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Falkordb

FalkorDBGraphStore #

Bases: GraphStore

FalkorDB Graph Store.

In this graph store, triplets are stored within FalkorDB.

Parameters:

Name Type Description Default
simple_graph_store_data_dict Optional[dict]

data dict containing the triplets. See FalkorDBGraphStoreData for more details.

 required
Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/base.py 
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class FalkorDBGraphStore(GraphStore):
    """FalkorDB Graph Store.

    In this graph store, triplets are stored within FalkorDB.

    Args:
        simple_graph_store_data_dict (Optional[dict]): data dict
            containing the triplets. See FalkorDBGraphStoreData
            for more details.
    """

    def __init__(
        self,
        url: str,
        database: str = "falkor",
        node_label: str = "Entity",
        **kwargs: Any,
    ) -> None:
        """Initialize params."""
        self._node_label = node_label

        self._driver = FalkorDB.from_url(url).select_graph(database)

        try:
            self._driver.query(f"CREATE INDEX FOR (n:`{self._node_label}`) ON (n.id)")
        except redis.ResponseError as e:
            # TODO: to find an appropriate way to handle this issue.
            logger.warning("Create index failed: %s", e)

        self._database = database

        self.schema = ""
        self.get_query = f"""
            MATCH (n1:`{self._node_label}`)-[r]->(n2:`{self._node_label}`)
            WHERE n1.id = $subj RETURN type(r), n2.id
        """

    @property
    def client(self) -> None:
        return self._driver

    def get(self, subj: str) -> List[List[str]]:
        """Get triplets."""
        result = self._driver.query(
            self.get_query, params={"subj": subj}, read_only=True
        )
        return result.result_set

    def get_rel_map(
        self, subjs: Optional[List[str]] = None, depth: int = 2, limit: int = 30
    ) -> Dict[str, List[List[str]]]:
        """Get flat rel map."""
        # The flat means for multi-hop relation path, we could get
        # knowledge like: subj -> rel -> obj -> rel -> obj -> rel -> obj.
        # This type of knowledge is useful for some tasks.
        # +-------------+------------------------------------+
        # | subj        | flattened_rels                     |
        # +-------------+------------------------------------+
        # | "player101" | [95, "player125", 2002, "team204"] |
        # | "player100" | [1997, "team204"]                  |
        # ...
        # +-------------+------------------------------------+

        rel_map: Dict[Any, List[Any]] = {}
        if subjs is None or len(subjs) == 0:
            # unlike simple graph_store, we don't do get_all here
            return rel_map

        query = f"""
            MATCH (n1:{self._node_label})
            WHERE n1.id IN $subjs
            WITH n1
            MATCH p=(n1)-[e*1..{depth}]->(z)
            RETURN p LIMIT {limit}
        """

        data = self.query(query, params={"subjs": subjs})
        if not data:
            return rel_map

        for record in data:
            nodes = record[0].nodes()
            edges = record[0].edges()

            subj_id = nodes[0].properties["id"]
            path = []
            for i, edge in enumerate(edges):
                dest = nodes[i + 1]
                dest_id = dest.properties["id"]
                path.append(edge.relation)
                path.append(dest_id)

            paths = rel_map[subj_id] if subj_id in rel_map else []
            paths.append(path)
            rel_map[subj_id] = paths

        return rel_map

    def upsert_triplet(self, subj: str, rel: str, obj: str) -> None:
        """Add triplet."""
        query = """
            MERGE (n1:`%s` {id:$subj})
            MERGE (n2:`%s` {id:$obj})
            MERGE (n1)-[:`%s`]->(n2)
        """

        prepared_statement = query % (
            self._node_label,
            self._node_label,
            rel.replace(" ", "_").upper(),
        )

        # Call FalkorDB with prepared statement
        self._driver.query(prepared_statement, params={"subj": subj, "obj": obj})

