feat(providers): add models.json for models list

Feat/system instructions

.github/workflows/docker-build.yaml

@@ -114,6 +114,11 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_PASSWORD }}
 
+      - name: Extract version from release tag
+        if: github.event_name == 'release'
+        id: version
+        run: echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
+
       - name: Create and push multi-arch manifest for main
         if: github.ref == 'refs/heads/master' && github.event_name == 'push'
         run: |

data/.gitignore

@@ -1,2 +1,3 @@
 *
+!models.json
 !.gitignore

data/models.json

@@ -0,0 +1,157 @@
+{
+  "_comment": "Ollama models are fetched from the Ollama API, so they are not included here.",
+  "chatModels": {
+    "openai": [
+      {
+        "displayName": "GPT-3.5 Turbo",
+        "key": "gpt-3.5-turbo"
+      },
+      {
+        "displayName": "GPT-4",
+        "key": "gpt-4"
+      },
+      {
+        "displayName": "GPT-4 Turbo",
+        "key": "gpt-4-turbo"
+      },
+      {
+        "displayName": "GPT-4 Omni",
+        "key": "gpt-4o"
+      },
+      {
+        "displayName": "GPT-4 Omni Mini",
+        "key": "gpt-4o-mini"
+      }
+    ],
+    "groq": [
+      {
+        "displayName": "Gemma2 9B IT",
+        "key": "gemma2-9b-it"
+      },
+      {
+        "displayName": "Llama 3.3 70B Versatile",
+        "key": "llama-3.3-70b-versatile"
+      },
+      {
+        "displayName": "Llama 3.1 8B Instant",
+        "key": "llama-3.1-8b-instant"
+      },
+      {
+        "displayName": "Llama3 70B 8192",
+        "key": "llama3-70b-8192"
+      },
+      {
+        "displayName": "Llama3 8B 8192",
+        "key": "llama3-8b-8192"
+      },
+      {
+        "displayName": "Mixtral 8x7B 32768",
+        "key": "mixtral-8x7b-32768"
+      },
+      {
+        "displayName": "Qwen QWQ 32B (Preview)",
+        "key": "qwen-qwq-32b"
+      },
+      {
+        "displayName": "Mistral Saba 24B (Preview)",
+        "key": "mistral-saba-24b"
+      },
+      {
+        "displayName": "DeepSeek R1 Distill Llama 70B (Preview)",
+        "key": "deepseek-r1-distill-llama-70b"
+      }
+    ],
+    "gemini": [
+      {
+        "displayName": "Gemini 2.5 Pro Experimental",
+        "key": "gemini-2.5-pro-exp-03-25"
+      },
+      {
+        "displayName": "Gemini 2.0 Flash",
+        "key": "gemini-2.0-flash"
+      },
+      {
+        "displayName": "Gemini 2.0 Flash-Lite",
+        "key": "gemini-2.0-flash-lite"
+      },
+      {
+        "displayName": "Gemini 2.0 Flash Thinking Experimental",
+        "key": "gemini-2.0-flash-thinking-exp-01-21"
+      },
+      {
+        "displayName": "Gemini 1.5 Flash",
+        "key": "gemini-1.5-flash"
+      },
+      {
+        "displayName": "Gemini 1.5 Flash-8B",
+        "key": "gemini-1.5-flash-8b"
+      },
+      {
+        "displayName": "Gemini 1.5 Pro",
+        "key": "gemini-1.5-pro"
+      }
+    ],
+    "anthropic": [
+      {
+        "displayName": "Claude 3.7 Sonnet",
+        "key": "claude-3-7-sonnet-20250219"
+      },
+      {
+        "displayName": "Claude 3.5 Haiku",
+        "key": "claude-3-5-haiku-20241022"
+      },
+      {
+        "displayName": "Claude 3.5 Sonnet v2",
+        "key": "claude-3-5-sonnet-20241022"
+      },
+      {
+        "displayName": "Claude 3.5 Sonnet",
+        "key": "claude-3-5-sonnet-20240620"
+      },
+      {
+        "displayName": "Claude 3 Opus",
+        "key": "claude-3-opus-20240229"
+      },
+      {
+        "displayName": "Claude 3 Sonnet",
+        "key": "claude-3-sonnet-20240229"
+      },
+      {
+        "displayName": "Claude 3 Haiku",
+        "key": "claude-3-haiku-20240307"
+      }
+    ]
+  },
+  "embeddingModels": {
+    "openai": [
+      {
+        "displayName": "Text Embedding 3 Large",
+        "key": "text-embedding-3-large"
+      },
+      {
+        "displayName": "Text Embedding 3 Small",
+        "key": "text-embedding-3-small"
+      }
+    ],
+    "gemini": [
+      {
+        "displayName": "Gemini Embedding",
+        "key": "gemini-embedding-exp"
+      }
+    ],
+    "transformers": [
+      {
+        "displayName": "BGE Small",
+        "key": "xenova-bge-small-en-v1.5"
+      },
+      {
+        "displayName": "GTE Small",
+        "key": "xenova-gte-small"
+      },
+      {
+        "displayName": "Bert Multilingual",
+        "key": "xenova-bert-base-multilingual-uncased"
+      }
+    ]
+  }
+}

