feat(models): remove old providers

drizzle/0002_daffy_wrecker.sql

@@ -0,0 +1,15 @@
+PRAGMA foreign_keys=OFF;--> statement-breakpoint
+CREATE TABLE `__new_messages` (
+	`id` integer PRIMARY KEY NOT NULL,
+	`messageId` text NOT NULL,
+	`chatId` text NOT NULL,
+	`backendId` text NOT NULL,
+	`query` text NOT NULL,
+	`createdAt` text NOT NULL,
+	`responseBlocks` text DEFAULT '[]',
+	`status` text DEFAULT 'answering'
+);
+--> statement-breakpoint
+DROP TABLE `messages`;--> statement-breakpoint
+ALTER TABLE `__new_messages` RENAME TO `messages`;--> statement-breakpoint
+PRAGMA foreign_keys=ON;

drizzle/meta/0002_snapshot.json

@@ -0,0 +1,132 @@
+{
+  "version": "6",
+  "dialect": "sqlite",
+  "id": "1c5eb804-d6b4-48ec-9a8f-75fb729c8e52",
+  "prevId": "6dedf55f-0e44-478f-82cf-14a21ac686f8",
+  "tables": {
+    "chats": {
+      "name": "chats",
+      "columns": {
+        "id": {
+          "name": "id",
+          "type": "text",
+          "primaryKey": true,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "title": {
+          "name": "title",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "createdAt": {
+          "name": "createdAt",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "focusMode": {
+          "name": "focusMode",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "files": {
+          "name": "files",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'[]'"
+        }
+      },
+      "indexes": {},
+      "foreignKeys": {},
+      "compositePrimaryKeys": {},
+      "uniqueConstraints": {},
+      "checkConstraints": {}
+    },
+    "messages": {
+      "name": "messages",
+      "columns": {
+        "id": {
+          "name": "id",
+          "type": "integer",
+          "primaryKey": true,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "messageId": {
+          "name": "messageId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "chatId": {
+          "name": "chatId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "backendId": {
+          "name": "backendId",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "query": {
+          "name": "query",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "createdAt": {
+          "name": "createdAt",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": true,
+          "autoincrement": false
+        },
+        "responseBlocks": {
+          "name": "responseBlocks",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'[]'"
+        },
+        "status": {
+          "name": "status",
+          "type": "text",
+          "primaryKey": false,
+          "notNull": false,
+          "autoincrement": false,
+          "default": "'answering'"
+        }
+      },
+      "indexes": {},
+      "foreignKeys": {},
+      "compositePrimaryKeys": {},
+      "uniqueConstraints": {},
+      "checkConstraints": {}
+    }
+  },
+  "views": {},
+  "enums": {},
+  "_meta": {
+    "schemas": {},
+    "tables": {},
+    "columns": {}
+  },
+  "internal": {
+    "indexes": {}
+  }
+}

drizzle/meta/_journal.json

@@ -15,6 +15,13 @@
       "when": 1758863991284,
       "tag": "0001_wise_rockslide",
       "breakpoints": true
+    },
+    {
+      "idx": 2,
+      "version": "6",
+      "when": 1763732708332,
+      "tag": "0002_daffy_wrecker",
+      "breakpoints": true
     }
   ]
 }

package.json

@@ -40,11 +40,15 @@
     "markdown-to-jsx": "^7.7.2",
     "next": "^15.2.2",
     "next-themes": "^0.3.0",
+    "ollama": "^0.6.3",
+    "openai": "^6.9.0",
+    "partial-json": "^0.1.7",
     "pdf-parse": "^1.1.1",
     "react": "^18",
     "react-dom": "^18",
     "react-text-to-speech": "^0.14.5",
     "react-textarea-autosize": "^8.5.3",
+    "rfc6902": "^5.1.2",
     "sonner": "^1.4.41",
     "tailwind-merge": "^2.2.2",
     "winston": "^3.17.0",

src/lib/agents/search/index.ts

@@ -0,0 +1,48 @@
+import { ResearcherOutput, SearchAgentInput } from './types';
+import SessionManager from '@/lib/session';
+import Classifier from './classifier';
+import { WidgetRegistry } from './widgets';
+import Researcher from './researcher';
+
+class SearchAgent {
+  async searchAsync(session: SessionManager, input: SearchAgentInput) {
+    const classifier = new Classifier();
+
+    const classification = await classifier.classify({
+      chatHistory: input.chatHistory,
+      enabledSources: input.config.sources,
+      query: input.followUp,
+      llm: input.config.llm,
+    });
+
+    session.emit('data', {
+      type: 'classification',
+      classification: classification,
+    });
+
+    const widgetPromise = WidgetRegistry.executeAll(classification.widgets, {
+      llm: input.config.llm,
+      embedding: input.config.embedding,
+      session: session,
+    });
+
+    let searchPromise: Promise<ResearcherOutput> | null = null;
+
+    if (!classification.skipSearch) {
+      const researcher = new Researcher();
+      searchPromise = researcher.research(session, {
+        chatHistory: input.chatHistory,
+        followUp: input.followUp,
+        classification: classification,
+        config: input.config,
+      });
+    }
+
+    const [widgetOutputs, searchResults] = await Promise.all([
+      widgetPromise,
+      searchPromise,
+    ]);
+  }
+}
+
+export default SearchAgent;

src/lib/agents/search/researcher/actions/done.ts

@@ -0,0 +1,19 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+
+const doneAction: ResearchAction<any> = {
+  name: 'done',
+  description:
+    "Indicates that the research process is complete and no further actions are needed. Use this action when you have gathered sufficient information to answer the user's query.",
+  enabled: (_) => true,
+  schema: z.object({
+    type: z.literal('done'),
+  }),
+  execute: async (params, additionalConfig) => {
+    return {
+      type: 'done',
+    };
+  },
+};
+
+export default doneAction;

src/lib/agents/search/researcher/actions/index.ts

@@ -0,0 +1,8 @@
+import doneAction from './done';
+import ActionRegistry from './registry';
+import webSearchAction from './webSearch';
+
+ActionRegistry.register(webSearchAction);
+ActionRegistry.register(doneAction);
+
+export { ActionRegistry };

src/lib/agents/search/researcher/actions/registry.ts

@@ -0,0 +1,73 @@
+import {
+  ActionConfig,
+  ActionOutput,
+  AdditionalConfig,
+  ClassifierOutput,
+  ResearchAction,
+} from '../../types';
+
+class ActionRegistry {
+  private static actions: Map<string, ResearchAction> = new Map();
+
+  static register(action: ResearchAction<any>) {
+    this.actions.set(action.name, action);
+  }
+
+  static get(name: string): ResearchAction | undefined {
+    return this.actions.get(name);
+  }
+
+  static getAvailableActions(config: {
+    classification: ClassifierOutput;
+  }): ResearchAction[] {
+    return Array.from(
+      this.actions.values().filter((action) => action.enabled(config)),
+    );
+  }
+
+  static getAvailableActionsDescriptions(config: {
+    classification: ClassifierOutput;
+  }): string {
+    const availableActions = this.getAvailableActions(config);
+
+    return availableActions
+      .map((action) => `------------\n##${action.name}\n${action.description}`)
+      .join('\n\n');
+  }
+
+  static async execute(
+    name: string,
+    params: any,
+    additionalConfig: AdditionalConfig,
+  ) {
+    const action = this.actions.get(name);
+
+    if (!action) {
+      throw new Error(`Action with name ${name} not found`);
+    }
+
+    return action.execute(params, additionalConfig);
+  }
+
+  static async executeAll(
+    actions: ActionConfig[],
+    additionalConfig: AdditionalConfig,
+  ): Promise<ActionOutput[]> {
+    const results: ActionOutput[] = [];
+
+    await Promise.all(
+      actions.map(async (actionConfig) => {
+        const output = await this.execute(
+          actionConfig.type,
+          actionConfig.params,
+          additionalConfig,
+        );
+        results.push(output);
+      }),
+    );
+
+    return results;
+  }
+}
+
+export default ActionRegistry;

