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from dataclasses import dataclass
from .prompts import (
MAPREDUCE_SYSTEM_PROMPT,
QUERY_FORMAT_PROMPT,
PLANNING_SYSTEM_PROMPT,
MAP_PROMPT,
REDUCE_PROMPT,
COLLAPSE_PROMPT,
SUMMARY_PROMPT,
)
@dataclass
class LongCepoConfig:
temperature_plan: float = 0.7 # Temperature for planning stage
temperature_map: float = 0.7 # Temperature for map stage
temperature_collapse: float = 0.7 # Temperature for collapse stage
temperature_reduce: float = 0.7 # Temperature for reduce stage
chunk_size: int = 4096 # Max tokens per chunk when splitting context
max_output_tokens: int = 1024 # Max output tokens per LLM API call (except for summary generation)
max_context_window: int = 8192 # Total model context window available
max_output_tokens_summary: int = 300 # Max output tokens per LLM API call (summary generation)
num_neighbor_summaries: int = 5 # Number of adjacent summaries from before/after in the context included in mapping stage
system_prompt: str = MAPREDUCE_SYSTEM_PROMPT # System prompt used in map/collapse/reduce stages
summary_prompt: str = SUMMARY_PROMPT # Prompt template for generating summaries in map phase
map_prompt: str = MAP_PROMPT # Prompt template for map stage
collapse_prompt: str = COLLAPSE_PROMPT # Prompt template for collapse stage
reduce_prompt: str = REDUCE_PROMPT # Prompt template for reduce stage
query_format_prompt: str = QUERY_FORMAT_PROMPT # Query normalization step prompt
planning_system_prompt: str = PLANNING_SYSTEM_PROMPT # Planning stage prompt
context_query_delimiter: str = "<CONTEXT_END>" # Delimiter used to split initial input into context and query
# tokenizer_name: str = "meta-llama/Llama-4-Maverick-17B-128E-Instruct" # Tokenizer to use to determine token lengths
tokenizer_name: str = "meta-llama/Llama-3.1-8B-Instruct" # Tokenizer to use to determine token lengths
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