src/app/api/chat/route.ts

@@ -49,6 +49,7 @@ type Body = {
   files: Array<string>;
   chatModel: ChatModel;
   embeddingModel: EmbeddingModel;
+  systemInstructions: string;
 };
 
 const handleEmitterEvents = async (
@@ -278,6 +279,7 @@ export const POST = async (req: Request) => {
       embedding,
       body.optimizationMode,
       body.files,
+      body.systemInstructions,
     );
 
     const responseStream = new TransformStream();

src/app/api/search/route.ts

@@ -125,6 +125,7 @@ export const POST = async (req: Request) => {
       embeddings,
       body.optimizationMode,
       [],
+      '',
     );
 
     if (!body.stream) {

src/app/settings/page.tsx

@@ -54,6 +54,38 @@ const Input = ({ className, isSaving, onSave, ...restProps }: InputProps) => {
   );
 };
 
+interface TextareaProps extends React.InputHTMLAttributes<HTMLTextAreaElement> {
+  isSaving?: boolean;
+  onSave?: (value: string) => void;
+}
+
+const Textarea = ({
+  className,
+  isSaving,
+  onSave,
+  ...restProps
+}: TextareaProps) => {
+  return (
+    <div className="relative">
+      <textarea
+        placeholder="Any special instructions for the LLM"
+        className="placeholder:text-sm text-sm w-full flex items-center justify-between p-3 bg-light-secondary dark:bg-dark-secondary rounded-lg hover:bg-light-200 dark:hover:bg-dark-200 transition-colors"
+        rows={4}
+        onBlur={(e) => onSave?.(e.target.value)}
+        {...restProps}
+      />
+      {isSaving && (
+        <div className="absolute right-3 top-3">
+          <Loader2
+            size={16}
+            className="animate-spin text-black/70 dark:text-white/70"
+          />
+        </div>
+      )}
+    </div>
+  );
+};
+
 const Select = ({
   className,
   options,
@@ -111,6 +143,7 @@ const Page = () => {
   const [isLoading, setIsLoading] = useState(false);
   const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
+  const [systemInstructions, setSystemInstructions] = useState<string>('');
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
 
   useEffect(() => {
@@ -172,6 +205,8 @@ const Page = () => {
         localStorage.getItem('autoVideoSearch') === 'true',
       );
 
+      setSystemInstructions(localStorage.getItem('systemInstructions')!);
+
       setIsLoading(false);
     };
 
@@ -328,6 +363,8 @@ const Page = () => {
         localStorage.setItem('embeddingModelProvider', value);
       } else if (key === 'embeddingModel') {
         localStorage.setItem('embeddingModel', value);
+      } else if (key === 'systemInstructions') {
+        localStorage.setItem('systemInstructions', value);
       }
     } catch (err) {
       console.error('Failed to save:', err);
@@ -473,6 +510,19 @@ const Page = () => {
               </div>
             </SettingsSection>
 
