diff --git a/pyproject.toml b/pyproject.toml
index e66cacdb..20baecde 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,7 @@ dependencies = [
     "mcp>=1.23.3",
     "pydantic>=2.12.4",
     "jinja2>=3.1.6",
+    "docling>=2.70.0",
 ]
 default-optional-dependency-keys = [
     "mysql",
diff --git a/src/databao_context_engine/plugins/files/pdf_plugin.py b/src/databao_context_engine/plugins/files/pdf_plugin.py
new file mode 100644
index 00000000..3d408b1a
--- /dev/null
+++ b/src/databao_context_engine/plugins/files/pdf_plugin.py
@@ -0,0 +1,36 @@
+from io import BufferedReader, BytesIO
+from typing import Any
+
+from docling.datamodel.base_models import DocumentStream, InputFormat
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+
+from databao_context_engine import BuildFilePlugin
+from databao_context_engine.pluginlib.build_plugin import EmbeddableChunk
+from databao_context_engine.services.docling_chunker import DoclingChunker
+
+
+class PDFPlugin(BuildFilePlugin):
+    id = "jetbrains/pdf"
+    name = "PDF Plugin"
+
+    def supported_types(self) -> set[str]:
+        return {"pdf"}
+
+    def build_file_context(self, full_type: str, file_name: str, file_buffer: BufferedReader) -> Any:
+        pdf_bytes = file_buffer.read()
+
+        opts = PdfPipelineOptions()
+        opts.do_ocr = False
+        opts.do_picture_description = False
+        opts.do_picture_classification = False
+        opts.generate_page_images = False
+        opts.generate_picture_images = False
+
+        converter = DocumentConverter(format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=opts)})
+        stream = DocumentStream(name=file_name, stream=BytesIO(pdf_bytes))
+        return converter.convert(stream).document
+
+    def divide_context_into_chunks(self, context: Any) -> list[EmbeddableChunk]:
+        indexer = DoclingChunker()
+        return indexer.index(context)
diff --git a/src/databao_context_engine/plugins/plugin_loader.py b/src/databao_context_engine/plugins/plugin_loader.py
index d9650297..d04bfc0d 100644
--- a/src/databao_context_engine/plugins/plugin_loader.py
+++ b/src/databao_context_engine/plugins/plugin_loader.py
@@ -34,11 +34,10 @@ def _load_builtin_plugins(exclude_file_plugins: bool = False) -> list[BuildPlugi
 
 
 def _load_builtin_file_plugins() -> list[BuildFilePlugin]:
+    from databao_context_engine.plugins.files.pdf_plugin import PDFPlugin
     from databao_context_engine.plugins.files.unstructured_files_plugin import InternalUnstructuredFilesPlugin
 
-    return [
-        InternalUnstructuredFilesPlugin(),
-    ]
+    return [InternalUnstructuredFilesPlugin(), PDFPlugin()]
 
 
 def _load_builtin_datasource_plugins() -> list[BuildDatasourcePlugin]:
diff --git a/src/databao_context_engine/services/docling_chunker.py b/src/databao_context_engine/services/docling_chunker.py
new file mode 100644
index 00000000..76120cde
--- /dev/null
+++ b/src/databao_context_engine/services/docling_chunker.py
@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+from docling_core.transforms.chunker import HybridChunker
+from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer
+from docling_core.types.doc.labels import DocItemLabel
+
+from databao_context_engine.pluginlib.build_plugin import EmbeddableChunk
+
+
+@dataclass(frozen=True)
+class EmbeddingPolicy:
+    """Configuration for embedding oriented chunking.
+
+    Attributes:
+            model_name: Tokenizer/model identifier used for token counting
+            tokens_budget: Max number of tokens allowed in each chunk
+            tokens_overlap: Overlap between consecutive splits to reduce boundary loss
+            tokenizer_unbounded_max: a very large value used to suppress HF tokenizer warnings when encoding long texts
+            hard_truncate_chars: last resert char cap for single rows
+    """
+
+    model_name: str = "nomic-ai/nomic-embed-text-v1.5"
+    tokens_budget: int = 1900
+    tokens_overlap: int = 80
+
+    tokenizer_unbounded_max: int = 1_000_000_000
+
+    hard_truncate_chars: int = 6000
+
+
+class TokenSplitter:
+    def __init__(self, policy: EmbeddingPolicy):
+        self.policy = policy
+
+    def split(self, text: str, *, tokenizer: HuggingFaceTokenizer) -> list[str]:
+        """Return `text` split into parts that fit within the policy token budget.