src/lib/agents/search/researcher/actions/webSearch.ts

@@ -0,0 +1,54 @@
+import z from 'zod';
+import { ResearchAction } from '../../types';
+import { searchSearxng } from '@/lib/searxng';
+
+const actionSchema = z.object({
+  type: z.literal('web_search'),
+  queries: z
+    .array(z.string())
+    .describe('An array of search queries to perform web searches for.'),
+});
+
+const actionDescription = `
+You have to use this action aggressively to find relevant information from the web to answer user queries. You can combine this action with other actions to gather comprehensive data. Always ensure that you provide accurate and up-to-date information by leveraging web search results.
+When this action is present, you must use it to obtain current information from the web. 
+
+### How to use:
+1. For fast search mode, you can use this action once. Make sure to cover all aspects of the user's query in that single search.
+2. If you're on quality mode, you'll get to use this action up to two times. Use the first search to gather general information, and the second search to fill in any gaps or get more specific details based on the initial findings.
+3. If you're set on Deep research mode, then you will get to use this action multiple times to gather more information. Use your judgment to decide when additional searches are necessary to provide a thorough and accurate response.
+
+Input: An array of search queries. Make sure the queries are relevant to the user's request and cover different aspects if necessary. You can include a maximum of 3 queries. Make sure the queries are SEO friendly and not sentences rather keywords which can be used to search a search engine like Google, Bing, etc.
+`;
+
+const webSearchAction: ResearchAction<typeof actionSchema> = {
+  name: 'web_search',
+  description: actionDescription,
+  schema: actionSchema,
+  enabled: (config) => config.classification.intents.includes('web_search'),
+  execute: async (input, _) => {
+    let results: Chunk[] = [];
+
+    const search = async (q: string) => {
+      const res = await searchSearxng(q);
+      res.results.forEach((r) => {
+        results.push({
+          content: r.content || r.title,
+          metadata: {
+            title: r.title,
+            url: r.url,
+          },
+        });
+      });
+    };
+
+    await Promise.all(input.queries.map(search));
+
+    return {
+      type: 'search_results',
+      results,
+    };
+  },
+};
+
+export default webSearchAction;

src/lib/agents/search/researcher/index.ts

@@ -0,0 +1,113 @@
+import z from 'zod';
+import {
+  ActionConfig,
+  ActionOutput,
+  ResearcherInput,
+  ResearcherOutput,
+} from '../types';
+import { ActionRegistry } from './actions';
+import { getResearcherPrompt } from '@/lib/prompts/search/researcher';
+import SessionManager from '@/lib/session';
+
+class Researcher {
+  async research(
+    session: SessionManager,
+    input: ResearcherInput,
+  ): Promise<ResearcherOutput> {
+    let findings: string = '';
+    let actionOutput: ActionOutput[] = [];
+    let maxIteration =
+      input.config.mode === 'fast'
+        ? 1
+        : input.config.mode === 'balanced'
+          ? 3
+          : 25;
+
+    const availableActions = ActionRegistry.getAvailableActions({
+      classification: input.classification,
+    });
+
+    const schema = z.object({
+      reasoning: z
+        .string()
+        .describe('The reasoning behind choosing the next action.'),
+      action: z
+        .union(availableActions.map((a) => a.schema))
+        .describe('The action to be performed next.'),
+    });
+
+    const availableActionsDescription =
+      ActionRegistry.getAvailableActionsDescriptions({
+        classification: input.classification,
+      });
+
+    for (let i = 0; i < maxIteration; i++) {
+      const researcherPrompt = getResearcherPrompt(availableActionsDescription);
+
+      const res = await input.config.llm.generateObject<z.infer<typeof schema>>(
+        {
+          messages: [
+            {
+              role: 'system',
+              content: researcherPrompt,
+            },
+            {
+              role: 'user',
+              content: `
+                    <research_query>
+                    ${input.classification.standaloneFollowUp}
+                    </research_query>
+
+                    <previous_actions>
+                    ${findings}
+                    </previous_actions>
+                    `,
+            },
+          ],
+          schema,
+        },
+      );
+
+
+      if (res.action.type === 'done') {
+        console.log('Research complete - "done" action selected');
+        break;
+      }
+
+      const actionConfig: ActionConfig = {
+        type: res.action.type as string,
+        params: res.action,
+      };
+
+      findings += 'Reasoning: ' + res.reasoning + '\n';
+      findings += `Executing Action: ${actionConfig.type} with params ${JSON.stringify(actionConfig.params)}\n`;
+
+      const actionResult = await ActionRegistry.execute(
+        actionConfig.type,
+        actionConfig.params,
+        {
+          llm: input.config.llm,
+          embedding: input.config.embedding,
+          session: session,
+        },
+      );
+
+      actionOutput.push(actionResult);
+
+      if (actionResult.type === 'search_results') {
+        findings += actionResult.results
+          .map(
+            (r) =>
+              `Title: ${r.metadata.title}\nURL: ${r.metadata.url}\nContent: ${r.content}\n`,
+          )
+          .join('\n');
+      }
+    }
+
+    return {
+      findings: actionOutput,
+    };
+  }
+}
+
+export default Researcher;

src/lib/agents/search/types.ts

@@ -1,7 +1,7 @@
-import { EventEmitter } from 'stream';
 import z from 'zod';
 import BaseLLM from '../../models/base/llm';
 import BaseEmbedding from '@/lib/models/base/embedding';
+import SessionManager from '@/lib/session';
 
 export type SearchSources = 'web' | 'discussions' | 'academic';
 
@@ -9,10 +9,11 @@ export type SearchAgentConfig = {
   sources: SearchSources[];
   llm: BaseLLM<any>;
   embedding: BaseEmbedding<any>;
+  mode: 'fast' | 'balanced' | 'deep_research';
 };
 
 export type SearchAgentInput = {
-  chatHistory: Message[];
+  chatHistory: ChatTurnMessage[];
   followUp: string;
   config: SearchAgentConfig;
 };
@@ -48,7 +49,7 @@ export type ClassifierInput = {
   llm: BaseLLM<any>;
   enabledSources: SearchSources[];
   query: string;
-  chatHistory: Message[];
+  chatHistory: ChatTurnMessage[];
 };
 
 export type ClassifierOutput = {
@@ -60,6 +61,46 @@ export type ClassifierOutput = {
 
 export type AdditionalConfig = {
   llm: BaseLLM<any>;
-  embedding: BaseLLM<any>;
-  emitter: EventEmitter;
+  embedding: BaseEmbedding<any>;
+  session: SessionManager;
+};
+
+export type ResearcherInput = {
+  chatHistory: ChatTurnMessage[];
+  followUp: string;
+  classification: ClassifierOutput;
+  config: SearchAgentConfig;
+};
+
+export type ResearcherOutput = {
+  findings: ActionOutput[];
+};
+
+export type SearchActionOutput = {
+  type: 'search_results';
+  results: Chunk[];
+};
+
+export type DoneActionOutput = {
+  type: 'done';
+};
+
+export type ActionOutput = SearchActionOutput | DoneActionOutput;
+
+export interface ResearchAction<
+  TSchema extends z.ZodObject<any> = z.ZodObject<any>,
+> {
+  name: string;
+  description: string;
+  schema: z.ZodObject<any>;
+  enabled: (config: { classification: ClassifierOutput }) => boolean;
+  execute: (
+    params: z.infer<TSchema>,
+    additionalConfig: AdditionalConfig,
+  ) => Promise<ActionOutput>;
+}
+
+export type ActionConfig = {
+  type: string;
+  params: Record<string, any>;
 };

src/lib/db/migrate.ts

@@ -18,12 +18,18 @@ db.exec(`
 `);
 
 function sanitizeSql(content: string) {
-  return content
-    .split(/\r?\n/)
-    .filter(
-      (l) => !l.trim().startsWith('-->') && !l.includes('statement-breakpoint'),
+  const statements = content
+    .split(/--> statement-breakpoint/g)
+    .map((stmt) =>
+      stmt
+        .split(/\r?\n/)
+        .filter((l) => !l.trim().startsWith('-->'))
+        .join('\n')
+        .trim(),
     )
-    .join('\n');
+    .filter((stmt) => stmt.length > 0);
+
+  return statements;
 }
 
 fs.readdirSync(migrationsFolder)
@@ -32,7 +38,7 @@ fs.readdirSync(migrationsFolder)
   .forEach((file) => {
     const filePath = path.join(migrationsFolder, file);
     let content = fs.readFileSync(filePath, 'utf-8');
-    content = sanitizeSql(content);
+    const statements = sanitizeSql(content);
 
     const migrationName = file.split('_')[0] || file;
 