+            <SettingsSection title="System Instructions">
+              <div className="flex flex-col space-y-4">
+                <Textarea
+                  value={systemInstructions}
+                  isSaving={savingStates['systemInstructions']}
+                  onChange={(e) => {
+                    setSystemInstructions(e.target.value);
+                  }}
+                  onSave={(value) => saveConfig('systemInstructions', value)}
+                />
+              </div>
+            </SettingsSection>
+
             <SettingsSection title="Model Settings">
               {config.chatModelProviders && (
                 <div className="flex flex-col space-y-4">

src/components/ChatWindow.tsx

@@ -480,6 +480,7 @@ const ChatWindow = ({ id }: { id?: string }) => {
           name: embeddingModelProvider.name,
           provider: embeddingModelProvider.provider,
         },
+        systemInstructions: localStorage.getItem('systemInstructions'),
       }),
     });
 

src/lib/prompts/academicSearch.ts

@@ -51,6 +51,10 @@ export const academicSearchResponsePrompt = `
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Academic', this means you will be searching for academic papers and articles on the web.
     
+    ### User instructions
+    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+    {systemInstructions}
+
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/lib/prompts/redditSearch.ts

@@ -51,6 +51,10 @@ export const redditSearchResponsePrompt = `
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Reddit', this means you will be searching for information, opinions and discussions on the web using Reddit.
     
+    ### User instructions
+    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+    {systemInstructions}
+
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/lib/prompts/webSearch.ts

@@ -1,6 +1,6 @@
 export const webSearchRetrieverPrompt = `
 You are an AI question rephraser. You will be given a conversation and a follow-up question,  you will have to rephrase the follow up question so it is a standalone question and can be used by another LLM to search the web for information to answer it.
-If it is a smple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
+If it is a simple writing task or a greeting (unless the greeting contains a question after it) like Hi, Hello, How are you, etc. than a question then you need to return \`not_needed\` as the response (This is because the LLM won't need to search the web for finding information on this topic).
 If the user asks some question from some URL or wants you to summarize a PDF or a webpage (via URL) you need to return the links inside the \`links\` XML block and the question inside the \`question\` XML block. If the user wants to you to summarize the webpage or the PDF you need to return \`summarize\` inside the \`question\` XML block in place of a question and the link to summarize in the \`links\` XML block.
 You must always return the rephrased question inside the \`question\` XML block, if there are no links in the follow-up question then don't insert a \`links\` XML block in your response.
 
@@ -92,6 +92,10 @@ export const webSearchResponsePrompt = `
     - If the user provides vague input or if relevant information is missing, explain what additional details might help refine the search.
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
 
+    ### User instructions
+    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+    {systemInstructions}
+
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/lib/prompts/wolframAlpha.ts

@@ -51,6 +51,10 @@ export const wolframAlphaSearchResponsePrompt = `
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Wolfram Alpha', this means you will be searching for information on the web using Wolfram Alpha. It is a computational knowledge engine that can answer factual queries and perform computations.
     
+    ### User instructions
+    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+    {systemInstructions}
+
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/lib/prompts/writingAssistant.ts

@@ -7,6 +7,10 @@ You have to cite the answer using [number] notation. You must cite the sentences
 Place these citations at the end of that particular sentence. You can cite the same sentence multiple times if it is relevant to the user's query like [number1][number2].
 However you do not need to cite it using the same number. You can use different numbers to cite the same sentence multiple times. The number refers to the number of the search result (passed in the context) used to generate that part of the answer.
 
