diff --git a/CHANGELOG.md b/CHANGELOG.md
index cf113d6b..142d0a7c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,9 @@ All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 ## [Unreleased]
 
+### Fixed
+- `pw.xpacks.llm.document_store.DocumentStore` no longer requires `_metadata` column in the input table.
+
 ## [0.16.0] - 2024-11-29
 
 ### Added
diff --git a/python/pathway/xpacks/llm/document_store.py b/python/pathway/xpacks/llm/document_store.py
index 33632ae4..a412d339 100644
--- a/python/pathway/xpacks/llm/document_store.py
+++ b/python/pathway/xpacks/llm/document_store.py
@@ -7,6 +7,7 @@
 multiple methods for querying.
 """
 
+import warnings
 from collections.abc import Callable
 from typing import TYPE_CHECKING, Iterable, TypeAlias
 
@@ -35,6 +36,11 @@ class DocumentStore:
 
     Args:
         - docs: pathway tables typically coming out of connectors which contain source documents.
+            The table needs to contain a ``data`` column of type bytes - usually by setting
+            format of the connector to be ``"raw""``. Optionally, it can contain
+            a ``_metadata`` column containing a dictionary with metadata which is then
+            used for filters. Some connectors offer ``with_metadata`` argument for returning
+            ``_metadata`` column.
         - retriever_factory: factory for building an index, which will be provided
             texts by the ``DocumentStore``.
         - parser: callable that parses file contents into a list of documents.
@@ -262,22 +268,32 @@ def split_doc(text: str, metadata: pw.Json) -> list[dict]:
 
         return self._apply_processor(post_processed_docs, split_doc)
 
+    def _clean_tables(self, docs: pw.Table | Iterable[pw.Table]) -> list[pw.Table]:
+        if isinstance(docs, pw.Table):
+            docs = [docs]
+
+        def _clean_table(doc: pw.Table) -> pw.Table:
+            if "_metadata" not in doc.column_names():
+                warnings.warn(
+                    f"`_metadata` column is not present in Table {doc}. Filtering will not work for this Table"
+                )
+                doc = doc.with_columns(_metadata=dict())
+
+            return doc.select(pw.this.data, pw.this._metadata)
+
+        return [_clean_table(doc) for doc in docs]
+
     def build_pipeline(self):
 
-        if isinstance(self.docs, pw.Table):
-            docs = self.docs
-        else:
-            docs_list = list(self.docs)
-            if len(docs_list) == 0:
-                raise ValueError(
-                    """Please provide at least one data source, e.g. read files from disk:
+        cleaned_tables = self._clean_tables(self.docs)
+        if len(cleaned_tables) == 0:
+            raise ValueError(
+                """Please provide at least one data source, e.g. read files from disk:
 pw.io.fs.read('./sample_docs', format='binary', mode='static', with_metadata=True)
 """
-                )
-            elif len(docs_list) == 1:
-                (docs,) = self.docs
-            else:
-                docs = docs_list[0].concat_reindex(*docs_list[1:])
+            )
+
+        docs = pw.Table.concat_reindex(*cleaned_tables)
 
         self.input_docs = docs.select(text=pw.this.data, metadata=pw.this._metadata)
         self.parsed_docs = self.parse_documents(self.input_docs)
diff --git a/python/pathway/xpacks/llm/tests/test_document_store.py b/python/pathway/xpacks/llm/tests/test_document_store.py
index 1a0ff932..59c372e6 100644
--- a/python/pathway/xpacks/llm/tests/test_document_store.py
+++ b/python/pathway/xpacks/llm/tests/test_document_store.py
@@ -594,3 +594,72 @@ def fake_embeddings_model(x: str) -> list[float]:
     (query_result,) = val.as_list()  # extract the single match
     assert isinstance(query_result, dict)
     assert query_result["text"]  # just check if some text was returned
+
+
+def test_docstore_on_table_without_metadata():
+    @pw.udf
+    def fake_embeddings_model(x: str) -> list[float]:
+        return [1.0, 1.0, 0.0]
+
+    docs = pw.debug.table_from_rows(
+        schema=pw.schema_from_types(data=bytes),
+        rows=[("test".encode("utf-8"),)],
+    )
+
+    index_factory = BruteForceKnnFactory(
+        dimensions=3,
+        reserved_space=10,
+        embedder=fake_embeddings_model,
+        metric=BruteForceKnnMetricKind.COS,
+    )
+
+    document_store = DocumentStore(docs, retriever_factory=index_factory)
+
+    retrieve_queries = pw.debug.table_from_rows(
+        schema=DocumentStore.RetrieveQuerySchema,
+        rows=[("Foo", 1, None, None)],
+    )
+
+    retrieve_outputs = document_store.retrieve_query(retrieve_queries)
+    _, rows = pw.debug.table_to_dicts(retrieve_outputs)
+    (val,) = rows["result"].values()
+    assert isinstance(val, pw.Json)
+    (query_result,) = val.as_list()  # extract the single match
+    assert isinstance(query_result, dict)
+    assert query_result["text"] == "test"  # just check if some text was returned
+
+
+def test_docstore_on_tables_with_different_schemas():
+    @pw.udf
+    def fake_embeddings_model(x: str) -> list[float]:
+        return [1.0, 1.0, 0.0]
+
+    docs1 = pw.debug.table_from_rows(
+        schema=pw.schema_from_types(data=bytes),
+        rows=[("test".encode("utf-8"),)],
+    )
+
+    docs2 = pw.debug.table_from_rows(
+        schema=pw.schema_from_types(data=bytes, _metadata=dict, val=int),
+        rows=[("test2".encode("utf-8"), {}, 1)],
+    )
+
+    index_factory = BruteForceKnnFactory(
+        dimensions=3,
+        reserved_space=10,
+        embedder=fake_embeddings_model,
+        metric=BruteForceKnnMetricKind.COS,
+    )
+
+    document_store = DocumentStore([docs1, docs2], retriever_factory=index_factory)
+
+    retrieve_queries = pw.debug.table_from_rows(
+        schema=DocumentStore.RetrieveQuerySchema,
+        rows=[("Foo", 2, None, None)],
+    )
+
+    retrieve_outputs = document_store.retrieve_query(retrieve_queries)
+    _, rows = pw.debug.table_to_dicts(retrieve_outputs)
+    (val,) = rows["result"].values()
+    assert isinstance(val, pw.Json)
+    assert len(val.as_list()) == 2