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

        def delete_rel(subj: str, obj: str, rel: str) -> None:
            rel = rel.replace(" ", "_").upper()
            query = f"""
                MATCH (n1:`{self._node_label}`)-[r:`{rel}`]->(n2:`{self._node_label}`)
                WHERE n1.id = $subj AND n2.id = $obj DELETE r
            """

            # Call FalkorDB with prepared statement
            self._driver.query(query, params={"subj": subj, "obj": obj})

        def delete_entity(entity: str) -> None:
            query = f"MATCH (n:`{self._node_label}`) WHERE n.id = $entity DELETE n"

            # Call FalkorDB with prepared statement
            self._driver.query(query, params={"entity": entity})

        def check_edges(entity: str) -> bool:
            query = f"""
                MATCH (n1:`{self._node_label}`)--()
                WHERE n1.id = $entity RETURN count(*)
            """

            # Call FalkorDB with prepared statement
            result = self._driver.query(
                query, params={"entity": entity}, read_only=True
            )
            return bool(result.result_set)

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

    def refresh_schema(self) -> None:
        """
        Refreshes the FalkorDB graph schema information.
        """
        node_properties = self.query("CALL DB.PROPERTYKEYS()")
        relationships = self.query("CALL DB.RELATIONSHIPTYPES()")

        self.schema = f"""
        Properties: {node_properties}
        Relationships: {relationships}
        """

    def get_schema(self, refresh: bool = False) -> str:
        """Get the schema of the FalkorDBGraph store."""
        if self.schema and not refresh:
            return self.schema
        self.refresh_schema()
        logger.debug(f"get_schema() schema:\n{self.schema}")
        return self.schema

    def query(self, query: str, params: Optional[Dict[str, Any]] = None) -> Any:
        result = self._driver.query(query, params=params)
        return result.result_set

get #

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

Get triplets.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/base.py 
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def get(self, subj: str) -> List[List[str]]:
    """Get triplets."""
    result = self._driver.query(
        self.get_query, params={"subj": subj}, read_only=True
    )
    return result.result_set

get_rel_map #

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

Get flat rel map.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/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 flat rel map."""
    # The flat means for multi-hop relation path, we could get
    # knowledge like: subj -> rel -> obj -> rel -> obj -> rel -> obj.
    # This type of knowledge is useful for some tasks.
    # +-------------+------------------------------------+
    # | subj        | flattened_rels                     |
    # +-------------+------------------------------------+
    # | "player101" | [95, "player125", 2002, "team204"] |
    # | "player100" | [1997, "team204"]                  |
    # ...
    # +-------------+------------------------------------+

    rel_map: Dict[Any, List[Any]] = {}
    if subjs is None or len(subjs) == 0:
        # unlike simple graph_store, we don't do get_all here
        return rel_map

    query = f"""
        MATCH (n1:{self._node_label})
        WHERE n1.id IN $subjs
        WITH n1
        MATCH p=(n1)-[e*1..{depth}]->(z)
        RETURN p LIMIT {limit}
    """

    data = self.query(query, params={"subjs": subjs})
    if not data:
        return rel_map

    for record in data:
        nodes = record[0].nodes()
        edges = record[0].edges()

        subj_id = nodes[0].properties["id"]
        path = []
        for i, edge in enumerate(edges):
            dest = nodes[i + 1]
            dest_id = dest.properties["id"]
            path.append(edge.relation)
            path.append(dest_id)

        paths = rel_map[subj_id] if subj_id in rel_map else []
        paths.append(path)
        rel_map[subj_id] = paths

    return rel_map

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-falkordb/llama_index/graph_stores/falkordb/base.py 
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def upsert_triplet(self, subj: str, rel: str, obj: str) -> None:
    """Add triplet."""
    query = """
        MERGE (n1:`%s` {id:$subj})
        MERGE (n2:`%s` {id:$obj})
        MERGE (n1)-[:`%s`]->(n2)
    """

    prepared_statement = query % (
        self._node_label,
        self._node_label,
        rel.replace(" ", "_").upper(),
    )

    # Call FalkorDB with prepared statement
    self._driver.query(prepared_statement, params={"subj": subj, "obj": obj})

delete #

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

Delete triplet.

