86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
import torch.cuda
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import torch.backends
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import os
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import logging
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import uuid
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LOG_FORMAT = "%(levelname) -5s %(asctime)s" "-1d: %(message)s"
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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logging.basicConfig(format=LOG_FORMAT)
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embedding_model_dict = {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec-base": "shibing624/text2vec-base-chinese",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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}
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# Embedding model name
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EMBEDDING_MODEL = "text2vec"
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# Embedding running device
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EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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# supported LLM models
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llm_model_dict = {
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"chatyuan": "ClueAI/ChatYuan-large-v2",
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"chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
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"chatglm-6b-int4": "THUDM/chatglm-6b-int4",
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"chatglm-6b-int8": "THUDM/chatglm-6b-int8",
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"chatglm-6b": "THUDM/chatglm-6b",
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}
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# LLM model name
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LLM_MODEL = "chatglm-6b"
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# LLM lora path，默认为空，如果有请直接指定文件夹路径
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LLM_LORA_PATH = ""
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USE_LORA = True if LLM_LORA_PATH else False
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# LLM streaming reponse
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STREAMING = True
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# Use p-tuning-v2 PrefixEncoder
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USE_PTUNING_V2 = False
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# LLM running device
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LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vector_store")
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UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content")
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# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
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PROMPT_TEMPLATE = """已知信息：
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{context}
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根据上述已知信息，简洁和专业的来回答用户的问题。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” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
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# 文本分句长度
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SENTENCE_SIZE = 100
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# 匹配后单段上下文长度
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CHUNK_SIZE = 250
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# LLM input history length
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LLM_HISTORY_LEN = 3
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 5
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# 如果为0，则不生效，经测试小于500值的结果更精准
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VECTOR_SEARCH_SCORE_THRESHOLD = 0
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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FLAG_USER_NAME = uuid.uuid4().hex
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logger.info(f"""
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loading model config
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llm device: {LLM_DEVICE}
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embedding device: {EMBEDDING_DEVICE}
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dir: {os.path.dirname(os.path.dirname(__file__))}
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flagging username: {FLAG_USER_NAME}
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""")
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