@@ -108,7 +114,12 @@ fs.readdirSync(migrationsFolder)
         db.exec('DROP TABLE messages;');
         db.exec('ALTER TABLE messages_with_sources RENAME TO messages;');
       } else {
-        db.exec(content);
+        // Execute each statement separately
+        statements.forEach((stmt) => {
+          if (stmt.trim()) {
+            db.exec(stmt);
+          }
+        });
       }
 
       db.prepare('INSERT OR IGNORE INTO ran_migrations (name) VALUES (?)').run(

src/lib/db/schema.ts

@@ -1,26 +1,22 @@
 import { sql } from 'drizzle-orm';
 import { text, integer, sqliteTable } from 'drizzle-orm/sqlite-core';
-import { Document } from '@langchain/core/documents';
 
 export const messages = sqliteTable('messages', {
   id: integer('id').primaryKey(),
-  role: text('type', { enum: ['assistant', 'user', 'source'] }).notNull(),
-  chatId: text('chatId').notNull(),
-  createdAt: text('createdAt')
-    .notNull()
-    .default(sql`CURRENT_TIMESTAMP`),
   messageId: text('messageId').notNull(),
-
-  content: text('content'),
-
-  sources: text('sources', {
-    mode: 'json',
-  })
-    .$type<Document[]>()
+  chatId: text('chatId').notNull(),
+  backendId: text('backendId').notNull(),
+  query: text('query').notNull(),
+  createdAt: text('createdAt').notNull(),
+  responseBlocks: text('responseBlocks', { mode: 'json' })
+    .$type<Block[]>()
     .default(sql`'[]'`),
+  status: text({ enum: ['answering', 'completed', 'error'] }).default(
+    'answering',
+  ),
 });
 
-interface File {
+interface DBFile {
   name: string;
   fileId: string;
 }
@@ -31,6 +27,6 @@ export const chats = sqliteTable('chats', {
   createdAt: text('createdAt').notNull(),
   focusMode: text('focusMode').notNull(),
   files: text('files', { mode: 'json' })
-    .$type<File[]>()
+    .$type<DBFile[]>()
     .default(sql`'[]'`),
 });

src/lib/models/providers/aiml.ts

@@ -1,152 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface AimlConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your AI/ML API key',
-    required: true,
-    placeholder: 'AI/ML API Key',
-    env: 'AIML_API_KEY',
-    scope: 'server',
-  },
-];
-
-class AimlProvider extends BaseModelProvider<AimlConfig> {
-  constructor(id: string, name: string, config: AimlConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const res = await fetch('https://api.aimlapi.com/models', {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-          Authorization: `Bearer ${this.config.apiKey}`,
-        },
-      });
-
-      const data = await res.json();
-
-      const chatModels: Model[] = data.data
-        .filter((m: any) => m.type === 'chat-completion')
-        .map((m: any) => {
-          return {
-            name: m.id,
-            key: m.id,
-          };
-        });
-
-      const embeddingModels: Model[] = data.data
-        .filter((m: any) => m.type === 'embedding')
-        .map((m: any) => {
-          return {
-            name: m.id,
-            key: m.id,
-          };
-        });
-
-      return {
-        embedding: embeddingModels,
-        chat: chatModels,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to AI/ML API. Please ensure your API key is correct and the service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading AI/ML API Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.aimlapi.com',
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading AI/ML API Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: this.config.apiKey,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.aimlapi.com',
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): AimlConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'aiml',
-      name: 'AI/ML API',
-    };
-  }
-}
-
-export default AimlProvider;

src/lib/models/providers/anthropic.ts

@@ -1,115 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatAnthropic } from '@langchain/anthropic';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface AnthropicConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Anthropic API key',
-    required: true,
-    placeholder: 'Anthropic API Key',
-    env: 'ANTHROPIC_API_KEY',
-    scope: 'server',
-  },
-];
-
-class AnthropicProvider extends BaseModelProvider<AnthropicConfig> {
-  constructor(id: string, name: string, config: AnthropicConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    const res = await fetch('https://api.anthropic.com/v1/models?limit=999', {
-      method: 'GET',
-      headers: {
-        'x-api-key': this.config.apiKey,
-        'anthropic-version': '2023-06-01',
-        'Content-type': 'application/json',
-      },
-    });
-
-    if (!res.ok) {
-      throw new Error(`Failed to fetch Anthropic models: ${res.statusText}`);
-    }
-
-    const data = (await res.json()).data;
-
-    const models: Model[] = data.map((m: any) => {
-      return {
-        key: m.id,
-        name: m.display_name,
-      };
-    });
-
-    return {
-      embedding: [],
-      chat: models,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Anthropic Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatAnthropic({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('Anthropic provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): AnthropicConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'anthropic',
-      name: 'Anthropic',
-    };
-  }
-}
-
-export default AnthropicProvider;

src/lib/models/providers/deepseek.ts

@@ -1,107 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI } from '@langchain/openai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface DeepSeekConfig {
-  apiKey: string;
-}
-
-const defaultChatModels: Model[] = [
-  {
-    name: 'Deepseek Chat / DeepSeek V3.2 Exp',
-    key: 'deepseek-chat',
-  },
-  {
-    name: 'Deepseek Reasoner / DeepSeek V3.2 Exp',
-    key: 'deepseek-reasoner',
-  },
-];
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your DeepSeek API key',
-    required: true,
-    placeholder: 'DeepSeek API Key',
-    env: 'DEEPSEEK_API_KEY',
-    scope: 'server',
-  },
-];
-
-class DeepSeekProvider extends BaseModelProvider<DeepSeekConfig> {
-  constructor(id: string, name: string, config: DeepSeekConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    return {
-      embedding: [],
-      chat: defaultChatModels,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading DeepSeek Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: 'https://api.deepseek.com',
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('DeepSeek provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): DeepSeekConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'deepseek',
-      name: 'Deepseek AI',
-    };
-  }
-}
-
-export default DeepSeekProvider;

src/lib/models/providers/gemini.ts

@@ -1,145 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import {
-  ChatGoogleGenerativeAI,
-  GoogleGenerativeAIEmbeddings,
-} from '@langchain/google-genai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface GeminiConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Google Gemini API key',
-    required: true,
-    placeholder: 'Google Gemini API Key',
-    env: 'GEMINI_API_KEY',
-    scope: 'server',
-  },
-];
-
-class GeminiProvider extends BaseModelProvider<GeminiConfig> {
-  constructor(id: string, name: string, config: GeminiConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    const res = await fetch(
-      `https://generativelanguage.googleapis.com/v1beta/models?key=${this.config.apiKey}`,
-      {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      },
-    );
-
-    const data = await res.json();
-
-    let defaultEmbeddingModels: Model[] = [];
-    let defaultChatModels: Model[] = [];
-
-    data.models.forEach((m: any) => {
-      if (
-        m.supportedGenerationMethods.some(
-          (genMethod: string) =>
-            genMethod === 'embedText' || genMethod === 'embedContent',
-        )
-      ) {
-        defaultEmbeddingModels.push({
-          key: m.name,
-          name: m.displayName,
-        });
-      } else if (m.supportedGenerationMethods.includes('generateContent')) {
-        defaultChatModels.push({
-          key: m.name,
-          name: m.displayName,
-        });
-      }
-    });
-
-    return {
-      embedding: defaultEmbeddingModels,
-      chat: defaultChatModels,
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Gemini Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatGoogleGenerativeAI({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Gemini Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new GoogleGenerativeAIEmbeddings({
-      apiKey: this.config.apiKey,
-      model: key,
-    });
-  }
-
-  static parseAndValidate(raw: any): GeminiConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'gemini',
-      name: 'Google Gemini',
-    };
-  }
-}
-
-export default GeminiProvider;

src/lib/models/providers/groq.ts

@@ -1,118 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatGroq } from '@langchain/groq';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface GroqConfig {
-  apiKey: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Groq API key',
-    required: true,
-    placeholder: 'Groq API Key',
-    env: 'GROQ_API_KEY',
-    scope: 'server',
-  },
-];
-
-class GroqProvider extends BaseModelProvider<GroqConfig> {
-  constructor(id: string, name: string, config: GroqConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const res = await fetch('https://api.groq.com/openai/v1/models', {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-          Authorization: `Bearer ${this.config.apiKey}`,
-        },
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: [],
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to Groq API. Please ensure your API key is correct and the Groq service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error('Error Loading Groq Chat Model. Invalid Model Selected');
-    }
-
-    return new ChatGroq({
-      apiKey: this.config.apiKey,
-      temperature: 0.7,
-      model: key,
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    throw new Error('Groq provider does not support embedding models.');
-  }
-
-  static parseAndValidate(raw: any): GroqConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.apiKey)
-      throw new Error('Invalid config provided. API key must be provided');
-
-    return {
-      apiKey: String(raw.apiKey),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'groq',
-      name: 'Groq',
-    };
-  }
-}
-
-export default GroqProvider;

