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LlamaIndex is a data framework for your LLM applications
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[Bug]: Querying neo4j knowledge graph #13741

Closed data-scientist-shivam799 closed 5 months ago

data-scientist-shivam799 commented 5 months ago

Bug Description

Hiii guys, I have been using neo4j and llama index for building and querying knowledge graph, I am able to build knowledge graph successfully but while query the system I am getting ValidationError: 1 validation error for LLMPredictStartEvent template none is not an allowed value. 

from llama_index.core import StorageContext
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
from llama_index.graph_stores.neo4j import Neo4jGraphStore
from llama_index.core import ServiceContext

username = "neo4j"
password = "ZlXSOpUBLwCH8In2lLcPvf-beyfb87egf7e6fwefgfegf"
url = "neo4j+s://f44bb72e.databases.neo4j.io"
database = "neo4j"

service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)

graph_store = Neo4jGraphStore(
        username=username,
        password=password,
        url=url,
        database=database
    )

storage_context = StorageContext.from_defaults(graph_store=graph_store)

query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    service_context=service_context,
    llm=llm,
    verbose=True,
    refresh_schema=True
)

response = query_engine.query(
    "Tell me more about what the author worked on at Interleaf",
)
print(response)

I have used nebula as well and got the same error.

Version

latest

Steps to Reproduce

I have been using this notebook https://github.com/Joshua-Yu/graph-rag/blob/main/openai%2Bllamaparse/demo_neo4j_vectordb.ipynb

Relevant Logs/Tracbacks

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
<ipython-input-14-f34ecfdbdf7d> in <cell line: 41>()
     39 )
     40 
---> 41 response = query_engine.query(
     42     "Tell me more about what the author worked on at Interleaf",
     43 )

7 frames
/usr/local/lib/python3.10/dist-packages/llama_index/core/instrumentation/dispatcher.py in wrapper(func, instance, args, kwargs)
    272             )
    273             try:
--> 274                 result = func(*args, **kwargs)
    275             except BaseException as e:
    276                 self.event(SpanDropEvent(span_id=id_, err_str=str(e)))

/usr/local/lib/python3.10/dist-packages/llama_index/core/base/base_query_engine.py in query(self, str_or_query_bundle)
     51             if isinstance(str_or_query_bundle, str):
     52                 str_or_query_bundle = QueryBundle(str_or_query_bundle)
---> 53             query_result = self._query(str_or_query_bundle)
     54         dispatch_event(QueryEndEvent(query=str_or_query_bundle, response=query_result))
     55         return query_result

/usr/local/lib/python3.10/dist-packages/llama_index/core/query_engine/knowledge_graph_query_engine.py in _query(self, query_bundle)
    197             CBEventType.QUERY, payload={EventPayload.QUERY_STR: query_bundle.query_str}
    198         ) as query_event:
--> 199             nodes: List[NodeWithScore] = self._retrieve(query_bundle)
    200 
    201             response = self._response_synthesizer.synthesize(

/usr/local/lib/python3.10/dist-packages/llama_index/core/query_engine/knowledge_graph_query_engine.py in _retrieve(self, query_bundle)
    152     def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
    153         """Get nodes for response."""
--> 154         graph_store_query = self.generate_query(query_bundle.query_str)
    155         if self._verbose:
    156             print_text(f"Graph Store Query:\n{graph_store_query}\n", color="yellow")

/usr/local/lib/python3.10/dist-packages/llama_index/core/query_engine/knowledge_graph_query_engine.py in generate_query(self, query_str)
    130         # Get the query engine query string
    131 
--> 132         graph_store_query: str = self._llm.predict(
    133             self._graph_query_synthesis_prompt,
    134             query_str=query_str,

/usr/local/lib/python3.10/dist-packages/llama_index/core/instrumentation/dispatcher.py in wrapper(func, instance, args, kwargs)
    272             )
    273             try:
--> 274                 result = func(*args, **kwargs)
    275             except BaseException as e:
    276                 self.event(SpanDropEvent(span_id=id_, err_str=str(e)))

