feat(arcadedb): add metadata query methods to ArcadeDBDocumentStore

integrations/arcadedb/pyproject.toml

@@ -24,7 +24,7 @@ classifiers = [
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
 dependencies = [
-  "haystack-ai>=2.24.0",
+  "haystack-ai>=2.26.1",
   "requests>=2.28.0",
 ]
 

integrations/arcadedb/src/haystack_integrations/document_stores/arcadedb/document_store.py

@@ -56,6 +56,9 @@ class ArcadeDBDocumentStore:
         "dot": "DOT_PRODUCT",
     }
 
+    # Limit for projection documents
+    SCHEMA_SAMPLING_LIMIT: ClassVar[int] = 1000
+
     def __init__(
         self,
         *,
@@ -234,6 +237,70 @@ def count_documents(self) -> int:
             return int(rows[0].get("cnt", 0))
         return 0
 
+    @staticmethod
+    def _extract_distinct_values(rows: list[dict[str, Any]]) -> set[str]:
+        """
+        Extracts and flattens unique non-None strings from 'val' column result rows.
+        :param rows: Raw result rows from ``_command``.
+        :returns: A set of unique string values.
+        """
+        result: set[str] = set()
+        for row in rows:
+            val = row.get("val")
+            if isinstance(val, list):
+                result.update(str(item) for item in val if item is not None)
+            elif val is not None:
+                result.add(str(val))
+        return result
+
+    def _get_metadata_projection_documents(self) -> list[dict[str, Any]]:
+        """
+        Private helper to fetch sample documents for schema inference.
+        Note: Does not `_ensure_initialized()`. To avoid redundant
+        initialization checks during internal calls, the caller is responsible for
+        ensuring the document store is initialized before invoking this method.
+        """
+        sql = f"SELECT content, meta FROM `{self._type_name}` LIMIT {self.SCHEMA_SAMPLING_LIMIT}"
+        return self._command(sql)
+
+    @staticmethod
+    def _infer_metadata_field_type(values: list[Any]) -> str:
+        """
+        Infers the metadata field type from a list of sampled values.
+        :param values: A list of raw Python values sampled from the field.
+        :returns: A type string — one of ``"boolean"``, ``"double"``, ``"long"``, or ``"keyword"``.
+            Returns ``"keyword"`` if values are empty or of mixed types.
+        """
+        inferred_types = set()
+        for value in values:
+            if isinstance(value, list):
+                for item in value:
+                    if isinstance(item, bool):
+                        inferred_types.add("boolean")
+                    elif isinstance(item, float):
+                        inferred_types.add("double")
+                    elif isinstance(item, int):
+                        inferred_types.add("long")
+                    elif isinstance(item, str):
+                        inferred_types.add("keyword")
+            elif isinstance(value, bool):
+                inferred_types.add("boolean")
+            elif isinstance(value, float):
+                inferred_types.add("double")
+            elif isinstance(value, int):
+                inferred_types.add("long")
+            elif isinstance(value, str):
+                inferred_types.add("keyword")
+
+        if not inferred_types:
+            return "keyword"
+
+        if len(inferred_types) > 1:
+            logger.warning("Field has mixed metadata types %s. Defaulting to 'keyword'.", inferred_types)
+            return "keyword"
+
+        return next(iter(inferred_types))
+
     def filter_documents(
         self,
         filters: dict[str, Any] | None = None,
@@ -357,7 +424,14 @@ def delete_by_filter(self, filters: dict[str, Any]) -> int:
         :returns: The number of documents deleted.
         """
         self._ensure_initialized()
-        where = _convert_filters(filters)
+        try:
+            where = _convert_filters(filters)
+        except ValueError as e:
+            raise FilterError(str(e)) from e
+
+        if not where:
+            msg = "delete_by_filter requires a non-empty filter. Use delete_all_documents() to delete all documents."
+            raise FilterError(msg)
 
         count_result = self._command(f"DELETE FROM `{self._type_name}` WHERE {where}")
 
