feat(custom-openai): use apiKey instead of openAIApiKey

package.json

@@ -15,11 +15,12 @@
     "@headlessui/react": "^2.2.0",
     "@iarna/toml": "^2.2.5",
     "@icons-pack/react-simple-icons": "^12.3.0",
-    "@langchain/anthropic": "^0.3.15",
-    "@langchain/community": "^0.3.36",
-    "@langchain/core": "^0.3.42",
-    "@langchain/google-genai": "^0.1.12",
-    "@langchain/openai": "^0.0.25",
+    "@langchain/anthropic": "^0.3.24",
+    "@langchain/community": "^0.3.49",
+    "@langchain/core": "^0.3.66",
+    "@langchain/google-genai": "^0.2.15",
+    "@langchain/ollama": "^0.2.3",
+    "@langchain/openai": "^0.6.2",
     "@langchain/textsplitters": "^0.1.0",
     "@tailwindcss/typography": "^0.5.12",
     "@xenova/transformers": "^2.17.2",
@@ -31,7 +32,7 @@
     "drizzle-orm": "^0.40.1",
     "html-to-text": "^9.0.5",
     "jspdf": "^3.0.1",
-    "langchain": "^0.1.30",
+    "langchain": "^0.3.30",
     "lucide-react": "^0.363.0",
     "mammoth": "^1.9.1",
     "markdown-to-jsx": "^7.7.2",

src/app/api/chat/route.ts

@@ -223,7 +223,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/images/route.ts

@@ -49,7 +49,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/search/route.ts

@@ -81,7 +81,7 @@ export const POST = async (req: Request) => {
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
         modelName: body.chatModel?.name || getCustomOpenaiModelName(),
-        openAIApiKey:
+        apiKey:
           body.chatModel?.customOpenAIKey || getCustomOpenaiApiKey(),
         temperature: 0.7,
         configuration: {

src/app/api/suggestions/route.ts

@@ -48,7 +48,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/app/api/videos/route.ts

@@ -49,7 +49,7 @@ export const POST = async (req: Request) => {
 
     if (body.chatModel?.provider === 'custom_openai') {
       llm = new ChatOpenAI({
-        openAIApiKey: getCustomOpenaiApiKey(),
+        apiKey: getCustomOpenaiApiKey(),
         modelName: getCustomOpenaiModelName(),
         temperature: 0.7,
         configuration: {

src/lib/prompts/webSearch.ts

@@ -1,63 +1,41 @@
 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 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.
+You are an AI question rephraser. You will be given a conversation and a follow-up question; rephrase it into a standalone question that another LLM can use to search the web.
 
-There are several examples attached for your reference inside the below \`examples\` XML block
+Return ONLY a JSON object that matches this schema:
+query: string   // the standalone question (or "summarize")
+links: string[] // URLs extracted from the user query (empty if none)
+searchRequired: boolean // true if web search is needed, false for greetings/simple writing tasks
+searchMode: "" | "normal" | "news" // "" when searchRequired is false; "news" if the user asks for news/articles, otherwise "normal"
 
-<examples>
-1. Follow up question: What is the capital of France
-Rephrased question:\`
-<question>
-Capital of france
-</question>
-\`
+Rules
+- Greetings / simple writing tasks → query:"", links:[], searchRequired:false, searchMode:""
+- Summarizing a URL → query:"summarize", links:[url...], searchRequired:true, searchMode:"normal"
+- Asking for news/articles → searchMode:"news"
+
+Examples
+1. Follow-up: What is the capital of France?
+"query":"capital of France","links":[],"searchRequired":true,"searchMode":"normal"
 
 2. Hi, how are you?
-Rephrased question\`
-<question>
-not_needed
-</question>
-\`
+"query":"","links":[],"searchRequired":false,"searchMode":""
 
-3. Follow up question: What is Docker?
-Rephrased question: \`
-<question>
-What is Docker
-</question>
-\`
+3. Follow-up: What is Docker?
+"query":"what is Docker","links":[],"searchRequired":true,"searchMode":"normal"
 
