community: update Memgraph integration (#27017)

**Description:**
- **Memgraph** no longer relies on `Neo4jGraphStore` but **implements
`GraphStore`**, just like other graph databases.
- **Memgraph** no longer relies on `GraphQAChain`, but implements
`MemgraphQAChain`, just like other graph databases.
- The refresh schema procedure has been updated to try using `SHOW
SCHEMA INFO`. The fallback uses Cypher queries (a combination of schema
and Cypher) → **LangChain integration no longer relies on MAGE
library**.
- The **schema structure** has been reformatted. Regardless of the
procedures used to get schema, schema structure is the same.
- The `add_graph_documents()` method has been implemented. It transforms
`GraphDocument` into Cypher queries and creates a graph in Memgraph. It
implements the ability to use `baseEntityLabel` to improve speed
(`baseEntityLabel` has an index on the `id` property). It also
implements the ability to include sources by creating a `MENTIONS`
relationship to the source document.
- Jupyter Notebook for Memgraph has been updated.
- **Issue:** /
- **Dependencies:** /
- **Twitter handle:** supe_katarina (DX Engineer @ Memgraph)

Closes #25606

libs/community/langchain_community/chains/graph_qa/memgraph.py

@@ -0,0 +1,316 @@
+"""Question answering over a graph."""
+
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List, Optional, Union
+
+from langchain.chains.base import Chain
+from langchain_core.callbacks import CallbackManagerForChainRun
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    SystemMessage,
+    ToolMessage,
+)
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import (
+    BasePromptTemplate,
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
+from langchain_core.runnables import Runnable
+from pydantic import Field
+
+from langchain_community.chains.graph_qa.prompts import (
+    MEMGRAPH_GENERATION_PROMPT,
+    MEMGRAPH_QA_PROMPT,
+)
+from langchain_community.graphs.memgraph_graph import MemgraphGraph
+
+INTERMEDIATE_STEPS_KEY = "intermediate_steps"
+
+FUNCTION_RESPONSE_SYSTEM = """You are an assistant that helps to form nice and human 
+understandable answers based on the provided information from tools.
+Do not add any other information that wasn't present in the tools, and use 
+very concise style in interpreting results!
+"""
+
+
+def extract_cypher(text: str) -> str:
+    """Extract Cypher code from a text.
+
+    Args:
+        text: Text to extract Cypher code from.
+
+    Returns:
+        Cypher code extracted from the text.
+    """
+    # The pattern to find Cypher code enclosed in triple backticks
+    pattern = r"```(.*?)```"
+
+    # Find all matches in the input text
+    matches = re.findall(pattern, text, re.DOTALL)
+
+    return matches[0] if matches else text
+
+
+def get_function_response(
+    question: str, context: List[Dict[str, Any]]
+) -> List[BaseMessage]:
+    TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
+    messages = [
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "tool_calls": [
+                    {
+                        "id": TOOL_ID,
+                        "function": {
+                            "arguments": '{"question":"' + question + '"}',
+                            "name": "GetInformation",
+                        },
+                        "type": "function",
+                    }
+                ]
+            },
+        ),
+        ToolMessage(content=str(context), tool_call_id=TOOL_ID),
+    ]
+    return messages
+
+
+class MemgraphQAChain(Chain):
+    """Chain for question-answering against a graph by generating Cypher statements.
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    graph: MemgraphGraph = Field(exclude=True)
+    cypher_generation_chain: Runnable
+    qa_chain: Runnable
+    graph_schema: str
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+    top_k: int = 10
+    """Number of results to return from the query"""
+    return_intermediate_steps: bool = False
+    """Whether or not to return the intermediate steps along with the final answer."""
+    return_direct: bool = False
+    """Optional cypher validation tool"""
+    use_function_response: bool = False
+    """Whether to wrap the database context as tool/function response"""
+    allow_dangerous_requests: bool = False
+    """Forced user opt-in to acknowledge that the chain can make dangerous requests.
+    
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include necessary permissions.
+        Failure to do so may result in data corruption or loss, since the calling
+        code may attempt commands that would result in deletion, mutation
+        of data if appropriately prompted or reading sensitive data if such
+        data is present in the database.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this tool.
+
+        See https://python.langchain.com/docs/security for more information.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the chain."""
+        super().__init__(**kwargs)
+        if self.allow_dangerous_requests is not True:
+            raise ValueError(
+                "In order to use this chain, you must acknowledge that it can make "
+                "dangerous requests by setting `allow_dangerous_requests` to `True`."
+                "You must narrowly scope the permissions of the database connection "
+                "to only include necessary permissions. Failure to do so may result "
+                "in data corruption or loss or reading sensitive data if such data is "
+                "present in the database."
+                "Only use this chain if you understand the risks and have taken the "
+                "necessary precautions. "
+                "See https://python.langchain.com/docs/security for more information."
+            )
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Return the input keys.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return the output keys.
+
+        :meta private:
+        """
+        _output_keys = [self.output_key]
+        return _output_keys
+
+    @property
+    def _chain_type(self) -> str:
