diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1d995a26..ba9606b3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,19 @@
 # Changelog
 ## [Unreleased]
 
+### Fixed
+
+- **Large Document Storage Overflow**: Fixed "byte array offset overflow" panic when vacuuming/rebuilding databases with many large PDF documents ([#225](https://github.com/ggozad/haiku.rag/issues/225))
+  - Root cause: Arrow's 32-bit string column offsets limited to ~2GB per fragment
+  - Changed `docling_document_json` (string) to `docling_document` (bytes) with `large_binary` Arrow type (64-bit offsets)
+  - Added gzip compression for DoclingDocument JSON (~1.4x compression ratio)
+  - Migration automatically compresses existing documents in batches to avoid memory issues
+  - **Breaking**: Migration is destructive - all table version history is lost after upgrade
+
+### Changed
+
+- **Dependencies**: Updated lancedb 0.26.0 → 0.26.1, docling 2.65.0 → 2.67.0
+
 ## [0.24.2] - 2026-01-08
 
 ### Fixed
diff --git a/haiku_rag_slim/haiku/rag/store/upgrades/__init__.py b/haiku_rag_slim/haiku/rag/store/upgrades/__init__.py
index 43a93dc4..3dbd02e6 100644
--- a/haiku_rag_slim/haiku/rag/store/upgrades/__init__.py
+++ b/haiku_rag_slim/haiku/rag/store/upgrades/__init__.py
@@ -69,6 +69,9 @@ from haiku.rag.store.upgrades.v0_20_0 import (
 from haiku.rag.store.upgrades.v0_23_1 import (
     upgrade_contextualize_chunks as upgrade_0_23_1_contextualize,
 )
+from haiku.rag.store.upgrades.v0_25_0 import (
+    upgrade_compress_docling_document as upgrade_0_25_0_compress,
+)
 