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

    def delete_rel(subj: str, obj: str, rel: str) -> None:
        rel = rel.replace(" ", "_").upper()
        query = f"""
            MATCH (n1:`{self._node_label}`)-[r:`{rel}`]->(n2:`{self._node_label}`)
            WHERE n1.id = $subj AND n2.id = $obj DELETE r
        """

        # Call FalkorDB with prepared statement
        self._driver.query(query, params={"subj": subj, "obj": obj})

    def delete_entity(entity: str) -> None:
        query = f"MATCH (n:`{self._node_label}`) WHERE n.id = $entity DELETE n"

        # Call FalkorDB with prepared statement
        self._driver.query(query, params={"entity": entity})

    def check_edges(entity: str) -> bool:
        query = f"""
            MATCH (n1:`{self._node_label}`)--()
            WHERE n1.id = $entity RETURN count(*)
        """

        # Call FalkorDB with prepared statement
        result = self._driver.query(
            query, params={"entity": entity}, read_only=True
        )
        return bool(result.result_set)

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

refresh_schema #

refresh_schema() -> None

Refreshes the FalkorDB graph schema information.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/base.py 
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def refresh_schema(self) -> None:
    """
    Refreshes the FalkorDB graph schema information.
    """
    node_properties = self.query("CALL DB.PROPERTYKEYS()")
    relationships = self.query("CALL DB.RELATIONSHIPTYPES()")

    self.schema = f"""
    Properties: {node_properties}
    Relationships: {relationships}
    """

get_schema #

get_schema(refresh: bool = False) -> str

Get the schema of the FalkorDBGraph store.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/base.py 
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def get_schema(self, refresh: bool = False) -> str:
    """Get the schema of the FalkorDBGraph store."""
    if self.schema and not refresh:
        return self.schema
    self.refresh_schema()
    logger.debug(f"get_schema() schema:\n{self.schema}")
    return self.schema

FalkorDBPropertyGraphStore #

Bases: PropertyGraphStore

FalkorDB Property Graph Store.

This class implements a FalkorDB property graph store.

If you are using local FalkorDB instead of FalkorDB Cloud, here's a helpful command for launching the docker container:

docker run \
    -p 3000:3000 -p 6379:6379 \
    -v $PWD/data:/data \
    falkordb/falkordb:latest

Parameters:

Name Type Description Default
url str

The URL for the FalkorDB database.

 required
database Optional[str]

The name of the database to connect to. Defaults to "falkor".

 'falkor'

Examples:

pip install llama-index-graph-stores-falkordb

from llama_index.core.indices.property_graph import PropertyGraphIndex
from llama_index.graph_stores.falkordb import FalkorDBPropertyGraphStore

# Create a FalkorDBPropertyGraphStore instance
graph_store = FalkorDBPropertyGraphStore(
    url="falkordb://localhost:6379",
    database="falkor"
)

# create the index
index = PropertyGraphIndex.from_documents(
    documents,
    property_graph_store=graph_store,
)
Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_property_graph.py 
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class FalkorDBPropertyGraphStore(PropertyGraphStore):
    r"""
    FalkorDB Property Graph Store.

    This class implements a FalkorDB property graph store.

    If you are using local FalkorDB instead of FalkorDB Cloud, here's a helpful
    command for launching the docker container:

    ```bash
    docker run \
        -p 3000:3000 -p 6379:6379 \
        -v $PWD/data:/data \
        falkordb/falkordb:latest
    ```

    Args:
        url (str): The URL for the FalkorDB database.
        database (Optional[str]): The name of the database to connect to. Defaults to "falkor".

