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Kuzu

KuzuGraphStore #

Bases: GraphStore

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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class KuzuGraphStore(GraphStore):
    def __init__(
        self,
        database: Any,
        node_table_name: str = "entity",
        rel_table_name: str = "links",
        **kwargs: Any,
    ) -> None:
        self.database = database
        self.connection = kuzu.Connection(database)
        self.node_table_name = node_table_name
        self.rel_table_name = rel_table_name
        self.init_schema()

    def init_schema(self) -> None:
        """Initialize schema if the tables do not exist."""
        node_tables = self.connection._get_node_table_names()
        if self.node_table_name not in node_tables:
            self.connection.execute(
                "CREATE NODE TABLE %s (ID STRING, PRIMARY KEY(ID))"
                % self.node_table_name
            )
        rel_tables = self.connection._get_rel_table_names()
        rel_tables = [rel_table["name"] for rel_table in rel_tables]
        if self.rel_table_name not in rel_tables:
            self.connection.execute(
                "CREATE REL TABLE {} (FROM {} TO {}, predicate STRING)".format(
                    self.rel_table_name, self.node_table_name, self.node_table_name
                )
            )

    @property
    def client(self) -> Any:
        return self.connection

    def get(self, subj: str) -> List[List[str]]:
        """Get triplets."""
        query = """
            MATCH (n1:%s)-[r:%s]->(n2:%s)
            WHERE n1.ID = $subj
            RETURN r.predicate, n2.ID;
        """
        prepared_statement = self.connection.prepare(
            query % (self.node_table_name, self.rel_table_name, self.node_table_name)
        )
        query_result = self.connection.execute(prepared_statement, {"subj": subj})
        retval = []
        while query_result.has_next():
            row = query_result.get_next()
            retval.append([row[0], row[1]])
        return retval

    def get_rel_map(
        self, subjs: Optional[List[str]] = None, depth: int = 2, limit: int = 30
    ) -> Dict[str, List[List[str]]]:
        """Get depth-aware rel map."""
        rel_wildcard = "r:%s*1..%d" % (self.rel_table_name, depth)
        match_clause = "MATCH (n1:{})-[{}]->(n2:{})".format(
            self.node_table_name,
            rel_wildcard,
            self.node_table_name,
        )
        return_clause = "RETURN n1, r, n2 LIMIT %d" % limit
        params = []
        if subjs is not None:
            for i, curr_subj in enumerate(subjs):
                if i == 0:
                    where_clause = "WHERE n1.ID = $%d" % i
                else:
                    where_clause += " OR n1.ID = $%d" % i
                params.append((str(i), curr_subj))
        else:
            where_clause = ""
        query = f"{match_clause} {where_clause} {return_clause}"
        prepared_statement = self.connection.prepare(query)
        if subjs is not None:
            query_result = self.connection.execute(prepared_statement, dict(params))
        else:
            query_result = self.connection.execute(prepared_statement)
        retval: Dict[str, List[List[str]]] = {}
        while query_result.has_next():
            row = query_result.get_next()
            curr_path = []
            subj = row[0]
            recursive_rel = row[1]
            obj = row[2]
            nodes_map = {}
            nodes_map[(subj["_id"]["table"], subj["_id"]["offset"])] = subj["ID"]
            nodes_map[(obj["_id"]["table"], obj["_id"]["offset"])] = obj["ID"]
            for node in recursive_rel["_nodes"]:
                nodes_map[(node["_id"]["table"], node["_id"]["offset"])] = node["ID"]
            for rel in recursive_rel["_rels"]:
                predicate = rel["predicate"]
                curr_subj_id = nodes_map[(rel["_src"]["table"], rel["_src"]["offset"])]
                curr_path.append(curr_subj_id)
                curr_path.append(predicate)
            # Add the last node
            curr_path.append(obj["ID"])
            if subj["ID"] not in retval:
                retval[subj["ID"]] = []
            retval[subj["ID"]].append(curr_path)
        return retval

    def upsert_triplet(self, subj: str, rel: str, obj: str) -> None:
        """Add triplet."""

        def check_entity_exists(connection: Any, entity: str) -> bool:
            is_exists_result = connection.execute(
                "MATCH (n:%s) WHERE n.ID = $entity RETURN n.ID" % self.node_table_name,
                {"entity": entity},
            )
            return is_exists_result.has_next()

        def create_entity(connection: Any, entity: str) -> None:
            connection.execute(
                "CREATE (n:%s {ID: $entity})" % self.node_table_name,
                {"entity": entity},
            )

        def check_rel_exists(connection: Any, subj: str, obj: str, rel: str) -> bool:
            is_exists_result = connection.execute(
                (
                    "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID = "
                    "$obj AND r.predicate = $pred RETURN r.predicate"
                ).format(
                    self.node_table_name, self.rel_table_name, self.node_table_name
                ),
                {"subj": subj, "obj": obj, "pred": rel},
            )
            return is_exists_result.has_next()

        def create_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
            connection.execute(
                (
                    "MATCH (n1:{}), (n2:{}) WHERE n1.ID = $subj AND n2.ID = $obj "
                    "CREATE (n1)-[r:{} {{predicate: $pred}}]->(n2)"
                ).format(
                    self.node_table_name, self.node_table_name, self.rel_table_name
                ),
                {"subj": subj, "obj": obj, "pred": rel},
            )

        is_subj_exists = check_entity_exists(self.connection, subj)
        is_obj_exists = check_entity_exists(self.connection, obj)

        if not is_subj_exists:
            create_entity(self.connection, subj)
        if not is_obj_exists:
            create_entity(self.connection, obj)

        if is_subj_exists and is_obj_exists:
            is_rel_exists = check_rel_exists(self.connection, subj, obj, rel)
            if is_rel_exists:
                return

        create_rel(self.connection, subj, obj, rel)

    def delete(self, subj: str, rel: str, obj: str) -> None:
        """Delete triplet."""