+
+        Returns:
+            A list of strings. If `text` is empty, returns [""]
+
+        Behavior:
+            - If `text` fits the budget, returns [text]
+            - Otherwise tokenizes and slices into windows of size `tokens_budget` with overlap `tokens_overlap`
+        """
+        if not text:
+            return [""]
+
+        if tokenizer.count_tokens(text) <= self.policy.tokens_budget:
+            return [text]
+
+        hf = tokenizer.get_tokenizer()
+        hf.model_max_length = self.policy.tokenizer_unbounded_max
+
+        ids = hf.encode(text, add_special_tokens=False)
+        if not ids:
+            return [text]
+
+        step = max(1, self.policy.tokens_budget - self.policy.tokens_overlap)
+        parts: list[str] = []
+
+        for start in range(0, len(ids), step):
+            end = min(len(ids), start + self.policy.tokens_budget)
+            parts.append(hf.decode(ids[start:end]))
+            if end == len(ids):
+                break
+
+        return parts or [text]
+
+
+class DoclingChunker:
+    """Convert a Docling document into EmbeddableChunks.
+
+    Pipeline:
+        1. Create a Docling `HybridChunker` for structural chunking.
+        2. For non-table chunks: contextualize and token-split into embeddable parts.
+        3. For tables: chunk separately.
+            - Fast path: embed caption + markdown if it fits
+            - Slow path: export to dataframe and row-batch without losing rows
+    """
+
+    def __init__(self, policy: Optional[EmbeddingPolicy] = None):
+        self.policy = policy or EmbeddingPolicy()
+        self.splitter = TokenSplitter(self.policy)
+
+    def index(self, doc: Any) -> list[EmbeddableChunk]:
+        """Create embeddable chunks from a Docling document.
+
+        Returns:
+            A list of EmbeddableChunks.
+        """
+        tokenizer = self._make_tokenizer()
+        chunker = self._make_chunker(tokenizer)
+
+        chunks: list[EmbeddableChunk] = []
+        chunks.extend(self._index_text(doc=doc, chunker=chunker, tokenizer=tokenizer))
+        chunks.extend(self._index_tables(doc=doc, tokenizer=tokenizer))
+        return chunks
+
+    def _make_tokenizer(self) -> HuggingFaceTokenizer:
+        return HuggingFaceTokenizer.from_pretrained(
+            model_name=self.policy.model_name,
+            max_tokens=self.policy.tokens_budget,
+        )
+
+    def _make_chunker(self, tokenizer: HuggingFaceTokenizer) -> HybridChunker:
+        return HybridChunker(tokenizer=tokenizer, merge_peers=False)
+
+    def _index_text(
+        self,
+        *,
+        doc: Any,
+        chunker: HybridChunker,
+        tokenizer: HuggingFaceTokenizer,
+    ) -> list[EmbeddableChunk]:
+        """Index non-table text chunks produced by Docling's chunker."""
+        out: list[EmbeddableChunk] = []
+
+        for chunk in chunker.chunk(dl_doc=doc):
+            if self._is_table_chunk(chunk):
+                continue
+
+            raw_text = getattr(chunk, "text", "")
+            display_text = (raw_text or "").strip()
+            if not display_text:
+                continue
+
+            embed_text = chunker.contextualize(chunk=chunk)
+
+            if tokenizer.count_tokens(embed_text) <= self.policy.tokens_budget:
+                out.append(EmbeddableChunk(embeddable_text=embed_text, content=display_text))
+                continue
+
+            delim = getattr(chunker, "delim", "\n")
+            if delim in embed_text:
+                header, body = embed_text.rsplit(delim, 1)
+                header = header.strip()
+                body = body.strip()
+
+                if not body or not header:
+                    parts = self.splitter.split(embed_text, tokenizer=tokenizer)
+                else:
+                    parts = []
+                    for body_part in self.splitter.split(body, tokenizer=tokenizer):
+                        candidate = f"{header}{delim}{body_part}".strip()
+                        parts.append(candidate)
+
+            else:
+                parts = self.splitter.split(embed_text, tokenizer=tokenizer)
+
+            for part in parts:
+                out.append(EmbeddableChunk(embeddable_text=part, content=display_text))
+
+        return out
+
+    def _is_table_chunk(self, chunk: Any) -> bool:
+        """Return True if the Docling chunk references a table item."""
+        meta = getattr(chunk, "meta", None)
+        doc_items = getattr(meta, "doc_items", None) or []
+
+        for it in doc_items:
+            label = getattr(it, "label", None)
+            if label == DocItemLabel.TABLE:
+                return True
+
+            ref = getattr(it, "self_ref", None)
+            if isinstance(ref, str) and "/tables/" in ref:
+                return True
+
+        return False
+
+    def _index_tables(self, *, doc: Any, tokenizer: HuggingFaceTokenizer) -> list[EmbeddableChunk]:
+        """Index all the tables in the document using the table-specific strategy."""