src/lib/models/providers/lemonade.ts

@@ -1,158 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface LemonadeConfig {
-  baseURL: string;
-  apiKey?: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'string',
-    name: 'Base URL',
-    key: 'baseURL',
-    description: 'The base URL for Lemonade API',
-    required: true,
-    placeholder: 'https://api.lemonade.ai/v1',
-    env: 'LEMONADE_BASE_URL',
-    scope: 'server',
-  },
-  {
-    type: 'password',
-    name: 'API Key',
-    key: 'apiKey',
-    description: 'Your Lemonade API key (optional)',
-    required: false,
-    placeholder: 'Lemonade API Key',
-    env: 'LEMONADE_API_KEY',
-    scope: 'server',
-  },
-];
-
-class LemonadeProvider extends BaseModelProvider<LemonadeConfig> {
-  constructor(id: string, name: string, config: LemonadeConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const headers: Record<string, string> = {
-        'Content-Type': 'application/json',
-      };
-
-      if (this.config.apiKey) {
-        headers['Authorization'] = `Bearer ${this.config.apiKey}`;
-      }
-
-      const res = await fetch(`${this.config.baseURL}/models`, {
-        method: 'GET',
-        headers,
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: models,
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to Lemonade API. Please ensure the base URL is correct and the service is available.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Lemonade Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: this.config.apiKey || 'not-needed',
-      temperature: 0.7,
-      model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading Lemonade Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: this.config.apiKey || 'not-needed',
-      model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): LemonadeConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.baseURL)
-      throw new Error('Invalid config provided. Base URL must be provided');
-
-    return {
-      baseURL: String(raw.baseURL),
-      apiKey: raw.apiKey ? String(raw.apiKey) : undefined,
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'lemonade',
-      name: 'Lemonade',
-    };
-  }
-}
-
-export default LemonadeProvider;

src/lib/models/providers/lmstudio.ts

@@ -1,148 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-
-interface LMStudioConfig {
-  baseURL: string;
-}
-
-const providerConfigFields: UIConfigField[] = [
-  {
-    type: 'string',
-    name: 'Base URL',
-    key: 'baseURL',
-    description: 'The base URL for LM Studio server',
-    required: true,
-    placeholder: 'http://localhost:1234',
-    env: 'LM_STUDIO_BASE_URL',
-    scope: 'server',
-  },
-];
-
-class LMStudioProvider extends BaseModelProvider<LMStudioConfig> {
-  constructor(id: string, name: string, config: LMStudioConfig) {
-    super(id, name, config);
-  }
-
-  private normalizeBaseURL(url: string): string {
-    const trimmed = url.trim().replace(/\/+$/, '');
-    return trimmed.endsWith('/v1') ? trimmed : `${trimmed}/v1`;
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    try {
-      const baseURL = this.normalizeBaseURL(this.config.baseURL);
-
-      const res = await fetch(`${baseURL}/models`, {
-        method: 'GET',
-        headers: {
-          'Content-Type': 'application/json',
-        },
-      });
-
-      const data = await res.json();
-
-      const models: Model[] = data.data.map((m: any) => {
-        return {
-          name: m.id,
-          key: m.id,
-        };
-      });
-
-      return {
-        embedding: models,
-        chat: models,
-      };
-    } catch (err) {
-      if (err instanceof TypeError) {
-        throw new Error(
-          'Error connecting to LM Studio. Please ensure the base URL is correct and the LM Studio server is running.',
-        );
-      }
-
-      throw err;
-    }
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [...defaultModels.chat, ...configProvider.chatModels],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    const modelList = await this.getModelList();
-
-    const exists = modelList.chat.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading LM Studio Chat Model. Invalid Model Selected',
-      );
-    }
-
-    return new ChatOpenAI({
-      apiKey: 'lm-studio',
-      temperature: 0.7,
-      model: key,
-      streaming: true,
-      configuration: {
-        baseURL: this.normalizeBaseURL(this.config.baseURL),
-      },
-    });
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading LM Studio Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new OpenAIEmbeddings({
-      apiKey: 'lm-studio',
-      model: key,
-      configuration: {
-        baseURL: this.normalizeBaseURL(this.config.baseURL),
-      },
-    });
-  }
-
-  static parseAndValidate(raw: any): LMStudioConfig {
-    if (!raw || typeof raw !== 'object')
-      throw new Error('Invalid config provided. Expected object');
-    if (!raw.baseURL)
-      throw new Error('Invalid config provided. Base URL must be provided');
-
-    return {
-      baseURL: String(raw.baseURL),
-    };
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'lmstudio',
-      name: 'LM Studio',
-    };
-  }
-}
-
-export default LMStudioProvider;

src/lib/models/providers/transformers.ts

@@ -1,87 +0,0 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { Embeddings } from '@langchain/core/embeddings';
-import { UIConfigField } from '@/lib/config/types';
-import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
-import { HuggingFaceTransformersEmbeddings } from '@langchain/community/embeddings/huggingface_transformers';
-interface TransformersConfig {}
-
-const defaultEmbeddingModels: Model[] = [
-  {
-    name: 'all-MiniLM-L6-v2',
-    key: 'Xenova/all-MiniLM-L6-v2',
-  },
-  {
-    name: 'mxbai-embed-large-v1',
-    key: 'mixedbread-ai/mxbai-embed-large-v1',
-  },
-  {
-    name: 'nomic-embed-text-v1',
-    key: 'Xenova/nomic-embed-text-v1',
-  },
-];
-
-const providerConfigFields: UIConfigField[] = [];
-
-class TransformersProvider extends BaseModelProvider<TransformersConfig> {
-  constructor(id: string, name: string, config: TransformersConfig) {
-    super(id, name, config);
-  }
-
-  async getDefaultModels(): Promise<ModelList> {
-    return {
-      embedding: [...defaultEmbeddingModels],
-      chat: [],
-    };
-  }
-
-  async getModelList(): Promise<ModelList> {
-    const defaultModels = await this.getDefaultModels();
-    const configProvider = getConfiguredModelProviderById(this.id)!;
-
-    return {
-      embedding: [
-        ...defaultModels.embedding,
-        ...configProvider.embeddingModels,
-      ],
-      chat: [],
-    };
-  }
-
-  async loadChatModel(key: string): Promise<BaseChatModel> {
-    throw new Error('Transformers Provider does not support chat models.');
-  }
-
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
-    const modelList = await this.getModelList();
-    const exists = modelList.embedding.find((m) => m.key === key);
-
-    if (!exists) {
-      throw new Error(
-        'Error Loading OpenAI Embedding Model. Invalid Model Selected.',
-      );
-    }
-
-    return new HuggingFaceTransformersEmbeddings({
-      model: key,
-    });
-  }
-
-  static parseAndValidate(raw: any): TransformersConfig {
-    return {};
-  }
-
-  static getProviderConfigFields(): UIConfigField[] {
-    return providerConfigFields;
-  }
-
-  static getProviderMetadata(): ProviderMetadata {
-    return {
-      key: 'transformers',
-      name: 'Transformers',
-    };
-  }
-}
-
-export default TransformersProvider;

src/lib/prompts/index.ts

@@ -1,13 +0,0 @@
-import {
-  webSearchResponsePrompt,
-  webSearchRetrieverFewShots,
-  webSearchRetrieverPrompt,
-} from './webSearch';
-import { writingAssistantPrompt } from './writingAssistant';
-
-export default {
-  webSearchResponsePrompt,
-  webSearchRetrieverPrompt,
-  webSearchRetrieverFewShots,
-  writingAssistantPrompt,
-};