-4. Follow up question: Can you tell me what is X from https://example.com
-Rephrased question: \`
-<question>
-Can you tell me what is X?
-</question>
+4. Follow-up: Can you tell me what is X from https://example.com?
+"query":"what is X","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
 
-<links>
-https://example.com
-</links>
-\`
+5. Follow-up: Summarize the content from https://example.com
+"query":"summarize","links":["https://example.com"],"searchRequired":true,"searchMode":"normal"
 
-5. Follow up question: Summarize the content from https://example.com
-Rephrased question: \`
-<question>
-summarize
-</question>
-
-<links>
-https://example.com
-</links>
-\`
-</examples>
-
-Anything below is the part of the actual conversation and you need to use conversation and the follow-up question to rephrase the follow-up question as a standalone question based on the guidelines shared above.
+6. Follow-up: Latest news about AI
+"query":"latest news about AI","links":[],"searchRequired":true,"searchMode":"news"
 
 <conversation>
 {chat_history}
 </conversation>
 
-Follow up question: {query}
+Follow-up question: {query}
 Rephrased question:
 `;
 

src/lib/providers/aimlapi.ts

@@ -38,7 +38,7 @@ export const loadAimlApiChatModels = async () => {
         chatModels[model.id] = {
           displayName: model.name || model.id,
           model: new ChatOpenAI({
-            openAIApiKey: apiKey,
+            apiKey: apiKey,
             modelName: model.id,
             temperature: 0.7,
             configuration: {
@@ -76,7 +76,7 @@ export const loadAimlApiEmbeddingModels = async () => {
         embeddingModels[model.id] = {
           displayName: model.name || model.id,
           model: new OpenAIEmbeddings({
-            openAIApiKey: apiKey,
+            apiKey: apiKey,
             modelName: model.id,
             configuration: {
               baseURL: API_URL,

src/lib/providers/deepseek.ts

@@ -31,7 +31,7 @@ export const loadDeepseekChatModels = async () => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey: deepseekApiKey,
+          apiKey: deepseekApiKey,
           modelName: model.key,
           temperature: 0.7,
           configuration: {

src/lib/providers/groq.ts

@@ -29,12 +29,15 @@ export const loadGroqChatModels = async () => {
       chatModels[model.id] = {
         displayName: model.id,
         model: new ChatOpenAI({
-          openAIApiKey: groqApiKey,
+          apiKey: groqApiKey,
           modelName: model.id,
           temperature: 0.7,
           configuration: {
             baseURL: 'https://api.groq.com/openai/v1',
           },
+          metadata: {
+            'model-type': 'groq',
+          },
         }) as unknown as BaseChatModel,
       };
     });

src/lib/providers/index.ts

@@ -118,7 +118,7 @@ export const getAvailableChatModelProviders = async () => {
           [customOpenAiModelName]: {
             displayName: customOpenAiModelName,
             model: new ChatOpenAI({
-              openAIApiKey: customOpenAiApiKey,
+              apiKey: customOpenAiApiKey,
               modelName: customOpenAiModelName,
               temperature: 0.7,
               configuration: {

src/lib/providers/lmstudio.ts

@@ -47,7 +47,7 @@ export const loadLMStudioChatModels = async () => {
       chatModels[model.id] = {
         displayName: model.name || model.id,
         model: new ChatOpenAI({
-          openAIApiKey: 'lm-studio',
+          apiKey: 'lm-studio',
           configuration: {
             baseURL: ensureV1Endpoint(endpoint),
           },
@@ -83,7 +83,7 @@ export const loadLMStudioEmbeddingsModels = async () => {
       embeddingsModels[model.id] = {
         displayName: model.name || model.id,
         model: new OpenAIEmbeddings({
-          openAIApiKey: 'lm-studio',
+          apiKey: 'lm-studio',
           configuration: {
             baseURL: ensureV1Endpoint(endpoint),
           },

