from poma.integrations.langchain import (
    PomaCheatsheetRetrieverLC,
    PomaChunksetSplitter,
    PomaFileLoader,
)

PomaFileLoader

PomaFileLoader(input_path: str | Path)

Load one file or every supported file under a directory into LangChain Document objects.

Method:

• load() -> list[Document]

Behavior notes:

• Each output Document includes metadata["source_path"] and metadata["doc_id"].
• PDF files are represented with empty page_content; the actual ingestion happens later through PrimeCut.
• Unsupported or unreadable binary files are skipped.

PomaChunksetSplitter

PomaChunksetSplitter(
    client: PrimeCut,
    *,
    verbose: bool = False,
    **kwargs,
)

Call the POMA API for each input document and return chunkset Document objects.

Method:

• split_documents(documents: Iterable[Document]) -> list[Document]

Behavior notes:

• Input documents must include a valid metadata["source_path"].
• Output documents store chunkset text in page_content.
• Output metadata includes doc_id, chunkset_index, chunkset, chunks, and source_path.
• split_text(...) is intentionally not implemented; use split_documents(...).

PomaCheatsheetRetrieverLC

PomaCheatsheetRetrieverLC(
    vector_store: VectorStore,
    *,
    top_k: int = 6,
    **kwargs,
)

Wrap a LangChain vector store and return one cheatsheet Document per source document.

Behavior notes:

• Retrieval uses vector_store.similarity_search(query, k=top_k).
• Hits are grouped by doc_id, then converted into one cheatsheet Document per source document.
• Use .invoke(query) through the standard LangChain retriever interface.