/usr/local/lib/python3.10/dist-packages/llama_index/core/llms/llm.py in predict(self, prompt, **prompt_args)
    431         dispatch_event = dispatcher.get_dispatch_event()
    432 
--> 433         dispatch_event(LLMPredictStartEvent(template=prompt, template_args=prompt_args))
    434         self._log_template_data(prompt, **prompt_args)
    435 

/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in __init__(__pydantic_self__, **data)
    339         values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
    340         if validation_error:
--> 341             raise validation_error
    342         try:
    343             object_setattr(__pydantic_self__, '__dict__', values)

ValidationError: 1 validation error for LLMPredictStartEvent
template
  none is not an allowed value (type=type_error.none.not_allowed)
dosubot[bot] commented 5 months ago

To resolve the ValidationError: 1 validation error for LLMPredictStartEvent template none is not an allowed value error, ensure that the graph_query_synthesis_prompt and graph_response_answer_prompt are properly initialized and not set to None. The error indicates that a None value is being passed where a valid prompt template is expected.

Here is an example of how to initialize the KnowledgeGraphQueryEngine with the required prompts:

from llama_index.core.prompts.base import PromptTemplate, PromptType
from llama_index.core import StorageContext, ServiceContext
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
from llama_index.graph_stores.neo4j import Neo4jGraphStore

# Define your custom prompt templates or use existing ones
graph_query_synthesis_prompt = PromptTemplate(
    "Your graph query synthesis prompt template here",
    prompt_type=PromptType.QUESTION_ANSWER,
)

# Initialize the LLM and ServiceContext
llm = your_llm_instance  # Replace with your actual LLM instance
service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)

# Initialize the Neo4jGraphStore
username = "neo4j"
password = "ZlXSOpUBLwCH8In2lLcPvf-beyfb87egf7e6fwefgfegf"
url = "neo4j+s://f44bb72e.databases.neo4j.io"
database = "neo4j"
graph_store = Neo4jGraphStore(
    username=username,
    password=password,
    url=url,
    database=database
)

# Create a StorageContext with your GraphStore
storage_context = StorageContext.from_defaults(graph_store=graph_store)

# Initialize the KnowledgeGraphQueryEngine with the required prompts
query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    service_context=service_context,
    llm=llm,
    graph_query_synthesis_prompt=graph_query_synthesis_prompt,
    graph_response_answer_prompt=DEFAULT_KG_RESPONSE_ANSWER_PROMPT,  # or your custom prompt
    verbose=True,
    refresh_schema=True
)

# Query the knowledge graph
response = query_engine.query(
    "Tell me more about what the author worked on at Interleaf",
)
print(response)

Ensure that graph_query_synthesis_prompt is not None and is a valid PromptTemplate instance. This should resolve the ValidationError [1][2].

#### Details **About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

data-scientist-shivam799 commented 5 months ago

@dosu Your solution is cool, it solved the issue but I am getting this now

---------------------------------------------------------------------------
CypherSyntaxError                         Traceback (most recent call last)
[<ipython-input-27-45c62b95ed91>](https://localhost:8080/#) in <cell line: 2>()
      1 # Query the knowledge graph
----> 2 response = query_engine.query(
      3     "Tell me the context"
      4 )
      5 print(response.response)

11 frames
[/usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py](https://localhost:8080/#) in on_failure(self, metadata)
    243         handler = self.handlers.get("on_summary")
    244         Util.callback(handler)
--> 245         raise Neo4jError.hydrate(**metadata)
    246 
    247     def on_ignored(self, metadata=None):

CypherSyntaxError: {code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input 'The': expected
  "ALTER"
  "CALL"
  "CREATE"
  "DEALLOCATE"
  "DELETE"
  "DENY"
  "DETACH"
  "DROP"
  "DRYRUN"
  "ENABLE"
  "FINISH"
  "FOREACH"
  "GRANT"
  "INSERT"
  "LOAD"
  "MATCH"
  "MERGE"
  "NODETACH"
  "OPTIONAL"
  "REALLOCATE"
  "REMOVE"
  "RENAME"
  "RETURN"
  "REVOKE"
  "SET"
  "SHOW"
  "START"
  "STOP"
  "TERMINATE"
  "UNWIND"
  "USE"
  "USING"
  "WITH" (line 1, column 1 (offset: 0))
"The query is related to the impact of COVID-19 on the global economy. The data shows that the pandemic has caused a significant economic downturn, with global GDP expected to contract by 4.4% in 2020. This has led to job losses, reduced consumer spending, and disruptions in supply chains. Governments around the world have implemented stimulus measures to support their economies, but the recovery is expected to be slow and uneven. The long-term effects of the pandemic on the global economy remain uncertain."
 ^}
dosubot[bot] commented 5 months ago