+        return "graph_cypher_chain"
+
+    @classmethod
+    def from_llm(
+        cls,
+        llm: Optional[BaseLanguageModel] = None,
+        *,
+        qa_prompt: Optional[BasePromptTemplate] = None,
+        cypher_prompt: Optional[BasePromptTemplate] = None,
+        cypher_llm: Optional[BaseLanguageModel] = None,
+        qa_llm: Optional[Union[BaseLanguageModel, Any]] = None,
+        qa_llm_kwargs: Optional[Dict[str, Any]] = None,
+        cypher_llm_kwargs: Optional[Dict[str, Any]] = None,
+        use_function_response: bool = False,
+        function_response_system: str = FUNCTION_RESPONSE_SYSTEM,
+        **kwargs: Any,
+    ) -> MemgraphQAChain:
+        """Initialize from LLM."""
+
+        if not cypher_llm and not llm:
+            raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
+        if not qa_llm and not llm:
+            raise ValueError("Either `llm` or `qa_llm` parameters must be provided")
+        if cypher_llm and qa_llm and llm:
+            raise ValueError(
+                "You can specify up to two of 'cypher_llm', 'qa_llm'"
+                ", and 'llm', but not all three simultaneously."
+            )
+        if cypher_prompt and cypher_llm_kwargs:
+            raise ValueError(
+                "Specifying cypher_prompt and cypher_llm_kwargs together is"
+                " not allowed. Please pass prompt via cypher_llm_kwargs."
+            )
+        if qa_prompt and qa_llm_kwargs:
+            raise ValueError(
+                "Specifying qa_prompt and qa_llm_kwargs together is"
+                " not allowed. Please pass prompt via qa_llm_kwargs."
+            )
+        use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {}
+        use_cypher_llm_kwargs = (
+            cypher_llm_kwargs if cypher_llm_kwargs is not None else {}
+        )
+        if "prompt" not in use_qa_llm_kwargs:
+            use_qa_llm_kwargs["prompt"] = (
+                qa_prompt if qa_prompt is not None else MEMGRAPH_QA_PROMPT
+            )
+        if "prompt" not in use_cypher_llm_kwargs:
+            use_cypher_llm_kwargs["prompt"] = (
+                cypher_prompt
+                if cypher_prompt is not None
+                else MEMGRAPH_GENERATION_PROMPT
+            )
+
+        qa_llm = qa_llm or llm
+        if use_function_response:
+            try:
+                qa_llm.bind_tools({})  # type: ignore[union-attr]
+                response_prompt = ChatPromptTemplate.from_messages(
+                    [
+                        SystemMessage(content=function_response_system),
+                        HumanMessagePromptTemplate.from_template("{question}"),
+                        MessagesPlaceholder(variable_name="function_response"),
+                    ]
+                )
+                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+            except (NotImplementedError, AttributeError):
+                raise ValueError("Provided LLM does not support native tools/functions")
+        else:
+            qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser()  # type: ignore
+
+        prompt = use_cypher_llm_kwargs["prompt"]
+        llm_to_use = cypher_llm if cypher_llm is not None else llm
+
+        if prompt is not None and llm_to_use is not None:
+            cypher_generation_chain = prompt | llm_to_use | StrOutputParser()  # type: ignore[arg-type]
+        else:
+            raise ValueError(
+                "Missing required components for the cypher generation chain: "
+                "'prompt' or 'llm'"
+            )
+
+        graph_schema = kwargs["graph"].get_schema
+
+        return cls(
+            graph_schema=graph_schema,
+            qa_chain=qa_chain,
+            cypher_generation_chain=cypher_generation_chain,
+            use_function_response=use_function_response,
+            **kwargs,
+        )
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Generate Cypher statement, use it to look up in db and answer question."""
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        callbacks = _run_manager.get_child()
+        question = inputs[self.input_key]
+        args = {
+            "question": question,
+            "schema": self.graph_schema,
+        }
+        args.update(inputs)
+
+        intermediate_steps: List = []
+
+        generated_cypher = self.cypher_generation_chain.invoke(
+            args, callbacks=callbacks
+        )
+        # Extract Cypher code if it is wrapped in backticks
+        generated_cypher = extract_cypher(generated_cypher)
+
+        _run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
+        _run_manager.on_text(
+            generated_cypher, color="green", end="\n", verbose=self.verbose
+        )
+
+        intermediate_steps.append({"query": generated_cypher})
+
+        # Retrieve and limit the number of results
+        # Generated Cypher be null if query corrector identifies invalid schema
+        if generated_cypher:
+            context = self.graph.query(generated_cypher)[: self.top_k]
+        else:
+            context = []
+
+        if self.return_direct:
+            result = context
+        else:
+            _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
+            _run_manager.on_text(
+                str(context), color="green", end="\n", verbose=self.verbose
+            )
+
+            intermediate_steps.append({"context": context})
+            if self.use_function_response:
+                function_response = get_function_response(question, context)
+                result = self.qa_chain.invoke(  # type: ignore
+                    {"question": question, "function_response": function_response},
+                )
+            else:
+                result = self.qa_chain.invoke(  # type: ignore
+                    {"question": question, "context": context},
+                    callbacks=callbacks,
+                )
+
+        chain_result: Dict[str, Any] = {"result": result}
+        if self.return_intermediate_steps:
+            chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
+
+        return chain_result

