feat(providers): implement custom classes

src/lib/models/providers/baseProvider.ts

@@ -1,45 +0,0 @@
-import { Embeddings } from '@langchain/core/embeddings';
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import { UIConfigField } from '@/lib/config/types';
-
-abstract class BaseModelProvider<CONFIG> {
-  constructor(
-    protected id: string,
-    protected name: string,
-    protected config: CONFIG,
-  ) {}
-  abstract getDefaultModels(): Promise<ModelList>;
-  abstract getModelList(): Promise<ModelList>;
-  abstract loadChatModel(modelName: string): Promise<BaseChatModel>;
-  abstract loadEmbeddingModel(modelName: string): Promise<Embeddings>;
-  static getProviderConfigFields(): UIConfigField[] {
-    throw new Error('Method not implemented.');
-  }
-  static getProviderMetadata(): ProviderMetadata {
-    throw new Error('Method not Implemented.');
-  }
-  static parseAndValidate(raw: any): any {
-    /* Static methods can't access class type parameters */
-    throw new Error('Method not Implemented.');
-  }
-}
-
-export type ProviderConstructor<CONFIG> = {
-  new (id: string, name: string, config: CONFIG): BaseModelProvider<CONFIG>;
-  parseAndValidate(raw: any): CONFIG;
-  getProviderConfigFields: () => UIConfigField[];
-  getProviderMetadata: () => ProviderMetadata;
-};
-
-export const createProviderInstance = <P extends ProviderConstructor<any>>(
-  Provider: P,
-  id: string,
-  name: string,
-  rawConfig: unknown,
-): InstanceType<P> => {
-  const cfg = Provider.parseAndValidate(rawConfig);
-  return new Provider(id, name, cfg) as InstanceType<P>;
-};
-
-export default BaseModelProvider;

src/lib/models/providers/index.ts

@@ -1,27 +1,11 @@
 import { ModelProviderUISection } from '@/lib/config/types';
-import { ProviderConstructor } from './baseProvider';
+import { ProviderConstructor } from '../base/provider';
 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/ollama/index.ts

@@ -1,10 +1,11 @@
-import { BaseChatModel } from '@langchain/core/language_models/chat_models';
-import { Model, ModelList, ProviderMetadata } from '../types';
-import BaseModelProvider from './baseProvider';
-import { ChatOllama, OllamaEmbeddings } from '@langchain/ollama';
-import { Embeddings } from '@langchain/core/embeddings';
 import { UIConfigField } from '@/lib/config/types';
 import { getConfiguredModelProviderById } from '@/lib/config/serverRegistry';
+import BaseModelProvider from '../../base/provider';
+import { Model, ModelList, ProviderMetadata } from '../../types';
+import BaseLLM from '../../base/llm';
+import BaseEmbedding from '../../base/embedding';
+import OllamaLLM from './ollamaLLM';
+import OllamaEmbedding from './ollamaEmbedding';
 
 interface OllamaConfig {
   baseURL: string;
@@ -76,7 +77,7 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
     };
   }
 
-  async loadChatModel(key: string): Promise<BaseChatModel> {
+  async loadChatModel(key: string): Promise<BaseLLM<any>> {
     const modelList = await this.getModelList();
 
     const exists = modelList.chat.find((m) => m.key === key);
@@ -87,14 +88,13 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
       );
     }
 
-    return new ChatOllama({
-      temperature: 0.7,
+    return new OllamaLLM({
+      baseURL: this.config.baseURL,
       model: key,
-      baseUrl: this.config.baseURL,
     });
   }
 
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
+  async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
     const modelList = await this.getModelList();
     const exists = modelList.embedding.find((m) => m.key === key);
 
@@ -104,9 +104,9 @@ class OllamaProvider extends BaseModelProvider<OllamaConfig> {
       );
     }
 
-    return new OllamaEmbeddings({
+    return new OllamaEmbedding({
       model: key,
-      baseUrl: this.config.baseURL,
+      baseURL: this.config.baseURL,
     });
   }
 

src/lib/models/providers/ollama/ollamaEmbedding.ts

@@ -0,0 +1,39 @@
+import { Ollama } from 'ollama';
+import BaseEmbedding from '../../base/embedding';
+
+type OllamaConfig = {
+  model: string;
+  baseURL?: string;
+};
+
+class OllamaEmbedding extends BaseEmbedding<OllamaConfig> {
+  ollamaClient: Ollama;
+
+  constructor(protected config: OllamaConfig) {
+    super(config);
+
+    this.ollamaClient = new Ollama({
+      host: this.config.baseURL || 'http://localhost:11434',
+    });
+  }
+
+  async embedText(texts: string[]): Promise<number[][]> {
+    const response = await this.ollamaClient.embed({
+      input: texts,
+      model: this.config.model,
+    });
+
+    return response.embeddings;
+  }
+
+  async embedChunks(chunks: Chunk[]): Promise<number[][]> {
+    const response = await this.ollamaClient.embed({
+      input: chunks.map((c) => c.content),
+      model: this.config.model,
+    });
+
+    return response.embeddings;
+  }
+}
+
+export default OllamaEmbedding;