+        out: list[EmbeddableChunk] = []
+        for table in getattr(doc, "tables", None) or []:
+            out.extend(self._table_to_chunks(table=table, doc=doc, tokenizer=tokenizer))
+        return out
+
+    def _table_to_chunks(self, *, table: Any, doc: Any, tokenizer: HuggingFaceTokenizer) -> list[EmbeddableChunk]:
+        """Convert a single table into one or more embeddable chunks."""
+        table_md = table.export_to_markdown(doc=doc)
+        caption = self._get_table_caption(table)
+
+        fast_embed = "\n".join([line for line in [f"TABLE: {caption}".strip(), table_md] if line.strip()])
+        if tokenizer.count_tokens(fast_embed) <= self.policy.tokens_budget:
+            return [EmbeddableChunk(embeddable_text=fast_embed, content=table_md)]
+
+        df = table.export_to_dataframe(doc=doc)
+        headers = [str(c) for c in df.columns]
+        rows = df.astype(str).values.tolist()
+
+        prefix = self._format_table_prefix(caption=caption, headers=headers)
+        base = prefix + "\nROWS:\n"
+
+        chunks: list[EmbeddableChunk] = []
+        current: list[str] = []
+
+        for r in rows:
+            line = "- " + " | ".join((c or "").strip() for c in r)
+            candidate = base + "\n".join(current + [line])
+
+            if tokenizer.count_tokens(candidate) > self.policy.tokens_budget:
+                if current:
+                    chunks.append(EmbeddableChunk(embeddable_text=base + "\n".join(current), content=table_md))
+                    current = [line]
+                else:
+                    chunks.append(
+                        EmbeddableChunk(
+                            embeddable_text=(base + line)[: self.policy.hard_truncate_chars] + "\n…[TRUNCATED]…",
+                            content=table_md,
+                        )
+                    )
+                    current = []
+            else:
+                current.append(line)
+
+        if current:
+            chunks.append(EmbeddableChunk(embeddable_text=base + "\n".join(current), content=table_md))
+
+        return chunks
+
+    def _get_table_caption(self, table: Any) -> str:
+        """Best-effort extraction of a table caption as plain text."""
+        caption = getattr(table, "caption", None)
+        if caption is None:
+            return ""
+
+        text = getattr(caption, "text", None)
+        if isinstance(text, str):
+            return text
+        if isinstance(caption, str):
+            return caption
+        return str(caption)
+
+    def _format_table_prefix(self, *, caption: str, headers: list[str]) -> str:
+        """Format the common prefix included in every table embedding chunk."""
+        lines: list[str] = []
+        if caption.strip():
+            lines.append(f"TABLE: {caption.strip()}")
+        if headers:
+            lines.append("COLUMNS: " + " | ".join(headers))
+        return "\n".join(lines).strip()
+
+    def _display_excerpt(self, text: str, *, max_chars: int = 600) -> str:
+        """A short, user-facing snippet when chunk.text is empty/missing."""