libs/community/langchain_community/chains/graph_qa/prompts.py

@@ -411,3 +411,58 @@ NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT = PromptTemplate(
     input_variables=["schema", "question", "extra_instructions"],
     template=NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_TEMPLATE,
 )
+
+MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database. 
+You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database.
+Instructions: 
+- Use provided node and relationship labels and property names from the
+schema which describes the database's structure. Upon receiving a user
+question, synthesize the schema to craft a precise Cypher query that
+directly corresponds to the user's intent. 
+- Generate valid executable Cypher queries on top of Memgraph database. 
+Any explanation, context, or additional information that is not a part 
+of the Cypher query syntax should be omitted entirely. 
+- Use Memgraph MAGE procedures instead of Neo4j APOC procedures. 
+- Do not include any explanations or apologies in your responses. 
+- Do not include any text except the generated Cypher statement.
+- For queries that ask for information or functionalities outside the direct
+generation of Cypher queries, use the Cypher query format to communicate
+limitations or capabilities. For example: RETURN "I am designed to generate
+Cypher queries based on the provided schema only."
+Schema: 
+{schema}
+
+With all the above information and instructions, generate Cypher query for the
+user question. 
+
+The question is:
+{question}"""
+
+MEMGRAPH_GENERATION_PROMPT = PromptTemplate(
+    input_variables=["schema", "question"], template=MEMGRAPH_GENERATION_TEMPLATE
+)
+
+
+MEMGRAPH_QA_TEMPLATE = """Your task is to form nice and human
+understandable answers. The information part contains the provided
+information that you must use to construct an answer.
+The provided information is authoritative, you must never doubt it or try to
+use your internal knowledge to correct it. Make the answer sound as a
+response to the question. Do not mention that you based the result on the
+given information. Here is an example:
+
+Question: Which managers own Neo4j stocks?
+Context:[manager:CTL LLC, manager:JANE STREET GROUP LLC]
+Helpful Answer: CTL LLC, JANE STREET GROUP LLC owns Neo4j stocks.
+
+Follow this example when generating answers. If the provided information is
+empty, say that you don't know the answer.
+
+Information:
+{context}
+
+Question: {question}
+Helpful Answer:"""
+MEMGRAPH_QA_PROMPT = PromptTemplate(
+    input_variables=["context", "question"], template=MEMGRAPH_QA_TEMPLATE
+)

libs/community/langchain_community/graphs/memgraph_graph.py

@@ -1,15 +1,272 @@
-from langchain_community.graphs.neo4j_graph import Neo4jGraph
+import logging
+from hashlib import md5
+from typing import Any, Dict, List, Optional
+
+from langchain_core.utils import get_from_dict_or_env
+
+from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
+from langchain_community.graphs.graph_store import GraphStore
+
+logger = logging.getLogger(__name__)
+
+
+BASE_ENTITY_LABEL = "__Entity__"
 
