Merge branch 'ItzCrazyKns:master' into master

feat(search): add optional systemInstructions to API request body

.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: |

docs/API/SEARCH.md

@@ -33,6 +33,7 @@ The API accepts a JSON object in the request body, where you define the focus mo
     ["human", "Hi, how are you?"],
     ["assistant", "I am doing well, how can I help you today?"]
   ],
+  "systemInstructions": "Focus on providing technical details about Perplexica's architecture.",
   "stream": false
 }
 ```
@@ -63,6 +64,8 @@ The API accepts a JSON object in the request body, where you define the focus mo
 
 - **`query`** (string, required): The search query or question.
 
+- **`systemInstructions`** (string, optional): Custom instructions provided by the user to guide the AI's response. These instructions are treated as user preferences and have lower priority than the system's core instructions. For example, you can specify a particular writing style, format, or focus area.
+
 - **`history`** (array, optional): An array of message pairs representing the conversation history. Each pair consists of a role (either 'human' or 'assistant') and the message content. This allows the system to use the context of the conversation to refine results. Example:
 
   ```json

package.json

@@ -1,6 +1,6 @@
 {
   "name": "perplexica-frontend",
-  "version": "1.10.1",
+  "version": "1.10.2",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {

sample.config.toml

@@ -22,5 +22,12 @@ MODEL_NAME = ""
 [MODELS.OLLAMA]
 API_URL = "" # Ollama API URL - http://host.docker.internal:11434
 
+[MODELS.DEEPSEEK]
+API_KEY = ""
+
 [API_ENDPOINTS]
 SEARXNG = "" # SearxNG API URL - http://localhost:32768
+TAVILY = "" # Tavily API key
+
+[SEARCH]
+ENGINE = "searxng" # "searxng" or "tavily"

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/config/route.ts

@@ -7,6 +7,9 @@ import {
   getGroqApiKey,
   getOllamaApiEndpoint,
   getOpenaiApiKey,
+  getDeepseekApiKey,
+  getSearchEngine,
+  getTavilyApiKey,
   updateConfig,
 } from '@/lib/config';
 import {
@@ -53,9 +56,12 @@ export const GET = async (req: Request) => {
     config['anthropicApiKey'] = getAnthropicApiKey();
     config['groqApiKey'] = getGroqApiKey();
     config['geminiApiKey'] = getGeminiApiKey();
+    config['deepseekApiKey'] = getDeepseekApiKey();
     config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
     config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
     config['customOpenaiModelName'] = getCustomOpenaiModelName();
+    config['searchEngine'] = getSearchEngine();
+    config['tavilyApiKey'] = getTavilyApiKey();
 
     return Response.json({ ...config }, { status: 200 });
   } catch (err) {
@@ -88,12 +94,21 @@ export const POST = async (req: Request) => {
         OLLAMA: {
           API_URL: config.ollamaApiUrl,
         },
+        DEEPSEEK: {
+          API_KEY: config.deepseekApiKey,
+        },
         CUSTOM_OPENAI: {
           API_URL: config.customOpenaiApiUrl,
           API_KEY: config.customOpenaiApiKey,
           MODEL_NAME: config.customOpenaiModelName,
         },
       },
+      SEARCH: {
+        ENGINE: config.searchEngine,
+      },
+      API_ENDPOINTS: {
+        TAVILY: config.tavilyApiKey || '',
+      },
     };
 
     updateConfig(updatedConfig);

src/app/api/discover/route.ts

@@ -1,4 +1,4 @@
-import { searchSearxng } from '@/lib/searxng';
+import { searchSearxng } from '../../../lib/searchEngines/searxng';
 
 const articleWebsites = [
   'yahoo.com',

src/app/api/search/route.ts

@@ -34,6 +34,7 @@ interface ChatRequestBody {
   query: string;
   history: Array<[string, string]>;
   stream?: boolean;
+  systemInstructions?: string;
 }
 
 export const POST = async (req: Request) => {
@@ -125,6 +126,7 @@ export const POST = async (req: Request) => {
       embeddings,
       body.optimizationMode,
       [],
+      body.systemInstructions || '',
     );
 
