### What problem does this PR solve? Add GPUStack as a new model provider. [GPUStack](https://github.com/gpustack/gpustack) is an open-source GPU cluster manager for running LLMs. Currently, locally deployed models in GPUStack cannot integrate well with RAGFlow. GPUStack provides both OpenAI compatible APIs (Models / Chat Completions / Embeddings / Speech2Text / TTS) and other APIs like Rerank. We would like to use GPUStack as a model provider in ragflow. [GPUStack Docs](https://docs.gpustack.ai/latest/quickstart/) Related issue: https://github.com/infiniflow/ragflow/issues/4064. ### Type of change - [x] New Feature (non-breaking change which adds functionality) ### Testing Instructions 1. Install GPUStack and deploy the `llama-3.2-1b-instruct` llm, `bge-m3` text embedding model, `bge-reranker-v2-m3` rerank model, `faster-whisper-medium` Speech-to-Text model, `cosyvoice-300m-sft` in GPUStack. 2. Add provider in ragflow settings. 3. Testing in ragflow.
201 lines
6.0 KiB
TypeScript
201 lines
6.0 KiB
TypeScript
import { useTranslate } from '@/hooks/common-hooks';
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import { IModalProps } from '@/interfaces/common';
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import { IAddLlmRequestBody } from '@/interfaces/request/llm';
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import {
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Flex,
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Form,
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Input,
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InputNumber,
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Modal,
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Select,
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Space,
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Switch,
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} from 'antd';
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import omit from 'lodash/omit';
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type FieldType = IAddLlmRequestBody & { vision: boolean };
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const { Option } = Select;
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const llmFactoryToUrlMap = {
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Ollama:
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'https://github.com/infiniflow/ragflow/blob/main/docs/guides/deploy_local_llm.mdx',
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Xinference: 'https://inference.readthedocs.io/en/latest/user_guide',
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LocalAI: 'https://localai.io/docs/getting-started/models/',
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'LM-Studio': 'https://lmstudio.ai/docs/basics',
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'OpenAI-API-Compatible': 'https://platform.openai.com/docs/models/gpt-4',
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TogetherAI: 'https://docs.together.ai/docs/deployment-options',
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Replicate: 'https://replicate.com/docs/topics/deployments',
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OpenRouter: 'https://openrouter.ai/docs',
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HuggingFace:
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'https://huggingface.co/docs/text-embeddings-inference/quick_tour',
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GPUStack: 'https://docs.gpustack.ai/latest/quickstart',
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};
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type LlmFactory = keyof typeof llmFactoryToUrlMap;
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const OllamaModal = ({
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visible,
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hideModal,
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onOk,
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loading,
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llmFactory,
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}: IModalProps<IAddLlmRequestBody> & { llmFactory: string }) => {
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const [form] = Form.useForm<FieldType>();
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const { t } = useTranslate('setting');
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const handleOk = async () => {
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const values = await form.validateFields();
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const modelType =
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values.model_type === 'chat' && values.vision
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? 'image2text'
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: values.model_type;
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const data = {
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...omit(values, ['vision']),
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model_type: modelType,
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llm_factory: llmFactory,
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max_tokens: values.max_tokens,
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};
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console.info(data);
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onOk?.(data);
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};
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const url =
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llmFactoryToUrlMap[llmFactory as LlmFactory] ||
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'https://github.com/infiniflow/ragflow/blob/main/docs/guides/deploy_local_llm.mdx';
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const optionsMap = {
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HuggingFace: [
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{ value: 'embedding', label: 'embedding' },
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{ value: 'chat', label: 'chat' },
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],
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Xinference: [
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{ value: 'chat', label: 'chat' },
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{ value: 'embedding', label: 'embedding' },
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{ value: 'rerank', label: 'rerank' },
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{ value: 'image2text', label: 'image2text' },
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{ value: 'speech2text', label: 'sequence2text' },
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{ value: 'tts', label: 'tts' },
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],
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GPUStack: [
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{ value: 'chat', label: 'chat' },
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{ value: 'embedding', label: 'embedding' },
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{ value: 'rerank', label: 'rerank' },
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{ value: 'speech2text', label: 'sequence2text' },
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{ value: 'tts', label: 'tts' },
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],
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Default: [
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{ value: 'chat', label: 'chat' },
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{ value: 'embedding', label: 'embedding' },
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{ value: 'rerank', label: 'rerank' },
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{ value: 'image2text', label: 'image2text' },
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],
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};
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const getOptions = (factory: string) => {
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return optionsMap[factory as keyof typeof optionsMap] || optionsMap.Default;
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};
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return (
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<Modal
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title={t('addLlmTitle', { name: llmFactory })}
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open={visible}
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onOk={handleOk}
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onCancel={hideModal}
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okButtonProps={{ loading }}
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footer={(originNode: React.ReactNode) => {
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return (
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<Flex justify={'space-between'}>
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<a href={url} target="_blank" rel="noreferrer">
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{t('ollamaLink', { name: llmFactory })}
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</a>
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<Space>{originNode}</Space>
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</Flex>
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);
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}}
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>
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<Form
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name="basic"
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style={{ maxWidth: 600 }}
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autoComplete="off"
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layout={'vertical'}
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form={form}
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>
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<Form.Item<FieldType>
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label={t('modelType')}
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name="model_type"
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initialValue={'embedding'}
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rules={[{ required: true, message: t('modelTypeMessage') }]}
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>
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<Select placeholder={t('modelTypeMessage')}>
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{getOptions(llmFactory).map((option) => (
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<Option key={option.value} value={option.value}>
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{option.label}
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</Option>
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))}
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</Select>
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</Form.Item>
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<Form.Item<FieldType>
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label={t(llmFactory === 'Xinference' ? 'modelUid' : 'modelName')}
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name="llm_name"
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rules={[{ required: true, message: t('modelNameMessage') }]}
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>
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<Input placeholder={t('modelNameMessage')} />
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</Form.Item>
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<Form.Item<FieldType>
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label={t('addLlmBaseUrl')}
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name="api_base"
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rules={[{ required: true, message: t('baseUrlNameMessage') }]}
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>
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<Input placeholder={t('baseUrlNameMessage')} />
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</Form.Item>
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<Form.Item<FieldType>
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label={t('apiKey')}
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name="api_key"
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rules={[{ required: false, message: t('apiKeyMessage') }]}
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>
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<Input placeholder={t('apiKeyMessage')} />
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</Form.Item>
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<Form.Item<FieldType>
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label={t('maxTokens')}
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name="max_tokens"
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rules={[
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{ required: true, message: t('maxTokensMessage') },
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{
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type: 'number',
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message: t('maxTokensInvalidMessage'),
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},
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({ getFieldValue }) => ({
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validator(_, value) {
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if (value < 0) {
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return Promise.reject(new Error(t('maxTokensMinMessage')));
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}
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return Promise.resolve();
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},
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}),
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]}
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>
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<InputNumber
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placeholder={t('maxTokensTip')}
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style={{ width: '100%' }}
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/>
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</Form.Item>
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<Form.Item noStyle dependencies={['model_type']}>
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{({ getFieldValue }) =>
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getFieldValue('model_type') === 'chat' && (
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<Form.Item
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label={t('vision')}
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valuePropName="checked"
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name={'vision'}
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>
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<Switch />
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</Form.Item>
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)
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}
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</Form.Item>
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</Form>
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</Modal>
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);
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};
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export default OllamaModal;
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