 SCHEMA_QUERY = """
-CALL llm_util.schema("raw")
-YIELD *
-RETURN *
+SHOW SCHEMA INFO
+"""
+
+NODE_PROPERTIES_QUERY = """
+CALL schema.node_type_properties()
+YIELD nodeType AS label, propertyName AS property, propertyTypes AS type
+WITH label AS nodeLabels, collect({key: property, types: type}) AS properties
+RETURN {labels: nodeLabels, properties: properties} AS output
+"""
+
+REL_QUERY = """
+MATCH (n)-[e]->(m)
+WITH DISTINCT
+    labels(n) AS start_node_labels,
+    type(e) AS rel_type,
+    labels(m) AS end_node_labels,
+    e,
+    keys(e) AS properties
+UNWIND CASE WHEN size(properties) > 0 THEN properties ELSE [null] END AS prop
+WITH
+    start_node_labels,
+    rel_type,
+    end_node_labels,
+    CASE WHEN prop IS NULL THEN [] ELSE [prop, valueType(e[prop])] END AS property_info
+RETURN
+    start_node_labels,
+    rel_type,
+    end_node_labels,
+    COLLECT(DISTINCT CASE 
+    WHEN property_info <> [] 
+    THEN property_info 
+    ELSE null END) AS properties_info
+"""
+
+NODE_IMPORT_QUERY = """
+UNWIND $data AS row
+CALL merge.node(row.label, row.properties, {}, {}) 
+YIELD node 
+RETURN distinct 'done' AS result
+"""
+
+REL_NODES_IMPORT_QUERY = """
+UNWIND $data AS row
+MERGE (source {id: row.source_id})
+MERGE (target {id: row.target_id})
+RETURN distinct 'done' AS result
+"""
+
+REL_IMPORT_QUERY = """
+UNWIND $data AS row
+MATCH (source {id: row.source_id})
+MATCH (target {id: row.target_id})
+WITH source, target, row
+CALL merge.relationship(source, row.type, {}, {}, target, {})
+YIELD rel
+RETURN distinct 'done' AS result
+"""
+
+INCLUDE_DOCS_QUERY = """
+MERGE (d:Document {id:$document.metadata.id})
+SET d.content = $document.page_content
+SET d += $document.metadata
+RETURN distinct 'done' AS result
+"""
+
+INCLUDE_DOCS_SOURCE_QUERY = """
+UNWIND $data AS row
+MATCH (source {id: row.source_id}), (d:Document {id: $document.metadata.id})
+MERGE (d)-[:MENTIONS]->(source)
+RETURN distinct 'done' AS result
+"""
+
+NODE_PROPS_TEXT = """
+Node labels and properties (name and type) are:
+"""
+
+REL_PROPS_TEXT = """
+Relationship labels and properties are:
+"""
+
+REL_TEXT = """
+Nodes are connected with the following relationships:
 """
 
 
-class MemgraphGraph(Neo4jGraph):
+def get_schema_subset(data: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        "edges": [
+            {
+                "end_node_labels": edge["end_node_labels"],
+                "properties": [
+                    {
+                        "key": prop["key"],
+                        "types": [
+                            {"type": type_item["type"].lower()}
+                            for type_item in prop["types"]
+                        ],
+                    }
+                    for prop in edge["properties"]
+                ],
+                "start_node_labels": edge["start_node_labels"],
+                "type": edge["type"],
+            }
+            for edge in data["edges"]
+        ],
+        "nodes": [
+            {
+                "labels": node["labels"],
+                "properties": [
+                    {
+                        "key": prop["key"],
+                        "types": [
+                            {"type": type_item["type"].lower()}
+                            for type_item in prop["types"]
+                        ],
+                    }
+                    for prop in node["properties"]
+                ],
+            }
+            for node in data["nodes"]
+        ],
+    }
+
+
+def get_reformated_schema(
+    nodes: List[Dict[str, Any]], rels: List[Dict[str, Any]]
+) -> Dict[str, Any]:
+    return {
+        "edges": [
+            {
+                "end_node_labels": rel["end_node_labels"],
+                "properties": [
+                    {"key": prop[0], "types": [{"type": prop[1].lower()}]}
+                    for prop in rel["properties_info"]
+                ],
+                "start_node_labels": rel["start_node_labels"],
+                "type": rel["rel_type"],
+            }
+            for rel in rels
+        ],
+        "nodes": [
+            {
+                "labels": [_remove_backticks(node["labels"])[1:]],
+                "properties": [
+                    {
+                        "key": prop["key"],
+                        "types": [
+                            {"type": type_item.lower()} for type_item in prop["types"]
+                        ],
+                    }
+                    for prop in node["properties"]
+                    if node["properties"][0]["key"] != ""
+                ],
+            }
+            for node in nodes
+        ],
+    }
+
+
+def transform_schema_to_text(schema: Dict[str, Any]) -> str:
+    node_props_data = ""
+    rel_props_data = ""
+    rel_data = ""
+
+    for node in schema["nodes"]:
+        node_props_data += f"- labels: (:{':'.join(node['labels'])})\n"
+        if node["properties"] == []:
+            continue
+        node_props_data += "  properties:\n"
+        for prop in node["properties"]:
+            prop_types_str = " or ".join(
+                {prop_types["type"] for prop_types in prop["types"]}
+            )
+            node_props_data += f"    - {prop['key']}: {prop_types_str}\n"
+
+    for rel in schema["edges"]:
+        rel_type = rel["type"]
+        start_labels = ":".join(rel["start_node_labels"])
+        end_labels = ":".join(rel["end_node_labels"])
+        rel_data += f"(:{start_labels})-[:{rel_type}]->(:{end_labels})\n"
+
+        if rel["properties"] == []:
+            continue
+
+        rel_props_data += f"- labels: {rel_type}\n  properties:\n"
+        for prop in rel["properties"]:
+            prop_types_str = " or ".join(
+                {prop_types["type"].lower() for prop_types in prop["types"]}
+            )
+            rel_props_data += f"    - {prop['key']}: {prop_types_str}\n"
+
+    return "".join(
+        [
+            NODE_PROPS_TEXT + node_props_data if node_props_data else "",
+            REL_PROPS_TEXT + rel_props_data if rel_props_data else "",
+            REL_TEXT + rel_data if rel_data else "",
+        ]
+    )
+
+
+def _remove_backticks(text: str) -> str:
+    return text.replace("`", "")
+
+
+def _transform_nodes(nodes: list[Node], baseEntityLabel: bool) -> List[dict]:
+    transformed_nodes = []
+    for node in nodes:
+        properties_dict = node.properties | {"id": node.id}
+        label = (
+            [_remove_backticks(node.type), BASE_ENTITY_LABEL]
+            if baseEntityLabel
+            else [_remove_backticks(node.type)]
+        )
+        node_dict = {"label": label, "properties": properties_dict}
+        transformed_nodes.append(node_dict)
+    return transformed_nodes
+
+
+def _transform_relationships(
+    relationships: list[Relationship], baseEntityLabel: bool
+) -> List[dict]:
+    transformed_relationships = []
+    for rel in relationships:
+        rel_dict = {
+            "type": _remove_backticks(rel.type),
+            "source_label": (
+                [BASE_ENTITY_LABEL]
+                if baseEntityLabel
+                else [_remove_backticks(rel.source.type)]
+            ),
+            "source_id": rel.source.id,
+            "target_label": (
+                [BASE_ENTITY_LABEL]
+                if baseEntityLabel
+                else [_remove_backticks(rel.target.type)]
+            ),
+            "target_id": rel.target.id,
+        }
+        transformed_relationships.append(rel_dict)
+    return transformed_relationships
+
+
+class MemgraphGraph(GraphStore):
     """Memgraph wrapper for graph operations.
 