+        s = (text or "").strip()
+        if not s:
+            return ""
+        return s if len(s) <= max_chars else s[: max_chars - 1] + "…"
diff --git a/tests/plugins/files/__init__.py b/tests/plugins/files/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/plugins/files/test_pdf_plugin.py b/tests/plugins/files/test_pdf_plugin.py
new file mode 100644
index 00000000..02412320
--- /dev/null
+++ b/tests/plugins/files/test_pdf_plugin.py
@@ -0,0 +1,110 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer
+
+from databao_context_engine.plugins.files.pdf_plugin import PDFPlugin
+from databao_context_engine.services.docling_chunker import EmbeddingPolicy
+
+FIXTURES_DIR = Path(__file__).parent.parent / "fixtures"
+
+
+def _embed_tokenizer(policy: EmbeddingPolicy) -> HuggingFaceTokenizer:
+    return HuggingFaceTokenizer.from_pretrained(
+        model_name=policy.model_name,
+        max_tokens=policy.tokens_budget,
+    )
+
+
+def test_pdf_plugin_smoke_table_pdf_produces_chunks_under_budget():
+    pdf_path = FIXTURES_DIR / "long_table.pdf"
+
+    plugin = PDFPlugin()
+
+    with pdf_path.open("rb") as f:
+        doc = plugin.build_file_context(full_type="files/pdf", file_name=pdf_path.name, file_buffer=f)
+
+    chunks = plugin.divide_context_into_chunks(doc)
+
+    assert chunks
+    assert 1 < len(chunks) < 5000
+    assert all(c.embeddable_text.strip() for c in chunks)
+    assert all(c.content.strip() for c in chunks)
+
+    unique_embed_texts = {c.embeddable_text for c in chunks}
+    assert len(unique_embed_texts) >= int(len(chunks) * 0.8)
+
+    policy = EmbeddingPolicy()
+    tok = _embed_tokenizer(policy)
+    over_budget = [c for c in chunks if tok.count_tokens(c.embeddable_text) > policy.tokens_budget]
+    assert not over_budget
+
+    table_chunks = [c for c in chunks if _is_table_chunk_text(c.embeddable_text)]
+    non_table_chunks = [c for c in chunks if not _is_table_chunk_text(c.embeddable_text)]
+
+    assert table_chunks
+    assert len(table_chunks) >= 2
+
+    assert len(non_table_chunks) <= 2
+
+    assert any("\n- " in c.embeddable_text for c in table_chunks), (
+        "Expected at least one '- ...' row line in table chunks"
+    )
+    assert any(" | " in c.embeddable_text for c in table_chunks)
+
+    assert all("|" in c.content for c in table_chunks)
+
+
+def test_pdf_plugin_smoke_mixed_text_pdf_produces_chunks_under_budget():
+    pdf_path = FIXTURES_DIR / "mixed_text.pdf"
+
+    plugin = PDFPlugin()
+
+    with pdf_path.open("rb") as f:
+        doc = plugin.build_file_context(full_type="files/pdf", file_name=pdf_path.name, file_buffer=f)
+
+    chunks = plugin.divide_context_into_chunks(doc)
+
+    assert chunks
+    assert 1 < len(chunks) < 5000
+    assert all(c.embeddable_text.strip() for c in chunks)
+    assert all(c.content.strip() for c in chunks)
+
+    unique_embed_texts = {c.embeddable_text for c in chunks}
+    assert len(unique_embed_texts) >= int(len(chunks) * 0.8)
+
+    policy = EmbeddingPolicy()
+    tok = _embed_tokenizer(policy)
+    over_budget = [c for c in chunks if tok.count_tokens(c.embeddable_text) > policy.tokens_budget]
+    assert not over_budget
+
+    table_chunks = [c for c in chunks if _is_table_chunk_text(c.embeddable_text)]
+    prose_chunks = [c for c in chunks if not _is_table_chunk_text(c.embeddable_text)]
+
+    assert prose_chunks
+    assert table_chunks
+
+    assert all(not _is_table_chunk_text(c.embeddable_text) for c in prose_chunks)
+
+    assert all(_is_table_chunk_text(c.embeddable_text) for c in table_chunks)
+
+    assert any(c.embeddable_text.strip().startswith("TABLE:") for c in table_chunks) or any(
+        ("COLUMNS:" in c.embeddable_text and "ROWS:" in c.embeddable_text) for c in table_chunks
+    )
+
+    row_batched = [c for c in table_chunks if ("COLUMNS:" in c.embeddable_text and "ROWS:" in c.embeddable_text)]
+    if row_batched:
+        assert any("\n- " in c.embeddable_text for c in row_batched), (
+            "Expected at least one row line in row-batched table chunks"
+        )