+### User instructions
+These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+{systemInstructions}
+
 <context>
 {context}
 </context>

src/lib/prompts/youtubeSearch.ts

@@ -51,6 +51,10 @@ export const youtubeSearchResponsePrompt = `
     - If no relevant information is found, say: "Hmm, sorry I could not find any relevant information on this topic. Would you like me to search again or ask something else?" Be transparent about limitations and suggest alternatives or ways to reframe the query.
     - You are set on focus mode 'Youtube', this means you will be searching for videos on the web using Youtube and providing information based on the video's transcrip
     
+    ### User instructions
+    These instructions are shared to you by the user and not by the system. You will have to follow them but give them less priority than the above instructions. If the user has provided specific instructions or preferences, incorporate them into your response while adhering to the overall guidelines.
+    {systemInstructions}
+
     ### Example Output
     - Begin with a brief introduction summarizing the event or query topic.
     - Follow with detailed sections under clear headings, covering all aspects of the query if possible.

src/lib/providers/anthropic.ts

@@ -1,48 +1,22 @@
 import { ChatAnthropic } from '@langchain/anthropic';
-import { ChatModel } from '.';
+import { ChatModel, getModelsList, RawModel } from '.';
 import { getAnthropicApiKey } from '../config';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
-const anthropicChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Claude 3.7 Sonnet',
-    key: 'claude-3-7-sonnet-20250219',
-  },
-  {
-    displayName: 'Claude 3.5 Haiku',
-    key: 'claude-3-5-haiku-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet v2',
-    key: 'claude-3-5-sonnet-20241022',
-  },
-  {
-    displayName: 'Claude 3.5 Sonnet',
-    key: 'claude-3-5-sonnet-20240620',
-  },
-  {
-    displayName: 'Claude 3 Opus',
-    key: 'claude-3-opus-20240229',
-  },
-  {
-    displayName: 'Claude 3 Sonnet',
-    key: 'claude-3-sonnet-20240229',
-  },
-  {
-    displayName: 'Claude 3 Haiku',
-    key: 'claude-3-haiku-20240307',
-  },
-];
+const loadModels = () => {
+  return getModelsList()?.['chatModels']['anthropic']  as unknown as RawModel[]
+}
 
 export const loadAnthropicChatModels = async () => {
   const anthropicApiKey = getAnthropicApiKey();
-
   if (!anthropicApiKey) return {};
 
+  const models = loadModels()
+
   try {
     const chatModels: Record<string, ChatModel> = {};
 
-    anthropicChatModels.forEach((model) => {
+    models.forEach((model) => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatAnthropic({

src/lib/providers/gemini.ts

@@ -3,57 +3,24 @@ import {
   GoogleGenerativeAIEmbeddings,
 } from '@langchain/google-genai';
 import { getGeminiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
+import { ChatModel, EmbeddingModel, getModelsList, RawModel } from '.';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 import { Embeddings } from '@langchain/core/embeddings';
 
-const geminiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemini 2.5 Pro Experimental',
-    key: 'gemini-2.5-pro-exp-03-25',
-  },
-  {
-    displayName: 'Gemini 2.0 Flash',
-    key: 'gemini-2.0-flash',
-  },
-  {
-    displayName: 'Gemini 2.0 Flash-Lite',
-    key: 'gemini-2.0-flash-lite',
-  },
-  {
-    displayName: 'Gemini 2.0 Flash Thinking Experimental',
-    key: 'gemini-2.0-flash-thinking-exp-01-21',
-  },
-  {
-    displayName: 'Gemini 1.5 Flash',
-    key: 'gemini-1.5-flash',
-  },
-  {
-    displayName: 'Gemini 1.5 Flash-8B',
-    key: 'gemini-1.5-flash-8b',
-  },
-  {
-    displayName: 'Gemini 1.5 Pro',
-    key: 'gemini-1.5-pro',
-  },
-];
-
-const geminiEmbeddingModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemini Embedding',
-    key: 'gemini-embedding-exp',
-  },
-];
+const loadModels = (modelType: 'chat' | 'embedding') => {
+  return getModelsList()?.[modelType === 'chat' ? 'chatModels' : 'embeddingModels']['gemini']  as unknown as RawModel[]
+}
 
 export const loadGeminiChatModels = async () => {
   const geminiApiKey = getGeminiApiKey();
-
   if (!geminiApiKey) return {};
 
+  const models = loadModels('chat');
+
   try {
     const chatModels: Record<string, ChatModel> = {};
 