     if (!body.stream) {

src/app/settings/page.tsx

@@ -20,9 +20,12 @@ interface SettingsType {
   anthropicApiKey: string;
   geminiApiKey: string;
   ollamaApiUrl: string;
+  deepseekApiKey: string;
   customOpenaiApiKey: string;
   customOpenaiApiUrl: string;
   customOpenaiModelName: string;
+  searchEngine: string;
+  tavilyApiKey?: string;
 }
 
 interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
@@ -54,6 +57,38 @@ 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 +146,8 @@ const Page = () => {
   const [isLoading, setIsLoading] = useState(false);
   const [automaticImageSearch, setAutomaticImageSearch] = useState(false);
   const [automaticVideoSearch, setAutomaticVideoSearch] = useState(false);
+  const [systemInstructions, setSystemInstructions] = useState<string>('');
+  const [searchEngine, setSearchEngine] = useState<string>('searxng');
   const [savingStates, setSavingStates] = useState<Record<string, boolean>>({});
 
   useEffect(() => {
@@ -172,6 +209,9 @@ const Page = () => {
         localStorage.getItem('autoVideoSearch') === 'true',
       );
 
+      setSystemInstructions(localStorage.getItem('systemInstructions')!);
+      setSearchEngine(localStorage.getItem('searchEngine') || 'searxng');
+
       setIsLoading(false);
     };
 
@@ -328,6 +368,12 @@ const Page = () => {
         localStorage.setItem('embeddingModelProvider', value);
       } else if (key === 'embeddingModel') {
         localStorage.setItem('embeddingModel', value);
+      } else if (key === 'systemInstructions') {
+        localStorage.setItem('systemInstructions', value);
+      } else if (key === 'searchEngine') {
+        localStorage.setItem('searchEngine', value);
+      } else if (key === 'tavilyApiKey') {
+        localStorage.setItem('tavilyApiKey', value);
       }
     } catch (err) {
       console.error('Failed to save:', err);
@@ -470,6 +516,45 @@ const Page = () => {
                     />
                   </Switch>
                 </div>
+
+                <div className="flex flex-col space-y-1 mt-2">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    Search Engine
+                  </p>
+                  <Select
+                    value={searchEngine}
+                    onChange={(e) => {
+                      const value = e.target.value;
+                      setSearchEngine(value);
+                      saveConfig('searchEngine', value);
+                    }}
+                    options={[
+                      { value: 'searxng', label: 'SearxNG' },
+                      ...(config.tavilyApiKey ? [{ value: 'tavily', label: 'Tavily' }] : []),
+                    ]}
+                  />
+                  <p className="text-xs text-black/60 dark:text-white/60 mt-1">
+                    Select which search engine to use for web searches
+                  </p>
+                  {searchEngine === 'tavily' && !config.tavilyApiKey && (
+                    <p className="text-xs text-red-500 mt-1">
+                      Tavily API key is required to use this search engine
+                    </p>
+                  )}
+                </div>
+              </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>
 
@@ -788,6 +873,51 @@ const Page = () => {
                     onSave={(value) => saveConfig('geminiApiKey', value)}
                   />
                 </div>
+
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    Deepseek API Key
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="Deepseek API Key"
+                    value={config.deepseekApiKey}
+                    isSaving={savingStates['deepseekApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        deepseekApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('deepseekApiKey', value)}
+                  />
+                </div>
+
+                <div className="flex flex-col space-y-1 mt-4 pt-4 border-t border-light-200 dark:border-dark-200">
+                  <p className="text-black/90 dark:text-white/90 font-medium">Search Engine API Keys</p>
+                  <p className="text-sm text-black/60 dark:text-white/60 mt-0.5">
+                    API keys for search engines used in the application
+                  </p>
+                </div>
+
+                <div className="flex flex-col space-y-1">
+                  <p className="text-black/70 dark:text-white/70 text-sm">
+                    Tavily API Key
+                  </p>
+                  <Input
+                    type="text"
+                    placeholder="Tavily API key"
+                    value={config.tavilyApiKey || ''}
+                    isSaving={savingStates['tavilyApiKey']}
+                    onChange={(e) => {
+                      setConfig((prev) => ({
+                        ...prev!,
+                        tavilyApiKey: e.target.value,
+                      }));
+                    }}
+                    onSave={(value) => saveConfig('tavilyApiKey', value)}
+                  />
+                </div>
               </div>
             </SettingsSection>
           </div>