+        assert any(" | " in c.embeddable_text for c in row_batched), (
+            "Expected pipe-separated cells in row-batched table chunks"
+        )
+
+    assert all("|" in c.content for c in table_chunks)
+
+
+def _is_table_chunk_text(s: str) -> bool:
+    s = s.strip()
+    return s.startswith("TABLE:") or (("COLUMNS:" in s) and ("ROWS:" in s))
diff --git a/tests/plugins/fixtures/long_table.pdf b/tests/plugins/fixtures/long_table.pdf
new file mode 100644
index 00000000..4152fbdb
Binary files /dev/null and b/tests/plugins/fixtures/long_table.pdf differ
diff --git a/tests/plugins/fixtures/mixed_text.pdf b/tests/plugins/fixtures/mixed_text.pdf
new file mode 100644
index 00000000..cd0bd024
Binary files /dev/null and b/tests/plugins/fixtures/mixed_text.pdf differ
diff --git a/tests/plugins/test_plugin_loader.py b/tests/plugins/test_plugin_loader.py
index 8f999984..923c7285 100644
--- a/tests/plugins/test_plugin_loader.py
+++ b/tests/plugins/test_plugin_loader.py
@@ -56,6 +56,7 @@ def test_loaded_plugins_no_extra():
     assert plugin_ids == {
         "jetbrains/duckdb",
         "jetbrains/parquet",
+        "jetbrains/pdf",
         "jetbrains/sqlite",
         "jetbrains/unstructured_files",
         "jetbrains/dbt",
@@ -70,6 +71,7 @@ def test_loaded_plugins_all_extras():
         "jetbrains/duckdb",
         "jetbrains/mssql",
         "jetbrains/mysql",
+        "jetbrains/pdf",
         "jetbrains/postgres",
         "jetbrains/snowflake",
         "jetbrains/parquet",
diff --git a/tests/services/__init__.py b/tests/services/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/services/test_docling_chunker.py b/tests/services/test_docling_chunker.py
new file mode 100644
index 00000000..e452ffba
--- /dev/null
+++ b/tests/services/test_docling_chunker.py
@@ -0,0 +1,219 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from docling_core.types.doc.labels import DocItemLabel
+
+from databao_context_engine.services.docling_chunker import DoclingChunker, EmbeddingPolicy, TokenSplitter
+
+
+class FakeHfTokenizer:
+    def __init__(self):
+        self.model_max_length = 8192
+        self._last_tokens: list[str] = []
+
+    def encode(self, text: str, add_special_tokens: bool = False) -> list[int]:
+        self._last_tokens = text.split()
+        return list(range(len(self._last_tokens)))
+
+    def decode(self, ids: list[int]) -> str:
+        return " ".join(self._last_tokens[i] for i in ids)
+
+
+class FakeDoclingTokenizer:
+    def __init__(self):
+        self._hf = FakeHfTokenizer()
+
+    def count_tokens(self, text: str) -> int:
+        return len(text.split())
+
+    def get_tokenizer(self) -> FakeHfTokenizer:
+        return self._hf
+
+
+@dataclass
+class FakeDocItem:
+    label: object | None = None
+    self_ref: str | None = None
+
+
+@dataclass
+class FakeMeta:
+    doc_items: list[FakeDocItem]
+
+
+@dataclass
+class FakeChunk:
+    text: str
+    meta: FakeMeta
+
+
+class FakeChunker:
+    def __init__(self, chunks: list[FakeChunk]):
+        self._chunks = chunks
+
+    def chunk(self, dl_doc):
+        yield from self._chunks
+
+    def contextualize(self, chunk: FakeChunk) -> str:
+        return chunk.text
+
+
+class FakeValues:
+    def __init__(self, rows: list[list[str]]):
+        self._rows = rows
+
+    def tolist(self) -> list[list[str]]:
+        return self._rows
+
+
+class FakeDataFrame:
+    def __init__(self, columns: list[str], rows: list[list[str]]):
+        self.columns = columns
+        self._rows = rows
+        self.values = FakeValues(rows)
+
+    def astype(self, _type):
+        return self
+
+
+class FakeTable:
+    def __init__(self, markdown: str, df: FakeDataFrame | None, caption: str = ""):
+        self._markdown = markdown
+        self._df = df
+        self.caption = caption
+
+    def export_to_markdown(self, doc):
+        return self._markdown
+
+    def export_to_dataframe(self, doc):
+        if self._df is None:
+            raise AssertionError("export_to_dataframe() should NOT be called in this test.")