+    Parameters:
+    url (Optional[str]): The URL of the Memgraph database server.
+    username (Optional[str]): The username for database authentication.
+    password (Optional[str]): The password for database authentication.
+    database (str): The name of the database to connect to. Default is 'memgraph'.
+    refresh_schema (bool): A flag whether to refresh schema information
+    at initialization. Default is True.
+    driver_config (Dict): Configuration passed to Neo4j Driver.
+
     *Security note*: Make sure that the database connection uses credentials
         that are narrowly-scoped to only include necessary permissions.
         Failure to do so may result in data corruption or loss, since the calling
@@ -23,49 +280,247 @@ class MemgraphGraph(Neo4jGraph):
     """
 
     def __init__(
-        self, url: str, username: str, password: str, *, database: str = "memgraph"
+        self,
+        url: Optional[str] = None,
+        username: Optional[str] = None,
+        password: Optional[str] = None,
+        database: Optional[str] = None,
+        refresh_schema: bool = True,
+        *,
+        driver_config: Optional[Dict] = None,
     ) -> None:
         """Create a new Memgraph graph wrapper instance."""
-        super().__init__(url, username, password, database=database)
+        try:
+            import neo4j
+        except ImportError:
+            raise ImportError(
+                "Could not import neo4j python package. "
+                "Please install it with `pip install neo4j`."
+            )
+
+        url = get_from_dict_or_env({"url": url}, "url", "MEMGRAPH_URI")
+
+        # if username and password are "", assume auth is disabled
+        if username == "" and password == "":
+            auth = None
+        else:
+            username = get_from_dict_or_env(
+                {"username": username},
+                "username",
+                "MEMGRAPH_USERNAME",
+            )
+            password = get_from_dict_or_env(
+                {"password": password},
+                "password",
+                "MEMGRAPH_PASSWORD",
+            )
+            auth = (username, password)
+        database = get_from_dict_or_env(
+            {"database": database}, "database", "MEMGRAPH_DATABASE", "memgraph"
+        )
+
+        self._driver = neo4j.GraphDatabase.driver(
+            url, auth=auth, **(driver_config or {})
+        )
+
+        self._database = database
+        self.schema: str = ""
+        self.structured_schema: Dict[str, Any] = {}
+
+        # Verify connection
+        try:
+            self._driver.verify_connectivity()
+        except neo4j.exceptions.ServiceUnavailable:
+            raise ValueError(
+                "Could not connect to Memgraph database. "
+                "Please ensure that the url is correct"
+            )
+        except neo4j.exceptions.AuthError:
+            raise ValueError(
+                "Could not connect to Memgraph database. "
+                "Please ensure that the username and password are correct"
+            )
+
+        # Set schema
+        if refresh_schema:
+            try:
+                self.refresh_schema()
+            except neo4j.exceptions.ClientError as e:
+                raise e
+
+    def close(self) -> None:
+        if self._driver:
+            logger.info("Closing the driver connection.")
+            self._driver.close()
+            self._driver = None
+
+    @property
+    def get_schema(self) -> str:
+        """Returns the schema of the Graph database"""
+        return self.schema
+
+    @property
+    def get_structured_schema(self) -> Dict[str, Any]:
+        """Returns the structured schema of the Graph database"""
+        return self.structured_schema
+
+    def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:
+        """Query the graph.
+
+        Args:
+            query (str): The Cypher query to execute.
+            params (dict): The parameters to pass to the query.
+
+        Returns:
+            List[Dict[str, Any]]: The list of dictionaries containing the query results.
+        """
+        from neo4j.exceptions import Neo4jError
+
+        try:
+            data, _, _ = self._driver.execute_query(
+                query,
+                database_=self._database,
+                parameters_=params,
+            )
+            json_data = [r.data() for r in data]
+            return json_data
+        except Neo4jError as e:
+            if not (
+                (
+                    (  # isCallInTransactionError
+                        e.code == "Neo.DatabaseError.Statement.ExecutionFailed"
+                        or e.code
+                        == "Neo.DatabaseError.Transaction.TransactionStartFailed"
+                    )
+                    and "in an implicit transaction" in e.message
+                )
+                or (  # isPeriodicCommitError
+                    e.code == "Neo.ClientError.Statement.SemanticError"
+                    and (
+                        "in an open transaction is not possible" in e.message