@@ -373,14 +447,143 @@ def update_by_filter(self, filters: dict[str, Any], meta: dict[str, Any]) -> int
         :returns: The number of documents updated.
         """
         self._ensure_initialized()
-        where = _convert_filters(filters)
+        try:
+            where = _convert_filters(filters)
+        except ValueError as e:
+            raise FilterError(str(e)) from e
+
+        if not where:
+            msg = "update_by_filter requires a non-empty filter."
+            raise FilterError(msg)
 
         sql_set = ",".join(f"meta[{_sql_str(key)}] = {_map_literal_base(value)}" for key, value in meta.items())
         sql = f"UPDATE `{self._type_name}` SET {sql_set} WHERE {where}"
         count_result = self._command(sql)
 
         return count_result[0]["count"]
 
+    def count_documents_by_filter(self, filters: dict[str, Any]) -> int:
+        """
+        Counts the number of documents matching the provided filter
+        :param filters: The filters to apply to the documents
+        :returns: The number of documents that match the filter
+        """
+        self._ensure_initialized()
+        try:
+            where = _convert_filters(filters)
+        except ValueError as e:
+            raise FilterError(str(e)) from e
+
+        sql = f"SELECT count(*) AS cnt FROM `{self._type_name}`"
+        if where:
+            sql += f" WHERE {where}"
+
+        rows = self._command(sql)
+        if rows:
+            return int(rows[0].get("cnt", 0))
+        return 0
+
+    def count_unique_metadata_by_filter(self, filters: dict[str, Any], metadata_fields: list[str]) -> dict[str, int]:
+        """
+        Counts unique values for each metadata field in documents matching the provided filters.
+        :param filters: The filters to apply to the document list.
+        :param metadata_fields: Metadata fields for which to count unique values.
+        :returns: A dictionary where keys are metadata field names and values are the
+            counts of unique values for that field.
+        """
+        self._ensure_initialized()
+        try:
+            where = _convert_filters(filters)
+        except ValueError as e:
+            raise FilterError(str(e)) from e
+
+        if not metadata_fields:
+            return {}
+
+        counts = {}
+        for field in metadata_fields:  # Arcade doesn't support COUNT(DISTINCT..)
+            field_name = field.removeprefix("meta.")
+            sql = f"SELECT DISTINCT meta[{_sql_str(field_name)}] AS val FROM `{self._type_name}`"
+            if where:
+                sql += f" WHERE {where}"
+            rows = self._command(sql)
+            counts[field_name] = len(self._extract_distinct_values(rows))
+
+        return counts
+
+    def get_metadata_fields_info(self) -> dict[str, dict[str, str]]:
+        """
+        Returns the metadata fields and their corresponding types based on sampled documents.
+        :returns: A dictionary mapping field names to dictionaries with a `type` key.
+        """
+        self._ensure_initialized()
+        documents = self._get_metadata_projection_documents()
+
+        if not documents:
+            return {}
+
+        fields_info: dict[str, dict[str, str]] = {}
+
+        if any(document.get("content") is not None for document in documents):
+            fields_info["content"] = {"type": "text"}
+
+        field_values: dict[str, list[Any]] = {}
+        for document in documents:
+            for field, value in document.get("meta", {}).items():
+                field_values.setdefault(field, []).append(value)
+
+        for field, values in field_values.items():
+            fields_info[field] = {"type": self._infer_metadata_field_type(values)}
+
+        return fields_info
+
+    def get_metadata_field_min_max(self, metadata_field: str) -> dict[str, Any]:
+        """
+        For a given metadata field, finds its min and max values.
+        :param metadata_field: The metadata field to inspect.
+        :returns: A dictionary with `min` and `max` keys and their corresponding values.
+        """
+        self._ensure_initialized()
+
+        field_name = metadata_field.removeprefix("meta.")
+        field_ref = f"meta[{_sql_str(field_name)}]"
+        sql = f"SELECT MIN({field_ref}) AS min_value, MAX({field_ref}) AS max_value FROM `{self._type_name}`"
+        rows = self._command(sql)
+
+        if not rows:
+            return {"min": None, "max": None}
+
+        return {"min": rows[0].get("min_value"), "max": rows[0].get("max_value")}
+
+    def get_metadata_field_unique_values(
+        self, metadata_field: str, search_term: str | None = None, from_: int = 0, size: int = 10
+    ) -> tuple[list[str], int]:
+        """
+        Retrieves unique values for a field matching a search term or all possible values
+        if no search term is given.
+        :param metadata_field: The metadata field to inspect.
+        :param search_term: Optional case-insensitive substring search term.
+        :param from_: The starting index for pagination.
+        :param size: The number of values to return.
+        :returns: A tuple containing the paginated values and the total count.
+        """
+        self._ensure_initialized()
+
+        metadata_field = metadata_field.removeprefix("meta.")
+        field_ref = f"meta[{_sql_str(metadata_field)}]"
+        where = ""
+
+        if search_term:
+            search_val = _sql_str(f"%{search_term}%")
+            where = f" WHERE {field_ref} ILIKE {search_val}"
+
+        sql = f"SELECT DISTINCT {field_ref} AS val FROM `{self._type_name}`{where}"
+        rows = self._command(sql)
+
+        all_values = sorted(self._extract_distinct_values(rows))
+        total_count = len(all_values)
+        return all_values[from_ : from_ + size], total_count
+
     # ------------------------------------------------------------------
     # Retrieval (called by Retriever components)
     # ------------------------------------------------------------------
@@ -410,7 +613,10 @@ def _embedding_retrieval(
             return []
 