+        return self._df
+
+
+def test_token_splitter_splits_with_overlap():
+    policy = EmbeddingPolicy(tokens_budget=5, tokens_overlap=2)
+    splitter = TokenSplitter(policy)
+    tokenizer = FakeDoclingTokenizer()
+
+    text = "t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11"
+    parts = splitter.split(text, tokenizer=tokenizer)
+
+    assert parts == [
+        "t0 t1 t2 t3 t4",
+        "t3 t4 t5 t6 t7",
+        "t6 t7 t8 t9 t10",
+        "t9 t10 t11",
+    ]
+
+
+def test_token_splitter_returns_single_part_if_under_budget():
+    policy = EmbeddingPolicy(tokens_budget=10, tokens_overlap=2)
+    splitter = TokenSplitter(policy)
+    tokenizer = FakeDoclingTokenizer()
+
+    text = "a b c"
+    assert splitter.split(text, tokenizer=tokenizer) == ["a b c"]
+
+
+def test_is_table_chunk_detects_by_label():
+    idx = DoclingChunker()
+
+    chunk = FakeChunk(
+        text="doesn't matter",
+        meta=FakeMeta(doc_items=[FakeDocItem(label=DocItemLabel.TABLE)]),
+    )
+    assert idx._is_table_chunk(chunk) is True
+
+
+def test_is_table_chunk_detects_by_self_ref():
+    idx = DoclingChunker()
+
+    chunk = FakeChunk(
+        text="doesn't matter",
+        meta=FakeMeta(doc_items=[FakeDocItem(self_ref="#/tables/0")]),
+    )
+    assert idx._is_table_chunk(chunk) is True
+
+
+def test_is_table_chunk_false_for_normal_text():
+    idx = DoclingChunker()
+
+    chunk = FakeChunk(
+        text="hello",
+        meta=FakeMeta(doc_items=[FakeDocItem(label="TEXT", self_ref="#/texts/1")]),
+    )
+    assert idx._is_table_chunk(chunk) is False
+
+
+def test_table_fast_path_does_not_export_dataframe():
+    policy = EmbeddingPolicy(tokens_budget=50, tokens_overlap=5)
+    idx = DoclingChunker(policy=policy)
+    tokenizer = FakeDoclingTokenizer()
+
+    table = FakeTable(
+        markdown="| a | b |\n|---|---|\n| 1 | 2 |",
+        df=None,
+        caption="Small table",
+    )
+
+    chunks = idx._table_to_chunks(table=table, doc=object(), tokenizer=tokenizer)
+    assert len(chunks) == 1
+    assert "TABLE: Small table" in chunks[0].embeddable_text
+    assert "| a | b |" in chunks[0].content
+
+
+def test_table_slow_path_row_batches_without_losing_rows():
+    policy = EmbeddingPolicy(tokens_budget=20, tokens_overlap=5)
+    idx = DoclingChunker(policy=policy)
+    tokenizer = FakeDoclingTokenizer()
+
+    big_markdown = "x " * 200
+
+    columns = ["id", "notes"]
+    rows = [[str(i), f"row{i}"] for i in range(1, 11)]
+    df = FakeDataFrame(columns=columns, rows=rows)
+
+    table = FakeTable(markdown=big_markdown, df=df, caption="Big table")
+
+    chunks = idx._table_to_chunks(table=table, doc=object(), tokenizer=tokenizer)
+    assert len(chunks) >= 2
+
+    combined = "\n".join(c.embeddable_text for c in chunks)
+    for i in range(1, 11):
+        assert f"- {i} | row{i}" in combined
+
+
+def test_index_skips_table_chunks_from_text_and_indexes_tables_separately():
+    policy = EmbeddingPolicy(tokens_budget=50, tokens_overlap=5)
+    idx = DoclingChunker(policy=policy)
+
+    tokenizer = FakeDoclingTokenizer()
+
+    normal_chunk = FakeChunk(
+        text="normal text chunk",
+        meta=FakeMeta(doc_items=[FakeDocItem(label="TEXT", self_ref="#/texts/1")]),
+    )
+    table_chunk = FakeChunk(
+        text="table text that should be skipped",
+        meta=FakeMeta(doc_items=[FakeDocItem(label=DocItemLabel.TABLE, self_ref="#/tables/0")]),
+    )
+    chunker = FakeChunker([normal_chunk, table_chunk])
+
+    df = FakeDataFrame(columns=["id"], rows=[["1"], ["2"], ["3"]])
+    table = FakeTable(markdown="x " * 200, df=df, caption="Big table")
+
+    class FakeDoc:
+        tables = [table]
+
+    idx._make_tokenizer = lambda: tokenizer
+    idx._make_chunker = lambda _tok: chunker
+
+    results = idx.index(FakeDoc())
+
+    texts = [c.embeddable_text for c in results]
+
+    assert any("normal text chunk" in t for t in texts)
+    assert not any("table text that should be skipped" in t for t in texts)
+    assert any("COLUMNS:" in t or "ROWS:" in t for t in texts)
diff --git a/uv.lock b/uv.lock
index 443fa802..5407a648 100644
--- a/uv.lock
+++ b/uv.lock
@@ -13,6 +13,24 @@ overrides = [
     { name = "virtualenv", specifier = ">=20.36.1" },
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9f/d2/c581486aa6c4fbd7394c23c47b83fa1a919d34194e16944241daf9e762dd/accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11", size = 380935, upload-time = "2025-11-21T11:27:44.522Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -22,6 +40,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