To resolve the CypherSyntaxError when querying a Neo4j knowledge graph using the Llama Index, you can utilize the self-healing flow implemented in the run_request method. This method attempts to correct the Cypher query by providing the specific error to GPT-4 and requesting an improved query. Additionally, you can use the CypherQueryCorrector class to correct relationship directions in generated Cypher statements.

Here is the relevant code snippet that demonstrates this process:

def run_request(self, question, history=None, retry=True):
    """
    Executes a Cypher query based on a given question.

    Args:
        question (str): The question to execute the Cypher query for.
        history (list, optional): A list of previous interactions for context. Defaults to None.
        retry (bool, optional): Whether to retry in case of a syntax error. Defaults to True.

    Returns:
        list/str: The query results or an error message.
    """
    from neo4j.exceptions import CypherSyntaxError

    # Construct Cypher statement
    cypher = self.construct_cypher_query(question, history)
    # Validate Cypher statement
    if self.cypher_query_corrector:
        cypher = self.cypher_query_corrector(cypher)
    print(cypher)
    try:
        return self.query_graph_db(cypher)
    # Self-healing flow
    except CypherSyntaxError as e:
        # If out of retries
        if not retry:
            return "Invalid Cypher syntax"
        # Self-healing Cypher flow by
        # providing specific error to GPT-4
        print("Retrying")
        return self.run_request(
            question,
            [
                ChatMessage(role=MessageRole.ASSISTANT, content=cypher),
                ChatMessage(
                    role=MessageRole.SYSTEM,
                    content=f"This query returns an error: {e!s}\n"
                    "Give me an improved query that works without any explanations or apologies",
                ),
            ],
            retry=False,
        )

Additionally, the CypherQueryCorrector class can be used to correct relationship directions in generated Cypher statements. Here is an example of how you can use it:

class CypherQueryCorrector:
    """
    Used to correct relationship direction in generated Cypher statements.
    This code is copied from the winner's submission to the Cypher competition:
    https://github.com/sakusaku-rich/cypher-direction-competition.
    """

    # Patterns for detecting nodes, properties, and paths
    property_pattern = re.compile(r"\{.+?\}")
    node_pattern = re.compile(r"\(.+?\)")
    path_pattern = re.compile(r"\(.*\).*-.*-.*\(.*\)")
    node_relation_node_pattern = re.compile(
        r"(\()+(?P<left_node>[^()]*?)\)(?P<relation>.*?)\((?P<right_node>[^()]*?)(\))+"
    )
    relation_type_pattern = re.compile(r":(?P<relation_type>.+?)?(\{.+\})?]")

    def __init__(self, schemas: List[Schema]):
        """
        Args:
            schemas: list of schemas.
        """
        self.schemas = schemas

    def clean_node(self, node: str) -> str:
        """
        Args:
            node: node in string format.
        """
        node = re.sub(self.property_pattern, "", node)
        node = node.replace("(", "")
        node = node.replace(")", "")
        return node.strip()

    def detect_node_variables(self, query: str) -> Dict[str, List[str]]:
        """
        Args:
            query: cypher query.
        """
        nodes = re.findall(self.node_pattern, query)
        nodes = [self.clean_node(node) for node in nodes]
        res: Dict[str, Any] = {}
        for node in nodes:
            parts = node.split(":")
            if parts == "":
                continue
            variable = parts[0]
            if variable not in res:
                res[variable] = []
            res[variable] += parts[1:]
        return res

    def extract_paths(self, query: str) -> "List[str]":
        """
        Args:
            query: cypher query.
        """
        return re.findall(self.path_pattern, query)

    def judge_direction(self, relation: str) -> str:
        """
        Args:
            relation: relation in string format.
        """
        direction = "BIDIRECTIONAL"
        if relation[0] == "<":
            direction = "INCOMING"
        if relation[-1] == ">":
            direction = "OUTGOING"
        return direction