    Examples:
        `pip install llama-index-graph-stores-falkordb`

        ```python
        from llama_index.core.indices.property_graph import PropertyGraphIndex
        from llama_index.graph_stores.falkordb import FalkorDBPropertyGraphStore

        # Create a FalkorDBPropertyGraphStore instance
        graph_store = FalkorDBPropertyGraphStore(
            url="falkordb://localhost:6379",
            database="falkor"
        )

        # create the index
        index = PropertyGraphIndex.from_documents(
            documents,
            property_graph_store=graph_store,
        )
        ```
    """

    supports_structured_queries: bool = True
    supports_vector_queries: bool = True
    text_to_cypher_template: PromptTemplate = DEFAULT_CYPHER_TEMPALTE

    def __init__(
        self,
        url: str,
        database: str = "falkor",
        refresh_schema: bool = True,
        sanitize_query_output: bool = True,
        **falkordb_kwargs: Any,
    ) -> None:
        self.sanitize_query_output = sanitize_query_output
        self._driver = FalkorDB.from_url(url).select_graph(database)
        self._database = database
        self.structured_schema = {}
        if refresh_schema:
            self.refresh_schema()

    @property
    def client(self):
        return self._driver

    def refresh_schema(self) -> None:
        """Refresh the schema."""
        node_query_results = self.structured_query(
            node_properties_query,
            param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]},
        )
        node_properties = (
            [el[b"output"] for el in node_query_results] if node_query_results else []
        )

        rels_query_result = self.structured_query(
            rel_properties_query, param_map={"EXCLUDED_LABELS": EXCLUDED_RELS}
        )
        rel_properties = (
            [el[b"output"] for el in rels_query_result] if rels_query_result else []
        )

        rel_objs_query_result = self.structured_query(
            rel_query,
            param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]},
        )
        relationships = (
            [el[b"output"] for el in rel_objs_query_result]
            if rel_objs_query_result
            else []
        )

        # Get constraints & indexes
        try:
            constraint = self.structured_query("CALL db.constraints()")
            index = self.structured_query(
                "CALL db.indexes() YIELD label, properties, entitytype " "RETURN *"
            )
        except (
            redis.exceptions.ResponseError
        ):  # Read-only user might not have access to schema information
            constraint = []
            index = []

        self.structured_schema = {
            "node_props": {el["label"]: el["keys"] for el in node_properties},
            "rel_props": {el["type"]: el["keys"] for el in rel_properties},
            "relationships": relationships,
            "metadata": {"constraint": constraint, "index": index},
        }

    def upsert_nodes(self, nodes: List[LabelledNode]) -> None:
        # Lists to hold separated types
        entity_dicts: List[dict] = []
        chunk_dicts: List[dict] = []

        # Sort by type
        for item in nodes:
            if isinstance(item, EntityNode):
                entity_dicts.append({**item.dict(), "id": item.id})
            elif isinstance(item, ChunkNode):
                chunk_dicts.append({**item.dict(), "id": item.id})
            else:
                # Log that we do not support these types of nodes
                # Or raise an error?
                pass

        if chunk_dicts:
            self.structured_query(
                """
                UNWIND $data AS row
                MERGE (c:Chunk {id: row.id})
                SET c.text = row.text
                WITH c, row
                SET c += row.properties
                WITH c, row.embedding AS embedding
                WHERE embedding IS NOT NULL
                SET c.embedding = vecf32(embedding)
                RETURN count(*)
                """,
                param_map={"data": chunk_dicts},
            )

        if entity_dicts:
            for entity_dict in entity_dicts:
                self.structured_query(
                    f"""
                    MERGE (e:`__Entity__` {{id: $data.id}})
                    SET e += $data.properties
                    SET e.name = $data.name
                    WITH e
                    SET e:{entity_dict["label"]}
                    WITH e
                    CALL {{
                        WITH e
                        WITH e
                        WHERE $data.embedding IS NOT NULL
                        SET e.embedding = vecf32($data.embedding)
                        RETURN count(*) AS count
                    }}
                    WITH e WHERE $data.properties.triplet_source_id IS NOT NULL
                    MERGE (c:Chunk {{id: $data.properties.triplet_source_id}})
                    MERGE (e)<-[:MENTIONS]-(c)
                    """,
                    param_map={"data": entity_dict},
                )