+
 [[package]]
 name = "anyio"
 version = "4.12.1"
@@ -135,32 +159,45 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/83/7b/5652771e24fff12da9dde4c20ecf4682e606b104f26419d139758cc935a6/azure_identity-1.25.1-py3-none-any.whl", hash = "sha256:e9edd720af03dff020223cd269fa3a61e8f345ea75443858273bcb44844ab651", size = 191317, upload-time = "2025-10-06T20:30:04.251Z" },
 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.14.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "soupsieve" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
+]
+
 [[package]]
 name = "boto3"
-version = "1.42.36"
+version = "1.42.40"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "botocore" },
     { name = "jmespath" },
     { name = "s3transfer" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/54/06/50ff808cf4f40efada8edc20f9d563ab287864423c874dfb94f755a60c52/boto3-1.42.36.tar.gz", hash = "sha256:a4eb51105c8c5d7b2bc2a9e2316e69baf69a55611275b9f189c0cf59f1aae171", size = 112839, upload-time = "2026-01-27T20:38:26.992Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d1/5a/bc190b2e8f2f625cbe50e1226fd7ce6b3719159add6d8d3c01a60defb7c4/boto3-1.42.40.tar.gz", hash = "sha256:e9e08059ae1bd47de411d361e9bfaaa6f35c8f996d68025deefff2b4dda79318", size = 112843, upload-time = "2026-02-02T20:35:04.404Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/d1/35d12f04a7792e2f5e9ddff3c7b60493a32027761380dee7f24ee8ae80cc/boto3-1.42.36-py3-none-any.whl", hash = "sha256:e0ff6f2747bfdec63405b35ea185a7aea35239c3f4fe99e4d29368a6de9c4a84", size = 140604, upload-time = "2026-01-27T20:38:25.349Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/e7/e0d57bcb17aa0ef1d234c9685b67d26e92ee5d2031415a3ca051803ebc6c/boto3-1.42.40-py3-none-any.whl", hash = "sha256:91d776b8b68006c1aca204d384be191883c2a36443f4a90561165986dae17b74", size = 140604, upload-time = "2026-02-02T20:35:01.913Z" },
 ]
 
 [[package]]
 name = "botocore"
-version = "1.42.36"
+version = "1.42.40"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jmespath" },
     { name = "python-dateutil" },
     { name = "urllib3" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/66/4e/b24089cf7a77d38886ac4fbae300a3c4c6d68c1b9ccb66af03cb07b6c35c/botocore-1.42.36.tar.gz", hash = "sha256:2ebd89cc75927944e2cee51b7adce749f38e0cb269a758a6464a27f8bcca65fb", size = 14909073, upload-time = "2026-01-27T20:38:16.621Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c0/ea/912061bde3c36b951d7e56e53f48445f8ecf769c222ef46f604ecf8f574f/botocore-1.42.40.tar.gz", hash = "sha256:6cfa07cf35ad477daef4920324f6d81b8d3a10a35baeafaa5fca22fb3ad225e2", size = 14916776, upload-time = "2026-02-02T20:34:52.409Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/e8/f14d25bd768187424b385bc6a44e2ed5e96964e461ba019add03e48713c7/botocore-1.42.36-py3-none-any.whl", hash = "sha256:2cfae4c482e5e87bd835ab4289b711490c161ba57e852c06b65a03e7c25e08eb", size = 14583066, upload-time = "2026-01-27T20:38:14.02Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/6a/a8b4fa14774b576bd7410c32a63d06a3a55287457d67c6bf11ebfef22aeb/botocore-1.42.40-py3-none-any.whl", hash = "sha256:b115cdfece8162cb30f387fdff2ee4693713744c97ebb4b89742e53675dc521c", size = 14592684, upload-time = "2026-02-02T20:34:48.087Z" },
 ]
 
 [[package]]
@@ -355,6 +392,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
+name = "colorlog"
+version = "6.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" },
+]
+
 [[package]]
 name = "cryptography"
 version = "46.0.4"
@@ -408,12 +457,36 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/6a/bd2e7caa2facffedf172a45c1a02e551e6d7d4828658c9a245516a598d94/cryptography-46.0.4-cp38-abi3-win_amd64.whl", hash = "sha256:fa0900b9ef9c49728887d1576fd8d9e7e3ea872fa9b25ef9b64888adc434e976", size = 3466633, upload-time = "2026-01-28T00:24:21.851Z" },
 ]
 
+[[package]]
+name = "cuda-bindings"
+version = "12.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/c1/dabe88f52c3e3760d861401bb994df08f672ec893b8f7592dc91626adcf3/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fda147a344e8eaeca0c6ff113d2851ffca8f7dfc0a6c932374ee5c47caa649c8", size = 12151019, upload-time = "2025-10-21T14:51:43.167Z" },
