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"""
RAG (Retrieval-Augmented Generation) Subsystem for SPARKNET
Provides:
- Vector store interface with ChromaDB implementation
- Embedding adapters (Ollama, OpenAI)
- Document indexing with metadata
- Grounded retrieval with evidence
- Answer generation with citations
"""
from .store import (
VectorStoreConfig,
VectorStore,
VectorSearchResult,
ChromaVectorStore,
get_vector_store,
)
from .embeddings import (
EmbeddingConfig,
EmbeddingAdapter,
OllamaEmbedding,
get_embedding_adapter,
)
from .indexer import (
IndexerConfig,
IndexingResult,
DocumentIndexer,
get_document_indexer,
)
from .retriever import (
RetrieverConfig,
RetrievedChunk,
DocumentRetriever,
get_document_retriever,
)
from .generator import (
GeneratorConfig,
GeneratedAnswer,
Citation,
GroundedGenerator,
get_grounded_generator,
)
from .docint_bridge import (
DocIntIndexer,
DocIntRetriever,
get_docint_indexer,
get_docint_retriever,
reset_docint_components,
)
__all__ = [
# Store
"VectorStoreConfig",
"VectorStore",
"VectorSearchResult",
"ChromaVectorStore",
"get_vector_store",
# Embeddings
"EmbeddingConfig",
"EmbeddingAdapter",
"OllamaEmbedding",
"get_embedding_adapter",
# Indexer
"IndexerConfig",
"IndexingResult",
"DocumentIndexer",
"get_document_indexer",
# Retriever
"RetrieverConfig",
"RetrievedChunk",
"DocumentRetriever",
"get_document_retriever",
# Generator
"GeneratorConfig",
"GeneratedAnswer",
"Citation",
"GroundedGenerator",
"get_grounded_generator",
# Document Intelligence Bridge
"DocIntIndexer",
"DocIntRetriever",
"get_docint_indexer",
"get_docint_retriever",
"reset_docint_components",
]
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