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llm_token_limit=lim.get('llm_context_window', 300),
overlap_token_count=lim.get('window_overlap', 50),
model_token_limit=lim.get('target_chunk_size', 100),
system_prompt_base=prompts.get('system_instructions', '')
)
# -----------------------------------------------------------------------------
# Data Structures
# -----------------------------------------------------------------------------
@dataclass
class Line:
number: int
text: str
token_count: int
@dataclass
class PreChunkSegment:
lines: List[Line]
segment_id: str = field(default_factory=lambda: str(uuid.uuid4()))
@property
def formatted_text(self) -> str:
return "\n".join([f"{line.number} | {line.text}" for line in self.lines])
@dataclass
class SemanticGroup:
line_numbers: Set[int]
# -----------------------------------------------------------------------------
# Service Implementation
# -----------------------------------------------------------------------------
class DocumentChunkingService:
def __init__(self, config_path: str = "config.yaml"):
# 1. Load Config
try:
self.config = ChunkingConfig.from_yaml(config_path)
except Exception as e:
logger.critical(f"Failed to load config: {e}")
sys.exit(1)
# 2. Setup Tokenizer based on Method
self.hf_tokenizer = None
if self.config.tokenizer_method == "huggingface":
if not TRANSFORMERS_AVAILABLE:
logger.critical("Config requests 'huggingface', but library is missing. Install 'transformers'.")
sys.exit(1)
try:
logger.info(f"Initializing HuggingFace Tokenizer: {self.config.hf_model_name}")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
self.hf_tokenizer = AutoTokenizer.from_pretrained(self.config.hf_model_name)
except Exception as e:
logger.critical(f"Failed to load HF Tokenizer: {e}")
sys.exit(1)
elif self.config.tokenizer_method == "heuristic":
logger.info(f"Using Heuristic Tokenizer ({self.config.heuristic_chars_per_token} chars/token)")
else:
logger.warning(f"Unknown tokenizer method '{self.config.tokenizer_method}'. Defaulting to heuristic.")
# 3. Setup OpenAI
if self.config.api_key == "MISSING_KEY":
logger.critical("No valid API Key found.")
self.client = None
else:
try:
self.client = OpenAI(api_key=self.config.api_key)
except Exception as e:
logger.error(f"Failed to initialize OpenAI Client: {e}")
sys.exit(1)
def _count_tokens(self, text: str) -> int:
"""
Determines token count based on the configured method.
"""
if not text:
return 0
if self.config.tokenizer_method == "huggingface" and self.hf_tokenizer:
# HuggingFace Count
return len(self.hf_tokenizer.encode(text, add_special_tokens=False))
else:
# Heuristic Count
return math.ceil(len(text) / self.config.heuristic_chars_per_token)
def _prepare_lines(self, document_text: str) -> List[Line]:
logger.debug(f"Preparing lines using {self.config.tokenizer_method} method...")
raw_lines = document_text.split('\n')
processed_lines = []
for idx, text in enumerate(raw_lines, start=1):
if not text.strip(): continue
count = self._count_tokens(text)
processed_lines.append(Line(idx, text, count))