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"""Knowledge base initialization (llamaindex-only)."""
from __future__ import annotations
import argparse
import asyncio
from datetime import datetime
import json
import os
from pathlib import Path
import shutil
from typing import Optional
from deeptutor.logging import get_logger
from deeptutor.services.rag.components.routing import FileTypeRouter
from deeptutor.services.rag.factory import DEFAULT_PROVIDER, normalize_provider_name
from deeptutor.services.rag.service import RAGService
from deeptutor.services.path_service import PathService
from deeptutor.knowledge.progress_tracker import ProgressStage, ProgressTracker
logger = get_logger("KnowledgeInit")
class KnowledgeBaseInitializer:
"""Knowledge base initializer."""
def __init__(
self,
kb_name: str,
base_dir: str = str(PathService.get_instance().get_knowledge_bases_dir()),
api_key: str | None = None,
base_url: str | None = None,
progress_tracker: ProgressTracker | None = None,
rag_provider: str | None = None,
):
self.kb_name = kb_name
self.base_dir = Path(base_dir)
self.kb_dir = self.base_dir / kb_name
self.raw_dir = self.kb_dir / "raw"
self.llamaindex_storage_dir = self.kb_dir / "llamaindex_storage"
self.api_key = api_key
self.base_url = base_url
self.progress_tracker = progress_tracker or ProgressTracker(kb_name, self.base_dir)
self.rag_provider = normalize_provider_name(rag_provider or DEFAULT_PROVIDER)
def _register_to_config(self) -> None:
"""Register KB in kb_config.json with initializing state."""
try:
from deeptutor.knowledge.manager import KnowledgeBaseManager
manager = KnowledgeBaseManager(base_dir=str(self.base_dir))
manager.config = manager._load_config()
if self.kb_name in manager.config.get("knowledge_bases", {}):
return
manager.update_kb_status(
name=self.kb_name,
status="initializing",
progress={
"stage": "initializing",
"message": "Creating directory structure...",
"percent": 0,
"current": 0,
"total": 0,
},
)
manager.config = manager._load_config()
manager.config.setdefault("knowledge_bases", {}).setdefault(self.kb_name, {})[
"rag_provider"
] = DEFAULT_PROVIDER
manager._save_config()
except Exception as e:
logger.warning(f"Failed to register KB to config: {e}")
def _update_metadata_with_provider(self, provider: str) -> None:
metadata_file = self.kb_dir / "metadata.json"
metadata: dict = {}
if metadata_file.exists():
try:
with open(metadata_file, "r", encoding="utf-8") as f:
metadata = json.load(f)
except Exception:
metadata = {}
metadata["rag_provider"] = normalize_provider_name(provider)
metadata["last_updated"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
with open(metadata_file, "w", encoding="utf-8") as f:
json.dump(metadata, f, indent=2, ensure_ascii=False)
try:
from deeptutor.services.config import get_kb_config_service
service = get_kb_config_service()
service.set_rag_provider(self.kb_name, DEFAULT_PROVIDER)
service.set_kb_config(self.kb_name, {"needs_reindex": False})
except Exception as config_err:
logger.warning(f"Failed to persist provider in centralized config: {config_err}")
def create_directory_structure(self) -> None:
"""Create KB directory structure."""
logger.info(f"Creating directory structure for knowledge base: {self.kb_name}")
for dir_path in [
self.raw_dir,
self.llamaindex_storage_dir,
]:
dir_path.mkdir(parents=True, exist_ok=True)
metadata = {
"name": self.kb_name,
"created_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"description": f"Knowledge base: {self.kb_name}",
"version": "1.0",
"rag_provider": DEFAULT_PROVIDER,
"needs_reindex": False,
}
with open(self.kb_dir / "metadata.json", "w", encoding="utf-8") as f:
json.dump(metadata, indent=2, ensure_ascii=False, fp=f)
self._register_to_config()
def copy_documents(self, source_files: list[str]) -> list[str]:
"""Copy source documents into raw directory."""
copied_files: list[str] = []
for source in source_files:
source_path = Path(source)
if not source_path.exists() or not source_path.is_file():
logger.warning(f"Source file not found: {source}")
continue
dest_path = self.raw_dir / source_path.name
shutil.copy2(source_path, dest_path)
copied_files.append(str(dest_path))
return copied_files
async def process_documents(
self,
) -> bool:
"""Process documents with llamaindex provider.
