# coding=utf-8 """ @project: maxkb @Author：虎 @file： ollama_model_provider.py @date：2024/3/5 17:23 @desc: """ import json import os from typing import Dict, Iterator from urllib.parse import urlparse, ParseResult import requests from common.utils.common import get_file_content from models_provider.base_model_provider import IModelProvider, ModelProvideInfo, ModelInfo, ModelTypeConst, \ BaseModelCredential, DownModelChunk, DownModelChunkStatus, ValidCode, ModelInfoManage from models_provider.impl.ollama_model_provider.credential.embedding import OllamaEmbeddingModelCredential from models_provider.impl.ollama_model_provider.credential.image import OllamaImageModelCredential from models_provider.impl.ollama_model_provider.credential.llm import OllamaLLMModelCredential from models_provider.impl.ollama_model_provider.credential.reranker import OllamaReRankModelCredential from models_provider.impl.ollama_model_provider.model.embedding import OllamaEmbedding from models_provider.impl.ollama_model_provider.model.image import OllamaImage from models_provider.impl.ollama_model_provider.model.llm import OllamaChatModel from models_provider.impl.ollama_model_provider.model.reranker import OllamaReranker from maxkb.conf import PROJECT_DIR from django.utils.translation import gettext as _ "" ollama_llm_model_credential = OllamaLLMModelCredential() model_info_list = [ ModelInfo( 'deepseek-r1:1.5b', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'deepseek-r1:7b', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'deepseek-r1:8b', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'deepseek-r1:14b', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'deepseek-r1:32b', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama2', _('Llama 2 is a set of pretrained and fine-tuned generative text models ranging in size from 7 billion to 70 billion. This is a repository of 7B pretrained models. Links to other models can be found in the index at the bottom.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama2:13b', _('Llama 2 is a set of pretrained and fine-tuned generative text models ranging in size from 7 billion to 70 billion. This is a repository of 13B pretrained models. Links to other models can be found in the index at the bottom.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama2:70b', _('Llama 2 is a set of pretrained and fine-tuned generative text models ranging in size from 7 billion to 70 billion. This is a repository of 70B pretrained models. Links to other models can be found in the index at the bottom.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama2-chinese:13b', _('Since the Chinese alignment of Llama2 itself is weak, we use the Chinese instruction set to fine-tune meta-llama/Llama-2-13b-chat-hf with LoRA so that it has strong Chinese conversation capabilities.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama3:8b', _('Meta Llama 3: The most capable public product LLM to date. 8 billion parameters.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'llama3:70b', _('Meta Llama 3: The most capable public product LLM to date. 70 billion parameters.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:0.5b', _("Compared with previous versions, qwen 1.5 0.5b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 500 million parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:1.8b', _("Compared with previous versions, qwen 1.5 1.8b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 1.8 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:4b', _("Compared with previous versions, qwen 1.5 4b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 4 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:7b', _("Compared with previous versions, qwen 1.5 7b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 7 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:14b', _("Compared with previous versions, qwen 1.5 14b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 14 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:32b', _("Compared with previous versions, qwen 1.5 32b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 32 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:72b', _("Compared with previous versions, qwen 1.5 72b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 72 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen:110b', _("Compared with previous versions, qwen 1.5 110b has significantly enhanced the model's alignment with human preferences and its multi-language processing capabilities. Models of all sizes support a context length of 32768 tokens. 110 billion parameters."