-    geminiChatModels.forEach((model) => {
+    models.forEach((model) => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatGoogleGenerativeAI({
@@ -73,13 +40,14 @@ export const loadGeminiChatModels = async () => {
 
 export const loadGeminiEmbeddingModels = async () => {
   const geminiApiKey = getGeminiApiKey();
-
   if (!geminiApiKey) return {};
 
+  const models = loadModels('embedding');
+
   try {
     const embeddingModels: Record<string, EmbeddingModel> = {};
 
-    geminiEmbeddingModels.forEach((model) => {
+    models.forEach((model) => {
       embeddingModels[model.key] = {
         displayName: model.displayName,
         model: new GoogleGenerativeAIEmbeddings({

src/lib/providers/groq.ts

@@ -1,88 +1,22 @@
 import { ChatOpenAI } from '@langchain/openai';
 import { getGroqApiKey } from '../config';
-import { ChatModel } from '.';
+import { ChatModel, getModelsList, RawModel } from '.';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
-const groqChatModels: Record<string, string>[] = [
-  {
-    displayName: 'Gemma2 9B IT',
-    key: 'gemma2-9b-it',
-  },
-  {
-    displayName: 'Llama 3.3 70B Versatile',
-    key: 'llama-3.3-70b-versatile',
-  },
-  {
-    displayName: 'Llama 3.1 8B Instant',
-    key: 'llama-3.1-8b-instant',
-  },
-  {
-    displayName: 'Llama3 70B 8192',
-    key: 'llama3-70b-8192',
-  },
-  {
-    displayName: 'Llama3 8B 8192',
-    key: 'llama3-8b-8192',
-  },
-  {
-    displayName: 'Mixtral 8x7B 32768',
-    key: 'mixtral-8x7b-32768',
-  },
-  {
-    displayName: 'Qwen QWQ 32B (Preview)',
-    key: 'qwen-qwq-32b',
-  },
-  {
-    displayName: 'Mistral Saba 24B (Preview)',
-    key: 'mistral-saba-24b',
-  },
-  {
-    displayName: 'Qwen 2.5 Coder 32B (Preview)',
-    key: 'qwen-2.5-coder-32b',
-  },
-  {
-    displayName: 'Qwen 2.5 32B (Preview)',
-    key: 'qwen-2.5-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
-    key: 'deepseek-r1-distill-qwen-32b',
-  },
-  {
-    displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
-    key: 'deepseek-r1-distill-llama-70b',
-  },
-  {
-    displayName: 'Llama 3.3 70B SpecDec (Preview)',
-    key: 'llama-3.3-70b-specdec',
-  },
-  {
-    displayName: 'Llama 3.2 1B Preview (Preview)',
-    key: 'llama-3.2-1b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 3B Preview (Preview)',
-    key: 'llama-3.2-3b-preview',
-  },
-  {
-    displayName: 'Llama 3.2 11B Vision Preview (Preview)',
-    key: 'llama-3.2-11b-vision-preview',
-  },
-  {
-    displayName: 'Llama 3.2 90B Vision Preview (Preview)',
-    key: 'llama-3.2-90b-vision-preview',
-  },
-];
+const loadModels = () => {
+  return getModelsList()?.chatModels['groq'] as unknown as RawModel[]
+}
 
 export const loadGroqChatModels = async () => {
   const groqApiKey = getGroqApiKey();
-
   if (!groqApiKey) return {};
 
+  const models = loadModels()
+
   try {
     const chatModels: Record<string, ChatModel> = {};
 
-    groqChatModels.forEach((model) => {
+    models.forEach((model) => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({

src/lib/providers/index.ts

@@ -1,26 +1,39 @@
-import { Embeddings } from '@langchain/core/embeddings';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
+import { Embeddings } from '@langchain/core/embeddings'
+import { BaseChatModel } from '@langchain/core/language_models/chat_models'
+import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai'
 import {
   getCustomOpenaiApiKey,
   getCustomOpenaiApiUrl,
   getCustomOpenaiModelName,
-} from '../config';
-import { ChatOpenAI } from '@langchain/openai';
-import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
-import { loadGroqChatModels } from './groq';
-import { loadAnthropicChatModels } from './anthropic';
-import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
-import { loadTransformersEmbeddingsModels } from './transformers';
+} from '../config'
+import { ChatOpenAI } from '@langchain/openai'
+import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama'
+import { loadGroqChatModels } from './groq'
+import { loadAnthropicChatModels } from './anthropic'
+import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini'
+import { loadTransformersEmbeddingsModels } from './transformers'
+import path from 'path'
+import fs from 'fs'
 