src/lib/providers/ollama.ts

@@ -6,8 +6,8 @@ export const PROVIDER_INFO = {
   key: 'ollama',
   displayName: 'Ollama',
 };
-import { ChatOllama } from '@langchain/community/chat_models/ollama';
-import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
+import { ChatOllama } from '@langchain/ollama';
+import { OllamaEmbeddings } from '@langchain/ollama';
 
 export const loadOllamaChatModels = async () => {
   const ollamaApiEndpoint = getOllamaApiEndpoint();

src/lib/providers/openai.ts

@@ -67,7 +67,7 @@ export const loadOpenAIChatModels = async () => {
       chatModels[model.key] = {
         displayName: model.displayName,
         model: new ChatOpenAI({
-          openAIApiKey: openaiApiKey,
+          apiKey: openaiApiKey,
           modelName: model.key,
           temperature: 0.7,
         }) as unknown as BaseChatModel,
@@ -93,7 +93,7 @@ export const loadOpenAIEmbeddingModels = async () => {
       embeddingModels[model.key] = {
         displayName: model.displayName,
         model: new OpenAIEmbeddings({
-          openAIApiKey: openaiApiKey,
+          apiKey: openaiApiKey,
           modelName: model.key,
         }) as unknown as Embeddings,
       };

src/lib/search/metaSearchAgent.ts

@@ -24,6 +24,7 @@ import computeSimilarity from '../utils/computeSimilarity';
 import formatChatHistoryAsString from '../utils/formatHistory';
 import eventEmitter from 'events';
 import { StreamEvent } from '@langchain/core/tracers/log_stream';
+import { z } from 'zod';
 
 export interface MetaSearchAgentType {
   searchAndAnswer: (
@@ -52,6 +53,17 @@ type BasicChainInput = {
   query: string;
 };
 
+const retrieverLLMOutputSchema = z.object({
+  query: z.string().describe('The query to search the web for.'),
+  links: z
+    .array(z.string())
+    .describe('The links to search/summarize if present'),
+  searchRequired: z
+    .boolean()
+    .describe('Wether there is a need to search the web'),
+  searchMode: z.enum(['', 'normal', 'news']).describe('The search mode.'),
+});
+
 class MetaSearchAgent implements MetaSearchAgentType {
   private config: Config;
   private strParser = new StringOutputParser();
@@ -62,73 +74,71 @@ class MetaSearchAgent implements MetaSearchAgentType {
 
   private async createSearchRetrieverChain(llm: BaseChatModel) {
     (llm as unknown as ChatOpenAI).temperature = 0;
-
     return RunnableSequence.from([
       PromptTemplate.fromTemplate(this.config.queryGeneratorPrompt),
-      llm,
-      this.strParser,
-      RunnableLambda.from(async (input: string) => {
-        const linksOutputParser = new LineListOutputParser({
-          key: 'links',
-        });
+      Object.assign(
+        Object.create(Object.getPrototypeOf(llm)),
+        llm,
+      ).withStructuredOutput(retrieverLLMOutputSchema, {
+        ...(llm.metadata?.['model-type'] === 'groq'
+          ? {
+              method: 'json-object',
+            }
+          : {}),
+      }),
+      RunnableLambda.from(
+        async (input: z.infer<typeof retrieverLLMOutputSchema>) => {
+          let question = input.query;
+          const links = input.links;
 
-        const questionOutputParser = new LineOutputParser({
-          key: 'question',
-        });
-
-        const links = await linksOutputParser.parse(input);
-        let question = this.config.summarizer
-          ? await questionOutputParser.parse(input)
-          : input;
-
-        if (question === 'not_needed') {
-          return { query: '', docs: [] };
-        }
-
-        if (links.length > 0) {
-          if (question.length === 0) {
-            question = 'summarize';
+          if (!input.searchRequired) {
+            return { query: '', docs: [] };
           }
 