+                        or "tried to execute in an explicit transaction" in e.message
+                    )
+                )
+                or (
+                    e.code == "Memgraph.ClientError.MemgraphError.MemgraphError"
+                    and ("in multicommand transactions" in e.message)
+                )
+                or (
+                    e.code == "Memgraph.ClientError.MemgraphError.MemgraphError"
+                    and "SchemaInfo disabled" in e.message
+                )
+            ):
+                raise
+
+        # fallback to allow implicit transactions
+        with self._driver.session(database=self._database) as session:
+            data = session.run(query, params)
+            json_data = [r.data() for r in data]
+            return json_data
 
     def refresh_schema(self) -> None:
         """
         Refreshes the Memgraph graph schema information.
         """
+        import ast
 
-        db_structured_schema = self.query(SCHEMA_QUERY)[0].get("schema")
-        assert db_structured_schema is not None
-        self.structured_schema = db_structured_schema
+        from neo4j.exceptions import Neo4jError
 
-        # Format node properties
-        formatted_node_props = []
+        # leave schema empty if db is empty
+        if self.query("MATCH (n) RETURN n LIMIT 1") == []:
+            return
 
-        for node_name, properties in db_structured_schema["node_props"].items():
-            formatted_node_props.append(
-                f"Node name: '{node_name}', Node properties: {properties}"
+        # first try with SHOW SCHEMA INFO
+        try:
+            result = self.query(SCHEMA_QUERY)[0].get("schema")
+            if result is not None and isinstance(result, (str, ast.AST)):
+                schema_result = ast.literal_eval(result)
+            else:
+                schema_result = result
+            assert schema_result is not None
+            structured_schema = get_schema_subset(schema_result)
+            self.structured_schema = structured_schema
+            self.schema = transform_schema_to_text(structured_schema)
+            return
+        except Neo4jError as e:
+            if (
+                e.code == "Memgraph.ClientError.MemgraphError.MemgraphError"
+                and "SchemaInfo disabled" in e.message
+            ):
+                logger.info(
+                    "Schema generation with SHOW SCHEMA INFO query failed. "
+                    "Set --schema-info-enabled=true to use SHOW SCHEMA INFO query. "
+                    "Falling back to alternative queries."
+                )
+
+        # fallback on Cypher without SHOW SCHEMA INFO
+        nodes = [query["output"] for query in self.query(NODE_PROPERTIES_QUERY)]
+        rels = self.query(REL_QUERY)
+
+        structured_schema = get_reformated_schema(nodes, rels)
+        self.structured_schema = structured_schema
+        self.schema = transform_schema_to_text(structured_schema)
+
+    def add_graph_documents(
+        self,
+        graph_documents: List[GraphDocument],
+        include_source: bool = False,
+        baseEntityLabel: bool = False,
+    ) -> None:
+        """
+        Take GraphDocument as input as uses it to construct a graph in Memgraph.
+
+        Parameters:
+        - graph_documents (List[GraphDocument]): A list of GraphDocument objects
+        that contain the nodes and relationships to be added to the graph. Each
+        GraphDocument should encapsulate the structure of part of the graph,
+        including nodes, relationships, and the source document information.
+        - include_source (bool, optional): If True, stores the source document
+        and links it to nodes in the graph using the MENTIONS relationship.
+        This is useful for tracing back the origin of data. Merges source
+        documents based on the `id` property from the source document metadata
+        if available; otherwise it calculates the MD5 hash of `page_content`
+        for merging process. Defaults to False.
+        - baseEntityLabel (bool, optional): If True, each newly created node
+        gets a secondary __Entity__ label, which is indexed and improves import
+        speed and performance. Defaults to False.
+        """
+
+        if baseEntityLabel:
+            self.query(
+                f"CREATE CONSTRAINT ON (b:{BASE_ENTITY_LABEL}) "
+                "ASSERT b.id IS UNIQUE;"
+            )
+            self.query(f"CREATE INDEX ON :{BASE_ENTITY_LABEL}(id);")
+            self.query(f"CREATE INDEX ON :{BASE_ENTITY_LABEL};")
+
+        for document in graph_documents:
+            if include_source:
+                if not document.source.metadata.get("id"):
+                    document.source.metadata["id"] = md5(
+                        document.source.page_content.encode("utf-8")
+                    ).hexdigest()
+
+                self.query(INCLUDE_DOCS_QUERY, {"document": document.source.__dict__})
+
+            self.query(
+                NODE_IMPORT_QUERY,
+                {"data": _transform_nodes(document.nodes, baseEntityLabel)},
             )
 
