import os # 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。 # 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录。 # 如果模型目录名称和 MODEL_PATH 中的 key 或 value 相同，程序会自动检测加载，无需修改 MODEL_PATH 中的路径。 MODEL_ROOT_PATH = "" # 选用的 Embedding 名称 EMBEDDING_MODEL = "m3e-base" # bge-large-zh # Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。 EMBEDDING_DEVICE = "auto" # 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置 EMBEDDING_KEYWORD_FILE = "keywords.txt" EMBEDDING_MODEL_OUTPUT_PATH = "output" # 要运行的 LLM 名称，可以包括本地模型和在线模型。 # 第一个将作为 API 和 WEBUI 的默认模型 LLM_MODELS = ["chatglm2-6b", "zhipu-api", "openai-api"] # AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0]) Agent_MODEL = None # LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。 LLM_DEVICE = "auto" # 历史对话轮数 HISTORY_LEN = 3 # 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度 MAX_TOKENS = None # LLM通用对话参数 TEMPERATURE = 0.7 # TOP_P = 0.95 # ChatOpenAI暂不支持该参数 ONLINE_LLM_MODEL = { # 线上模型。请在server_config中为每个在线API设置不同的端口 "openai-api": { "model_name": "gpt-35-turbo", "api_base_url": "https://api.openai.com/v1", "api_key": "", "openai_proxy": "", }, # 具体注册及api key获取请前往 http://open.bigmodel.cn "zhipu-api": { "api_key": "", "version": "chatglm_turbo", # 可选包括 "chatglm_turbo" "provider": "ChatGLMWorker", }, # 具体注册及api key获取请前往 https://api.minimax.chat/ "minimax-api": { "group_id": "", "api_key": "", "is_pro": False, "provider": "MiniMaxWorker", }, # 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/ "xinghuo-api": { "APPID": "", "APISecret": "", "api_key": "", "version": "v1.5", # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0" "provider": "XingHuoWorker", }, # 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf "qianfan-api": { "version": "ERNIE-Bot", # 注意大小写。当前支持 "ERNIE-Bot" 或 "ERNIE-Bot-turbo"， 更多的见官方文档。 "version_url": "", # 也可以不填写version，直接填写在千帆申请模型发布的API地址 "api_key": "", "secret_key": "", "provider": "QianFanWorker", }, # 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379 "fangzhou-api": { "version": "chatglm-6b-model", # 当前支持 "chatglm-6b-model"， 更多的见文档模型支持列表中方舟部分。 "version_url": "", # 可以不填写version，直接填写在方舟申请模型发布的API地址 "api_key": "", "secret_key": "", "provider": "FangZhouWorker", }, # 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details "qwen-api": { "version": "qwen-turbo", # 可选包括 "qwen-turbo", "qwen-plus" "api_key": "", # 请在阿里云控制台模型服务灵积API-KEY管理页面创建 "provider": "QwenWorker", }, # 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter "baichuan-api": { "version": "Baichuan2-53B", # 当前支持 "Baichuan2-53B"， 见官方文档。 "api_key": "", "secret_key": "", "provider": "BaiChuanWorker", }, # Azure API "azure-api": { "deployment_name": "", # 部署容器的名字 "resource_name": "", # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写 "api_version": "", # API的版本，不是模型版本 "api_key": "", "provider": "AzureWorker", }, } # 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法： # 1、将对应的值修改为模型绝对路径 # 2、不修改此处的值（以 text2vec 为例）： # 2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录： # - text2vec # - GanymedeNil/text2vec-large-chinese # - text2vec-large-chinese # 2.2 如果以上本地路径不存在，则使用huggingface模型 MODEL_PATH = { "embed_model": { "ernie-tiny": "nghuyong/ernie-3.0-nano-zh", "ernie-base": "nghuyong/ernie-3.0-base-zh", "text2vec-base": "shibing624/text2vec-base-chinese", "text2vec": "GanymedeNil/text2vec-large-chinese", "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase", "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence", "text2vec-multilingual": "shibing624/text2vec-base-multilingual", "text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese", "m3e-small": "moka-ai/m3e-small", "m3e-base": "moka-ai/m3e-base", "m3e-large": "moka-ai/m3e-large", "bge-small-zh": "BAAI/bge-small-zh", "bge-base-zh": "BAAI/bge-base-zh", "bge-large-zh": "BAAI/bge-large-zh", "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct", "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5", "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5", "piccolo-base-zh": "sensenova/piccolo-base-zh", "piccolo-large-zh": "sensenova/piccolo-large-zh", "text-embedding-ada-002": "your