    def extract_node_variable(self, part: str) -> Optional[str]:
        """
        Args:
            part: node in string format.
        """
        part = part.lstrip("(").rstrip(")")
        idx = part.find(":")
        if idx != -1:
            part = part[:idx]
        return None if part == "" else part

    def detect_labels(
        self, str_node: str, node_variable_dict: Dict[str, Any]
    ) -> List[str]:
        """
        Args:
            str_node: node in string format
            node_variable_dict: dictionary of node variables.
        """
        splitted_node = str_node.split(":")
        variable = splitted_node[0]
        labels = []
        if variable in node_variable_dict:
            labels = node_variable_dict[variable]
        elif variable == "" and len(splitted_node) > 1:
            labels = splitted_node[1:]
        return labels

    def verify_schema(
        self,
        from_node_labels: List[str],
        relation_types: List[str],
        to_node_labels: List[str],
    ) -> bool:
        """
        Args:
            from_node_labels: labels of the from node
            relation_type: type of the relation
            to_node_labels: labels of the to node.
        """
        valid_schemas = self.schemas
        if from_node_labels != []:
            from_node_labels = [label.strip("`") for label in from_node_labels]
            valid_schemas = [
                schema for schema in valid_schemas if schema[0] in from_node_labels
            ]
        if to_node_labels != []:
            to_node_labels = [label.strip("`") for label in to_node_labels]
            valid_schemas = [
                schema for schema in valid_schemas if schema[2] in to_node_labels
            ]
        if relation_types != []:
            relation_types = [type.strip("`") for type in relation_types]
            valid_schemas = [
                schema for schema in valid_schemas if schema[1] in relation_types
            ]
        return valid_schemas != []

    def detect_relation_types(self, str_relation: str) -> Tuple[str, List[str]]:
        """
        Args:
            str_relation: relation in string format.
        """
        relation_direction = self.judge_direction(str_relation)
        relation_type = self.relation_type_pattern.search(str_relation)
        if relation_type is None or relation_type.group("relation_type") is None:
            return relation_direction, []
        relation_types = [
            t.strip().strip("!")
            for t in relation_type.group("relation_type").split("|")
        ]
        return relation_direction, relation_types

    def correct_query(self, query: str) -> str:
        """
        Args:
            query: cypher query.
        """
        node_variable_dict = self.detect_node_variables(query)
        paths = self.extract_paths(query)
        for path in paths:
            original_path = path
            start_idx = 0
            while start_idx < len(path):
                match_res = re.match(self.node_relation_node_pattern, path[start_idx:])
                if match_res is None:
                    break
                start_idx += match_res.start()
                match_dict = match_res.groupdict()
                left_node_labels = self.detect_labels(
                    match_dict["left_node"], node_variable_dict
                )
                right_node_labels = self.detect_labels(
                    match_dict["right_node"], node_variable_dict
                )
                end_idx = (
                    start_idx
                    + 4
                    + len(match_dict["left_node"])
                    + len(match_dict["relation"])
                    + len(match_dict["right_node"])
                )
                original_partial_path = original_path[start_idx : end_idx + 1]
                relation_direction, relation_types = self.detect_relation_types(
                    match_dict["relation"]
                )

                if relation_types != [] and "".join(relation_types).find("*") != -1:
                    start_idx += (
                        len(match_dict["left_node"]) + len(match_dict["relation"]) + 2
                    )
                    continue
                if relation_direction == "OUTGOING":
                    is_legal = self.verify_schema(
                        left_node_labels, relation_types, right_node_labels
                    )
                    if not is_legal:
                        is_legal = self.verify_schema(
                            right_node_labels, relation_types, left_node_labels
                        )
                        if is_legal:
                            corrected_relation = "<" + match_dict["relation"][:-1]
                            corrected_partial_path = original_partial_path.replace(
                                match_dict["relation"], corrected_relation
                            )
                            query = query.replace(
                                original_partial_path, corrected_partial_path
                            )
                        else:
                            return ""
                elif relation_direction == "INCOMING":
                    is_legal = self.verify_schema(
                        right_node_labels, relation_types, left_node_labels
                    )
                    if not is_legal:
                        is_legal = self.verify_schema(
                            left_node_labels, relation_types, right_node_labels
                        )
                        if is_legal:
                            corrected_relation = match_dict["relation"][1:] + ">"
                            corrected_partial_path = original_partial_path.replace(
                                match_dict["relation"], corrected_relation
                            )
                            query = query.replace(
                                original_partial_path, corrected_partial_path
                            )
                        else:
                            return ""
                else:
                    is_legal = self.verify_schema(
                        left_node_labels, relation_types, right_node_labels
                    )
                    is_legal |= self.verify_schema(
                        right_node_labels, relation_types, left_node_labels
                    )
                    if not is_legal:
                        return ""
                start_idx += (
                    len(match_dict["left_node"]) + len(match_dict["relation"]) + 2
                )
        return query