src/lib/prompts/search/researcher.ts

@@ -0,0 +1,241 @@
+export const getResearcherPrompt = (
+  actionDesc: string,
+  mode: 'fast' | 'balanced' | 'deep_research',
+) => {
+  const today = new Date().toLocaleDateString('en-US', {
+    year: 'numeric',
+    month: 'long',
+    day: 'numeric',
+  });
+
+  return `
+You are an action orchestrator. Your job is to fulfill user requests by selecting and executing appropriate actions - whether that's searching for information, creating calendar events, sending emails, or any other available action.
+
+Today's date: ${today}
+
+You are operating in "${mode}" mode. ${
+    mode === 'fast'
+      ? 'Prioritize speed - use as few actions as possible to get the needed information quickly.'
+      : mode === 'balanced'
+        ? 'Balance speed and depth - use a moderate number of actions to get good information efficiently. Never stop at the first action unless there is no action available or the query is simple.'
+        : 'Conduct deep research - use multiple actions to gather comprehensive information, even if it takes longer.'
+  }
+
+<available_actions>
+${actionDesc}
+</available_actions>
+
+<core_principle>
+
+NEVER ASSUME - your knowledge may be outdated. When a user asks about something you're not certain about, go find out. Don't assume it exists or doesn't exist - just look it up directly.
+
+</core_principle>
+
+<reasoning_approach>
+
+Think like a human would. Your reasoning should be natural and show:
+- What the user is asking for
+- What you need to find out or do
+- Your plan to accomplish it
+
+Keep it to 2-3 natural sentences.
+
+</reasoning_approach>
+
+<examples>
+
+## Example 1: Unknown Subject
+
+User: "What is Kimi K2?"
+
+Good reasoning:
+"I'm not sure what Kimi K2 is - could be an AI model, a product, or something else. Let me look it up to find out what it actually is and get the relevant details."
+
+Actions: web_search ["Kimi K2", "Kimi K2 AI"]
+
+## Example 2: Subject You're Uncertain About
+
+User: "What are the features of GPT-5.1?"
+
+Good reasoning:
+"I don't have current information on GPT-5.1 - my knowledge might be outdated. Let me look up GPT-5.1 to see what's available and what features it has."
+
+Actions: web_search ["GPT-5.1", "GPT-5.1 features", "GPT-5.1 release"]
+
+Bad reasoning (wastes time on verification):
+"GPT-5.1 might not exist based on my knowledge. I need to verify if it exists first before looking for features."
+
+## Example 3: After Actions Return Results
+
+User: "What are the features of GPT-5.1?"
+[Previous actions returned information about GPT-5.1]
+
+Good reasoning:
+"Got the information I needed about GPT-5.1. The results cover its features and capabilities - I can now provide a complete answer."
+
+Action: done
+
+## Example 4: Ambiguous Query
+
+User: "Tell me about Mercury"
+
+Good reasoning:
+"Mercury could refer to several things - the planet, the element, or something else. I'll look up both main interpretations to give a useful answer."
+
+Actions: web_search ["Mercury planet facts", "Mercury element"]
+
+## Example 5: Current Events
+
+User: "What's happening with AI regulation?"
+
+Good reasoning:
+"I need current news on AI regulation developments. Let me find the latest updates on this topic."
+
+Actions: web_search ["AI regulation news 2024", "AI regulation bill latest"]
+
+## Example 6: Technical Query
+
+User: "How do I set up authentication in Next.js 14?"
+
+Good reasoning:
+"This is a technical implementation question. I'll find the current best practices and documentation for Next.js 14 authentication."
+
+Actions: web_search ["Next.js 14 authentication guide", "NextAuth.js App Router"]
+
+## Example 7: Comparison Query
+
+User: "Prisma vs Drizzle - which should I use?"
+
+Good reasoning:
+"Need to find factual comparisons between these ORMs - performance, features, trade-offs. Let me gather objective information."
+
+Actions: web_search ["Prisma vs Drizzle comparison 2024", "Drizzle ORM performance"]
+
+## Example 8: Fact-Check
+
+User: "Is it true you only use 10% of your brain?"
+
+Good reasoning:
+"This is a common claim that needs scientific verification. Let me find what the actual research says about this."
+
+Actions: web_search ["10 percent brain myth science", "brain usage neuroscience"]
+
+## Example 9: Recent Product
+
+User: "What are the specs of MacBook Pro M4?"
+
+Good reasoning:
+"I need current information on the MacBook Pro M4. Let me look up the latest specs and details."
+
+Actions: web_search ["MacBook Pro M4 specs", "MacBook Pro M4 specifications Apple"]
+
+## Example 10: Multi-Part Query
+
+User: "Population of Tokyo vs New York?"
+
+Good reasoning:
+"Need current population stats for both cities. I'll look up the comparison data."
+
+Actions: web_search ["Tokyo population 2024", "Tokyo vs New York population"]
+
+## Example 11: Calendar Task
+
+User: "Add a meeting with John tomorrow at 3pm"
+
+Good reasoning:
+"This is a calendar task. I have all the details - meeting with John, tomorrow, 3pm. I'll create the event."
+
+Action: create_calendar_event with the provided details
+
+## Example 12: Email Task
+
+User: "Send an email to sarah@company.com about the project update"
+
+Good reasoning:
+"Need to send an email. I have the recipient but need to compose appropriate content about the project update."
+
+Action: send_email to sarah@company.com with project update content
+
+## Example 13: Multi-Step Task
+
+User: "What's the weather in Tokyo and add a reminder to pack an umbrella if it's rainy"
+
+Good reasoning:
+"Two things here - first I need to check Tokyo's weather, then based on that I might need to create a reminder. Let me start with the weather lookup."
+
+Actions: web_search ["Tokyo weather today forecast"]
+
+## Example 14: Research Then Act
+
+User: "Find the best Italian restaurant near me and make a reservation for 7pm"
+
+Good reasoning:
+"I need to first find top Italian restaurants in the area, then make a reservation. Let me start by finding the options."
+
+Actions: web_search ["best Italian restaurant near me", "top rated Italian restaurants"]
+
+</examples>
+
+<action_guidelines>
+
+## For Information Queries:
+- Just look it up - don't overthink whether something exists
+- Use 1-3 targeted queries
+- Done when you have useful information to answer with
+
+## For Task Execution:
+- Calendar, email, reminders: execute directly with the provided details
+- If details are missing, note what you need
+
+## For Multi-Step Requests:
+- Break it down logically
+- Complete one part before moving to the next
+- Some tasks require information before you can act
+
+## When to Select "done":
+- You have the information needed to answer
+- You've completed the requested task
+- Further actions would be redundant
+
+</action_guidelines>
+
+<query_formulation>
+
+**General subjects:**
+- ["subject name", "subject name + context"]
+
+**Current events:**
+- Include year: "topic 2024", "topic latest news"
+
+**Technical topics:**
+- Include versions: "framework v14 guide"
+- Add context: "documentation", "tutorial", "how to"
+
+**Comparisons:**
+- "X vs Y comparison", "X vs Y benchmarks"
+
+**Keep it simple:**
+- 1-3 actions per iteration
+- Don't over-complicate queries
+
+</query_formulation>
+
+<mistakes_to_avoid>
+
+1. **Over-assuming**: Don't assume things exist or don't exist - just look them up
+
+2. **Verification obsession**: Don't waste actions "verifying existence" - just search for the thing directly
+
+3. **Endless loops**: If 2-3 actions don't find something, it probably doesn't exist - report that and move on
+
+4. **Ignoring task context**: If user wants a calendar event, don't just search - create the event
+
+5. **Overthinking**: Keep reasoning simple and action-focused
+
+</mistakes_to_avoid>
+
+<output_format>
+Reasoning should be 2-3 natural sentences showing your thought process and plan. Then select and configure the appropriate action(s).
+</output_format>
+`;
+};

