langchain-xberg 1.0.0

LangChain document loader for Xberg — extract 88+ file formats into Documents with async, batching, chunking, and rich metadata

langchain-xberg

A LangChain document loader backed by Xberg. XbergLoader extracts text and metadata from 98+ formats — running OCR where needed — and returns LangChain Document objects. Extraction is async at the core; multiple sources go through Xberg's extract_batch in a single native call, so concurrency happens Rust-side.

Install

pip install langchain-xberg

Requires Python 3.10+.

Load

Pass a path, a list of paths, a directory, or raw bytes. One source becomes one Document.

from langchain_xberg import XbergLoader

# Single file
docs = XbergLoader(file_path="report.pdf").load()
print(docs[0].page_content)           # extracted markdown
print(docs[0].metadata["title"])      # source, mime_type, title, authors, detected_languages, page_count, ...

# Multiple files — one batched extraction
docs = XbergLoader(file_path=["report.pdf", "notes.docx"]).load()

# A directory with a glob
docs = XbergLoader(file_path="./corpus/", glob="**/*.pdf").load()

# Raw bytes (mime_type required)
docs = XbergLoader(data=raw_bytes, mime_type="application/pdf").load()

Chunk for retrieval

Enable Xberg's native chunking to emit one Document per chunk, sized for embedding. Each chunk carries chunk_index, total_chunks, heading_path, page, and token_count in its metadata.

from langchain_xberg import XbergLoader
from xberg import ChunkingConfig, ExtractionConfig

config = ExtractionConfig(chunking=ChunkingConfig(max_characters=1000, overlap=200))
docs = XbergLoader(file_path="report.pdf", config=config).load()  # one Document per chunk

To split by page instead, pass pages=PageConfig(extract_pages=True); each Document then gets a 0-indexed page.

Configure extraction

Pass any Xberg ExtractionConfig to control OCR, output format, and batch concurrency.

from xberg import ExtractionConfig, OcrConfig

config = ExtractionConfig(
    output_format="markdown",
    ocr=OcrConfig(backend="tesseract"),
    force_ocr=True,
    max_concurrent_extractions=8,
)
docs = XbergLoader(file_path="./corpus/", config=config).load()

Async

Inside an event loop, use the async API — await loader.aload() or async for doc in loader.alazy_load(). These use Xberg's native async extraction end to end. The synchronous load / lazy_load bridge to it and must not run inside a running loop.

loader = XbergLoader(file_path="report.pdf")
docs = await loader.aload()

Errors

A per-input failure raises xberg.XbergError with the offending source and message. In batch mode the failure comes from ExtractionResult.errors; a single load surfaces the raised exception directly.

For the full API, see the Xberg documentation.