Langchain-Chatchat/configs/model_config.py.example
2024-03-06 13:32:43 +08:00

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import os
MODEL_ROOT_PATH = ""
EMBEDDING_MODEL = "bge-large-zh-v1.5" # bge-large-zh
EMBEDDING_DEVICE = "auto"
# 选用的reranker模型
RERANKER_MODEL = "bge-reranker-large"
# 是否启用reranker模型
USE_RERANKER = False
RERANKER_MAX_LENGTH = 1024
# 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"
SUPPORT_AGENT_MODELS = [
"chatglm3-6b",
"openai-api"
]
LLM_MODEL_CONFIG = {
# 意图识别不需要输出,模型后台知道就行
"preprocess_model": {
"zhipu-api": {
"temperature": 0.4,
"max_tokens": 2048,
"history_len": 100,
"prompt_name": "default",
"callbacks": False
},
},
"llm_model": {
"chatglm3-6b": {
"temperature": 0.9,
"max_tokens": 4096,
"history_len": 3,
"prompt_name": "default",
"callbacks": True
},
"zhipu-api": {
"temperature": 0.9,
"max_tokens": 4000,
"history_len": 5,
"prompt_name": "default",
"callbacks": True
},
"Qwen-1_8B-Chat": {
"temperature": 0.4,
"max_tokens": 2048,
"history_len": 100,
"prompt_name": "default",
"callbacks": True
},
},
"action_model": {
"chatglm3-6b": {
"temperature": 0.01,
"max_tokens": 4096,
"prompt_name": "ChatGLM3",
"callbacks": True
},
"openai-api": {
"temperature": 0.01,
"max_tokens": 4096,
"prompt_name": "GPT-4",
"callbacks": True
},
},
"postprocess_model": {
"chatglm3-6b": {
"temperature": 0.01,
"max_tokens": 4096,
"prompt_name": "default",
"callbacks": True
}
},
}
TOOL_CONFIG = {
"search_local_knowledgebase": {
"use": True,
"top_k": 10,
"score_threshold": 1,
"conclude_prompt": {
"with_result":
'<指令>根据已知信息,简洁和专业的来回答问题。如果无法从中得到答案,请说 "根据已知信息无法回答该问题"'
'不允许在答案中添加编造成分,答案请使用中文。 </指令>\n'
'<已知信息>{{ context }}</已知信息>\n'
'<问题>{{ question }}</问题>\n',
"without_result":
'请你根据我的提问回答我的问题:\n'
'{{ question }}\n'
'请注意,你必须在回答结束后强调,你的回答是根据你的经验回答而不是参考资料回答的。\n',
}
},
"search_internet": {
"use": True,
"search_engine_name": "bing",
"search_engine_config":
{
"bing": {
"result_len": 3,
"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
"bing_key": "",
},
"metaphor": {
"result_len": 3,
"metaphor_api_key": "",
"split_result": False,
"chunk_size": 500,
"chunk_overlap": 0,
},
"duckduckgo": {
"result_len": 3
}
},
"top_k": 10,
"verbose": "Origin",
"conclude_prompt":
"<指令>这是搜索到的互联网信息,请你根据这些信息进行提取并有调理,简洁的回答问题。如果无法从中得到答案,请说 “无法搜索到能回答问题的内容”。 "
"</指令>\n<已知信息>{{ context }}</已知信息>\n"
"<问题>\n"
"{{ question }}\n"
"</问题>\n"
},
"arxiv": {
"use": True,
},
"shell": {
"use": True,
},
"weather_check": {
"use": True,
"api-key": "",
},
"search_youtube": {
"use": False,
},
"wolfram": {
"use": False,
},
"calculate": {
"use": False,
},
}
# LLM 模型运行设备。设为"auto"会自动检测(会有警告),也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
LLM_DEVICE = "auto"
ONLINE_LLM_MODEL = {
"openai-api": {
"model_name": "gpt-4-1106-preview",
"api_base_url": "https://api.openai.com/v1",
"api_key": "",
"openai_proxy": "",
},
"zhipu-api": {
"api_key": "",
"version": "chatglm_turbo",
"provider": "ChatGLMWorker",
},
"minimax-api": {
"group_id": "",
"api_key": "",
"is_pro": False,
"provider": "MiniMaxWorker",
},
"xinghuo-api": {
"APPID": "",
"APISecret": "",
"api_key": "",
"version": "v3.0",
"provider": "XingHuoWorker",
},
"qianfan-api": {
"version": "ernie-bot-4",
"version_url": "",
"api_key": "",
"secret_key": "",
"provider": "QianFanWorker",
},
"fangzhou-api": {
"version": "chatglm-6b-model",