src/components/ChatWindow.tsx

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

src/components/MessageBox.tsx

@@ -48,6 +48,7 @@ const MessageBox = ({
   const [speechMessage, setSpeechMessage] = useState(message.content);
 
   useEffect(() => {
+    const citationRegex = /\[([^\]]+)\]/g;
     const regex = /\[(\d+)\]/g;
     let processedMessage = message.content;
 
@@ -67,13 +68,36 @@ const MessageBox = ({
     ) {
       setParsedMessage(
         processedMessage.replace(
-          regex,
+          citationRegex,
-          (_, number) =>
+          (_, capturedContent: string) => {
-            `<a href="${
+            const numbers = capturedContent
-              message.sources?.[number - 1]?.metadata?.url
+              .split(',')
-            }" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${number}</a>`,
+              .map((numStr) => numStr.trim());
+
+            const linksHtml = numbers
+              .map((numStr) => {
+                const number = parseInt(numStr);
+
+                if (isNaN(number) || number <= 0) {
+                  return `[${numStr}]`;
+                }
+
+                const source = message.sources?.[number - 1];
+                const url = source?.metadata?.url;
+
+                if (url) {
+                  return `<a href="${url}" target="_blank" className="bg-light-secondary dark:bg-dark-secondary px-1 rounded ml-1 no-underline text-xs text-black/70 dark:text-white/70 relative">${numStr}</a>`;
+                } else {
+                  return `[${numStr}]`;
+                }
+              })
+              .join('');
+
+            return linksHtml;
+          },
         ),
       );
+      setSpeechMessage(message.content.replace(regex, ''));
       return;
     }
 

src/lib/chains/imageSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../searchEngines/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const imageSearchChainPrompt = `

src/lib/chains/videoSearchAgent.ts

@@ -7,7 +7,7 @@ import { PromptTemplate } from '@langchain/core/prompts';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import { BaseMessage } from '@langchain/core/messages';
 import { StringOutputParser } from '@langchain/core/output_parsers';
-import { searchSearxng } from '../searxng';
+import { searchSearxng } from '../searchEngines/searxng';
 import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
 
 const VideoSearchChainPrompt = `

src/lib/config.ts

@@ -25,6 +25,9 @@ interface Config {
     OLLAMA: {
       API_URL: string;
     };
+    DEEPSEEK: {
+      API_KEY: string;
+    };
     CUSTOM_OPENAI: {
       API_URL: string;
       API_KEY: string;
@@ -33,6 +36,10 @@ interface Config {
   };
   API_ENDPOINTS: {
     SEARXNG: string;
+    TAVILY: string;
+  };
+  SEARCH: {
+    ENGINE: string;
   };
 }
 
@@ -61,8 +68,16 @@ export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
 export const getSearxngApiEndpoint = () =>
   process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
 
+export const getTavilyApiKey = () =>
+  process.env.TAVILY_API_KEY || loadConfig().API_ENDPOINTS.TAVILY;
+
+export const getSearchEngine = () =>
+  process.env.SEARCH_ENGINE || loadConfig().SEARCH?.ENGINE || 'searxng';
+
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
 
+export const getDeepseekApiKey = () => loadConfig().MODELS.DEEPSEEK.API_KEY;
+
 export const getCustomOpenaiApiKey = () =>
   loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
 

