAL_AI_MODEL_PREF ?? null; case "openrouter": @@ -256,6 +254,8 @@ class AgentHandler { return process.env.NVIDIA_NIM_LLM_MODEL_PREF ?? null; case "ppio": return process.env.PPIO_MODEL_PREF ?? "qwen/qwen2.5-32b-instruct"; + case "gemini": + return process.env.GEMINI_LLM_MODEL_PREF ?? "gemini-2.0-flash-lite"; default: return null; }