Datasets:
infinex
/
data

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
131
import json
import logging
import uuid
import math
import os
import sys
from dataclasses import dataclass, field
from typing import List, Dict, Any, Set, Optional
# -----------------------------------------------------------------------------
# Imports & Dependency Checks
# -----------------------------------------------------------------------------
try:
import yaml
except ImportError:
print("Error: 'PyYAML' is required. Install via 'pip install pyyaml'.")
sys.exit(1)
try:
from openai import OpenAI, OpenAIError
except ImportError:
print("Error: 'openai' is required. Install via 'pip install openai'.")
sys.exit(1)
# We check for transformers inside the class to avoid crashing if
# the user wants heuristic mode but doesn't have transformers installed.
try:
from transformers import AutoTokenizer
TRANSFORMERS_AVAILABLE = True
except ImportError:
TRANSFORMERS_AVAILABLE = False
# -----------------------------------------------------------------------------
# Logging
# -----------------------------------------------------------------------------
logging.basicConfig(
level=logging.DEBUG,
format='[%(levelname)s] %(asctime)s - %(funcName)s:%(lineno)d - %(message)s',
datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)
# -----------------------------------------------------------------------------
# Configuration
# -----------------------------------------------------------------------------
@dataclass
class GroupInterval:
start: int
end: int
line_numbers: Set[int]
@dataclass
class ChunkingConfig:
"""Configuration object loaded from YAML."""
api_key: str
llm_model_name: str
temperature: float
# Tokenization
tokenizer_method: str
hf_model_name: str
heuristic_chars_per_token: int
# Limits
llm_token_limit: int
overlap_token_count: int
model_token_limit: int
# Prompts
system_prompt_base: str
@classmethod
def from_yaml(cls, path: str) -> 'ChunkingConfig':
if not os.path.exists(path):
raise FileNotFoundError(f"Config file not found at: {path}")
logger.info(f"Loading configuration from {path}...")
with open(path, 'r') as f:
data = yaml.safe_load(f)
oa = data.get('openai', {})
tok = data.get('tokenization', {})
tok_heu = tok.get('heuristic', {})
tok_hf = tok.get('huggingface', {})
lim = data.get('limits', {})
prompts = data.get('prompts', {})
raw_key = oa.get('api_key', 'ENV')
api_key = os.getenv("OPENAI_API_KEY") if raw_key == "ENV" else raw_key
return cls(
api_key=api_key or "MISSING_KEY",
llm_model_name=oa.get('model_name', 'gpt-4o-mini'),
temperature=oa.get('temperature', 0.0),
# Tokenizer Config
tokenizer_method=tok.get('method', 'heuristic'),
hf_model_name=tok_hf.get('model_name', 'gpt2'),
heuristic_chars_per_token=tok_heu.get('chars_per_token', 4),