        def delete_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
            connection.execute(
                (
                    "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID"
                    " = $obj AND r.predicate = $pred DELETE r"
                ).format(
                    self.node_table_name, self.rel_table_name, self.node_table_name
                ),
                {"subj": subj, "obj": obj, "pred": rel},
            )

        def delete_entity(connection: Any, entity: str) -> None:
            connection.execute(
                "MATCH (n:%s) WHERE n.ID = $entity DELETE n" % self.node_table_name,
                {"entity": entity},
            )

        def check_edges(connection: Any, entity: str) -> bool:
            is_exists_result = connection.execute(
                "MATCH (n1:{})-[r:{}]-(n2:{}) WHERE n2.ID = $entity RETURN r.predicate".format(
                    self.node_table_name, self.rel_table_name, self.node_table_name
                ),
                {"entity": entity},
            )
            return is_exists_result.has_next()

        delete_rel(self.connection, subj, obj, rel)
        if not check_edges(self.connection, subj):
            delete_entity(self.connection, subj)
        if not check_edges(self.connection, obj):
            delete_entity(self.connection, obj)

    @classmethod
    def from_persist_dir(
        cls,
        persist_dir: str,
        node_table_name: str = "entity",
        rel_table_name: str = "links",
    ) -> "KuzuGraphStore":
        """Load from persist dir."""
        database = kuzu.Database(persist_dir)
        return cls(database, node_table_name, rel_table_name)

    @classmethod
    def from_dict(cls, config_dict: Dict[str, Any]) -> "KuzuGraphStore":
        """
        Initialize graph store from configuration dictionary.

        Args:
            config_dict: Configuration dictionary.

        Returns:
            Graph store.

        """
        return cls(**config_dict)

init_schema #

init_schema() -> None

Initialize schema if the tables do not exist.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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def init_schema(self) -> None:
    """Initialize schema if the tables do not exist."""
    node_tables = self.connection._get_node_table_names()
    if self.node_table_name not in node_tables:
        self.connection.execute(
            "CREATE NODE TABLE %s (ID STRING, PRIMARY KEY(ID))"
            % self.node_table_name
        )
    rel_tables = self.connection._get_rel_table_names()
    rel_tables = [rel_table["name"] for rel_table in rel_tables]
    if self.rel_table_name not in rel_tables:
        self.connection.execute(
            "CREATE REL TABLE {} (FROM {} TO {}, predicate STRING)".format(
                self.rel_table_name, self.node_table_name, self.node_table_name
            )
        )

get #

get(subj: str) -> List[List[str]]

Get triplets.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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def get(self, subj: str) -> List[List[str]]:
    """Get triplets."""
    query = """
        MATCH (n1:%s)-[r:%s]->(n2:%s)
        WHERE n1.ID = $subj
        RETURN r.predicate, n2.ID;
    """
    prepared_statement = self.connection.prepare(
        query % (self.node_table_name, self.rel_table_name, self.node_table_name)
    )
    query_result = self.connection.execute(prepared_statement, {"subj": subj})
    retval = []
    while query_result.has_next():
        row = query_result.get_next()
        retval.append([row[0], row[1]])
    return retval

get_rel_map #

get_rel_map(subjs: Optional[List[str]] = None, depth: int = 2, limit: int = 30) -> Dict[str, List[List[str]]]

Get depth-aware rel map.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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def get_rel_map(
    self, subjs: Optional[List[str]] = None, depth: int = 2, limit: int = 30
) -> Dict[str, List[List[str]]]:
    """Get depth-aware rel map."""
    rel_wildcard = "r:%s*1..%d" % (self.rel_table_name, depth)
    match_clause = "MATCH (n1:{})-[{}]->(n2:{})".format(
        self.node_table_name,
        rel_wildcard,
        self.node_table_name,
    )
    return_clause = "RETURN n1, r, n2 LIMIT %d" % limit
    params = []
    if subjs is not None:
        for i, curr_subj in enumerate(subjs):
            if i == 0:
                where_clause = "WHERE n1.ID = $%d" % i
            else:
                where_clause += " OR n1.ID = $%d" % i
            params.append((str(i), curr_subj))
    else:
        where_clause = ""
    query = f"{match_clause} {where_clause} {return_clause}"
    prepared_statement = self.connection.prepare(query)
    if subjs is not None:
        query_result = self.connection.execute(prepared_statement, dict(params))
    else:
        query_result = self.connection.execute(prepared_statement)
    retval: Dict[str, List[List[str]]] = {}
    while query_result.has_next():
        row = query_result.get_next()
        curr_path = []
        subj = row[0]
        recursive_rel = row[1]
        obj = row[2]
        nodes_map = {}
        nodes_map[(subj["_id"]["table"], subj["_id"]["offset"])] = subj["ID"]
        nodes_map[(obj["_id"]["table"], obj["_id"]["offset"])] = obj["ID"]
        for node in recursive_rel["_nodes"]:
            nodes_map[(node["_id"]["table"], node["_id"]["offset"])] = node["ID"]
        for rel in recursive_rel["_rels"]:
            predicate = rel["predicate"]
            curr_subj_id = nodes_map[(rel["_src"]["table"], rel["_src"]["offset"])]
            curr_path.append(curr_subj_id)
            curr_path.append(predicate)
        # Add the last node
        curr_path.append(obj["ID"])
        if subj["ID"] not in retval:
            retval[subj["ID"]] = []
        retval[subj["ID"]].append(curr_path)
    return retval

upsert_triplet #

upsert_triplet(subj: str, rel: str, obj: str) -> None

Add triplet.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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def upsert_triplet(self, subj: str, rel: str, obj: str) -> None:
    """Add triplet."""