src/lib/models/providers/ollama/ollamaLLM.ts

@@ -0,0 +1,149 @@
+import z from 'zod';
+import BaseLLM from '../../base/llm';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../../types';
+import { Ollama } from 'ollama';
+import { parse } from 'partial-json';
+
+type OllamaConfig = {
+  baseURL: string;
+  model: string;
+  options?: GenerateOptions;
+};
+
+class OllamaLLM extends BaseLLM<OllamaConfig> {
+  ollamaClient: Ollama;
+
+  constructor(protected config: OllamaConfig) {
+    super(config);
+
+    this.ollamaClient = new Ollama({
+      host: this.config.baseURL || 'http://localhost:11434',
+    });
+  }
+
+  withOptions(options: GenerateOptions) {
+    this.config.options = {
+      ...this.config.options,
+      ...options,
+    };
+    return this;
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    this.withOptions(input.options || {});
+
+    const res = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    return {
+      content: res.message.content,
+      additionalInfo: {
+        reasoning: res.message.thinking,
+      },
+    };
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    this.withOptions(input.options || {});
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      stream: true,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    for await (const chunk of stream) {
+      yield {
+        contentChunk: chunk.message.content,
+        done: chunk.done,
+        additionalInfo: {
+          reasoning: chunk.message.thinking,
+        },
+      };
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    this.withOptions(input.options || {});
+
+    const response = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      format: z.toJSONSchema(input.schema),
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    try {
+      return input.schema.parse(JSON.parse(response.message.content)) as T;
+    } catch (err) {
+      throw new Error(`Error parsing response from Ollama: ${err}`);
+    }
+  }
+
+  async *streamObject<T>(input: GenerateObjectInput): AsyncGenerator<T> {
+    let recievedObj: string = '';
+
+    this.withOptions(input.options || {});
+
+    const stream = await this.ollamaClient.chat({
+      model: this.config.model,
+      messages: input.messages,
+      format: z.toJSONSchema(input.schema),
+      stream: true,
+      options: {
+        top_p: this.config.options?.topP,
+        temperature: this.config.options?.temperature,
+        num_predict: this.config.options?.maxTokens,
+        frequency_penalty: this.config.options?.frequencyPenalty,
+        presence_penalty: this.config.options?.presencePenalty,
+        stop: this.config.options?.stopSequences,
+      },
+    });
+
+    for await (const chunk of stream) {
+      recievedObj += chunk.message.content;
+
+      try {
+        yield parse(recievedObj) as T;
+      } catch (err) {
+        console.log('Error parsing partial object from Ollama:', err);
+        yield {} as T;
+      }
+    }
+  }
+}
+
+export default OllamaLLM;

src/lib/models/providers/openai/index.ts

@@ -1,10 +1,13 @@
 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';
+import { Model, ModelList, ProviderMetadata } from '../../types';
+import OpenAIEmbedding from './openaiEmbedding';
+import BaseEmbedding from '../../base/embedding';
+import BaseModelProvider from '../../base/provider';
+import BaseLLM from '../../base/llm';
+import OpenAILLM from './openaiLLM';
 
 interface OpenAIConfig {
   apiKey: string;
@@ -145,7 +148,7 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
     };
   }
 
-  async loadChatModel(key: string): Promise<BaseChatModel> {
+  async loadChatModel(key: string): Promise<BaseLLM<any>> {
     const modelList = await this.getModelList();
 
     const exists = modelList.chat.find((m) => m.key === key);
@@ -156,17 +159,14 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
       );
     }
 
-    return new ChatOpenAI({
+    return new OpenAILLM({
       apiKey: this.config.apiKey,
-      temperature: 0.7,
       model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
+      baseURL: this.config.baseURL,
     });
   }
 
-  async loadEmbeddingModel(key: string): Promise<Embeddings> {
+  async loadEmbeddingModel(key: string): Promise<BaseEmbedding<any>> {
     const modelList = await this.getModelList();
     const exists = modelList.embedding.find((m) => m.key === key);
 
@@ -176,12 +176,10 @@ class OpenAIProvider extends BaseModelProvider<OpenAIConfig> {
       );
     }
 
-    return new OpenAIEmbeddings({
+    return new OpenAIEmbedding({
       apiKey: this.config.apiKey,
       model: key,
-      configuration: {
-        baseURL: this.config.baseURL,
-      },
+      baseURL: this.config.baseURL,
     });
   }
 