"version_url": "",
"api_key": "",
"secret_key": "",
"provider": "FangZhouWorker",
},
"qwen-api": {
"version": "qwen-max",
"api_key": "",
"provider": "QwenWorker",
"embed_model": "text-embedding-v1" # embedding 模型名称
},
"baichuan-api": {
"version": "Baichuan2-53B",
"api_key": "",
"secret_key": "",
"provider": "BaiChuanWorker",
},
"azure-api": {
"deployment_name": "",
"resource_name": "",
"api_version": "2023-07-01-preview",
"api_key": "",
"provider": "AzureWorker",
},
# 昆仑万维天工 API https://model-platform.tiangong.cn/
"tiangong-api": {
"version": "SkyChat-MegaVerse",
"api_key": "",
"secret_key": "",
"provider": "TianGongWorker",
},
# Gemini API https://makersuite.google.com/app/apikey
"gemini-api": {
"api_key": "",
"provider": "GeminiWorker",
}
}
# 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": "/media/zr/Data/Models/Embedding/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": "/Models/bge-large-zh-v1.5",
"piccolo-base-zh": "sensenova/piccolo-base-zh",
"piccolo-large-zh": "sensenova/piccolo-large-zh",
"nlp_gte_sentence-embedding_chinese-large": "/Models/nlp_gte_sentence-embedding_chinese-large",
"text-embedding-ada-002": "Just write your OpenAI key like "sk-o3IGBhC9g8AiFvTGWVKsT*****" ",
},
"llm_model": {
"chatglm2-6b": "THUDM/chatglm2-6b",
"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
"chatglm3-6b": "/Models/chatglm3-6b",
"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
"Yi-34B-Chat": "/data/share/models/Yi-34B-Chat",
"BlueLM-7B-Chat": "/Models/BlueLM-7B-Chat",
"baichuan2-13b": "/media/zr/Data/Models/LLM/Baichuan2-13B-Chat",
"baichuan2-7b": "/media/zr/Data/Models/LLM/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",
"internlm2-chat-7b": "internlm/internlm2-chat-7b",
"internlm2-chat-20b": "internlm/internlm2-chat-20b",
"BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat",
"BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k",
"Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat",
"agentlm-7b": "THUDM/agentlm-7b",
"agentlm-13b": "THUDM/agentlm-13b",
"agentlm-70b": "THUDM/agentlm-70b",
"falcon-7b": "tiiuae/falcon-7b",
"falcon-40b": "tiiuae/falcon-40b",
"falcon-rw-7b": "tiiuae/falcon-rw-7b",
"aquila-7b": "BAAI/Aquila-7B",
"aquilachat-7b": "BAAI/AquilaChat-7B",
"open_llama_13b": "openlm-research/open_llama_13b",
"vicuna-13b-v1.5": "lmsys/vicuna-13b-v1.5",
"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",
"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-1_8B-Chat":"Qwen/Qwen-1_8B-Chat"
"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": "/media/zr/Data/Models/LLM/Qwen-14B-Chat-Int4", # 确保已经安装了auto-gptq optimum flash-attn
},
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
# 使用VLLM可能导致模型推理能力下降无法完成Agent任务
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',
'chatglm2-6b': 'THUDM/chatglm2-6b',
'chatglm2-6b-32k': 'THUDM/chatglm2-6b-32k',
'chatglm3-6b': 'THUDM/chatglm3-6b',
'chatglm3-6b-32k': 'THUDM/chatglm3-6b-32k',
"internlm-7b": "internlm/internlm-7b",
"internlm-chat-7b": "internlm/internlm-chat-7b",
"internlm2-chat-7b": "internlm/Models/internlm2-chat-7b",
"internlm2-chat-20b": "internlm/Models/internlm2-chat-20b",
"aquila-7b": "BAAI/Aquila-7B",
"aquilachat-7b": "BAAI/AquilaChat-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",
"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",
}