    def check_entity_exists(connection: Any, entity: str) -> bool:
        is_exists_result = connection.execute(
            "MATCH (n:%s) WHERE n.ID = $entity RETURN n.ID" % self.node_table_name,
            {"entity": entity},
        )
        return is_exists_result.has_next()

    def create_entity(connection: Any, entity: str) -> None:
        connection.execute(
            "CREATE (n:%s {ID: $entity})" % self.node_table_name,
            {"entity": entity},
        )

    def check_rel_exists(connection: Any, subj: str, obj: str, rel: str) -> bool:
        is_exists_result = connection.execute(
            (
                "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID = "
                "$obj AND r.predicate = $pred RETURN r.predicate"
            ).format(
                self.node_table_name, self.rel_table_name, self.node_table_name
            ),
            {"subj": subj, "obj": obj, "pred": rel},
        )
        return is_exists_result.has_next()

    def create_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
        connection.execute(
            (
                "MATCH (n1:{}), (n2:{}) WHERE n1.ID = $subj AND n2.ID = $obj "
                "CREATE (n1)-[r:{} {{predicate: $pred}}]->(n2)"
            ).format(
                self.node_table_name, self.node_table_name, self.rel_table_name
            ),
            {"subj": subj, "obj": obj, "pred": rel},
        )

    is_subj_exists = check_entity_exists(self.connection, subj)
    is_obj_exists = check_entity_exists(self.connection, obj)

    if not is_subj_exists:
        create_entity(self.connection, subj)
    if not is_obj_exists:
        create_entity(self.connection, obj)

    if is_subj_exists and is_obj_exists:
        is_rel_exists = check_rel_exists(self.connection, subj, obj, rel)
        if is_rel_exists:
            return

    create_rel(self.connection, subj, obj, rel)

delete #

delete(subj: str, rel: str, obj: str) -> None

Delete triplet.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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def delete(self, subj: str, rel: str, obj: str) -> None:
    """Delete triplet."""

    def delete_rel(connection: Any, subj: str, obj: str, rel: str) -> None:
        connection.execute(
            (
                "MATCH (n1:{})-[r:{}]->(n2:{}) WHERE n1.ID = $subj AND n2.ID"
                " = $obj AND r.predicate = $pred DELETE r"
            ).format(
                self.node_table_name, self.rel_table_name, self.node_table_name
            ),
            {"subj": subj, "obj": obj, "pred": rel},
        )

    def delete_entity(connection: Any, entity: str) -> None:
        connection.execute(
            "MATCH (n:%s) WHERE n.ID = $entity DELETE n" % self.node_table_name,
            {"entity": entity},
        )

    def check_edges(connection: Any, entity: str) -> bool:
        is_exists_result = connection.execute(
            "MATCH (n1:{})-[r:{}]-(n2:{}) WHERE n2.ID = $entity RETURN r.predicate".format(
                self.node_table_name, self.rel_table_name, self.node_table_name
            ),
            {"entity": entity},
        )
        return is_exists_result.has_next()

    delete_rel(self.connection, subj, obj, rel)
    if not check_edges(self.connection, subj):
        delete_entity(self.connection, subj)
    if not check_edges(self.connection, obj):
        delete_entity(self.connection, obj)

from_persist_dir classmethod #

from_persist_dir(persist_dir: str, node_table_name: str = 'entity', rel_table_name: str = 'links') -> KuzuGraphStore

Load from persist dir.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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@classmethod
def from_persist_dir(
    cls,
    persist_dir: str,
    node_table_name: str = "entity",
    rel_table_name: str = "links",
) -> "KuzuGraphStore":
    """Load from persist dir."""
    database = kuzu.Database(persist_dir)
    return cls(database, node_table_name, rel_table_name)

from_dict classmethod #

from_dict(config_dict: Dict[str, Any]) -> KuzuGraphStore

Initialize graph store from configuration dictionary.

Parameters:

Name Type Description Default
config_dict Dict[str, Any]

Configuration dictionary.

 required

Returns:

Type Description
KuzuGraphStore

Graph store.
Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/base.py 
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@classmethod
def from_dict(cls, config_dict: Dict[str, Any]) -> "KuzuGraphStore":
    """
    Initialize graph store from configuration dictionary.

    Args:
        config_dict: Configuration dictionary.

    Returns:
        Graph store.

    """
    return cls(**config_dict)

KuzuPropertyGraphStore #

Bases: PropertyGraphStore

Kùzu Property Graph Store.

This class implements a Kùzu property graph store.

Kùzu can be installed and used with this simple command:

pip install kuzu
Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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class KuzuPropertyGraphStore(PropertyGraphStore):
    """
    Kùzu Property Graph Store.

    This class implements a Kùzu property graph store.

    Kùzu can be installed and used with this simple command:

    ```
    pip install kuzu
    ```
    """

    def __init__(
        self,
        db: kuzu.Database,
        relationship_schema: Optional[List[Tuple[str, str, str]]] = None,
        has_structured_schema: Optional[bool] = False,
        sanitize_query_output: Optional[bool] = True,
        use_vector_index: bool = True,
        embed_model: Optional[Any] = None,
        embed_dimension: Optional[int] = None,
    ) -> None:
        self.db = db
        self.connection = kuzu.Connection(self.db)
        self.use_vector_index = use_vector_index

        # Initialize embedding dimension with auto-detection and fallback logic
        self.embed_dimension = self._initialize_embedding_dimension(
            embed_model, embed_dimension
        )

        # Install and load vector extension if using vector indexes
        if self.use_vector_index:
            try:
                self.connection.execute("INSTALL vector; LOAD vector;")
            except RuntimeError:
                # print("Warning: Skipping installing vector extension due to RuntimeError:", e)
                # print("Check that you can install Kuzu's vector extension by running: " \
                #       "`INSTALL vector; LOAD vector;` in a Kuzu CLI session."
                # )
                pass

        if has_structured_schema:
            if relationship_schema is None:
                raise ValueError(
                    "Please provide a relationship schema if structured_schema=True."
                )
            else:
                self.validate_relationship_schema(relationship_schema)
        else:
            # Use a generic schema with node types of 'Entity' if no schema is required
            relationship_schema = [("Entity", "LINKS", "Entity")]

        self.relationship_schema = relationship_schema
        self.entities = self.get_entities()
        self.has_structured_schema = has_structured_schema
        self.entities.extend(
            ["Chunk"]
        )  # Always include Chunk as an entity type, in all schemas
        self.sanitize_query_output = sanitize_query_output
        self.structured_schema = {}
        self.init_schema()

    def init_schema(self) -> None:
        """Initialize schema if the required tables do not exist."""
        utils.create_chunk_node_table(
            self.connection, embedding_dimension=self.embed_dimension
        )
        utils.create_entity_node_tables(self.connection, entities=self.entities)
        utils.create_relation_tables(
            self.connection,
            self.entities,
            relationship_schema=self.relationship_schema,
        )

    def validate_relationship_schema(self, relationship_schema: List[Triple]) -> None:
        # Check that validation schema is a list of tuples as required by Kùzu for relationships
        if not all(isinstance(item, tuple) for item in relationship_schema):
            raise ValueError(
                "Please specify the relationship schema as "
                "a list of tuples, for example: [('PERSON', 'IS_CEO_OF', 'ORGANIZATION')]"
            )