src/lib/prompts/webSearch.ts

@@ -1,137 +0,0 @@
-import { BaseMessageLike } from '@langchain/core/messages';
-
-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.
-
-**Note**: All user messages are individual entities and should be treated as such do not mix conversations.
-`;
-
-export const webSearchRetrieverFewShots: BaseMessageLike[] = [
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-What is the capital of France
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-Capital of france
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Hi, how are you?
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-not_needed
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-What is Docker?
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-What is Docker
-</question>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Can you tell me what is X from https://example.com
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-What is X?
-</question>
-<links>
-https://example.com
-</links>`,
-  ],
-  [
-    'user',
-    `<conversation>
-</conversation>
-<query>
-Summarize the content from https://example.com
-</query>`,
-  ],
-  [
-    'assistant',
-    `<question>
-summarize
-</question>
-<links>
-https://example.com
-</links>`,
-  ],
-];
-
-export const webSearchResponsePrompt = `
-    You are Perplexica, an AI model skilled in web search and crafting detailed, engaging, and well-structured answers. You excel at summarizing web pages and extracting relevant information to create professional, blog-style responses.
-
-    Your task is to provide answers that are:
-    - **Informative and relevant**: Thoroughly address the user's query using the given context.
-    - **Well-structured**: Include clear headings and subheadings, and use a professional tone to present information concisely and logically.
-    - **Engaging and detailed**: Write responses that read like a high-quality blog post, including extra details and relevant insights.
-    - **Cited and credible**: Use inline citations with [number] notation to refer to the context source(s) for each fact or detail included.
-    - **Explanatory and Comprehensive**: Strive to explain the topic in depth, offering detailed analysis, insights, and clarifications wherever applicable.
-
-    ### Formatting Instructions
-    - **Structure**: Use a well-organized format with proper headings (e.g., "## Example heading 1" or "## Example heading 2"). Present information in paragraphs or concise bullet points where appropriate.
-    - **Tone and Style**: Maintain a neutral, journalistic tone with engaging narrative flow. Write as though you're crafting an in-depth article for a professional audience.
-    - **Markdown Usage**: Format your response with Markdown for clarity. Use headings, subheadings, bold text, and italicized words as needed to enhance readability.
-    - **Length and Depth**: Provide comprehensive coverage of the topic. Avoid superficial responses and strive for depth without unnecessary repetition. Expand on technical or complex topics to make them easier to understand for a general audience.
-    - **No main heading/title**: Start your response directly with the introduction unless asked to provide a specific title.
-    - **Conclusion or Summary**: Include a concluding paragraph that synthesizes the provided information or suggests potential next steps, where appropriate.
-
-    ### Citation Requirements
-    - Cite every single fact, statement, or sentence using [number] notation corresponding to the source from the provided \`context\`.
-    - Integrate citations naturally at the end of sentences or clauses as appropriate. For example, "The Eiffel Tower is one of the most visited landmarks in the world[1]."
-    - Ensure that **every sentence in your response includes at least one citation**, even when information is inferred or connected to general knowledge available in the provided context.
-    - Use multiple sources for a single detail if applicable, such as, "Paris is a cultural hub, attracting millions of visitors annually[1][2]."
-    - Always prioritize credibility and accuracy by linking all statements back to their respective context sources.
-    - Avoid citing unsupported assumptions or personal interpretations; if no source supports a statement, clearly indicate the limitation.
-
-    ### Special Instructions
-    - If the query involves technical, historical, or complex topics, provide detailed background and explanatory sections to ensure clarity.
-    - 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.
-    - Provide explanations or historical context as needed to enhance understanding.
-    - End with a conclusion or overall perspective if relevant.
-
-    <context>
-    {context}
-    </context>
-
-    Current date & time in ISO format (UTC timezone) is: {date}.
-`;

src/lib/prompts/writingAssistant.ts

@@ -1,17 +0,0 @@
-export const writingAssistantPrompt = `
-You are Perplexica, an AI model who is expert at searching the web and answering user's queries. You are currently set on focus mode 'Writing Assistant', this means you will be helping the user write a response to a given query. 
-Since you are a writing assistant, you would not perform web searches. If you think you lack information to answer the query, you can ask the user for more information or suggest them to switch to a different focus mode.
-You will be shared a context that can contain information from files user has uploaded to get answers from. You will have to generate answers upon that.
-
-You have to cite the answer using [number] notation. You must cite the sentences with their relevent context number. You must cite each and every part of the answer so the user can know where the information is coming from.
-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/search/index.ts

@@ -1,59 +0,0 @@
-import MetaSearchAgent from '@/lib/search/metaSearchAgent';
-import prompts from '../prompts';
-
-export const searchHandlers: Record<string, MetaSearchAgent> = {
-  webSearch: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-  academicSearch: new MetaSearchAgent({
-    activeEngines: ['arxiv', 'google scholar', 'pubmed'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: true,
-  }),
-  writingAssistant: new MetaSearchAgent({
-    activeEngines: [],
-    queryGeneratorPrompt: '',
-    queryGeneratorFewShots: [],
-    responsePrompt: prompts.writingAssistantPrompt,
-    rerank: true,
-    rerankThreshold: 0,
-    searchWeb: false,
-  }),
-  wolframAlphaSearch: new MetaSearchAgent({
-    activeEngines: ['wolframalpha'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: false,
-    rerankThreshold: 0,
-    searchWeb: true,
-  }),
-  youtubeSearch: new MetaSearchAgent({
-    activeEngines: ['youtube'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-  redditSearch: new MetaSearchAgent({
-    activeEngines: ['reddit'],
-    queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
-    responsePrompt: prompts.webSearchResponsePrompt,
-    queryGeneratorFewShots: prompts.webSearchRetrieverFewShots,
-    rerank: true,
-    rerankThreshold: 0.3,
-    searchWeb: true,
-  }),
-};