 upgrades.append(upgrade_0_9_3_order)
 upgrades.append(upgrade_0_9_3_fts)
@@ -76,3 +79,4 @@ upgrades.append(upgrade_0_10_1_add_title)
 upgrades.append(upgrade_0_19_6_embeddings)
 upgrades.append(upgrade_0_20_0_docling)
 upgrades.append(upgrade_0_23_1_contextualize)
+upgrades.append(upgrade_0_25_0_compress)
diff --git a/haiku_rag_slim/haiku/rag/store/upgrades/v0_25_0.py b/haiku_rag_slim/haiku/rag/store/upgrades/v0_25_0.py
new file mode 100644
index 00000000..8c0a8eac
--- /dev/null
+++ b/haiku_rag_slim/haiku/rag/store/upgrades/v0_25_0.py
@@ -0,0 +1,200 @@
+import json
+import logging
+from datetime import timedelta
+
+import pyarrow as pa
+from lancedb.pydantic import LanceModel
+from pydantic import Field
+
+from haiku.rag.store.compression import compress_json, decompress_json
+from haiku.rag.store.engine import Store
+from haiku.rag.store.upgrades import Upgrade
+
+logger = logging.getLogger(__name__)
+
+BATCH_SIZE = 10
+
+
+def _apply_compress_docling_document(store: Store) -> None:  # pragma: no cover
+    """Migrate docling_document_json (str) to docling_document (compressed bytes)."""
+
+    class DocumentRecordV4(LanceModel):
+        id: str
+        content: str
+        uri: str | None = None
+        title: str | None = None
+        metadata: str = Field(default="{}")
+        docling_document: bytes | None = None
+        docling_version: str | None = None
+        created_at: str = Field(default_factory=lambda: "")
+        updated_at: str = Field(default_factory=lambda: "")
+
+    def get_documents_arrow_schema_v4() -> pa.Schema:
+        """Generate Arrow schema with large_binary for docling_document."""
+        base_schema = DocumentRecordV4.to_arrow_schema()
+        fields = []
+        for field in base_schema:
+            if field.name == "docling_document":
+                fields.append(pa.field("docling_document", pa.large_binary()))
+            else:
+                fields.append(field)
+        return pa.schema(fields)
+
+    def migrate_row(row: dict) -> DocumentRecordV4:
+        """Migrate a single row, compressing docling_document."""
+        docling_json = row.get("docling_document_json") or row.get("docling_document")
+        docling_bytes: bytes | None = None
+
+        if docling_json:
+            if isinstance(docling_json, str):
+                docling_bytes = compress_json(docling_json)
+            elif isinstance(docling_json, bytes):
+                try:
+                    decompress_json(docling_json)
+                    docling_bytes = docling_json  # Already compressed
+                except Exception:
+                    docling_bytes = compress_json(docling_json.decode("utf-8"))
+
+        metadata_raw = row.get("metadata")
+        metadata_str = (
+            metadata_raw
+            if isinstance(metadata_raw, str)
+            else json.dumps(metadata_raw or {})
+        )
+
+        return DocumentRecordV4(
+            id=row.get("id") or "",
+            content=row.get("content", ""),
+            uri=row.get("uri"),
+            title=row.get("title"),
+            metadata=metadata_str,
+            docling_document=docling_bytes,
+            docling_version=row.get("docling_version"),
+            created_at=row.get("created_at", ""),
+            updated_at=row.get("updated_at", ""),
+        )
+
+    # First pass: collect document IDs to process
+    try:
+        ids = [
+            row["id"]
+            for row in store.documents_table.search()
+            .select(["id"])
+            .to_arrow()
+            .to_pylist()
+        ]
+    except Exception:
+        ids = []
+
+    if not ids:
+        # No documents, just recreate table with new schema
+        try:
+            store.db.drop_table("documents")
+        except Exception:
+            pass
+        store.documents_table = store.db.create_table(
+            "documents", schema=get_documents_arrow_schema_v4()
+        )
+        return
+
+    # Create staging table with new schema
+    try:
+        store.db.drop_table("documents_v4_staging")
+    except Exception:
+        pass
+    staging_table = store.db.create_table(
+        "documents_v4_staging", schema=get_documents_arrow_schema_v4()
+    )
+
+    # Migrate in batches: read from old, compress, write to staging
+    total_docs = len(ids)
+    total_batches = (total_docs + BATCH_SIZE - 1) // BATCH_SIZE
+    logger.info("Compressing %d documents in %d batches", total_docs, total_batches)
+
+    for batch_num, i in enumerate(range(0, len(ids), BATCH_SIZE), 1):
+        batch_ids = ids[i : i + BATCH_SIZE]
+        id_list = ", ".join(f"'{id}'" for id in batch_ids)
+
+        batch = (
+            store.documents_table.search()
+            .where(f"id IN ({id_list})")
+            .to_arrow()
+            .to_pylist()
+        )
+
+        migrated_batch = [migrate_row(row) for row in batch]
+        if migrated_batch:
+            staging_table.add(migrated_batch)
+
+        logger.info(
+            "Compressed batch %d/%d (%d documents)",
+            batch_num,
+            total_batches,
+            len(migrated_batch),
+        )
+
+    # Replace old table with staging table
+    try:
+        store.db.drop_table("documents")
+    except Exception:
+        pass
+
+    store.documents_table = store.db.create_table(
+        "documents", schema=get_documents_arrow_schema_v4()
+    )
+
+    # Copy from staging to final table in batches
+    staging_ids = [
+        row["id"]
+        for row in staging_table.search().select(["id"]).to_arrow().to_pylist()
+    ]
+
+    logger.info("Copying %d documents to new table", len(staging_ids))
+
+    for batch_num, i in enumerate(range(0, len(staging_ids), BATCH_SIZE), 1):
+        batch_ids = staging_ids[i : i + BATCH_SIZE]
+        id_list = ", ".join(f"'{id}'" for id in batch_ids)
+
+        batch = (
+            staging_table.search().where(f"id IN ({id_list})").to_arrow().to_pylist()
+        )
+        records = [
+            DocumentRecordV4(
+                id=row["id"],
+                content=row["content"],
+                uri=row["uri"],
+                title=row["title"],
+                metadata=row["metadata"],
+                docling_document=row["docling_document"],
+                docling_version=row["docling_version"],
+                created_at=row["created_at"],
+                updated_at=row["updated_at"],
+            )
+            for row in batch
+        ]
+        if records:
+            store.documents_table.add(records)
+            logger.info("Copied batch %d/%d", batch_num, total_batches)
+
+    # Cleanup staging table
+    try:
+        store.db.drop_table("documents_v4_staging")
+    except Exception:
+        pass
+
+    # Vacuum all tables (destructive migration, no history preserved)
+    logger.info("Vacuuming database")
+    for table in [store.documents_table, store.chunks_table, store.settings_table]:
+        try:
+            table.optimize(cleanup_older_than=timedelta(seconds=0))
+        except Exception:
+            pass
+
+    logger.info("Migration complete")
+
+
+upgrade_compress_docling_document = Upgrade(
+    version="0.25.0",
+    apply=_apply_compress_docling_document,
+    description="Compress docling_document with gzip and use large_binary type",
+)
diff --git a/haiku_rag_slim/pyproject.toml b/haiku_rag_slim/pyproject.toml
index 27267c3b..17e1e4ce 100644
--- a/haiku_rag_slim/pyproject.toml
+++ b/haiku_rag_slim/pyproject.toml
@@ -2,7 +2,7 @@
 
 name = "haiku.rag-slim"
 description = "Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling - Minimal dependencies"
-version = "0.24.2"
+version = "0.25.0"
 authors = [{ name = "Yiorgis Gozadinos", email = "ggozadinos@gmail.com" }]
 license = { text = "MIT" }
 readme = { file = "README.md", content-type = "text/markdown" }
diff --git a/uv.lock b/uv.lock
index 7cac008d..caa987cd 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1362,7 +1362,7 @@ requires-dist = [
 
 [[package]]
 name = "haiku-rag-slim"
-version = "0.24.2"
+version = "0.25.0"
 source = { editable = "haiku_rag_slim" }
 dependencies = [
     { name = "docling-core" },