OPENAI_API_KEY", }, "llm_model": { # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持 "chatglm2-6b": "THUDM/chatglm2-6b", "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k", "baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat", "baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat", "baichuan-7b": "baichuan-inc/Baichuan-7B", "baichuan-13b": "baichuan-inc/Baichuan-13B", 'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat', "aquila-7b": "BAAI/Aquila-7B", "aquilachat-7b": "BAAI/AquilaChat-7B", "internlm-7b": "internlm/internlm-7b", "internlm-chat-7b": "internlm/internlm-chat-7b", "falcon-7b": "tiiuae/falcon-7b", "falcon-40b": "tiiuae/falcon-40b", "falcon-rw-7b": "tiiuae/falcon-rw-7b", "gpt2": "gpt2", "gpt2-xl": "gpt2-xl", "gpt-j-6b": "EleutherAI/gpt-j-6b", "gpt4all-j": "nomic-ai/gpt4all-j", "gpt-neox-20b": "EleutherAI/gpt-neox-20b", "pythia-12b": "EleutherAI/pythia-12b", "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "dolly-v2-12b": "databricks/dolly-v2-12b", "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b", "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf", "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf", "open_llama_13b": "openlm-research/open_llama_13b", "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3", "koala": "young-geng/koala", "mpt-7b": "mosaicml/mpt-7b", "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter", "mpt-30b": "mosaicml/mpt-30b", "opt-66b": "facebook/opt-66b", "opt-iml-max-30b": "facebook/opt-iml-max-30b", "Qwen-7B": "Qwen/Qwen-7B", "Qwen-14B": "Qwen/Qwen-14B", "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", "Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", # 确保已经安装了auto-gptq optimum flash-attn "Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4", # 确保已经安装了auto-gptq optimum flash-attn }, } # 通常情况下不需要更改以下内容 # nltk 模型存储路径 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data") VLLM_MODEL_DICT = { "aquila-7b": "BAAI/Aquila-7B", "aquilachat-7b": "BAAI/AquilaChat-7B", "baichuan-7b": "baichuan-inc/Baichuan-7B", "baichuan-13b": "baichuan-inc/Baichuan-13B", 'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat', # 注意：bloom系列的tokenizer与model是分离的，因此虽然vllm支持，但与fschat框架不兼容 # "bloom":"bigscience/bloom", # "bloomz":"bigscience/bloomz", # "bloomz-560m":"bigscience/bloomz-560m", # "bloomz-7b1":"bigscience/bloomz-7b1", # "bloomz-1b7":"bigscience/bloomz-1b7", "internlm-7b": "internlm/internlm-7b", "internlm-chat-7b": "internlm/internlm-chat-7b", "falcon-7b": "tiiuae/falcon-7b", "falcon-40b": "tiiuae/falcon-40b", "falcon-rw-7b": "tiiuae/falcon-rw-7b", "gpt2": "gpt2", "gpt2-xl": "gpt2-xl", "gpt-j-6b": "EleutherAI/gpt-j-6b", "gpt4all-j": "nomic-ai/gpt4all-j", "gpt-neox-20b": "EleutherAI/gpt-neox-20b", "pythia-12b": "EleutherAI/pythia-12b", "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", "dolly-v2-12b": "databricks/dolly-v2-12b", "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b", "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf", "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf", "open_llama_13b": "openlm-research/open_llama_13b", "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3", "koala": "young-geng/koala", "mpt-7b": "mosaicml/mpt-7b", "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter", "mpt-30b": "mosaicml/mpt-30b", "opt-66b": "facebook/opt-66b", "opt-iml-max-30b": "facebook/opt-iml-max-30b", "Qwen-7B": "Qwen/Qwen-7B", "Qwen-14B": "Qwen/Qwen-14B", "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", "agentlm-7b": "THUDM/agentlm-7b", "agentlm-13b": "THUDM/agentlm-13b", "agentlm-70b": "THUDM/agentlm-70b", } # 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告 SUPPORT_AGENT_MODEL = [ "azure-api", "openai-api", "claude-api", "zhipu-api", "qwen-api", "Qwen", "baichuan-api", "agentlm", "chatglm3", "xinghuo-api", ]