-        # Format relationship properties
-        formatted_rel_props = []
-        for rel_name, properties in db_structured_schema["rel_props"].items():
-            formatted_rel_props.append(
-                f"Relationship name: '{rel_name}', "
-                f"Relationship properties: {properties}"
+            rel_data = _transform_relationships(document.relationships, baseEntityLabel)
+            self.query(
+                REL_NODES_IMPORT_QUERY,
+                {"data": rel_data},
+            )
+            self.query(
+                REL_IMPORT_QUERY,
+                {"data": rel_data},
             )
 
-        # Format relationships
-        formatted_rels = [
-            f"(:{rel['start']})-[:{rel['type']}]->(:{rel['end']})"
-            for rel in db_structured_schema["relationships"]
-        ]
-
-        self.schema = "\n".join(
-            [
-                "Node properties are the following:",
-                *formatted_node_props,
-                "Relationship properties are the following:",
-                *formatted_rel_props,
-                "The relationships are the following:",
-                *formatted_rels,
-            ]
-        )
+            if include_source:
+                self.query(
+                    INCLUDE_DOCS_SOURCE_QUERY,
+                    {"data": rel_data, "document": document.source.__dict__},
+                )
+        self.refresh_schema()

libs/community/tests/integration_tests/graphs/test_memgraph.py

@@ -1,24 +1,44 @@
 import os
 
+from langchain_core.documents import Document
+
 from langchain_community.graphs import MemgraphGraph
+from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
+from langchain_community.graphs.memgraph_graph import NODE_PROPERTIES_QUERY, REL_QUERY
+
+test_data = [
+    GraphDocument(
+        nodes=[Node(id="foo", type="foo"), Node(id="bar", type="bar")],
+        relationships=[
+            Relationship(
+                source=Node(id="foo", type="foo"),
+                target=Node(id="bar", type="bar"),
+                type="REL",
+            )
+        ],
+        source=Document(page_content="source document"),
+    )
+]
 
 
 def test_cypher_return_correct_schema() -> None:
     """Test that chain returns direct results."""
+
     url = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
     username = os.environ.get("MEMGRAPH_USERNAME", "")
     password = os.environ.get("MEMGRAPH_PASSWORD", "")
+
     assert url is not None
     assert username is not None
     assert password is not None
 
-    graph = MemgraphGraph(
-        url=url,
-        username=username,
-        password=password,
-    )
-    # Delete all nodes in the graph
-    graph.query("MATCH (n) DETACH DELETE n")
+    graph = MemgraphGraph(url=url, username=username, password=password)
+
+    # Drop graph
+    graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
+    graph.query("DROP GRAPH")
+    graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")
+
     # Create two nodes and a relationship
     graph.query(
         """
@@ -31,32 +51,123 @@ def test_cypher_return_correct_schema() -> None:
     )
     # Refresh schema information
     graph.refresh_schema()
-    relationships = graph.query(
-        "CALL llm_util.schema('raw') YIELD schema "
-        "WITH schema.relationships AS relationships "
-        "UNWIND relationships AS relationship "
-        "RETURN relationship['start'] AS start, "
-        "relationship['type'] AS type, "
-        "relationship['end'] AS end "
-        "ORDER BY start, type, end;"
-    )
 