    @property
    def client(self) -> kuzu.Connection:
        return self.connection

    def get_entities(self) -> List[str]:
        return sorted(
            set(
                [rel[0] for rel in self.relationship_schema]
                + [rel[2] for rel in self.relationship_schema]
            )
        )

    def _initialize_embedding_dimension(
        self, embed_model: Optional[Any], embedding_dimension: Optional[int]
    ) -> Optional[int]:
        """
        Initialize embedding dimension using auto-detection and fallback logic.

        Args:
            embed_model: Optional embedding model for auto-detection
            embedding_dimension: Optional manual dimension specification

        Returns:
            Detected or specified embedding dimension, or None if unavailable

        """
        if embed_model is not None:
            # Try auto-detection first
            detected_dim = self._detect_embedding_dimension(embed_model)
            if detected_dim is not None:
                print(f"Auto-detected embedding dimension: {detected_dim}")
                return detected_dim
            elif embedding_dimension is not None:
                # Fall back to manual specification if auto-detection fails
                print(
                    f"Using manually specified embedding dimension: {embedding_dimension}"
                )
                return embedding_dimension
            else:
                # Neither auto-detection nor manual specification available
                print(
                    "Warning: Could not determine embedding dimension. Vector indexing may not work properly."
                )
                return None
        else:
            # No embed_model provided, use manual specification
            if embedding_dimension is not None:
                print(
                    f"Using manually specified embedding dimension: {embedding_dimension}"
                )
            return embedding_dimension

    def _detect_embedding_dimension(self, embed_model: Any) -> Optional[int]:
        """
        Detect embedding dimension by creating a test embedding.

        Args:
            embed_model: The embedding model instance

        Returns:
            Detected dimension or None if cannot be determined

        """
        try:
            test_embedding = embed_model.get_text_embedding("hello")
            if isinstance(test_embedding, list) and len(test_embedding) > 0:
                return len(test_embedding)
        except Exception:
            print(
                "Error: Could not detect embedding dimension from model. Please specify it manually via the `embedding_dimension` parameter."
            )  # noqa: E501

        return None

    def _create_vector_index(self, table_name: str) -> None:
        """Create a vector index for the embedding column of a table."""
        if not self.use_vector_index or table_name != "Chunk":
            return

        # Check if chunk_embedding_index already exists
        existing_indexes_result = self.connection.execute(
            "CALL SHOW_INDEXES() RETURN *"
        )
        for row in existing_indexes_result:
            if len(row) > 1 and row[1] == "chunk_embedding_index":
                return

        # Check if table has any data - Kuzu requires data before creating vector index
        count_result = self.connection.execute(
            f"MATCH (n:{table_name}) RETURN COUNT(n)"
        )
        if not any(int(row[0]) > 0 for row in count_result):
            return

        # Create vector index for Chunk table
        self.connection.execute("""
        CALL CREATE_VECTOR_INDEX(
            'Chunk',
            'chunk_embedding_index',
            'embedding',
            metric := 'cosine'
        )
        """)

    def _ensure_vector_indexes(self) -> None:
        """Ensure vector indexes are created for Chunk table only."""
        if not self.use_vector_index:
            return
        # Only create index for Chunk table since these have larger blobs of text
        # This makes the workflow easier to manage as a whole
        self._create_vector_index("Chunk")

    def refresh_vector_index(self) -> None:
        """Drop and recreate the vector index for Chunk table."""
        index_name = "chunk_embedding_index"
        # Drop existing index if it exists
        try:
            self.connection.execute(f"DROP INDEX {index_name}")
            print(f"Dropped vector index: {index_name}")
        except Exception:
            # Index may not exist, which is fine
            pass

        # Recreate the index
        self._create_vector_index("Chunk")
        print(f"Created vector index: {index_name}")

    def upsert_nodes(self, nodes: List[LabelledNode]) -> None:
        entity_list: List[EntityNode] = []
        chunk_list: List[ChunkNode] = []
        node_tables = self.connection._get_node_table_names()

        for item in nodes:
            if isinstance(item, EntityNode):
                entity_list.append(item)
            elif isinstance(item, ChunkNode):
                chunk_list.append(item)

        for chunk in chunk_list:
            upsert_chunk_node_query = """
                MERGE (c:Chunk {id: $id})
                  SET c.text = $text,
                      c.label = $label,
                      c.embedding = $embedding,
                      c.ref_doc_id = $ref_doc_id,
                      c.creation_date = date($creation_date),
                      c.last_modified_date = date($last_modified_date),
                      c.file_name = $file_name,
                      c.file_path = $file_path,
                      c.file_size = $file_size,
                      c.file_type = $file_type
                """

            self.connection.execute(
                upsert_chunk_node_query,
                parameters={
                    "id": chunk.id_,
                    "text": chunk.text.strip(),
                    "label": chunk.label,
                    "embedding": chunk.embedding,
                    "ref_doc_id": chunk.properties.get("ref_doc_id"),
                    "creation_date": chunk.properties.get("creation_date"),
                    "last_modified_date": chunk.properties.get("last_modified_date"),
                    "file_name": chunk.properties.get("file_name"),
                    "file_path": chunk.properties.get("file_path"),
                    "file_size": chunk.properties.get("file_size"),
                    "file_type": chunk.properties.get("file_type"),
                },
            )

        for entity in entity_list:
            entity_label = entity.label if entity.label in node_tables else "Entity"
            upsert_entity_node_query = f"""
                MERGE (e:{entity_label} {{id: $id}})
                SET e.label = $label,
                    e.name = $name,
                    e.creation_date = date($creation_date),
                    e.last_modified_date = date($last_modified_date),
                    e.file_name = $file_name,
                    e.file_path = $file_path,
                    e.file_size = $file_size,
                    e.file_type = $file_type,
                    e.triplet_source_id = $triplet_source_id
                """