         neighbors = rows[0]["neighbors"]
-        where = _convert_filters(filters)
+        try:
+            where = _convert_filters(filters)
+        except ValueError as e:
+            raise FilterError(str(e)) from e
 
         documents = []
         for neighbor in neighbors:

integrations/arcadedb/tests/test_document_store.py

@@ -9,7 +9,15 @@
 from haystack import Document
 from haystack.document_stores.errors import DuplicateDocumentError
 from haystack.document_stores.types import DuplicatePolicy
-from haystack.testing.document_store import DocumentStoreBaseExtendedTests
+from haystack.testing.document_store import (
+    CountDocumentsByFilterTest,
+    CountUniqueMetadataByFilterTest,
+    DocumentStoreBaseExtendedTests,
+    FilterableDocsFixtureMixin,
+    GetMetadataFieldMinMaxTest,
+    GetMetadataFieldsInfoTest,
+    GetMetadataFieldUniqueValuesTest,
+)
 
 from haystack_integrations.document_stores.arcadedb import ArcadeDBDocumentStore
 
@@ -48,7 +56,15 @@ def test_to_dict_from_dict(self):
     reason="Set ARCADEDB_PASSWORD (e.g. via repo secret in CI) to run integration tests.",
 )
 @pytest.mark.integration
-class TestArcadeDBDocumentStore(DocumentStoreBaseExtendedTests):
+class TestArcadeDBDocumentStore(
+    CountDocumentsByFilterTest,
+    CountUniqueMetadataByFilterTest,
+    DocumentStoreBaseExtendedTests,
+    FilterableDocsFixtureMixin,
+    GetMetadataFieldMinMaxTest,
+    GetMetadataFieldsInfoTest,
+    GetMetadataFieldUniqueValuesTest,
+):
     """
     Run Haystack DocumentStore mixin tests against ArcadeDBDocumentStore.
 