    def upsert_relations(self, relations: List[Relation]) -> None:
        """Add relations."""
        params = [r.dict() for r in relations]

        for param in params:
            self.structured_query(
                f"""
                MERGE (source {{id: $data.source_id}})
                ON CREATE SET source:Chunk
                MERGE (target {{id: $data.target_id}})
                ON CREATE SET target:Chunk
                WITH source, target
                CREATE (source)-[r:`{param["label"]}`]->(target)
                SET r += $data.properties
                RETURN count(*)
                """,
                param_map={"data": param},
            )

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

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

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

        if properties:
            prop_list = []
            for i, prop in enumerate(properties):
                prop_list.append(f"e.`{prop}` = $property_{i}")
                params[f"property_{i}"] = properties[prop]
            cypher_statement += " AND ".join(prop_list)

        return_statement = """
        WITH e
        RETURN e.id AS name,
               [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type,
               e{.* , embedding: Null, id: Null} AS properties
        """
        cypher_statement += return_statement

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

        nodes = []
        for record in response:
            # text indicates a chunk node
            # none on the type indicates an implicit node, likely a chunk node
            if "text" in record[b"properties"] or record[b"type"] is None:
                text = record[b"properties"].pop("text", "")
                nodes.append(
                    ChunkNode(
                        id_=record[b"name"],
                        text=text,
                        properties=remove_empty_values(record[b"properties"]),
                    )
                )
            else:
                nodes.append(
                    EntityNode(
                        name=record[b"name"],
                        label=record[b"type"],
                        properties=remove_empty_values(record[b"properties"]),
                    )
                )

        return nodes

    def get_triplets(
        self,
        entity_names: Optional[List[str]] = None,
        relation_names: Optional[List[str]] = None,
        properties: Optional[dict] = None,
        ids: Optional[List[str]] = None,
    ) -> List[Triplet]:
        # TODO: handle ids of chunk nodes
        cypher_statement = "MATCH (e:`__Entity__`) "

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

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

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

        if properties:
            prop_list = []
            for i, prop in enumerate(properties):
                prop_list.append(f"e.`{prop}` = $property_{i}")
                params[f"property_{i}"] = properties[prop]
            cypher_statement += " AND ".join(prop_list)

        return_statement = f"""
        WITH e
        CALL {{
            WITH e
            MATCH (e)-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]->(t:__Entity__)
            RETURN e.name AS source_id, [l in labels(e) WHERE l <> '__Entity__' | l][0] AS source_type,
                   e{{.* , embedding: Null, name: Null}} AS source_properties,
                   type(r) AS type,
                   t.name AS target_id, [l in labels(t) WHERE l <> '__Entity__' | l][0] AS target_type,
                   t{{.* , embedding: Null, name: Null}} AS target_properties
            UNION ALL
            WITH e
            MATCH (e)<-[r{':`' + '`|`'.join(relation_names) + '`' if relation_names else ''}]-(t:__Entity__)
            RETURN t.name AS source_id, [l in labels(t) WHERE l <> '__Entity__' | l][0] AS source_type,
                   e{{.* , embedding: Null, name: Null}} AS source_properties,
                   type(r) AS type,
                   e.name AS target_id, [l in labels(e) WHERE l <> '__Entity__' | l][0] AS target_type,
                   t{{.* , embedding: Null, name: Null}} AS target_properties
        }}
        RETURN source_id, source_type, type, target_id, target_type, source_properties, target_properties"""
        cypher_statement += return_statement