    def __call__(self, query: str) -> str:
        """Correct the query to make it valid. If
        Args:
            query: cypher query.
        """
        return self.correct_query(query)

This method first constructs the Cypher query and optionally validates it using the CypherQueryCorrector. If a CypherSyntaxError is caught, it retries the query by asking GPT-4 to provide an improved version of the query without any explanations or apologies. If the retry fails, it returns an "Invalid Cypher syntax" message. The CypherQueryCorrector class helps in correcting the relationship directions in the Cypher statements, ensuring the query is valid [1][2].

#### Details **About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

data-scientist-shivam799 commented 5 months ago

@dosu bro how can I implement this in my code, can you please help?

dosubot[bot] commented 5 months ago

To integrate the self-healing flow and CypherQueryCorrector into your code to handle and correct the CypherSyntaxError when querying a Neo4j knowledge graph using the Llama Index, you can follow the provided code snippets. Here is a consolidated and complete example:

from importlib.util import find_spec
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.graph_stores.neo4j import Neo4jGraphStore
from llama_index.tools.neo4j.query_validator import CypherQueryCorrector, Schema
from neo4j.exceptions import CypherSyntaxError
from llama_index.core.llms.base import LLM, ChatMessage, MessageRole

class Neo4jQueryToolSpec(BaseToolSpec):
    """
    This class is responsible for querying a Neo4j graph database based on a provided schema definition.
    """

    spec_functions = ["run_request"]

    def __init__(self, url, user, password, database, llm: LLM, validate_cypher: bool = False):
        """
        Initializes the Neo4jSchemaWiseQuery object.

        Args:
            url (str): The connection string for the Neo4j database.
            user (str): Username for the Neo4j database.
            password (str): Password for the Neo4j database.
            llm (obj): A language model for generating Cypher queries.
            validate_cypher (bool): Validate relationship directions in the generated Cypher statement. Default: False
        """
        if find_spec("neo4j") is None:
            raise ImportError("`neo4j` package not found, please run `pip install neo4j`")

        self.graph_store = Neo4jGraphStore(url=url, username=user, password=password, database=database)
        self.llm = llm
        self.cypher_query_corrector = None
        if validate_cypher:
            corrector_schema = [
                Schema(el["start"], el["type"], el["end"])
                for el in self.graph_store.structured_schema.get("relationships")
            ]
            self.cypher_query_corrector = CypherQueryCorrector(corrector_schema)

    def get_system_message(self):
        """
        Generates a system message detailing the task and schema.
        """
        return f"""
        Task: Generate Cypher queries to query a Neo4j graph database based on the provided schema definition.
        Instructions:
        Use only the provided relationship types and properties.
        Do not use any other relationship types or properties that are not provided.
        If you cannot generate a Cypher statement based on the provided schema, explain the reason to the user.
        Schema:
        {self.graph_store.schema}

        Note: Do not include any explanations or apologies in your responses.
        """

    def query_graph_db(self, neo4j_query, params=None):
        """
        Queries the Neo4j database.

        Args:
            neo4j_query (str): The Cypher query to be executed.
            params (dict, optional): Parameters for the Cypher query. Defaults to None.

        Returns:
            list: The query results.
        """
        if params is None:
            params = {}
        with self.graph_store.client.session() as session:
            result = session.run(neo4j_query, params)
            output = [r.values() for r in result]
            output.insert(0, list(result.keys()))
            return output

    def construct_cypher_query(self, question, history=None):
        """
        Constructs a Cypher query based on a given question and history.