-          let docs: Document[] = [];
-
-          const linkDocs = await getDocumentsFromLinks({ links });
-
-          const docGroups: Document[] = [];
-
-          linkDocs.map((doc) => {
-            const URLDocExists = docGroups.find(
-              (d) =>
-                d.metadata.url === doc.metadata.url &&
-                d.metadata.totalDocs < 10,
-            );
-
-            if (!URLDocExists) {
-              docGroups.push({
-                ...doc,
-                metadata: {
-                  ...doc.metadata,
-                  totalDocs: 1,
-                },
-              });
+          if (links.length > 0) {
+            if (question.length === 0) {
+              question = 'summarize';
             }
 
-            const docIndex = docGroups.findIndex(
-              (d) =>
-                d.metadata.url === doc.metadata.url &&
-                d.metadata.totalDocs < 10,
-            );
+            let docs: Document[] = [];
 
-            if (docIndex !== -1) {
-              docGroups[docIndex].pageContent =
-                docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
-              docGroups[docIndex].metadata.totalDocs += 1;
-            }
-          });
+            const linkDocs = await getDocumentsFromLinks({ links });
 
-          await Promise.all(
-            docGroups.map(async (doc) => {
-              const res = await llm.invoke(`
+            const docGroups: Document[] = [];
+
+            linkDocs.map((doc) => {
+              const URLDocExists = docGroups.find(
+                (d) =>
+                  d.metadata.url === doc.metadata.url &&
+                  d.metadata.totalDocs < 10,
+              );
+
+              if (!URLDocExists) {
+                docGroups.push({
+                  ...doc,
+                  metadata: {
+                    ...doc.metadata,
+                    totalDocs: 1,
+                  },
+                });
+              }
+
+              const docIndex = docGroups.findIndex(
+                (d) =>
+                  d.metadata.url === doc.metadata.url &&
+                  d.metadata.totalDocs < 10,
+              );
+
+              if (docIndex !== -1) {
+                docGroups[docIndex].pageContent =
+                  docGroups[docIndex].pageContent + `\n\n` + doc.pageContent;
+                docGroups[docIndex].metadata.totalDocs += 1;
+              }
+            });
+
+            await Promise.all(
+              docGroups.map(async (doc) => {
+                const res = await llm.invoke(`
             You are a web search summarizer, tasked with summarizing a piece of text retrieved from a web search. Your job is to summarize the 
             text into a detailed, 2-4 paragraph explanation that captures the main ideas and provides a comprehensive answer to the query.
             If the query is \"summarize\", you should provide a detailed summary of the text. If the query is a specific question, you should answer it in the summary.
@@ -189,46 +199,50 @@ class MetaSearchAgent implements MetaSearchAgentType {
             Make sure to answer the query in the summary.
           `);
 
-              const document = new Document({
-                pageContent: res.content as string,
-                metadata: {
-                  title: doc.metadata.title,
-                  url: doc.metadata.url,
-                },
-              });
+                const document = new Document({
+                  pageContent: res.content as string,
+                  metadata: {
+                    title: doc.metadata.title,
+                    url: doc.metadata.url,
+                  },
+                });
 
-              docs.push(document);
-            }),
-          );
-
-          return { query: question, docs: docs };
-        } else {
-          question = question.replace(/<think>.*?<\/think>/g, '');
-
-          const res = await searchSearxng(question, {
-            language: 'en',
-            engines: this.config.activeEngines,
-          });
-
-          const documents = 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 }),
-                },
+                docs.push(document);
               }),
-          );
+            );
 
-          return { query: question, docs: documents };
-        }
-      }),
+            return { query: question, docs: docs };
+          } else {
+            question = question.replace(/<think>.*?<\/think>/g, '');
+
+            const res = await searchSearxng(question, {
+              language: 'en',
+              engines:
+                input.searchMode === 'normal'
+                  ? this.config.activeEngines
+                  : ['bing news'],
+            });
+
+            const documents = 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 }),
+                  },
+                }),
+            );
+
+            return { query: question, docs: documents };
+          }
+        },
+      ),
     ]);
   }