            self.connection.execute(
                upsert_entity_node_query,
                parameters={
                    "id": entity.name,
                    "label": entity.label,
                    "name": entity.name,
                    "creation_date": entity.properties.get("creation_date"),
                    "last_modified_date": entity.properties.get("last_modified_date"),
                    "file_name": entity.properties.get("file_name"),
                    "file_path": entity.properties.get("file_path"),
                    "file_size": entity.properties.get("file_size"),
                    "file_type": entity.properties.get("file_type"),
                    "triplet_source_id": entity.properties.get("triplet_source_id"),
                },
            )
        # Create vector index for Chunk table if embeddings were inserted
        if self.use_vector_index and any(
            chunk.embedding is not None for chunk in chunk_list
        ):
            self._create_vector_index("Chunk")
        # Manual checkpoint
        self.connection.execute("CHECKPOINT;")

    def upsert_relations(self, relations: List[Relation]) -> None:
        for rel in relations:
            if self.has_structured_schema:
                src, rel_tbl_name, dst = utils.lookup_relation(
                    rel.label, self.relationship_schema
                )
            else:
                src, rel_tbl_name, dst = "Entity", "LINKS", "Entity"

            # Connect entities to each other
            self.connection.execute(
                f"""
                MATCH (a:{src} {{id: $source_id}}),
                      (b:{dst} {{id: $target_id}})
                MERGE (a)-[r:{rel_tbl_name} {{label: $label}}]->(b)
                    SET r.triplet_source_id = $triplet_source_id
                """,
                parameters={
                    "source_id": rel.source_id,
                    "target_id": rel.target_id,
                    "triplet_source_id": rel.properties.get("triplet_source_id"),
                    "label": rel.label,
                },
            )
            # Connect chunks to entities
            self.connection.execute(
                f"""
                MATCH (a:{src} {{id: $source_id}}),
                        (b:{dst} {{id: $target_id}}),
                        (c:Chunk {{id: $triplet_source_id}})
                MERGE (c)-[:MENTIONS]->(a)
                MERGE (c)-[:MENTIONS]->(b)
                """,
                parameters={
                    "source_id": rel.source_id,
                    "target_id": rel.target_id,
                    "triplet_source_id": rel.properties.get("triplet_source_id"),
                },
            )
        # Manual checkpoint
        self.connection.execute("CHECKPOINT;")

    def structured_query(
        self, query: str, param_map: Optional[Dict[str, Any]] = None
    ) -> Any:
        response = self.connection.execute(query, parameters=param_map)
        column_names = response.get_column_names()
        result = []
        for row in response:
            result.append(dict(zip(column_names, row)))

        if self.sanitize_query_output:
            return value_sanitize(result)
        self.connection.execute("CHECKPOINT;")

        return result

    def vector_query(
        self, query: VectorStoreQuery, **kwargs: Any
    ) -> Tuple[List[LabelledNode], List[float]]:
        """Perform vector similarity search on Chunk nodes."""
        self._ensure_vector_indexes()

        # Use Kuzu's vector index for similarity search
        result = self.connection.execute(
            """
            CALL QUERY_VECTOR_INDEX(
                'Chunk',
                'chunk_embedding_index',
                $query_embedding,
                $top_k
            )
            RETURN node.id as id, distance
            ORDER BY distance
            """,
            parameters={
                "query_embedding": query.query_embedding,
                "top_k": query.similarity_top_k,
            },
        )

        # Get matching chunk nodes and convert distances to similarities
        node_data = []

        for row in result:
            node_id, distance = row[0], row[1]

            # Fetch the chunk node
            chunk_result = self.structured_query(
                "MATCH (n:Chunk {id: $node_id}) RETURN n.*",
                param_map={"node_id": node_id},
            )

            if chunk_result:
                record = chunk_result[0]
                properties = {
                    k: v for k, v in record.items() if k not in ["n.id", "n.text"]
                }
                node = ChunkNode(
                    id_=record["n.id"],
                    text=record.get("n.text", ""),
                    properties=utils.remove_empty_values(properties),
                )
                # Convert distance to similarity (lower distance = higher similarity)
                similarity = 1.0 - distance
                node_data.append((node, similarity))

        # Sort by similarity in descending order
        node_data.sort(key=lambda x: x[1], reverse=True)

        # Separate nodes and similarities
        nodes = [item[0] for item in node_data]
        similarities = [item[1] for item in node_data]

        # Manual checkpoint
        self.connection.execute("CHECKPOINT;")

        return nodes, similarities

    def get(
        self,
        properties: Optional[dict] = None,
        ids: Optional[List[str]] = None,
    ) -> List[LabelledNode]:
        """Get nodes from the property graph store."""
        cypher_statement = "MATCH (e) "

        parameters = {}
        if ids:
            cypher_statement += "WHERE e.id in $ids "
            parameters["ids"] = ids

        return_statement = "RETURN e.*"
        cypher_statement += return_statement
        result = self.structured_query(cypher_statement, param_map=parameters)
        result = result if result else []

        nodes = []
        for record in result:
            # Text indicates a chunk node
            # None on the label indicates an implicit node, likely a chunk node
            if record.get("e.label") == "text_chunk":
                properties = {
                    k: v for k, v in record.items() if k not in ["e.id", "e.text"]
                }
                text = record.get("e.text")
                nodes.append(
                    ChunkNode(
                        id_=record["e.id"],
                        text=text,
                        properties=utils.remove_empty_values(properties),
                    )
                )
            else:
                properties = {
                    k: v for k, v in record.items() if k not in ["e.id", "e.name"]
                }
                name = record["e.name"] if record.get("e.name") else record["e.id"]
                label = record["e.label"] if record.get("e.label") else "Chunk"
                nodes.append(
                    EntityNode(
                        name=name,
                        label=label,
                        properties=utils.remove_empty_values(properties),
                    )
                )
        return nodes

    def get_triplets(
        self,
        entity_names: Optional[List[str]] = None,
        relation_names: Optional[List[str]] = None,
        ids: Optional[List[str]] = None,
    ) -> List[Triplet]:
        # Construct the Cypher query
        cypher_statement = "MATCH (e)-[r]->(t) "

        params = {}
        if entity_names or relation_names or ids:
            cypher_statement += "WHERE "

        if entity_names:
            cypher_statement += "e.name in $entity_names "
            params["entity_names"] = entity_names

        if relation_names and entity_names:
            cypher_statement += f"AND "
        if relation_names:
            cypher_statement += "r.label in $relation_names "
            params[f"relation_names"] = relation_names

        if ids:
            cypher_statement += "e.id in $ids "
            params["ids"] = ids