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/deepseek.ts

@@ -0,0 +1,44 @@
+import { ChatOpenAI } from '@langchain/openai';
+import { getDeepseekApiKey } from '../config';
+import { ChatModel } from '.';
+import { BaseChatModel } from '@langchain/core/language_models/chat_models';
+
+const deepseekChatModels: Record<string, string>[] = [
+  {
+    displayName: 'Deepseek Chat (Deepseek V3)',
+    key: 'deepseek-chat',
+  },
+  {
+    displayName: 'Deepseek Reasoner (Deepseek R1)',
+    key: 'deepseek-reasoner',
+  },
+];
+
+export const loadDeepseekChatModels = async () => {
+  const deepseekApiKey = getDeepseekApiKey();
+
+  if (!deepseekApiKey) return {};
+
+  try {
+    const chatModels: Record<string, ChatModel> = {};
+
+    deepseekChatModels.forEach((model) => {
+      chatModels[model.key] = {
+        displayName: model.displayName,
+        model: new ChatOpenAI({
+          openAIApiKey: deepseekApiKey,
+          modelName: model.key,
+          temperature: 0.7,
+          configuration: {
+            baseURL: 'https://api.deepseek.com',
+          },
+        }) as unknown as BaseChatModel,
+      };
+    });
+
+    return chatModels;
+  } catch (err) {
+    console.error(`Error loading Deepseek models: ${err}`);
+    return {};
+  }
+};

src/lib/providers/gemini.ts

@@ -40,8 +40,12 @@ const geminiChatModels: Record<string, string>[] = [
 
 const geminiEmbeddingModels: Record<string, string>[] = [
   {
-    displayName: 'Gemini Embedding',
+    displayName: 'Text Embedding 004',
-    key: 'gemini-embedding-exp',
+    key: 'models/text-embedding-004',
+  },
+  {
+    displayName: 'Embedding 001',
+    key: 'models/embedding-001',
   },
 ];
 

src/lib/providers/groq.ts

@@ -72,6 +72,14 @@ const groqChatModels: Record<string, string>[] = [
     displayName: 'Llama 3.2 90B Vision Preview (Preview)',
     key: 'llama-3.2-90b-vision-preview',
   },
+  /* {
+    displayName: 'Llama 4 Maverick 17B 128E Instruct (Preview)',
+    key: 'meta-llama/llama-4-maverick-17b-128e-instruct',
+  }, */
+  {
+    displayName: 'Llama 4 Scout 17B 16E Instruct (Preview)',
+    key: 'meta-llama/llama-4-scout-17b-16e-instruct',
+  },
 ];
 
 export const loadGroqChatModels = async () => {

src/lib/providers/index.ts

@@ -12,6 +12,7 @@ import { loadGroqChatModels } from './groq';
 import { loadAnthropicChatModels } from './anthropic';
 import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
 import { loadTransformersEmbeddingsModels } from './transformers';
+import { loadDeepseekChatModels } from './deepseek';
 
 export interface ChatModel {
   displayName: string;
@@ -32,6 +33,7 @@ export const chatModelProviders: Record<
   groq: loadGroqChatModels,
   anthropic: loadAnthropicChatModels,
   gemini: loadGeminiChatModels,
+  deepseek: loadDeepseekChatModels,
 };
 
 export const embeddingModelProviders: Record<

src/lib/search/metaSearchAgent.ts

@@ -17,7 +17,9 @@ import LineListOutputParser from '../outputParsers/listLineOutputParser';
 import LineOutputParser from '../outputParsers/lineOutputParser';
 import { getDocumentsFromLinks } from '../utils/documents';
 import { Document } from 'langchain/document';
-import { searchSearxng } from '../searxng';
+import { searchTavily } from '../searchEngines/tavily';
+import { searchSearxng } from '../searchEngines/searxng';
+import { getSearchEngine } from '../config';
 import path from 'node:path';
 import fs from 'node:fs';
 import computeSimilarity from '../utils/computeSimilarity';
@@ -33,6 +35,7 @@ export interface MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
+    systemInstructions: string,
   ) => Promise<eventEmitter>;
 }
 
@@ -204,25 +207,42 @@ class MetaSearchAgent implements MetaSearchAgentType {
         } else {
           question = question.replace(/<think>.*?<\/think>/g, '');
 