 export interface ChatModel {
-  displayName: string;
-  model: BaseChatModel;
+  displayName: string
+  model: BaseChatModel
 }
 
 export interface EmbeddingModel {
-  displayName: string;
-  model: Embeddings;
+  displayName: string
+  model: Embeddings
+}
+
+export type RawModel = {
+  displayName: string
+  key: string
+}
+
+type ModelsList = {
+  [key in "chatModels" | "embeddingModels"]: {
+    [key: string]: RawModel[]
+  }
 }
 
 export const chatModelProviders: Record<
@@ -32,7 +45,7 @@ export const chatModelProviders: Record<
   groq: loadGroqChatModels,
   anthropic: loadAnthropicChatModels,
   gemini: loadGeminiChatModels,
-};
+}
 
 export const embeddingModelProviders: Record<
   string,
@@ -42,21 +55,43 @@ export const embeddingModelProviders: Record<
   ollama: loadOllamaEmbeddingModels,
   gemini: loadGeminiEmbeddingModels,
   transformers: loadTransformersEmbeddingsModels,
-};
+}
+
+export const getModelsList = (): ModelsList | null => {
+  const modelFile = path.join(process.cwd(), 'data/models.json')
+  try {
+    const content = fs.readFileSync(modelFile, 'utf-8')
+    return JSON.parse(content) as ModelsList
+  } catch (err) {
+    console.error(`Error reading models file: ${err}`)
+    return null
+  }
+}
+
+export const updateModelsList = (models: ModelsList) => {
+  try {
+    const modelFile = path.join(process.cwd(), 'data/models.json')
+    const content = JSON.stringify(models, null, 2)
+
+    fs.writeFileSync(modelFile, content, 'utf-8')
+  } catch(err) {
+    console.error(`Error updating models file: ${err}`)
+  }
+}
 
 export const getAvailableChatModelProviders = async () => {
-  const models: Record<string, Record<string, ChatModel>> = {};
+  const models: Record<string, Record<string, ChatModel>> = {}
 
   for (const provider in chatModelProviders) {
-    const providerModels = await chatModelProviders[provider]();
+    const providerModels = await chatModelProviders[provider]()
     if (Object.keys(providerModels).length > 0) {
-      models[provider] = providerModels;
+      models[provider] = providerModels
     }
   }
 
-  const customOpenAiApiKey = getCustomOpenaiApiKey();
-  const customOpenAiApiUrl = getCustomOpenaiApiUrl();
-  const customOpenAiModelName = getCustomOpenaiModelName();
+  const customOpenAiApiKey = getCustomOpenaiApiKey()
+  const customOpenAiApiUrl = getCustomOpenaiApiUrl()
+  const customOpenAiModelName = getCustomOpenaiModelName()
 
   models['custom_openai'] = {
     ...(customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName
@@ -74,20 +109,20 @@ export const getAvailableChatModelProviders = async () => {
           },
         }
       : {}),
-  };
+  }
 
-  return models;
-};
+  return models
+}
 
 export const getAvailableEmbeddingModelProviders = async () => {
-  const models: Record<string, Record<string, EmbeddingModel>> = {};
+  const models: Record<string, Record<string, EmbeddingModel>> = {}
 
   for (const provider in embeddingModelProviders) {
-    const providerModels = await embeddingModelProviders[provider]();
+    const providerModels = await embeddingModelProviders[provider]()
     if (Object.keys(providerModels).length > 0) {
-      models[provider] = providerModels;
+      models[provider] = providerModels
     }
   }
 