        Args:
            question (str): The question to construct the Cypher query for.
            history (list, optional): A list of previous interactions for context. Defaults to None.

        Returns:
            str: The constructed Cypher query.
        """
        messages = [
            ChatMessage(role=MessageRole.SYSTEM, content=self.get_system_message()),
            ChatMessage(role=MessageRole.USER, content=question),
        ]
        # Used for Cypher healing flows
        if history:
            messages.extend(history)

        completions = self.llm.chat(messages)
        return completions.message.content

    def run_request(self, question, history=None, retry=True):
        """
        Executes a Cypher query based on a given question.

        Args:
            question (str): The question to execute the Cypher query for.
            history (list, optional): A list of previous interactions for context. Defaults to None.
            retry (bool, optional): Whether to retry in case of a syntax error. Defaults to True.

        Returns:
            list/str: The query results or an error message.
        """
        # Construct Cypher statement
        cypher = self.construct_cypher_query(question, history)
        # Validate Cypher statement
        if self.cypher_query_corrector:
            cypher = self.cypher_query_corrector(cypher)
        print(cypher)
        try:
            return self.query_graph_db(cypher)
        # Self-healing flow
        except CypherSyntaxError as e:
            # If out of retries
            if not retry:
                return "Invalid Cypher syntax"
            # Self-healing Cypher flow by providing specific error to GPT-4
            print("Retrying")
            return self.run_request(
                question,
                [
                    ChatMessage(role=MessageRole.ASSISTANT, content=cypher),
                    ChatMessage(
                        role=MessageRole.SYSTEM,
                        content=f"This query returns an error: {e!s}\n"
                        "Give me an improved query that works without any explanations or apologies",
                    ),
                ],
                retry=False,
            )

This code integrates the self-healing flow and CypherQueryCorrector to handle and correct CypherSyntaxError when querying a Neo4j knowledge graph using the Llama Index. The CypherQueryCorrector class is designed to correct relationship directions in generated Cypher statements, ensuring that the queries conform to the schema definitions [1][2].

#### Details **About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

data-scientist-shivam799 commented 5 months ago

@dosu i think you have misunderstood me, I need to query neo4j database and retrieve data as like response to a particular question

dosubot[bot] commented 5 months ago

To query a Neo4j database and retrieve data as a response to a particular question using the LlamaIndex framework, you can use the run_request method of the Neo4jQueryToolSpec class. Here is an example of how you can do this:

from importlib.util import find_spec
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.graph_stores.neo4j import Neo4jGraphStore
from llama_index.tools.neo4j.query_validator import CypherQueryCorrector, Schema
from neo4j.exceptions import CypherSyntaxError
from llama_index.core.llms.base import LLM, ChatMessage, MessageRole

class Neo4jQueryToolSpec(BaseToolSpec):
    spec_functions = ["run_request"]

    def __init__(self, url, user, password, database, llm: LLM, validate_cypher: bool = False):
        if find_spec("neo4j") is None:
            raise ImportError("`neo4j` package not found, please run `pip install neo4j`")

        self.graph_store = Neo4jGraphStore(url=url, username=user, password=password, database=database)
        self.llm = llm
        self.cypher_query_corrector = None
        if validate_cypher:
            corrector_schema = [
                Schema(el["start"], el["type"], el["end"])
                for el in self.graph_store.structured_schema.get("relationships")
            ]
            self.cypher_query_corrector = CypherQueryCorrector(corrector_schema)

    def get_system_message(self):
        return f"""
        Task: Generate Cypher queries to query a Neo4j graph database based on the provided schema definition.
        Instructions:
        Use only the provided relationship types and properties.
        Do not use any other relationship types or properties that are not provided.
        If you cannot generate a Cypher statement based on the provided schema, explain the reason to the user.
        Schema:
        {self.graph_store.schema}