@@ -73,15 +89,16 @@ def assert_documents_are_equal(self, received: list[Document], expected: list[Do
         received = sorted(received, key=lambda x: x.id)
         expected = sorted(expected, key=lambda x: x.id)
         for received_doc, expected_doc in zip(received, expected, strict=True):
-            received_doc.score = None
+            actual = dataclasses.replace(received_doc, score=None)
             if expected_doc.embedding is None:
-                received_doc.embedding = None
-            elif received_doc.embedding is None:
+                actual = dataclasses.replace(actual, embedding=None)
+            elif actual.embedding is None:
                 assert expected_doc.embedding is None
             else:
-                assert received_doc.embedding == pytest.approx(expected_doc.embedding)
-            received_doc.embedding, expected_doc.embedding = None, None
-            assert received_doc == expected_doc
+                assert actual.embedding == pytest.approx(expected_doc.embedding)
+            actual = dataclasses.replace(actual, embedding=None)
+            expected_clean = dataclasses.replace(expected_doc, embedding=None)
+            assert actual == expected_clean
 
     def test_write_documents(self, document_store: ArcadeDBDocumentStore):
         """Override mixin: test default write_documents and duplicate fail behaviour."""
@@ -122,3 +139,74 @@ def test_embedding_retrieval(self, document_store: ArcadeDBDocumentStore):
         )
         assert len(results) <= 3
         assert results[0].score is not None
+
+    def test_count_documents_by_empty_filter(self, document_store: ArcadeDBDocumentStore):
+        """Counts all documents when an empty filter is provided."""
+        docs = [
+            Document(id="1", content="Doc 1", meta={"category": "news"}),
+        ]
+        document_store.write_documents(docs)
+
+        count = document_store.count_documents_by_filter({})
+
+        assert count == 1
+
+    def test_count_unique_metadata_by_filter_empty_fields(self, document_store: ArcadeDBDocumentStore):
+        """Returns an empty dict when no metadata fields are requested."""
+        docs = [
+            Document(id="1", content="Doc 1", meta={"category": "news"}),
+        ]
+        document_store.write_documents(docs)
+
+        counts = document_store.count_unique_metadata_by_filter(
+            {"field": "meta.status", "operator": "==", "value": "news"},
+            [],
+        )
+
+        assert counts == {}
+
+    def test_get_metadata_field_min_max_nonexistent_field(self, document_store: ArcadeDBDocumentStore):
+        """Returns None for both min and max when the field does not exist."""
+        docs = [Document(id="1", content="Doc 1", meta={"category": "news"})]
+        document_store.write_documents(docs)
+
+        result = document_store.get_metadata_field_min_max("nonexistent")
+
+        assert result == {"min": None, "max": None}
+
+    def test_get_metadata_field_unique_values_pagination(self, document_store: ArcadeDBDocumentStore):
+        """Respects size limit while total reflects the full unpaginated count."""
+        docs = [
+            Document(id="1", content="Doc 1", meta={"category": "alpha"}),
+            Document(id="2", content="Doc 2", meta={"category": "beta"}),
+            Document(id="3", content="Doc 3", meta={"category": "gamma"}),
+        ]
+        document_store.write_documents(docs)
+
+        values, total = document_store.get_metadata_field_unique_values("category", from_=0, size=2)
+
+        assert len(values) == 2
+        assert total == 3
+
+    def test_get_metadata_field_unique_values_case_insensitive(self, document_store: ArcadeDBDocumentStore):
+        """Matches values case-insensitively when a search term is provided."""
+        docs = [
+            Document(id="1", content="Doc 1", meta={"category": "Books"}),
+            Document(id="2", content="Doc 2", meta={"category": "books"}),
+            Document(id="3", content="Doc 3", meta={"category": "ELECTRONICS"}),
+        ]
+        document_store.write_documents(docs)
+
+        _, total = document_store.get_metadata_field_unique_values("category", search_term="book")
+
+        assert total == 2
+
+    def test_get_metadata_field_unique_values_no_matches(self, document_store: ArcadeDBDocumentStore):
+        """Returns empty results when no metadata values match the search term."""
+        docs = [Document(id="1", content="Doc 1", meta={"category": "news"})]
+        document_store.write_documents(docs)
+
+        values, total = document_store.get_metadata_field_unique_values("category", search_term="sports")
+
+        assert values == []
+        assert total == 0