-    node_props = graph.query(
-        "CALL llm_util.schema('raw') YIELD schema "
-        "WITH schema.node_props AS nodes "
-        "WITH nodes['LabelA'] AS properties "
-        "UNWIND properties AS property "
-        "RETURN property['property'] AS prop, "
-        "property['type'] AS type "
-        "ORDER BY prop ASC;"
-    )
+    node_properties = graph.query(NODE_PROPERTIES_QUERY)
+    relationships = graph.query(REL_QUERY)
 
-    expected_relationships = [
-        {"start": "LabelA", "type": "REL_TYPE", "end": "LabelB"},
-        {"start": "LabelA", "type": "REL_TYPE", "end": "LabelC"},
+    expected_node_properties = [
+        {
+            "output": {
+                "labels": ":`LabelA`",
+                "properties": [{"key": "property_a", "types": ["String"]}],
+            }
+        },
+        {"output": {"labels": ":`LabelB`", "properties": [{"key": "", "types": []}]}},
+        {"output": {"labels": ":`LabelC`", "properties": [{"key": "", "types": []}]}},
     ]
 
-    expected_node_props = [{"prop": "property_a", "type": "str"}]
+    expected_relationships = [
+        {
+            "start_node_labels": ["LabelA"],
+            "rel_type": "REL_TYPE",
+            "end_node_labels": ["LabelC"],
+            "properties_info": [["rel_prop", "STRING"]],
+        },
+        {
+            "start_node_labels": ["LabelA"],
+            "rel_type": "REL_TYPE",
+            "end_node_labels": ["LabelB"],
+            "properties_info": [],
+        },
+    ]
 
+    graph.close()
+
+    assert node_properties == expected_node_properties
     assert relationships == expected_relationships
-    assert node_props == expected_node_props
+
+
+def test_add_graph_documents() -> None:
+    """Test that Memgraph correctly imports graph document."""
+    url = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
+    username = os.environ.get("MEMGRAPH_USERNAME", "")
+    password = os.environ.get("MEMGRAPH_PASSWORD", "")
+
+    assert url is not None
+    assert username is not None
+    assert password is not None
+
+    graph = MemgraphGraph(
+        url=url, username=username, password=password, refresh_schema=False
+    )
+    # Drop graph
+    graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
+    graph.query("DROP GRAPH")
+    graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")
+    # Create KG
+    graph.add_graph_documents(test_data)
+    output = graph.query("MATCH (n) RETURN labels(n) AS label, count(*) AS count")
+    # Close the connection
+    graph.close()
+    assert output == [{"label": ["bar"], "count": 1}, {"label": ["foo"], "count": 1}]
+
+
+def test_add_graph_documents_base_entity() -> None:
+    """Test that Memgraph correctly imports graph document with Entity label."""
+    url = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
+    username = os.environ.get("MEMGRAPH_USERNAME", "")
+    password = os.environ.get("MEMGRAPH_PASSWORD", "")
+
+    assert url is not None
+    assert username is not None
+    assert password is not None
+
+    graph = MemgraphGraph(
+        url=url, username=username, password=password, refresh_schema=False
+    )
+    # Drop graph
+    graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
+    graph.query("DROP GRAPH")
+    graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")
+    # Create KG
+    graph.add_graph_documents(test_data, baseEntityLabel=True)
+    output = graph.query("MATCH (n) RETURN labels(n) AS label, count(*) AS count")
+
+    # Close the connection
+    graph.close()
+
+    assert output == [
+        {"label": ["__Entity__", "bar"], "count": 1},
+        {"label": ["__Entity__", "foo"], "count": 1},
+    ]
+
+
+def test_add_graph_documents_include_source() -> None:
+    """Test that Memgraph correctly imports graph document with source included."""
+    url = os.environ.get("MEMGRAPH_URI", "bolt://localhost:7687")
+    username = os.environ.get("MEMGRAPH_USERNAME", "")
+    password = os.environ.get("MEMGRAPH_PASSWORD", "")
+
+    assert url is not None
+    assert username is not None
+    assert password is not None
+
+    graph = MemgraphGraph(
+        url=url, username=username, password=password, refresh_schema=False
+    )
+    # Drop graph
+    graph.query("STORAGE MODE IN_MEMORY_ANALYTICAL")
+    graph.query("DROP GRAPH")
+    graph.query("STORAGE MODE IN_MEMORY_TRANSACTIONAL")
+    # Create KG
+    graph.add_graph_documents(test_data, include_source=True)
+    output = graph.query("MATCH (n) RETURN labels(n) AS label, count(*) AS count")
+
+    # Close the connection
+    graph.close()
+
+    assert output == [
+        {"label": ["bar"], "count": 1},
+        {"label": ["foo"], "count": 1},
+        {"label": ["Document"], "count": 1},
+    ]