+    { url = "https://files.pythonhosted.org/packages/63/56/e465c31dc9111be3441a9ba7df1941fe98f4aa6e71e8788a3fb4534ce24d/cuda_bindings-12.9.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:32bdc5a76906be4c61eb98f546a6786c5773a881f3b166486449b5d141e4a39f", size = 11906628, upload-time = "2025-10-21T14:51:49.905Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/84/1e6be415e37478070aeeee5884c2022713c1ecc735e6d82d744de0252eee/cuda_bindings-12.9.4-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56e0043c457a99ac473ddc926fe0dc4046694d99caef633e92601ab52cbe17eb", size = 11925991, upload-time = "2025-10-21T14:51:56.535Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/af/6dfd8f2ed90b1d4719bc053ff8940e494640fe4212dc3dd72f383e4992da/cuda_bindings-12.9.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b72ee72a9cc1b531db31eebaaee5c69a8ec3500e32c6933f2d3b15297b53686", size = 11922703, upload-time = "2025-10-21T14:52:03.585Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/19/90ac264acc00f6df8a49378eedec9fd2db3061bf9263bf9f39fd3d8377c3/cuda_bindings-12.9.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d80bffc357df9988dca279734bc9674c3934a654cab10cadeed27ce17d8635ee", size = 11924658, upload-time = "2025-10-21T14:52:10.411Z" },
+]
+
+[[package]]
+name = "cuda-pathfinder"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/02/4dbe7568a42e46582248942f54dc64ad094769532adbe21e525e4edf7bc4/cuda_pathfinder-1.3.3-py3-none-any.whl", hash = "sha256:9984b664e404f7c134954a771be8775dfd6180ea1e1aef4a5a37d4be05d9bbb1", size = 27154, upload-time = "2025-12-04T22:35:08.996Z" },
+]
+
 [[package]]
 name = "databao-context-engine"
 version = "0.1.7"
 source = { editable = "." }
 dependencies = [
     { name = "click" },
+    { name = "docling" },
     { name = "duckdb" },
     { name = "jinja2" },
     { name = "mcp" },
@@ -461,6 +534,7 @@ requires-dist = [
     { name = "asyncpg", marker = "extra == 'postgresql'", specifier = ">=0.31.0" },
     { name = "click", specifier = ">=8.3.0" },
     { name = "clickhouse-connect", marker = "extra == 'clickhouse'", specifier = ">=0.10.0" },
+    { name = "docling", specifier = ">=2.70.0" },
     { name = "duckdb", specifier = ">=1.4.3" },
     { name = "jinja2", specifier = ">=3.1.6" },
     { name = "mcp", specifier = ">=1.23.3" },
@@ -488,6 +562,15 @@ dev = [
     { name = "types-pyyaml", specifier = ">=6.0.12.20250915" },
 ]
 
+[[package]]
+name = "dill"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315, upload-time = "2026-01-19T02:36:56.85Z" }
+wheels = [
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+]
+
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 name = "distlib"
 version = "0.4.0"
@@ -511,6 +594,128 @@ wheels = [
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 ]
 
+[[package]]
+name = "docling"
+version = "2.72.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "accelerate" },
+    { name = "beautifulsoup4" },
+    { name = "certifi" },
+    { name = "docling-core", extra = ["chunking"] },
+    { name = "docling-ibm-models" },
+    { name = "docling-parse" },
+    { name = "filetype" },
+    { name = "huggingface-hub" },
+    { name = "lxml" },
+    { name = "marko" },
+    { name = "ocrmac", marker = "sys_platform == 'darwin'" },
+    { name = "openpyxl" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "pluggy" },
+    { name = "polyfactory" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
+    { name = "pylatexenc" },
+    { name = "pypdfium2" },
+    { name = "python-docx" },
+    { name = "python-pptx" },
+    { name = "rapidocr" },
+    { name = "requests" },
+    { name = "rtree" },
+    { name = "scipy" },
+    { name = "tqdm" },
+    { name = "typer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/32/01/4ac01f90e56297a8476399df2ec31b26e073a20b20aa5641a4b8f080a2f4/docling-2.72.0.tar.gz", hash = "sha256:3edd48bb7b6e5737647441b96fba8811f69cac495883fa28dbf2444a0322cabd", size = 284463, upload-time = "2026-02-03T15:09:42.752Z" }
+wheels = [
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+]
+
+[[package]]
+name = "docling-core"
+version = "2.63.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jsonref" },
+    { name = "jsonschema" },
+    { name = "latex2mathml" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "tabulate" },
+    { name = "typer" },
+    { name = "typing-extensions" },
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+]
+
+[package.optional-dependencies]
+chunking = [
+    { name = "semchunk" },
+    { name = "transformers" },
+    { name = "tree-sitter" },
+    { name = "tree-sitter-c" },
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