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

        triples = []
        for record in data:
            source = EntityNode(
                name=record[b"source_id"],
                label=record[b"source_type"],
                properties=remove_empty_values(record[b"source_properties"]),
            )
            target = EntityNode(
                name=record[b"target_id"],
                label=record[b"target_type"],
                properties=remove_empty_values(record[b"target_properties"]),
            )
            rel = Relation(
                source_id=record[b"source_id"],
                target_id=record[b"target_id"],
                label=record[b"type"],
            )
            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]:
        """Get depth-aware rel map."""
        triples = []

        ids = [node.id for node in graph_nodes]
        # Needs some optimization
        response = self.structured_query(
            f"""
            WITH $ids AS id_list
            UNWIND range(0, size(id_list) - 1) AS idx
            MATCH (e:`__Entity__`)
            WHERE e.id = id_list[idx]
            MATCH p=(e)-[r*1..{depth}]-(other)
            WHERE ALL(rel in relationships(p) WHERE type(rel) <> 'MENTIONS')
            UNWIND relationships(p) AS rel
            WITH distinct rel, idx
            WITH startNode(rel) AS source,
                type(rel) AS type,
                endNode(rel) AS endNode,
                idx
            LIMIT $limit
            RETURN source.id AS source_id, [l in labels(source) WHERE l <> '__Entity__' | l][0] AS source_type,
                source{{.* , embedding: Null, id: Null}} AS source_properties,
                type,
                endNode.id AS target_id, [l in labels(endNode) WHERE l <> '__Entity__' | l][0] AS target_type,
                endNode{{.* , embedding: Null, id: Null}} AS target_properties,
                idx
            ORDER BY idx
            LIMIT $limit
            """,
            param_map={"ids": ids, "limit": limit},
        )
        response = response if response else []

        ignore_rels = ignore_rels or []
        for record in response:
            if record[b"type"] in ignore_rels:
                continue

            source = EntityNode(
                name=record[b"source_id"],
                label=record[b"source_type"],
                properties=remove_empty_values(record[b"source_properties"]),
            )
            target = EntityNode(
                name=record[b"target_id"],
                label=record[b"target_type"],
                properties=remove_empty_values(record[b"target_properties"]),
            )
            rel = Relation(
                source_id=record[b"source_id"],
                target_id=record[b"target_id"],
                label=record[b"type"],
            )
            triples.append([source, rel, target])

        return triples

    def structured_query(
        self, query: str, param_map: Optional[Dict[str, Any]] = None
    ) -> Any:
        param_map = param_map or {}

        result = self._driver.query(query, param_map)
        full_result = [
            {h[1]: d[i] for i, h in enumerate(result.header)} for d in result.result_set
        ]

        if self.sanitize_query_output:
            return [value_sanitize(el) for el in full_result]
        return full_result

    def vector_query(
        self, query: VectorStoreQuery, **kwargs: Any
    ) -> Tuple[List[LabelledNode], List[float]]:
        """Query the graph store with a vector store query."""
        conditions = None
        if query.filters:
            conditions = [
                f"e.{filter.key} {filter.operator.value} {filter.value}"
                for filter in query.filters.filters
            ]
        filters = (
            f" {query.filters.condition.value} ".join(conditions).replace("==", "=")
            if conditions is not None
            else "1 = 1"
        )

        data = self.structured_query(
            f"""MATCH (e:`__Entity__`)
            WHERE e.embedding IS NOT NULL AND ({filters})
            WITH e, vec.euclideanDistance(e.embedding, vecf32($embedding)) AS score
            ORDER BY score DESC LIMIT $limit
            RETURN e.id AS name,
               [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type,
               e{{.* , embedding: Null, name: Null, id: Null}} AS properties,
               score""",
            param_map={
                "embedding": query.query_embedding,
                "dimension": len(query.query_embedding),
                "limit": query.similarity_top_k,
            },
        )
        data = data if data else []

        nodes = []
        scores = []
        for record in data:
            node = EntityNode(
                name=record[b"name"],
                label=record[b"type"],
                properties=remove_empty_values(record[b"properties"]),
            )
            nodes.append(node)
            scores.append(record[b"score"])

        return (nodes, scores)