-  return models;
-};
+  return models
+}

src/lib/providers/ollama.ts

@@ -1,24 +1,39 @@
-import axios from 'axios';
-import { getKeepAlive, getOllamaApiEndpoint } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
-
-export const loadOllamaChatModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
-
-  if (!ollamaApiEndpoint) return {};
+import axios from 'axios'
+import { getKeepAlive, getOllamaApiEndpoint } from '../config'
+import { ChatModel, EmbeddingModel } from '.'
+import { ChatOllama } from '@langchain/community/chat_models/ollama'
+import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama'
 
+const loadModels = async (apiURL: string) => {
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
+    const res = await axios.get(`${apiURL}/api/tags`, {
       headers: {
         'Content-Type': 'application/json',
       },
-    });
+    })
 
-    const { models } = res.data;
+    if (res.status !== 200) {
+      console.error(`Failed to load Ollama models: ${res.data}`)
+      return []
+    }
 
-    const chatModels: Record<string, ChatModel> = {};
+    const { models } = res.data
+
+    return models
+  } catch (err) {
+    console.error(`Error loading Ollama models: ${err}`)
+    return []
+  }
+}
+
+export const loadOllamaChatModels = async () => {
+  const ollamaApiEndpoint = getOllamaApiEndpoint()
+  if (!ollamaApiEndpoint) return {}
+
+  const models = await loadModels(ollamaApiEndpoint)
+
+  try {
+    const chatModels: Record<string, ChatModel> = {}
 
     models.forEach((model: any) => {
       chatModels[model.model] = {
@@ -29,31 +44,24 @@ export const loadOllamaChatModels = async () => {
           temperature: 0.7,
           keepAlive: getKeepAlive(),
         }),
-      };
-    });
+      }
+    })
 
-    return chatModels;
+    return chatModels
   } catch (err) {
-    console.error(`Error loading Ollama models: ${err}`);
-    return {};
+    console.error(`Error loading Ollama models: ${err}`)
+    return {}
   }
-};
+}
 
 export const loadOllamaEmbeddingModels = async () => {
-  const ollamaApiEndpoint = getOllamaApiEndpoint();
+  const ollamaApiEndpoint = getOllamaApiEndpoint()
+  if (!ollamaApiEndpoint) return {}
 
-  if (!ollamaApiEndpoint) return {};
+  const models = await loadModels(ollamaApiEndpoint)
 
   try {
-    const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
-      headers: {
-        'Content-Type': 'application/json',
-      },
-    });
-
-    const { models } = res.data;
-
-    const embeddingModels: Record<string, EmbeddingModel> = {};
+    const embeddingModels: Record<string, EmbeddingModel> = {}
 
     models.forEach((model: any) => {
       embeddingModels[model.model] = {
@@ -62,12 +70,12 @@ export const loadOllamaEmbeddingModels = async () => {
           baseUrl: ollamaApiEndpoint,
           model: model.model,
         }),
-      };
-    });
+      }
+    })
 
-    return embeddingModels;
+    return embeddingModels
   } catch (err) {
-    console.error(`Error loading Ollama embeddings models: ${err}`);
-    return {};
+    console.error(`Error loading Ollama embeddings models: ${err}`)
+    return {}
   }
-};
+}

src/lib/providers/openai.ts

@@ -1,52 +1,23 @@
 import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
 import { getOpenaiApiKey } from '../config';
-import { ChatModel, EmbeddingModel } from '.';
+import { ChatModel, EmbeddingModel, getModelsList, RawModel } from '.';
 import { BaseChatModel } from '@langchain/core/language_models/chat_models';
 import { Embeddings } from '@langchain/core/embeddings';
 
