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import logging
from enum import Enum
from pathlib import Path
from typing import List, Optional, Union
import fire
from langchain_core.embeddings import Embeddings
from src.chains import PresentationAnalysis
from src.config import EmbeddingConfig, Navigator, Provider
from src.config.logging import setup_logging
from src.rag.storage import (ChromaSlideStore, create_slides_database,
create_slides_database_async)
logger = logging.getLogger(__name__)
class Mode(str, Enum):
"""Available conversion modes"""
FRESH = "fresh" # Create new collection
APPEND = "append" # Add to existing collection
def load_openai_embeddings(
provider: Provider, model_name: Optional[str] = "text-embedding-3-small"
) -> Embeddings:
"""Get embeddings model based on provider and name
Args:
provider: Provider type (vsegpt or openai)
model_name: Optional model name override
Returns:
Configured embeddings model
"""
config = EmbeddingConfig()
model_name = model_name
logger.info(f"Using {provider} embeddings model: {model_name}")
if provider == Provider.VSEGPT:
return config.load_vsegpt(model=model_name)
elif provider == Provider.OPENAI:
return config.load_openai(model=model_name)
else:
raise ValueError(f"Unknown provider: {provider}")
class FindPresentationJsons:
"""Helper class for finding presentation JSON files"""
navigator: Navigator = Navigator()
def find_jsons(
self, patterns: Optional[List[str]] = None, base_dir: Optional[Path] = None
) -> List[Path]:
"""Find JSON files using patterns
Args:
patterns: List of substrings to search for, or None to get all JSONs
base_dir: Directory to search in (defaults to interim)
Returns:
List of found JSON file paths
"""
if base_dir is None:
base_dir = self.navigator.interim
if not patterns:
# Get all JSONs from interim if no patterns specified
return list(base_dir.rglob("*.json"))
found_files = []
for pattern in patterns:
found = self.navigator.find_file_by_substr(
substr=pattern, extension=".json", base_dir=base_dir, return_first=False
)
if found:
found_files.extend(found)
else:
logger.warning(f"No JSONs found matching '{pattern}'")
# Remove duplicates while preserving order
return list(dict.fromkeys(found_files))
def process_presentations(
json_paths: List[Path],
collection_name: str = "pres1",
mode: Mode = Mode.FRESH,
embeddings: Optional[Embeddings] = None,
) -> None:
"""Process presentation JSONs into ChromaDB collection
Args:
json_paths: List of JSON file paths
collection_name: Name for ChromaDB collection
mode: Processing mode (fresh or append)
embeddings: Optional embedding model (default OpenAI)
"""
logger.info(f"Processing presentations in {mode} mode")
logger.debug(f"JSON paths: {json_paths}")
# Load presentations from JSONs
presentations = []
for path in json_paths:
try:
pres = PresentationAnalysis.load(path)
presentations.append(pres)
logger.info(f"Loaded presentation: {path.stem}")
except Exception as e:
logger.error(f"Failed to load {path}: {str(e)}")
continue
if not presentations:
logger.error("No presentations loaded")
return
try:
if mode == Mode.FRESH:
logger.info(f"Creating new collection: {collection_name}")
store = create_slides_database(
presentations=presentations,
collection_name=collection_name,
embedding_model=embeddings,
)
else:
logger.info(f"Adding to existing collection: {collection_name}")
store = ChromaSlideStore(
collection_name=collection_name, embedding_model=embeddings
)
for pres in presentations:
for slide in pres.slides:
store.add_slide(slide)
logger.info("Processing completed successfully")
except Exception as e:
logger.error("Processing failed", exc_info=True)
async def process_presentations_async(
json_paths: List[Path],
collection_name: str = "pres0",
mode: Mode = Mode.FRESH,
embeddings: Optional[Embeddings] = None,
max_concurrent_slides: int = 5,
) -> None:
"""Process presentation JSONs into ChromaDB collection asynchronously"""
logger.info(f"Processing presentations in {mode} mode")
logger.debug(f"JSON paths: {json_paths}")
# Load presentations from JSONs
presentations = []
for path in json_paths:
try:
pres = PresentationAnalysis.load(path)
presentations.append(pres)
logger.info(f"Loaded presentation: {path.stem}")
except Exception as e:
logger.error(f"Failed to load {path}: {str(e)}")
continue
if not presentations:
logger.error("No presentations loaded")
return
try:
if mode == Mode.FRESH:
logger.info(f"Creating new collection: {collection_name}")
store = await create_slides_database_async(
presentations=presentations,
collection_name=collection_name,
embedding_model=embeddings,
max_concurrent_slides=max_concurrent_slides,
)
else:
logger.info(f"Adding to existing collection: {collection_name}")
store = ChromaSlideStore(
collection_name=collection_name,
embedding_model=embeddings,
)
for pres in presentations:
await store.process_presentation_async(
pres, max_concurrent=max_concurrent_slides
)
logger.info("Processing completed successfully")
except Exception as e:
logger.error("Processing failed", exc_info=True)
class ChromaCLI:
"""CLI for converting presentation JSONs to ChromaDB"""
def __init__(self):
"""Initialize CLI with logging setup"""
setup_logging(logger, Path("logs"))
self.navigator = Navigator()
self.finder = FindPresentationJsons()
def convert(
self,
*patterns: str,
collection: str = "pres1",
mode: str = "fresh",
provider: str = "openai",
model_name: Optional[str] = "text-embedding-3-small",
base_dir: Optional[str] = None,
max_concurrent: int = 5,
) -> None:
"""Convert presentation JSONs to ChromaDB collection
Args:
*patterns: Optional patterns to search for specific JSONs
collection: Name for ChromaDB collection
mode: Processing mode ('fresh' or 'append')
provider: Embedding provider ('vsegpt' or 'openai')
model_name: Optional specific model name
base_dir: Optional base directory to search in
"""
try:
mode = Mode(mode.lower())
provider = Provider(provider.lower())
except ValueError as e:
logger.error(f"Invalid parameter: {str(e)}")
return
# Get embeddings model
try:
embeddings = load_openai_embeddings(provider, model_name)
except Exception as e:
logger.error(f"Failed to initialize embeddings: {str(e)}")
return
# Set base directory
base_path = Path(base_dir) if base_dir else None
# Find JSON files
json_paths = self.finder.find_jsons(
patterns=list(patterns) if patterns else None, base_dir=base_path
)
if not json_paths:
logger.error("No JSON files found")
return
logger.info(f"Found {len(json_paths)} JSON files")
logger.debug(f"Files: {[p.name for p in json_paths]}")
try:
asyncio.run(
process_presentations_async(
json_paths=json_paths,
collection_name=collection,
mode=mode,
embeddings=embeddings,
max_concurrent_slides=max_concurrent,
)
)
except KeyboardInterrupt:
logger.warning("Processing interrupted by user")
except Exception as e:
logger.error("Processing failed with error", exc_info=True)
def main():
"""Entry point for Fire CLI"""
fire.Fire(ChromaCLI)
if __name__ == "__main__":
main()
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