        # Avoid returning a massive list of triplets that represent a large portion of the graph
        # This uses the LIMIT constant defined at the top of the file
        if not (entity_names or relation_names or ids):
            return_statement = f"WHERE e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};"
        else:
            return_statement = f"AND e.label <> 'text_chunk' RETURN * LIMIT {LIMIT};"

        cypher_statement += return_statement

        result = self.structured_query(cypher_statement, param_map=params)
        result = result if result else []

        triples = []
        for record in result:
            if record["e"]["_label"] == "Chunk":
                continue

            src_table = record["e"]["_id"]["table"]
            dst_table = record["t"]["_id"]["table"]
            id_map = {src_table: record["e"]["id"], dst_table: record["t"]["id"]}
            source = EntityNode(
                name=record["e"]["id"],
                label=record["e"]["_label"],
                properties=utils.get_filtered_props(record["e"], ["_id", "_label"]),
            )
            target = EntityNode(
                name=record["t"]["id"],
                label=record["t"]["_label"],
                properties=utils.get_filtered_props(record["t"], ["_id", "_label"]),
            )
            rel = Relation(
                source_id=id_map.get(record["r"]["_src"]["table"], "unknown"),
                target_id=id_map.get(record["r"]["_dst"]["table"], "unknown"),
                label=record["r"]["label"],
            )
            triples.append([source, rel, target])
        return triples

    def get_rel_map(
        self,
        graph_nodes: List[LabelledNode],
        depth: int = 2,
        limit: int = 30,
        ignore_rels: Optional[List[str]] = None,
    ) -> List[Triplet]:
        triples = []

        ids = [node.id for node in graph_nodes]
        if len(ids) > 0:
            # Run recursive query
            response = self.structured_query(
                f"""
                MATCH (e)
                WHERE e.id IN $ids
                MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other)
                RETURN *
                LIMIT {limit};
                """,
                param_map={"ids": ids},
            )
        else:
            response = self.structured_query(
                f"""
                MATCH (e)
                MATCH (e)-[rel*1..{depth} (r, n | WHERE r.label <> "MENTIONS") ]->(other)
                RETURN *
                LIMIT {limit};
                """
            )

        ignore_rels = ignore_rels or []
        for record in response:
            for item in record["rel"]["_rels"]:
                if item["label"] in ignore_rels:
                    continue

                src_table = item["_src"]["table"]
                dst_table = item["_src"]["table"]
                id_map = {
                    src_table: record["e"]["_id"],
                    dst_table: record["other"]["id"],
                }
                source = EntityNode(
                    name=record["e"]["name"],
                    label=record["e"]["_label"],
                    properties=utils.get_filtered_props(
                        record["e"], ["_id", "name", "_label"]
                    ),
                )
                target = EntityNode(
                    name=record["other"]["name"],
                    label=record["other"]["_label"],
                    properties=utils.get_filtered_props(
                        record["e"], ["_id", "name", "_label"]
                    ),
                )
                rel = Relation(
                    source_id=id_map.get(item["_src"]["table"], "unknown"),
                    target_id=id_map.get(item["_dst"]["table"], "unknown"),
                    label=item["label"],
                )
                triples.append([source, rel, target])

        return triples

    def delete(
        self,
        entity_names: Optional[List[str]] = None,
        relation_names: Optional[List[str]] = None,
        properties: Optional[dict] = None,
        ids: Optional[List[str]] = None,
    ) -> None:
        """Delete nodes and relationships from the property graph store."""
        if entity_names:
            self.structured_query(
                "MATCH (n) WHERE n.name IN $entity_names DETACH DELETE n",
                param_map={"entity_names": entity_names},
            )

        if ids:
            self.structured_query(
                "MATCH (n) WHERE n.id IN $ids DETACH DELETE n",
                param_map={"ids": ids},
            )

        if relation_names:
            for rel in relation_names:
                src, _, dst = utils.lookup_relation(rel, self.relationship_schema)
                self.structured_query(
                    f"""
                    MATCH (:{src})-[r {{label: $label}}]->(:{dst})
                    DELETE r
                    """,
                    param_map={"label": rel},
                )

        if properties:
            assert isinstance(properties, dict), (
                "`properties` should be a key-value mapping."
            )
            cypher = "MATCH (e) WHERE "
            prop_list = []
            params = {}
            for i, prop in enumerate(properties):
                prop_list.append(f"e.`{prop}` = $property_{i}")
                params[f"property_{i}"] = properties[prop]
            cypher += " AND ".join(prop_list)
            self.structured_query(cypher + " DETACH DELETE e", param_map=params)

    def get_schema(self) -> Any:
        """
        Returns a structured schema of the property graph store.