-const openaiChatModels: Record<string, string>[] = [
-  {
-    displayName: 'GPT-3.5 Turbo',
-    key: 'gpt-3.5-turbo',
-  },
-  {
-    displayName: 'GPT-4',
-    key: 'gpt-4',
-  },
-  {
-    displayName: 'GPT-4 turbo',
-    key: 'gpt-4-turbo',
-  },
-  {
-    displayName: 'GPT-4 omni',
-    key: 'gpt-4o',
-  },
-  {
-    displayName: 'GPT-4 omni mini',
-    key: 'gpt-4o-mini',
-  },
-];
-
-const openaiEmbeddingModels: Record<string, string>[] = [
-  {
-    displayName: 'Text Embedding 3 Small',
-    key: 'text-embedding-3-small',
-  },
-  {
-    displayName: 'Text Embedding 3 Large',
-    key: 'text-embedding-3-large',
-  },
-];
+const loadModels = (modelType: 'chat' | 'embedding') => {
+  return getModelsList()?.[modelType === 'chat' ? 'chatModels' : 'embeddingModels']['openai']  as unknown as RawModel[]
+}
 
 export const loadOpenAIChatModels = async () => {
   const openaiApiKey = getOpenaiApiKey();
+  const models = loadModels('chat');
 
-  if (!openaiApiKey) return {};
+  if (!openaiApiKey || !models) return {};
 
   try {
     const chatModels: Record<string, ChatModel> = {};
 
-    openaiChatModels.forEach((model) => {
+    models.forEach((model) => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({
@@ -66,13 +37,14 @@ export const loadOpenAIChatModels = async () => {
 
 export const loadOpenAIEmbeddingModels = async () => {
   const openaiApiKey = getOpenaiApiKey();
+  const models = loadModels('embedding');
 
-  if (!openaiApiKey) return {};
+  if (!openaiApiKey || !models) return {};
 
   try {
     const embeddingModels: Record<string, EmbeddingModel> = {};
 
-    openaiEmbeddingModels.forEach((model) => {
+    models.forEach((model) => {
       embeddingModels[model.key] = {
         displayName: model.displayName,
         model: new OpenAIEmbeddings({

src/lib/providers/transformers.ts

@@ -1,31 +1,30 @@
-import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
+import { EmbeddingModel, getModelsList, RawModel } from '.'
+import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer'
+
+const loadModels = () => {
+  return getModelsList()?.embeddingModels[
+    'transformers'
+  ] as unknown as RawModel[]
+}
 
 export const loadTransformersEmbeddingsModels = async () => {
   try {
-    const embeddingModels = {
-      'xenova-bge-small-en-v1.5': {
-        displayName: 'BGE Small',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/bge-small-en-v1.5',
-        }),
-      },
-      'xenova-gte-small': {
-        displayName: 'GTE Small',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/gte-small',
-        }),
-      },
-      'xenova-bert-base-multilingual-uncased': {
-        displayName: 'Bert Multilingual',
-        model: new HuggingFaceTransformersEmbeddings({
-          modelName: 'Xenova/bert-base-multilingual-uncased',
-        }),
-      },
-    };
+    const models = loadModels()
 
-    return embeddingModels;
+    const embeddingModels: Record<string, EmbeddingModel> = {}
+
+    models.forEach(model => {
+      embeddingModels[model.key] = {
+        displayName: model.displayName,
+        model: new HuggingFaceTransformersEmbeddings({
+          modelName: model.key,
+        }),
+      }
+    })
+
+    return embeddingModels
   } catch (err) {
-    console.error(`Error loading Transformers embeddings model: ${err}`);
-    return {};
+    console.error(`Error loading Transformers embeddings model: ${err}`)
+    return {}
   }
-};
+}

src/lib/search/metaSearchAgent.ts

@@ -33,6 +33,7 @@ export interface MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
+    systemInstructions: string,
   ) => Promise<eventEmitter>;
 }
 
@@ -236,9 +237,11 @@ class MetaSearchAgent implements MetaSearchAgentType {
     fileIds: string[],
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
+    systemInstructions: string,
   ) {
     return RunnableSequence.from([
       RunnableMap.from({
+        systemInstructions: () => systemInstructions,
         query: (input: BasicChainInput) => input.query,
         chat_history: (input: BasicChainInput) => input.chat_history,
         date: () => new Date().toISOString(),
@@ -468,6 +471,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
+    systemInstructions: string,
   ) {
     const emitter = new eventEmitter();
 
@@ -476,6 +480,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
       fileIds,
       embeddings,
       optimizationMode,
+      systemInstructions,
     );
 
     const stream = answeringChain.streamEvents(