feat(providers): add rest of the providers

src/lib/models/providers/aiml.ts

@@ -0,0 +1,152 @@
+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

@@ -0,0 +1,115 @@
+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

@@ -0,0 +1,107 @@
+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

@@ -0,0 +1,140 @@
+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.includes('embedText')) {
+        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

@@ -0,0 +1,118 @@
+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/index.ts

@@ -3,11 +3,25 @@ import { ProviderConstructor } from './baseProvider';
 import OpenAIProvider from './openai';
 import OllamaProvider from './ollama';
 import TransformersProvider from './transformers';
+import AnthropicProvider from './anthropic';
+import GeminiProvider from './gemini';
+import GroqProvider from './groq';
+import DeepSeekProvider from './deepseek';
+import LMStudioProvider from './lmstudio';
+import LemonadeProvider from './lemonade';
+import AimlProvider from '@/lib/models/providers/aiml';
 
 export const providers: Record<string, ProviderConstructor<any>> = {
   openai: OpenAIProvider,
   ollama: OllamaProvider,
   transformers: TransformersProvider,
+  anthropic: AnthropicProvider,
+  gemini: GeminiProvider,
+  groq: GroqProvider,
+  deepseek: DeepSeekProvider,
+  aiml: AimlProvider,
+  lmstudio: LMStudioProvider,
+  lemonade: LemonadeProvider,
 };
 
 export const getModelProvidersUIConfigSection =

src/lib/models/providers/lemonade.ts

@@ -0,0 +1,158 @@
+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

@@ -0,0 +1,148 @@
+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;