        The schema contains `node_props`, `rel_props`, and `relationships` keys and
        the associated metadata.
        Example output:
        {
            'node_props': {'Chunk': [{'property': 'id', 'type': 'STRING'},
                                    {'property': 'text', 'type': 'STRING'},
                                    {'property': 'label', 'type': 'STRING'},
                                    {'property': 'embedding', 'type': 'DOUBLE'},
                                    {'property': 'properties', 'type': 'STRING'},
                                    {'property': 'ref_doc_id', 'type': 'STRING'}],
                            'Entity': [{'property': 'id', 'type': 'STRING'},
                                    {'property': 'name', 'type': 'STRING'},
                                    {'property': 'label', 'type': 'STRING'},
                                    {'property': 'embedding', 'type': 'DOUBLE'},
                                    {'property': 'properties', 'type': 'STRING'}]},
            'rel_props': {'SOURCE': [{'property': 'label', 'type': 'STRING'}]},
            'relationships': [{'end': 'Chunk', 'start': 'Chunk', 'type': 'SOURCE'}]
        }
        """
        current_table_schema = {"node_props": {}, "rel_props": {}, "relationships": []}
        node_tables = self.connection._get_node_table_names()
        for table_name in node_tables:
            node_props = self.connection._get_node_property_names(table_name)
            current_table_schema["node_props"][table_name] = []
            for prop, attr in node_props.items():
                schema = {}
                schema["property"] = prop
                schema["type"] = attr["type"]
                current_table_schema["node_props"][table_name].append(schema)

        rel_tables = self.connection._get_rel_table_names()
        for i, table in enumerate(rel_tables):
            table_name = table["name"]
            prop_values = self.connection.execute(
                f"MATCH ()-[r:{table_name}]->() RETURN distinct r.label AS label;"
            )
            for row in prop_values:
                rel_label = row[0]
                src, dst = rel_tables[i]["src"], rel_tables[i]["dst"]
                current_table_schema["relationships"].append(
                    {"start": src, "type": rel_label, "end": dst}
                )
                current_table_schema["rel_props"][rel_label] = []
                table_details = self.connection.execute(
                    f"CALL TABLE_INFO('{table_name}') RETURN *;"
                )
                for props in table_details:
                    rel_props = {}
                    rel_props["property"] = props[1]
                    rel_props["type"] = props[2]
                    current_table_schema["rel_props"][rel_label].append(rel_props)

        self.structured_schema = current_table_schema

        return self.structured_schema

    def get_schema_str(self) -> str:
        schema = self.get_schema()

        formatted_node_props = []
        formatted_rel_props = []

        # Format node properties
        for label, props in schema["node_props"].items():
            props_str = ", ".join(
                [f"{prop['property']}: {prop['type']}" for prop in props]
            )
            formatted_node_props.append(f"{label} {{{props_str}}}")

        # Format relationship properties
        for type, props in schema["rel_props"].items():
            props_str = ", ".join(
                [f"{prop['property']}: {prop['type']}" for prop in props]
            )
            formatted_rel_props.append(f"{type} {{{props_str}}}")

        # Format relationships
        formatted_rels = [
            f"(:{rel['start']})-[:{rel['type']}]->(:{rel['end']})"
            for rel in schema["relationships"]
        ]

        return "\n".join(
            [
                "Node properties:",
                "\n".join(formatted_node_props),
                "Relationship properties:",
                "\n".join(formatted_rel_props),
                "The relationships:",
                "\n".join(formatted_rels),
            ]
        )

init_schema #

init_schema() -> None

Initialize schema if the required tables do not exist.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def init_schema(self) -> None:
    """Initialize schema if the required tables do not exist."""
    utils.create_chunk_node_table(
        self.connection, embedding_dimension=self.embed_dimension
    )
    utils.create_entity_node_tables(self.connection, entities=self.entities)
    utils.create_relation_tables(
        self.connection,
        self.entities,
        relationship_schema=self.relationship_schema,
    )

refresh_vector_index #

refresh_vector_index() -> None

Drop and recreate the vector index for Chunk table.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def refresh_vector_index(self) -> None:
    """Drop and recreate the vector index for Chunk table."""
    index_name = "chunk_embedding_index"
    # Drop existing index if it exists
    try:
        self.connection.execute(f"DROP INDEX {index_name}")
        print(f"Dropped vector index: {index_name}")
    except Exception:
        # Index may not exist, which is fine
        pass

    # Recreate the index
    self._create_vector_index("Chunk")
    print(f"Created vector index: {index_name}")

vector_query #

vector_query(query: VectorStoreQuery, **kwargs: Any) -> Tuple[List[LabelledNode], List[float]]

Perform vector similarity search on Chunk nodes.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def vector_query(
    self, query: VectorStoreQuery, **kwargs: Any
) -> Tuple[List[LabelledNode], List[float]]:
    """Perform vector similarity search on Chunk nodes."""
    self._ensure_vector_indexes()

    # Use Kuzu's vector index for similarity search
    result = self.connection.execute(
        """
        CALL QUERY_VECTOR_INDEX(
            'Chunk',
            'chunk_embedding_index',
            $query_embedding,
            $top_k
        )
        RETURN node.id as id, distance
        ORDER BY distance
        """,
        parameters={
            "query_embedding": query.query_embedding,
            "top_k": query.similarity_top_k,
        },
    )

    # Get matching chunk nodes and convert distances to similarities
    node_data = []

    for row in result:
        node_id, distance = row[0], row[1]

        # Fetch the chunk node
        chunk_result = self.structured_query(
            "MATCH (n:Chunk {id: $node_id}) RETURN n.*",
            param_map={"node_id": node_id},
        )

        if chunk_result:
            record = chunk_result[0]
            properties = {
                k: v for k, v in record.items() if k not in ["n.id", "n.text"]
            }
            node = ChunkNode(
                id_=record["n.id"],
                text=record.get("n.text", ""),
                properties=utils.remove_empty_values(properties),
            )
            # Convert distance to similarity (lower distance = higher similarity)
            similarity = 1.0 - distance
            node_data.append((node, similarity))

    # Sort by similarity in descending order
    node_data.sort(key=lambda x: x[1], reverse=True)

    # Separate nodes and similarities
    nodes = [item[0] for item in node_data]
    similarities = [item[1] for item in node_data]

    # Manual checkpoint
    self.connection.execute("CHECKPOINT;")

    return nodes, similarities

get #

get(properties: Optional[dict] = None, ids: Optional[List[str]] = None) -> List[LabelledNode]