src/lib/models/providers/openai/openaiEmbedding.ts

@@ -0,0 +1,41 @@
+import OpenAI from 'openai';
+import BaseEmbedding from '../../base/embedding';
+
+type OpenAIConfig = {
+  apiKey: string;
+  model: string;
+  baseURL?: string;
+};
+
+class OpenAIEmbedding extends BaseEmbedding<OpenAIConfig> {
+  openAIClient: OpenAI;
+
+  constructor(protected config: OpenAIConfig) {
+    super(config);
+
+    this.openAIClient = new OpenAI({
+      apiKey: config.apiKey,
+      baseURL: config.baseURL,
+    });
+  }
+
+  async embedText(texts: string[]): Promise<number[][]> {
+    const response = await this.openAIClient.embeddings.create({
+      model: this.config.model,
+      input: texts,
+    });
+
+    return response.data.map((embedding) => embedding.embedding);
+  }
+
+  async embedChunks(chunks: Chunk[]): Promise<number[][]> {
+    const response = await this.openAIClient.embeddings.create({
+      model: this.config.model,
+      input: chunks.map((c) => c.content),
+    });
+
+    return response.data.map((embedding) => embedding.embedding);
+  }
+}
+
+export default OpenAIEmbedding;

src/lib/models/providers/openai/openaiLLM.ts

@@ -0,0 +1,163 @@
+import OpenAI from 'openai';
+import BaseLLM from '../../base/llm';
+import { zodTextFormat, zodResponseFormat } from 'openai/helpers/zod';
+import {
+  GenerateObjectInput,
+  GenerateOptions,
+  GenerateTextInput,
+  GenerateTextOutput,
+  StreamTextOutput,
+} from '../../types';
+import { parse } from 'partial-json';
+
+type OpenAIConfig = {
+  apiKey: string;
+  model: string;
+  baseURL?: string;
+  options?: GenerateOptions;
+};
+
+class OpenAILLM extends BaseLLM<OpenAIConfig> {
+  openAIClient: OpenAI;
+
+  constructor(protected config: OpenAIConfig) {
+    super(config);
+
+    this.openAIClient = new OpenAI({
+      apiKey: this.config.apiKey,
+      baseURL: this.config.baseURL || 'https://api.openai.com/v1',
+    });
+  }
+
+  withOptions(options: GenerateOptions) {
+    this.config.options = {
+      ...this.config.options,
+      ...options,
+    };
+
+    return this;
+  }
+
+  async generateText(input: GenerateTextInput): Promise<GenerateTextOutput> {
+    this.withOptions(input.options || {});
+
+    const response = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      messages: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+    });
+
+    if (response.choices && response.choices.length > 0) {
+      return {
+        content: response.choices[0].message.content!,
+        additionalInfo: {
+          finishReason: response.choices[0].finish_reason,
+        },
+      };
+    }
+
+    throw new Error('No response from OpenAI');
+  }
+
+  async *streamText(
+    input: GenerateTextInput,
+  ): AsyncGenerator<StreamTextOutput> {
+    this.withOptions(input.options || {});
+
+    const stream = await this.openAIClient.chat.completions.create({
+      model: this.config.model,
+      messages: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      stream: true,
+    });
+
+    for await (const chunk of stream) {
+      if (chunk.choices && chunk.choices.length > 0) {
+        yield {
+          contentChunk: chunk.choices[0].delta.content || '',
+          done: chunk.choices[0].finish_reason !== null,
+          additionalInfo: {
+            finishReason: chunk.choices[0].finish_reason,
+          },
+        };
+      }
+    }
+  }
+
+  async generateObject<T>(input: GenerateObjectInput): Promise<T> {
+    this.withOptions(input.options || {});
+
+    const response = await this.openAIClient.chat.completions.parse({
+      messages: input.messages,
+      model: this.config.model,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      response_format: zodResponseFormat(input.schema, 'object'),
+    });
+
+    if (response.choices && response.choices.length > 0) {
+      try {
+        return input.schema.parse(response.choices[0].message.parsed) as T;
+      } catch (err) {
+        throw new Error(`Error parsing response from OpenAI: ${err}`);
+      }
+    }
+
+    throw new Error('No response from OpenAI');
+  }
+
+  async *streamObject<T>(input: GenerateObjectInput): AsyncGenerator<T> {
+    let recievedObj: string = '';
+
+    this.withOptions(input.options || {});
+
+    const stream = this.openAIClient.responses.stream({
+      model: this.config.model,
+      input: input.messages,
+      temperature: this.config.options?.temperature || 1.0,
+      top_p: this.config.options?.topP,
+      max_completion_tokens: this.config.options?.maxTokens,
+      stop: this.config.options?.stopSequences,
+      frequency_penalty: this.config.options?.frequencyPenalty,
+      presence_penalty: this.config.options?.presencePenalty,
+      text: {
+        format: zodTextFormat(input.schema, 'object'),
+      },
+    });
+
+    for await (const chunk of stream) {
+      if (chunk.type === 'response.output_text.delta' && chunk.delta) {
+        recievedObj += chunk.delta;
+
+        try {
+          yield parse(recievedObj) as T;
+        } catch (err) {
+          console.log('Error parsing partial object from OpenAI:', err);
+          yield {} as T;
+        }
+      } else if (chunk.type === 'response.output_text.done' && chunk.text) {
+        try {
+          yield parse(chunk.text) as T;
+        } catch (err) {
+          throw new Error(`Error parsing response from OpenAI: ${err}`);
+        }
+      }
+    }
+  }
+}
+
+export default OpenAILLM;