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61 lines
2.9 KiB
Python
61 lines
2.9 KiB
Python
from dataclasses import dataclass, field
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from typing import Literal, Dict
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@dataclass
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class Arguments(object):
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language: Literal["cn", "en"] = field(default="en", metadata={"help": "support en & cn now"})
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thread_pool_max_workers: int = field(default=5, metadata={"help": "thread pool max workers"})
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logger_name: str = field(default="memoryscope")
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logger_name_time_suffix: str = field(default="%Y%m%d_%H%M%S")
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logger_to_screen: bool = field(default=False, metadata={"help": "If false, it does not print to the screen."})
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memory_chat_class: str = field(default="cli_memory_chat", metadata={
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"help": "cli_memory_chat(Command-line interaction), api_memory_chat(API interface interaction), etc."})
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consolidate_memory_interval_time: int = field(default=1, metadata={
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"help": "If you feel that the token consumption is relatively high, please increase the time interval."})
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reflect_and_reconsolidate_interval_time: int = field(default=15, metadata={
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"help": "If you feel that the token consumption is relatively high, please increase the time interval."})
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worker_params: Dict[str, dict] = field(default_factory=lambda: {}, metadata={
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"help": "dict format: worker_name -> param_key -> param_value"})
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generation_backend: str = field(default="openai_generation", metadata={
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"help": "global generation backend: openai_generation, dashscope_generation, etc."})
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generation_model: str = field(default="gpt-4o", metadata={
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"help": "global generation model: gpt-4o, gpt-4, qwen-max, etc."})
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generation_params: dict = field(default_factory=lambda: {}, metadata={
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"help": "global generation params: max_tokens, top_p, temperature, etc."})
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embedding_backend: str = field(default="openai_embedding", metadata={
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"help": "global embedding backend: openai_embedding, dashscope_embedding, etc."})
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embedding_model: str = field(default="text-embedding-ada-002", metadata={
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"help": "global embedding model: text-embedding-ada-002, text-embedding-v2, etc."})
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embedding_params: dict = field(default_factory=lambda: {})
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use_dummy_ranker: bool = field(default=True, metadata={
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"help": "If a semantic ranking model is not available, MemoryScope will use cosine similarity scoring as a "
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"substitute. However, the ranking effectiveness will be somewhat compromised."})
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rank_backend: str = field(default="dashscope_rank", metadata={"help": "global rank backend: dashscope_rank, etc."})
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rank_model: str = field(default="gte-rerank", metadata={"help": "global rank model: gte-rerank, etc."})
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rank_params: dict = field(default_factory=lambda: {})
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es_index_name: str = field(default="memory_index")
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es_url: str = field(default="http://localhost:9200")
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retrieve_mode: str = field(default="dense", metadata={
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"help": "retrieve_mode: dense, sparse(not implemented), hybrid(not implemented)"})
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