    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 matching data."""
        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:
                self.structured_query(f"MATCH ()-[r:`{rel}`]->() DELETE r")

        if properties:
            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, refresh: bool = False) -> Any:
        if refresh:
            self.refresh_schema()

        return self.structured_schema

    def get_schema_str(self, refresh: bool = False) -> str:
        schema = self.get_schema(refresh=refresh)

        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 using structured_schema
        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"(:{el['start']})-[:{el['type']}]->(:{el['end']})"
            for el 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),
            ]
        )

refresh_schema #

refresh_schema() -> None

Refresh the schema.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_property_graph.py 
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def refresh_schema(self) -> None:
    """Refresh the schema."""
    node_query_results = self.structured_query(
        node_properties_query,
        param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]},
    )
    node_properties = (
        [el[b"output"] for el in node_query_results] if node_query_results else []
    )

    rels_query_result = self.structured_query(
        rel_properties_query, param_map={"EXCLUDED_LABELS": EXCLUDED_RELS}
    )
    rel_properties = (
        [el[b"output"] for el in rels_query_result] if rels_query_result else []
    )

    rel_objs_query_result = self.structured_query(
        rel_query,
        param_map={"EXCLUDED_LABELS": [*EXCLUDED_LABELS, BASE_ENTITY_LABEL]},
    )
    relationships = (
        [el[b"output"] for el in rel_objs_query_result]
        if rel_objs_query_result
        else []
    )

    # Get constraints & indexes
    try:
        constraint = self.structured_query("CALL db.constraints()")
        index = self.structured_query(
            "CALL db.indexes() YIELD label, properties, entitytype " "RETURN *"
        )
    except (
        redis.exceptions.ResponseError
    ):  # Read-only user might not have access to schema information
        constraint = []
        index = []

    self.structured_schema = {
        "node_props": {el["label"]: el["keys"] for el in node_properties},
        "rel_props": {el["type"]: el["keys"] for el in rel_properties},
        "relationships": relationships,
        "metadata": {"constraint": constraint, "index": index},
    }

upsert_relations #

upsert_relations(relations: List[Relation]) -> None

Add relations.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_property_graph.py 
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def upsert_relations(self, relations: List[Relation]) -> None:
    """Add relations."""
    params = [r.dict() for r in relations]

    for param in params:
        self.structured_query(
            f"""
            MERGE (source {{id: $data.source_id}})
            ON CREATE SET source:Chunk
            MERGE (target {{id: $data.target_id}})
            ON CREATE SET target:Chunk
            WITH source, target
            CREATE (source)-[r:`{param["label"]}`]->(target)
            SET r += $data.properties
            RETURN count(*)
            """,
            param_map={"data": param},
        )

get #

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

Get nodes.

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

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

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

    if properties:
        prop_list = []
        for i, prop in enumerate(properties):
            prop_list.append(f"e.`{prop}` = $property_{i}")
            params[f"property_{i}"] = properties[prop]
        cypher_statement += " AND ".join(prop_list)

    return_statement = """
    WITH e
    RETURN e.id AS name,
           [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type,
           e{.* , embedding: Null, id: Null} AS properties
    """
    cypher_statement += return_statement

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

    nodes = []
    for record in response:
        # text indicates a chunk node
        # none on the type indicates an implicit node, likely a chunk node
        if "text" in record[b"properties"] or record[b"type"] is None:
            text = record[b"properties"].pop("text", "")
            nodes.append(
                ChunkNode(
                    id_=record[b"name"],
                    text=text,
                    properties=remove_empty_values(record[b"properties"]),
                )
            )
        else:
            nodes.append(
                EntityNode(
                    name=record[b"name"],
                    label=record[b"type"],
                    properties=remove_empty_values(record[b"properties"]),
                )
            )