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2:72b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2:57b-a14b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2:7b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:72b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:32b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:14b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:7b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:1.5b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:0.5b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'qwen2.5:3b-instruct', '', ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ModelInfo( 'phi3', _("Phi-3 Mini is Microsoft's 3.8B parameter, lightweight, state-of-the-art open model."), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel), ] ollama_embedding_model_credential = OllamaEmbeddingModelCredential() ollama_image_model_credential = OllamaImageModelCredential() ollama_reranker_model_credential = OllamaReRankModelCredential() embedding_model_info = [ ModelInfo( 'nomic-embed-text', _('A high-performance open embedding model with a large token context window.'), ModelTypeConst.EMBEDDING, ollama_embedding_model_credential, OllamaEmbedding), ] reranker_model_info = [ ModelInfo( 'linux6200/bge-reranker-v2-m3', '', ModelTypeConst.RERANKER, ollama_reranker_model_credential, OllamaReranker), ] image_model_info = [ ModelInfo( 'llava:7b', '', ModelTypeConst.IMAGE, ollama_image_model_credential, OllamaImage), ModelInfo( 'llava:13b', '', ModelTypeConst.IMAGE, ollama_image_model_credential, OllamaImage), ModelInfo( 'llava:34b', '', ModelTypeConst.IMAGE, ollama_image_model_credential, OllamaImage), ] model_info_manage = ( ModelInfoManage.builder() .append_model_info_list(model_info_list) .append_model_info_list(embedding_model_info) .append_default_model_info(ModelInfo( 'phi3', _('Phi-3 Mini is Microsoft\'s 3.8B parameter, lightweight, state-of-the-art open model.'), ModelTypeConst.LLM, ollama_llm_model_credential, OllamaChatModel)) .append_default_model_info(ModelInfo( 'nomic-embed-text', _('A high-performance open embedding model with a large token context window.'), ModelTypeConst.EMBEDDING, ollama_embedding_model_credential, OllamaEmbedding), ) .append_model_info_list(image_model_info) .append_default_model_info(image_model_info[0]) .append_model_info_list(reranker_model_info) .append_default_model_info(reranker_model_info[0]) .build() ) def get_base_url(url: str): parse = urlparse(url) result_url = ParseResult(scheme=parse.scheme, netloc=parse.netloc, path=parse.path, params='', query='', fragment='').geturl() return result_url[:-1] if result_url.endswith("/") else result_url def convert_to_down_model_chunk(row_str: str, chunk_index: int): row = json.loads(row_str) status = DownModelChunkStatus.unknown digest = "" progress = 100 if 'status' in row: digest = row.get('status') if row.get('status') == 'success': status = DownModelChunkStatus.success if row.get('status').__contains__("pulling"): progress = 0 status = DownModelChunkStatus.pulling if 'total' in row and 'completed' in row and row.get('total'): progress = (row.get('completed') / row.get('total') * 100) elif 'error' in row: status = DownModelChunkStatus.error digest = row.get('error') return DownModelChunk(status=status, digest=digest, progress=progress, details=row_str, index=chunk_index) def convert(response_stream) -> Iterator[DownModelChunk]: temp = "" index = 0 for c in response_stream: index += 1 row_content = c.decode() temp += row_content if row_content.endswith('}') or row_content.endswith('\n'): rows = [t for t in temp.split("\n") if len(t) > 0] for row in rows: yield convert_to_down_model_chunk(row, index) temp = "" if len(temp) > 0: rows = [t for t in temp.split("\n") if len(t) > 0] for row in rows: yield convert_to_down_model_chunk(row, index) class OllamaModelProvider(IModelProvider): def get_model_info_manage(self): return model_info_manage def get_model_provide_info(self): return ModelProvideInfo(provider='model_ollama_provider', name='Ollama', icon=get_file_content( os.path.join(PROJECT_DIR, "apps", 'models_provider', 'impl', 'ollama_model_provider', 'icon', 'ollama_icon_svg'))) @staticmethod def get_base_model_list(api_base): base_url = get_base_url(api_base) r = requests.request(method="GET", url=f"{base_url}/api/tags", timeout=5) r.raise_for_status() return r.json() def down_model(self, model_type: str, model_name, model_credential: Dict[str, object]) -> Iterator[DownModelChunk]: api_base = model_credential.get('api_base', '') base_url = get_base_url(api_base) r = requests.request( method="POST", url=f"{base_url}/api/pull", data=json.dumps({"name": model_name}).encode(), stream=True, ) return convert(r)