-          const res = await searchSearxng(question, {
+          const searchEngine = getSearchEngine();
+
+          let res;
+          
+          if (searchEngine === 'tavily') {
+            res = await searchTavily(question, {
+              search_depth: 'basic',
+              max_results: 15,
+              include_images: true,
+            });
+          } else {
+            // Default to SearxNG
+            res = await searchSearxng(question, {
               language: 'en',
               engines: this.config.activeEngines,
             });
+          }
 
-          const documents = res.results.map(
+          let documents: Document[] = [];
+          
+          documents = documents.concat(
+            res.results.map(
               (result) =>
                 new Document({
                   pageContent:
                     result.content ||
                     (this.config.activeEngines.includes('youtube')
                       ? result.title
-                    : '') /* Todo: Implement transcript grabbing using Youtubei (source: https://www.npmjs.com/package/youtubei) */,
+                      : ''),
                   metadata: {
                     title: result.title,
                     url: result.url,
-                  ...(result.img_src && { img_src: result.img_src }),
+                    ...(result.img_src ? { img_src: result.img_src } : {}),
                   },
                 }),
+            )
           );
 
           return { query: question, docs: documents };
@@ -236,9 +256,11 @@ 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 +490,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
     embeddings: Embeddings,
     optimizationMode: 'speed' | 'balanced' | 'quality',
     fileIds: string[],
+    systemInstructions: string,
   ) {
     const emitter = new eventEmitter();
 
@@ -476,6 +499,7 @@ class MetaSearchAgent implements MetaSearchAgentType {
       fileIds,
       embeddings,
       optimizationMode,
+      systemInstructions,
     );
 
     const stream = answeringChain.streamEvents(

src/lib/searchEngines/searxng.ts

@@ -1,5 +1,5 @@
 import axios from 'axios';
-import { getSearxngApiEndpoint } from './config';
+import { getSearxngApiEndpoint } from '../config';
 
 interface SearxngSearchOptions {
   categories?: string[];

src/lib/searchEngines/tavily.ts

@@ -0,0 +1,79 @@
+import axios from 'axios';
+import { getTavilyApiKey } from '../config';
+
+interface TavilySearchOptions {
+  topic?: 'general' | 'news';
+  search_depth?: 'basic' | 'advanced';
+  chunks_per_source?: number;
+  max_results?: number;
+  time_range?: 'day' | 'week' | 'month' | 'year' | 'd' | 'w' | 'm' | 'y';
+  days?: number;
+  include_answer?: boolean | 'basic' | 'advanced';
+  include_raw_content?: boolean;
+  include_images?: boolean;
+  include_image_descriptions?: boolean;
+  include_domains?: string[];
+  exclude_domains?: string[];
+}
+
+interface TavilySearchResult {
+  title: string;
+  url: string;
+  content: string;
+  score: number;
+  raw_content?: string;
+}
+
+interface TavilySearchResponse {
+  query: string;
+  answer?: string;
+  images?: Array<{
+    url: string;
+    description?: string;
+  }>;
+  results: TavilySearchResult[];
+  response_time: string;
+}
+
+export const searchTavily = async (
+  query: string,
+  opts?: TavilySearchOptions,
+) => {
+  const tavilyApiKey = getTavilyApiKey();
+  
+  if (!tavilyApiKey) {
+    throw new Error('Tavily API key is not configured');
+  }
+
+  const url = 'https://api.tavily.com/search';
+  
+  const response = await axios.post<TavilySearchResponse>(
+    url,
+    {
+      query,
+      ...opts,
+    },
+    {
+      headers: {
+        'Content-Type': 'application/json',
+        'Authorization': `Bearer ${tavilyApiKey}`,
+      },
+    }
+  );
+
+  const results = response.data.results;
+  
+  // Convert Tavily results to match the format expected by the rest of the application
+  const formattedResults = results.map(result => ({
+    title: result.title,
+    url: result.url,
+    content: result.content,
+    img_src: undefined, // Tavily doesn't provide image URLs in the standard response
+  }));
+
+  return { 
+    results: formattedResults, 
+    suggestions: [], // Tavily doesn't provide suggestions, so return empty array
+    answer: response.data.answer, // Include the AI-generated answer if available
+  };
+};