    return nodes

get_rel_map #

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

Get depth-aware rel map.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_property_graph.py 
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def get_rel_map(
    self,
    graph_nodes: List[LabelledNode],
    depth: int = 2,
    limit: int = 30,
    ignore_rels: Optional[List[str]] = None,
) -> List[Triplet]:
    """Get depth-aware rel map."""
    triples = []

    ids = [node.id for node in graph_nodes]
    # Needs some optimization
    response = self.structured_query(
        f"""
        WITH $ids AS id_list
        UNWIND range(0, size(id_list) - 1) AS idx
        MATCH (e:`__Entity__`)
        WHERE e.id = id_list[idx]
        MATCH p=(e)-[r*1..{depth}]-(other)
        WHERE ALL(rel in relationships(p) WHERE type(rel) <> 'MENTIONS')
        UNWIND relationships(p) AS rel
        WITH distinct rel, idx
        WITH startNode(rel) AS source,
            type(rel) AS type,
            endNode(rel) AS endNode,
            idx
        LIMIT $limit
        RETURN source.id AS source_id, [l in labels(source) WHERE l <> '__Entity__' | l][0] AS source_type,
            source{{.* , embedding: Null, id: Null}} AS source_properties,
            type,
            endNode.id AS target_id, [l in labels(endNode) WHERE l <> '__Entity__' | l][0] AS target_type,
            endNode{{.* , embedding: Null, id: Null}} AS target_properties,
            idx
        ORDER BY idx
        LIMIT $limit
        """,
        param_map={"ids": ids, "limit": limit},
    )
    response = response if response else []

    ignore_rels = ignore_rels or []
    for record in response:
        if record[b"type"] in ignore_rels:
            continue

        source = EntityNode(
            name=record[b"source_id"],
            label=record[b"source_type"],
            properties=remove_empty_values(record[b"source_properties"]),
        )
        target = EntityNode(
            name=record[b"target_id"],
            label=record[b"target_type"],
            properties=remove_empty_values(record[b"target_properties"]),
        )
        rel = Relation(
            source_id=record[b"source_id"],
            target_id=record[b"target_id"],
            label=record[b"type"],
        )
        triples.append([source, rel, target])

    return triples

vector_query #

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

Query the graph store with a vector store query.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_property_graph.py 
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def vector_query(
    self, query: VectorStoreQuery, **kwargs: Any
) -> Tuple[List[LabelledNode], List[float]]:
    """Query the graph store with a vector store query."""
    conditions = None
    if query.filters:
        conditions = [
            f"e.{filter.key} {filter.operator.value} {filter.value}"
            for filter in query.filters.filters
        ]
    filters = (
        f" {query.filters.condition.value} ".join(conditions).replace("==", "=")
        if conditions is not None
        else "1 = 1"
    )

    data = self.structured_query(
        f"""MATCH (e:`__Entity__`)
        WHERE e.embedding IS NOT NULL AND ({filters})
        WITH e, vec.euclideanDistance(e.embedding, vecf32($embedding)) AS score
        ORDER BY score DESC LIMIT $limit
        RETURN e.id AS name,
           [l in labels(e) WHERE l <> '__Entity__' | l][0] AS type,
           e{{.* , embedding: Null, name: Null, id: Null}} AS properties,
           score""",
        param_map={
            "embedding": query.query_embedding,
            "dimension": len(query.query_embedding),
            "limit": query.similarity_top_k,
        },
    )
    data = data if data else []

    nodes = []
    scores = []
    for record in data:
        node = EntityNode(
            name=record[b"name"],
            label=record[b"type"],
            properties=remove_empty_values(record[b"properties"]),
        )
        nodes.append(node)
        scores.append(record[b"score"])

    return (nodes, scores)

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 matching data.

Source code in llama-index-integrations/graph_stores/llama-index-graph-stores-falkordb/llama_index/graph_stores/falkordb/falkordb_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 matching data."""
    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:
            self.structured_query(f"MATCH ()-[r:`{rel}`]->() DELETE r")

    if properties:
        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)