"""
provider = DEFAULT_PROVIDER
self.progress_tracker.update(
ProgressStage.PROCESSING_DOCUMENTS,
f"Starting to process documents with {provider} provider...",
current=0,
total=0,
)
doc_files: list[Path] = []
for pattern in FileTypeRouter.get_glob_patterns_for_provider(provider):
doc_files.extend(list(self.raw_dir.glob(pattern)))
if not doc_files:
self.progress_tracker.update(
ProgressStage.ERROR,
"No documents found to process",
error="No documents found",
)
raise ValueError("No documents found to process")
self.progress_tracker.update(
ProgressStage.PROCESSING_DOCUMENTS,
f"Found {len(doc_files)} documents, starting to process...",
current=0,
total=len(doc_files),
)
rag_service = RAGService(
kb_base_dir=str(self.base_dir),
provider=provider,
)
file_paths = [str(doc_file) for doc_file in doc_files]
def _on_progress(batch_num, total_batches):
self.progress_tracker.update(
ProgressStage.PROCESSING_DOCUMENTS,
f"Embedding batches: {batch_num}/{total_batches} complete",
current=batch_num,
total=total_batches,
)
try:
success = await rag_service.initialize(
kb_name=self.kb_name,
file_paths=file_paths,
progress_callback=_on_progress,
)
if not success:
self.progress_tracker.update(
ProgressStage.ERROR,
"Document processing failed",
error="RAG pipeline returned failure",
)
raise RuntimeError("RAG pipeline returned failure")
self._update_metadata_with_provider(provider)
self.progress_tracker.update(
ProgressStage.PROCESSING_DOCUMENTS,
"Documents processed successfully",
current=len(doc_files),
total=len(doc_files),
)
except Exception as e:
error_msg = str(e)
logger.error(f"Error processing documents: {error_msg}")
self.progress_tracker.update(
ProgressStage.ERROR,
"Failed to process documents",
error=error_msg,
)
raise
await self.fix_structure()
await self.display_statistics_generic()
return True
async def fix_structure(self) -> None:
"""No-op retained for compatibility with previous pipelines."""
logger.info("Skipping legacy structure cleanup (llamaindex-only mode)")
def extract_numbered_items(self, batch_size: int = 20) -> None:
"""Compatibility no-op: numbered-item extraction is deprecated."""
_ = batch_size
logger.info("Skipping numbered items extraction (deprecated feature removed)")
async def display_statistics_generic(self) -> None:
"""Display basic statistics."""
raw_files = list(self.raw_dir.glob("*")) if self.raw_dir.exists() else []
logger.info("=" * 50)
logger.info("Knowledge Base Statistics")
logger.info("=" * 50)
logger.info(f"Raw documents: {len(raw_files)}")
logger.info(f"LlamaIndex storage exists: {self.llamaindex_storage_dir.exists()}")
logger.info(f"Provider used: {DEFAULT_PROVIDER}")
logger.info("=" * 50)
async def initialize_knowledge_base(
kb_name: str,
source_files: list[str],
base_dir: str = str(PathService.get_instance().get_knowledge_bases_dir()),
api_key: Optional[str] = None,
base_url: Optional[str] = None,
skip_extract: bool = False,
) -> bool:
"""Convenience initializer used by CLI wrappers."""
from deeptutor.knowledge.manager import KnowledgeBaseManager
manager = KnowledgeBaseManager(base_dir=base_dir)
initializer = KnowledgeBaseInitializer(
kb_name=kb_name,
base_dir=base_dir,
api_key=api_key,
base_url=base_url,
rag_provider=DEFAULT_PROVIDER,
)
try:
initializer.create_directory_structure()
initializer.copy_documents(source_files)
await initializer.process_documents()
if not skip_extract:
initializer.extract_numbered_items()
manager.update_kb_status(
name=kb_name,
status="ready",
progress={
"stage": "completed",
"message": "Knowledge base initialization complete!",
"percent": 100,
"current": 1,
"total": 1,
"file_name": "",
"error": None,
"timestamp": datetime.now().isoformat(),
},
)
return True
except Exception as exc:
manager.update_kb_status(
name=kb_name,
status="error",
progress={
"stage": "error",
"message": "Knowledge base initialization failed",
"percent": 0,
"current": 0,
"total": 1,
"file_name": "",
"error": str(exc),
"timestamp": datetime.now().isoformat(),
},
)
raise
async def main() -> None:
parser = argparse.ArgumentParser(description="Initialize a new knowledge base from documents")
parser.add_argument("name", help="Knowledge base name")
parser.add_argument("--docs", nargs="+", help="Document files to process")
parser.add_argument("--docs-dir", help="Directory containing documents to process")
parser.add_argument("--base-dir", default="./knowledge_bases")
parser.add_argument("--api-key", default=os.getenv("LLM_API_KEY"))
parser.add_argument("--base-url", default=os.getenv("LLM_HOST"))
parser.add_argument("--skip-processing", action="store_true")
parser.add_argument("--skip-extract", action="store_true")
parser.add_argument("--batch-size", type=int, default=20)
args = parser.parse_args()
doc_files: list[str] = []
if args.docs:
doc_files.extend(args.docs)
if args.docs_dir:
docs_dir = Path(args.docs_dir)
if docs_dir.exists() and docs_dir.is_dir():
for pattern in FileTypeRouter.get_glob_patterns_for_provider(DEFAULT_PROVIDER):
doc_files.extend([str(f) for f in docs_dir.glob(pattern)])
initializer = KnowledgeBaseInitializer(
kb_name=args.name,
base_dir=args.base_dir,
api_key=args.api_key,
base_url=args.base_url,
)
initializer.create_directory_structure()
if doc_files:
initializer.copy_documents(doc_files)
if not args.skip_processing:
await initializer.process_documents()
if not args.skip_processing and not args.skip_extract:
initializer.extract_numbered_items(batch_size=args.batch_size)
if __name__ == "__main__":
asyncio.run(main())
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