src/lib/search/metaSearchAgent.ts

@@ -1,514 +0,0 @@
-import { ChatOpenAI } from '@langchain/openai';
-import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import type { Embeddings } from '@langchain/core/embeddings';
-import {
-  ChatPromptTemplate,
-  MessagesPlaceholder,
-  PromptTemplate,
-} from '@langchain/core/prompts';
-import {
-  RunnableLambda,
-  RunnableMap,
-  RunnableSequence,
-} from '@langchain/core/runnables';
-import { BaseMessage, BaseMessageLike } from '@langchain/core/messages';
-import { StringOutputParser } from '@langchain/core/output_parsers';
-import LineListOutputParser from '../outputParsers/listLineOutputParser';
-import LineOutputParser from '../outputParsers/lineOutputParser';
-import { getDocumentsFromLinks } from '../utils/documents';
-import { Document } from '@langchain/core/documents';
-import { searchSearxng } from '../searxng';
-import path from 'node:path';
-import fs from 'node:fs';
-import computeSimilarity from '../utils/computeSimilarity';
-import formatChatHistoryAsString from '../utils/formatHistory';
-import eventEmitter from 'events';
-import { StreamEvent } from '@langchain/core/tracers/log_stream';
-
-export interface MetaSearchAgentType {
-  searchAndAnswer: (
-    message: string,
-    history: BaseMessage[],
-    llm: BaseChatModel,
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-    fileIds: string[],
-    systemInstructions: string,
-  ) => Promise<eventEmitter>;
-}
-
-interface Config {
-  searchWeb: boolean;
-  rerank: boolean;
-  rerankThreshold: number;
-  queryGeneratorPrompt: string;
-  queryGeneratorFewShots: BaseMessageLike[];
-  responsePrompt: string;
-  activeEngines: string[];
-}
-
-type BasicChainInput = {
-  chat_history: BaseMessage[];
-  query: string;
-};
-
-class MetaSearchAgent implements MetaSearchAgentType {
-  private config: Config;
-  private strParser = new StringOutputParser();
-
-  constructor(config: Config) {
-    this.config = config;
-  }
-
-  private async createSearchRetrieverChain(llm: BaseChatModel) {
-    (llm as unknown as ChatOpenAI).temperature = 0;
-
-    return RunnableSequence.from([
-      ChatPromptTemplate.fromMessages([
-        ['system', this.config.queryGeneratorPrompt],
-        ...this.config.queryGeneratorFewShots,
-        [
-          'user',
-          `
-        <conversation>
-        {chat_history}
-        </conversation>
-
-        <query>
-        {query}
-        </query>
-       `,
-        ],
-      ]),
-      llm,
-      this.strParser,
-      RunnableLambda.from(async (input: string) => {
-        const linksOutputParser = new LineListOutputParser({
-          key: 'links',
-        });
-
-        const questionOutputParser = new LineOutputParser({
-          key: 'question',
-        });
-
-        const links = await linksOutputParser.parse(input);
-        let question = (await questionOutputParser.parse(input)) ?? input;
-
-        if (question === 'not_needed') {
-          return { query: '', docs: [] };
-        }
-
-        if (links.length > 0) {
-          if (question.length === 0) {
-            question = 'summarize';
-          }
-
-          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,
-                },
-              });
-            }
-
-            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.
-            
-            - **Journalistic tone**: The summary should sound professional and journalistic, not too casual or vague.
-            - **Thorough and detailed**: Ensure that every key point from the text is captured and that the summary directly answers the query.
-            - **Not too lengthy, but detailed**: The summary should be informative but not excessively long. Focus on providing detailed information in a concise format.
-
-            The text will be shared inside the \`text\` XML tag, and the query inside the \`query\` XML tag.
-
-            <example>
-            1. \`<text>
-            Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. 
-            It was first released in 2013 and is developed by Docker, Inc. Docker is designed to make it easier to create, deploy, and run applications 
-            by using containers.
-            </text>
-
-            <query>
-            What is Docker and how does it work?
-            </query>
-
-            Response:
-            Docker is a revolutionary platform-as-a-service product developed by Docker, Inc., that uses container technology to make application 
-            deployment more efficient. It allows developers to package their software with all necessary dependencies, making it easier to run in 
-            any environment. Released in 2013, Docker has transformed the way applications are built, deployed, and managed.
-            \`
-            2. \`<text>
-            The theory of relativity, or simply relativity, encompasses two interrelated theories of Albert Einstein: special relativity and general
-            relativity. However, the word "relativity" is sometimes used in reference to Galilean invariance. The term "theory of relativity" was based
-            on the expression "relative theory" used by Max Planck in 1906. The theory of relativity usually encompasses two interrelated theories by
-            Albert Einstein: special relativity and general relativity. Special relativity applies to all physical phenomena in the absence of gravity.
-            General relativity explains the law of gravitation and its relation to other forces of nature. It applies to the cosmological and astrophysical
-            realm, including astronomy.
-            </text>
-
-            <query>
-            summarize
-            </query>
-
-            Response:
-            The theory of relativity, developed by Albert Einstein, encompasses two main theories: special relativity and general relativity. Special
-            relativity applies to all physical phenomena in the absence of gravity, while general relativity explains the law of gravitation and its
-            relation to other forces of nature. The theory of relativity is based on the concept of "relative theory," as introduced by Max Planck in
-            1906. It is a fundamental theory in physics that has revolutionized our understanding of the universe.
-            \`
-            </example>
-
-            Everything below is the actual data you will be working with. Good luck!
-
-            <query>
-            ${question}
-            </query>
-
-            <text>
-            ${doc.pageContent}
-            </text>
-
-            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,
-                },
-              });
-
-              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 }),
-                },
-              }),
-          );
-
-          return { query: question, docs: documents };
-        }
-      }),
-    ]);
-  }
-
-  private async createAnsweringChain(
-    llm: BaseChatModel,
-    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(),
-        context: RunnableLambda.from(async (input: BasicChainInput) => {
-          const processedHistory = formatChatHistoryAsString(
-            input.chat_history,
-          );
-
-          let docs: Document[] | null = null;
-          let query = input.query;
-
-          if (this.config.searchWeb) {
-            const searchRetrieverChain =
-              await this.createSearchRetrieverChain(llm);
-
-            const searchRetrieverResult = await searchRetrieverChain.invoke({
-              chat_history: processedHistory,
-              query,
-            });
-
-            query = searchRetrieverResult.query;
-            docs = searchRetrieverResult.docs;
-          }
-
-          const sortedDocs = await this.rerankDocs(
-            query,
-            docs ?? [],
-            fileIds,
-            embeddings,
-            optimizationMode,
-          );
-
-          return sortedDocs;
-        })
-          .withConfig({
-            runName: 'FinalSourceRetriever',
-          })
-          .pipe(this.processDocs),
-      }),
-      ChatPromptTemplate.fromMessages([
-        ['system', this.config.responsePrompt],
-        new MessagesPlaceholder('chat_history'),
-        ['user', '{query}'],
-      ]),
-      llm,
-      this.strParser,
-    ]).withConfig({
-      runName: 'FinalResponseGenerator',
-    });
-  }
-
-  private async rerankDocs(
-    query: string,
-    docs: Document[],
-    fileIds: string[],
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-  ) {
-    if (docs.length === 0 && fileIds.length === 0) {
-      return docs;
-    }
-
-    const filesData = fileIds
-      .map((file) => {
-        const filePath = path.join(process.cwd(), 'uploads', file);
-
-        const contentPath = filePath + '-extracted.json';
-        const embeddingsPath = filePath + '-embeddings.json';
-
-        const content = JSON.parse(fs.readFileSync(contentPath, 'utf8'));
-        const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8'));
-
-        const fileSimilaritySearchObject = content.contents.map(
-          (c: string, i: number) => {
-            return {
-              fileName: content.title,
-              content: c,
-              embeddings: embeddings.embeddings[i],
-            };
-          },
-        );
-
-        return fileSimilaritySearchObject;
-      })
-      .flat();
-
-    if (query.toLocaleLowerCase() === 'summarize') {
-      return docs.slice(0, 15);
-    }
-
-    const docsWithContent = docs.filter(
-      (doc) => doc.pageContent && doc.pageContent.length > 0,
-    );
-
-    if (optimizationMode === 'speed' || this.config.rerank === false) {
-      if (filesData.length > 0) {
-        const [queryEmbedding] = await Promise.all([
-          embeddings.embedQuery(query),
-        ]);
-
-        const fileDocs = filesData.map((fileData) => {
-          return new Document({
-            pageContent: fileData.content,
-            metadata: {
-              title: fileData.fileName,
-              url: `File`,
-            },
-          });
-        });
-
-        const similarity = filesData.map((fileData, i) => {
-          const sim = computeSimilarity(queryEmbedding, fileData.embeddings);
-
-          return {
-            index: i,
-            similarity: sim,
-          };
-        });
-
-        let sortedDocs = similarity
-          .filter(
-            (sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3),
-          )
-          .sort((a, b) => b.similarity - a.similarity)
-          .slice(0, 15)
-          .map((sim) => fileDocs[sim.index]);
-
-        sortedDocs =
-          docsWithContent.length > 0 ? sortedDocs.slice(0, 8) : sortedDocs;
-
-        return [
-          ...sortedDocs,
-          ...docsWithContent.slice(0, 15 - sortedDocs.length),
-        ];
-      } else {
-        return docsWithContent.slice(0, 15);
-      }
-    } else if (optimizationMode === 'balanced') {
-      const [docEmbeddings, queryEmbedding] = await Promise.all([
-        embeddings.embedDocuments(
-          docsWithContent.map((doc) => doc.pageContent),
-        ),
-        embeddings.embedQuery(query),
-      ]);
-
-      docsWithContent.push(
-        ...filesData.map((fileData) => {
-          return new Document({
-            pageContent: fileData.content,
-            metadata: {
-              title: fileData.fileName,
-              url: `File`,
-            },
-          });
-        }),
-      );
-
-      docEmbeddings.push(...filesData.map((fileData) => fileData.embeddings));
-
-      const similarity = docEmbeddings.map((docEmbedding, i) => {
-        const sim = computeSimilarity(queryEmbedding, docEmbedding);
-
-        return {
-          index: i,
-          similarity: sim,
-        };
-      });
-
-      const sortedDocs = similarity
-        .filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3))
-        .sort((a, b) => b.similarity - a.similarity)
-        .slice(0, 15)
-        .map((sim) => docsWithContent[sim.index]);
-
-      return sortedDocs;
-    }
-
-    return [];
-  }
-
-  private processDocs(docs: Document[]) {
-    return docs
-      .map(
-        (_, index) =>
-          `${index + 1}. ${docs[index].metadata.title} ${docs[index].pageContent}`,
-      )
-      .join('\n');
-  }
-
-  private async handleStream(
-    stream: AsyncGenerator<StreamEvent, any, any>,
-    emitter: eventEmitter,
-  ) {
-    for await (const event of stream) {
-      if (
-        event.event === 'on_chain_end' &&
-        event.name === 'FinalSourceRetriever'
-      ) {
-        emitter.emit(
-          'data',
-          JSON.stringify({ type: 'sources', data: event.data.output }),
-        );
-      }
-      if (
-        event.event === 'on_chain_stream' &&
-        event.name === 'FinalResponseGenerator'
-      ) {
-        emitter.emit(
-          'data',
-          JSON.stringify({ type: 'response', data: event.data.chunk }),
-        );
-      }
-      if (
-        event.event === 'on_chain_end' &&
-        event.name === 'FinalResponseGenerator'
-      ) {
-        emitter.emit('end');
-      }
-    }
-  }
-
-  async searchAndAnswer(
-    message: string,
-    history: BaseMessage[],
-    llm: BaseChatModel,
-    embeddings: Embeddings,
-    optimizationMode: 'speed' | 'balanced' | 'quality',
-    fileIds: string[],
-    systemInstructions: string,
-  ) {
-    const emitter = new eventEmitter();
-
-    const answeringChain = await this.createAnsweringChain(
-      llm,
-      fileIds,
-      embeddings,
-      optimizationMode,
-      systemInstructions,
-    );
-
-    const stream = answeringChain.streamEvents(
-      {
-        chat_history: history,
-        query: message,
-      },
-      {
-        version: 'v1',
-      },
-    );
-
-    this.handleStream(stream, emitter);
-
-    return emitter;
-  }
-}
-
-export default MetaSearchAgent;