Get nodes from the property graph store.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def get(
    self,
    properties: Optional[dict] = None,
    ids: Optional[List[str]] = None,
) -> List[LabelledNode]:
    """Get nodes from the property graph store."""
    cypher_statement = "MATCH (e) "

    parameters = {}
    if ids:
        cypher_statement += "WHERE e.id in $ids "
        parameters["ids"] = ids

    return_statement = "RETURN e.*"
    cypher_statement += return_statement
    result = self.structured_query(cypher_statement, param_map=parameters)
    result = result if result else []

    nodes = []
    for record in result:
        # Text indicates a chunk node
        # None on the label indicates an implicit node, likely a chunk node
        if record.get("e.label") == "text_chunk":
            properties = {
                k: v for k, v in record.items() if k not in ["e.id", "e.text"]
            }
            text = record.get("e.text")
            nodes.append(
                ChunkNode(
                    id_=record["e.id"],
                    text=text,
                    properties=utils.remove_empty_values(properties),
                )
            )
        else:
            properties = {
                k: v for k, v in record.items() if k not in ["e.id", "e.name"]
            }
            name = record["e.name"] if record.get("e.name") else record["e.id"]
            label = record["e.label"] if record.get("e.label") else "Chunk"
            nodes.append(
                EntityNode(
                    name=name,
                    label=label,
                    properties=utils.remove_empty_values(properties),
                )
            )
    return nodes

delete #

delete(entity_names: Optional[List[str]] = None, relation_names: Optional[List[str]] = None, properties: Optional[dict] = None, ids: Optional[List[str]] = None) -> None

Delete nodes and relationships from the property graph store.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def delete(
    self,
    entity_names: Optional[List[str]] = None,
    relation_names: Optional[List[str]] = None,
    properties: Optional[dict] = None,
    ids: Optional[List[str]] = None,
) -> None:
    """Delete nodes and relationships from the property graph store."""
    if entity_names:
        self.structured_query(
            "MATCH (n) WHERE n.name IN $entity_names DETACH DELETE n",
            param_map={"entity_names": entity_names},
        )

    if ids:
        self.structured_query(
            "MATCH (n) WHERE n.id IN $ids DETACH DELETE n",
            param_map={"ids": ids},
        )

    if relation_names:
        for rel in relation_names:
            src, _, dst = utils.lookup_relation(rel, self.relationship_schema)
            self.structured_query(
                f"""
                MATCH (:{src})-[r {{label: $label}}]->(:{dst})
                DELETE r
                """,
                param_map={"label": rel},
            )

    if properties:
        assert isinstance(properties, dict), (
            "`properties` should be a key-value mapping."
        )
        cypher = "MATCH (e) WHERE "
        prop_list = []
        params = {}
        for i, prop in enumerate(properties):
            prop_list.append(f"e.`{prop}` = $property_{i}")
            params[f"property_{i}"] = properties[prop]
        cypher += " AND ".join(prop_list)
        self.structured_query(cypher + " DETACH DELETE e", param_map=params)

get_schema #

get_schema() -> Any

Returns a structured schema of the property graph store.

The schema contains node_props, rel_props, and relationships keys and the associated metadata. Example output: { 'node_props': {'Chunk': [{'property': 'id', 'type': 'STRING'}, {'property': 'text', 'type': 'STRING'}, {'property': 'label', 'type': 'STRING'}, {'property': 'embedding', 'type': 'DOUBLE'}, {'property': 'properties', 'type': 'STRING'}, {'property': 'ref_doc_id', 'type': 'STRING'}], 'Entity': [{'property': 'id', 'type': 'STRING'}, {'property': 'name', 'type': 'STRING'}, {'property': 'label', 'type': 'STRING'}, {'property': 'embedding', 'type': 'DOUBLE'}, {'property': 'properties', 'type': 'STRING'}]}, 'rel_props': {'SOURCE': [{'property': 'label', 'type': 'STRING'}]}, 'relationships': [{'end': 'Chunk', 'start': 'Chunk', 'type': 'SOURCE'}] }

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-kuzu/llama_index/graph_stores/kuzu/kuzu_property_graph.py 
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def get_schema(self) -> Any:
    """
    Returns a structured schema of the property graph store.

    The schema contains `node_props`, `rel_props`, and `relationships` keys and
    the associated metadata.
    Example output:
    {
        'node_props': {'Chunk': [{'property': 'id', 'type': 'STRING'},
                                {'property': 'text', 'type': 'STRING'},
                                {'property': 'label', 'type': 'STRING'},
                                {'property': 'embedding', 'type': 'DOUBLE'},
                                {'property': 'properties', 'type': 'STRING'},
                                {'property': 'ref_doc_id', 'type': 'STRING'}],
                        'Entity': [{'property': 'id', 'type': 'STRING'},
                                {'property': 'name', 'type': 'STRING'},
                                {'property': 'label', 'type': 'STRING'},
                                {'property': 'embedding', 'type': 'DOUBLE'},
                                {'property': 'properties', 'type': 'STRING'}]},
        'rel_props': {'SOURCE': [{'property': 'label', 'type': 'STRING'}]},
        'relationships': [{'end': 'Chunk', 'start': 'Chunk', 'type': 'SOURCE'}]
    }
    """
    current_table_schema = {"node_props": {}, "rel_props": {}, "relationships": []}
    node_tables = self.connection._get_node_table_names()
    for table_name in node_tables:
        node_props = self.connection._get_node_property_names(table_name)
        current_table_schema["node_props"][table_name] = []
        for prop, attr in node_props.items():
            schema = {}
            schema["property"] = prop
            schema["type"] = attr["type"]
            current_table_schema["node_props"][table_name].append(schema)

    rel_tables = self.connection._get_rel_table_names()
    for i, table in enumerate(rel_tables):
        table_name = table["name"]
        prop_values = self.connection.execute(
            f"MATCH ()-[r:{table_name}]->() RETURN distinct r.label AS label;"
        )
        for row in prop_values:
            rel_label = row[0]
            src, dst = rel_tables[i]["src"], rel_tables[i]["dst"]
            current_table_schema["relationships"].append(
                {"start": src, "type": rel_label, "end": dst}
            )
            current_table_schema["rel_props"][rel_label] = []
            table_details = self.connection.execute(
                f"CALL TABLE_INFO('{table_name}') RETURN *;"
            )
            for props in table_details:
                rel_props = {}
                rel_props["property"] = props[1]
                rel_props["type"] = props[2]
                current_table_schema["rel_props"][rel_label].append(rel_props)

    self.structured_schema = current_table_schema

    return self.structured_schema