src/lib/session.ts

@@ -1,13 +1,20 @@
 import { EventEmitter } from 'stream';
-/* todo implement history saving and better artifact typing and handling */
+import { applyPatch } from 'rfc6902';
+
 class SessionManager {
   private static sessions = new Map<string, SessionManager>();
   readonly id: string;
-  private artifacts = new Map<string, Artifact>();
+  private blocks = new Map<string, Block>();
+  private events: { event: string; data: any }[] = [];
   private emitter = new EventEmitter();
+  private TTL_MS = 30 * 60 * 1000;
 
-  constructor() {
-    this.id = crypto.randomUUID();
+  constructor(id?: string) {
+    this.id = id ?? crypto.randomUUID();
+
+    setTimeout(() => {
+      SessionManager.sessions.delete(this.id);
+    }, this.TTL_MS);
   }
 
   static getSession(id: string): SessionManager | undefined {
@@ -18,26 +25,55 @@ class SessionManager {
     return Array.from(this.sessions.values());
   }
 
+  static createSession(): SessionManager {
+    const session = new SessionManager();
+    this.sessions.set(session.id, session);
+    return session;
+  }
+
+  removeAllListeners() {
+    this.emitter.removeAllListeners();
+  }
+
   emit(event: string, data: any) {
     this.emitter.emit(event, data);
+    this.events.push({ event, data });
   }
 
-  emitArtifact(artifact: Artifact) {
-    this.artifacts.set(artifact.id, artifact);
-    this.emitter.emit('addArtifact', artifact);
+  emitBlock(block: Block) {
+    this.blocks.set(block.id, block);
+    this.emit('data', {
+      type: 'block',
+      block: block,
+    });
   }
 
-  appendToArtifact(artifactId: string, data: any) {
-    const artifact = this.artifacts.get(artifactId);
-    if (artifact) {
-      if (typeof artifact.data === 'string') {
-        artifact.data += data;
-      } else if (Array.isArray(artifact.data)) {
-        artifact.data.push(data);
-      } else if (typeof artifact.data === 'object') {
-        Object.assign(artifact.data, data);
-      }
-      this.emitter.emit('updateArtifact', artifact);
+  getBlock(blockId: string): Block | undefined {
+    return this.blocks.get(blockId);
+  }
+
+  updateBlock(blockId: string, patch: any[]) {
+    const block = this.blocks.get(blockId);
+
+    if (block) {
+      applyPatch(block, patch);
+      this.blocks.set(blockId, block);
+      this.emit('data', {
+        type: 'updateBlock',
+        blockId: blockId,
+        patch: patch,
+      });
+    }
+  }
+
+  addListener(event: string, listener: (data: any) => void) {
+    this.emitter.addListener(event, listener);
+  }
+
+  replay() {
+    for (const { event, data } of this.events) {
+      /* Using emitter directly to avoid infinite loop */
+      this.emitter.emit(event, data);
     }
   }
 }

src/lib/types.ts

@@ -1,4 +1,4 @@
-type Message = {
+type ChatTurnMessage = {
   role: 'user' | 'assistant' | 'system';
   content: string;
 };
@@ -8,8 +8,64 @@ type Chunk = {
   metadata: Record<string, any>;
 };
 
-type Artifact = {
+type TextBlock = {
   id: string;
-  type: string;
-  data: any;
+  type: 'text';
+  data: string;
 };
+
+type SourceBlock = {
+  id: string;
+  type: 'source';
+  data: Chunk[];
+};
+
+type SuggestionBlock = {
+  id: string;
+  type: 'suggestion';
+  data: string[];
+};
+
+type WidgetBlock = {
+  id: string;
+  type: 'widget';
+  data: {
+    widgetType: string;
+    params: Record<string, any>;
+  };
+};
+
+type ReasoningResearchBlock = {
+  id: string;
+  reasoning: string;
+};
+
+type SearchingResearchBlock = {
+  id: string;
+  searching: string[];
+};
+
+type ReadingResearchBlock = {
+  id: string;
+  reading: Chunk[];
+};
+
+type ResearchBlockSubStep =
+  | ReasoningResearchBlock
+  | SearchingResearchBlock
+  | ReadingResearchBlock;
+
+type ResearchBlock = {
+  id: string;
+  type: 'research';
+  data: {
+    subSteps: ResearchBlockSubStep[];
+  };
+};
+
+type Block =
+  | TextBlock
+  | SourceBlock
+  | SuggestionBlock
+  | WidgetBlock
+  | ResearchBlock;

src/lib/utils/formatHistory.ts

@@ -1,10 +1,8 @@
-import { BaseMessage, isAIMessage } from '@langchain/core/messages';
-
-const formatChatHistoryAsString = (history: BaseMessage[]) => {
+const formatChatHistoryAsString = (history: Message[]) => {
   return history
     .map(
       (message) =>
-        `${isAIMessage(message) ? 'AI' : 'User'}: ${message.content}`,
+        `${message.role === 'assistant' ? 'AI' : 'User'}: ${message.content}`,
     )
     .join('\n');
 };

yarn.lock

@@ -3992,6 +3992,13 @@ ollama@^0.5.12:
   dependencies:
     whatwg-fetch "^3.6.20"
 
+ollama@^0.6.3:
+  version "0.6.3"
+  resolved "https://registry.yarnpkg.com/ollama/-/ollama-0.6.3.tgz#b188573dd0ccb3b4759c1f8fa85067cb17f6673c"
+  integrity sha512-KEWEhIqE5wtfzEIZbDCLH51VFZ6Z3ZSa6sIOg/E/tBV8S51flyqBOXi+bRxlOYKDf8i327zG9eSTb8IJxvm3Zg==
+  dependencies:
+    whatwg-fetch "^3.6.20"
+
 once@^1.3.0, once@^1.3.1, once@^1.4.0:
   version "1.4.0"
   resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
@@ -4126,6 +4133,11 @@ parseley@^0.12.0:
     leac "^0.6.0"
     peberminta "^0.9.0"
 
+partial-json@^0.1.7:
+  version "0.1.7"
+  resolved "https://registry.yarnpkg.com/partial-json/-/partial-json-0.1.7.tgz#b735a89edb3e25f231a3c4caeaae71dc9f578605"
+  integrity sha512-Njv/59hHaokb/hRUjce3Hdv12wd60MtM9Z5Olmn+nehe0QDAsRtRbJPvJ0Z91TusF0SuZRIvnM+S4l6EIP8leA==
+
 path-exists@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/path-exists/-/path-exists-4.0.0.tgz#513bdbe2d3b95d7762e8c1137efa195c6c61b5b3"
@@ -4518,6 +4530,11 @@ reusify@^1.0.4:
   resolved "https://registry.yarnpkg.com/reusify/-/reusify-1.0.4.tgz#90da382b1e126efc02146e90845a88db12925d76"
   integrity sha512-U9nH88a3fc/ekCF1l0/UP1IosiuIjyTh7hBvXVMHYgVcfGvt897Xguj2UOLDeI5BG2m7/uwyaLVT6fbtCwTyzw==
 
+rfc6902@^5.1.2:
+  version "5.1.2"
+  resolved "https://registry.yarnpkg.com/rfc6902/-/rfc6902-5.1.2.tgz#774262ba7b032ab9abf9eb8e0312927e8f425062"
+  integrity sha512-zxcb+PWlE8PwX0tiKE6zP97THQ8/lHmeiwucRrJ3YFupWEmp25RmFSlB1dNTqjkovwqG4iq+u1gzJMBS3um8mA==
+
 rgbcolor@^1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/rgbcolor/-/rgbcolor-1.0.1.tgz#d6505ecdb304a6595da26fa4b43307306775945d"