        Note: Do not include any explanations or apologies in your responses.
        """

    def query_graph_db(self, neo4j_query, params=None):
        if params is None:
            params = {}
        with self.graph_store.client.session() as session:
            result = session.run(neo4j_query, params)
            output = [r.values() for r in result]
            output.insert(0, list(result.keys()))
            return output

    def construct_cypher_query(self, question, history=None):
        messages = [
            ChatMessage(role=MessageRole.SYSTEM, content=self.get_system_message()),
            ChatMessage(role=MessageRole.USER, content=question),
        ]
        if history:
            messages.extend(history)

        completions = self.llm.chat(messages)
        return completions.message.content

    def run_request(self, question, history=None, retry=True):
        cypher = self.construct_cypher_query(question, history)
        if self.cypher_query_corrector:
            cypher = self.cypher_query_corrector(cypher)
        print(cypher)
        try:
            return self.query_graph_db(cypher)
        except CypherSyntaxError as e:
            if not retry:
                return "Invalid Cypher syntax"
            print("Retrying")
            return self.run_request(
                question,
                [
                    ChatMessage(role=MessageRole.ASSISTANT, content=cypher),
                    ChatMessage(
                        role=MessageRole.SYSTEM,
                        content=f"This query returns an error: {e!s}\n"
                        "Give me an improved query that works without any explanations or apologies",
                    ),
                ],
                retry=False,
            )

# Initialize the Neo4jQueryToolSpec with the necessary parameters
neo4j_tool = Neo4jQueryToolSpec(
    url="bolt://localhost:7687",  # Replace with your Neo4j connection URL
    user="neo4j",                 # Replace with your Neo4j username
    password="password",          # Replace with your Neo4j password
    database="neo4j",             # Replace with your Neo4j database name
    llm=your_llm_instance,        # Replace with your LLM instance
    validate_cypher=True          # Set to True if you want to validate Cypher queries
)

# Define the question you want to query
question = "What are the names of all employees in the database?"

# Execute the query
results = neo4j_tool.run_request(question)

# Print the results
print(results)

In this example:

  1. Replace the placeholders (url, user, password, database, and your_llm_instance) with your actual Neo4j connection details and the instance of your language model.
  2. Define the question variable with the question you want to ask.
  3. Call the run_request method with the question to get the results from the Neo4j database.
  4. Print the results to see the output.

This setup ensures that your queries are constructed, validated, and executed correctly, with error handling for CypherSyntaxError included [1][2][3][4][5].

#### Details **About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

data-scientist-shivam799 commented 5 months ago

@dosu I am trying to do something like this

from llama_index.core.prompts.base import PromptTemplate, PromptType
from llama_index.core import StorageContext, ServiceContext
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
from llama_index.graph_stores.neo4j import Neo4jGraphStore

# Define your custom prompt templates
graph_query_synthesis_prompt = PromptTemplate(
    "Based on the provided data, briefly summarize the key points related to the query.",
    prompt_type=PromptType.QUESTION_ANSWER,
)

DEFAULT_KG_RESPONSE_ANSWER_PROMPT = PromptTemplate(
    "Provide a detailed and comprehensive answer to the query, including any relevant context and background information.",
    prompt_type=PromptType.QUESTION_ANSWER,
)

# Initialize the LLM and ServiceContext
service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)

# Initialize the Neo4jGraphStore
username = "neo4j"
password = ""
url = ""
database = "neo4j"

graph_store = Neo4jGraphStore(
    username=username,
    password=password,
    url=url,
    database=database
)

# Create a StorageContext with your GraphStore
storage_context = StorageContext.from_defaults(graph_store=graph_store)

# Initialize the KnowledgeGraphQueryEngine with the required prompts
query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    service_context=service_context,
    llm=llm,
    graph_query_synthesis_prompt=graph_query_synthesis_prompt,
    graph_response_answer_prompt=DEFAULT_KG_RESPONSE_ANSWER_PROMPT,  # or your custom prompt
    verbose=True,
    refresh_schema=True
)

# Query the knowledge graph
response = query_engine.query(
    "tell me what government did to prevent it"
)
print(response)

and getting error like this

Graph Store Query: The query is related to the impact of COVID-19 on the global economy. The key points include a significant decline in global GDP growth, increased unemployment rates, disruptions in supply chains, and a shift towards remote work and digitalization. The pandemic has also led to increased government spending and stimulus packages to support businesses and individuals affected by the crisis. Overall, the global economy has been severely impacted by the pandemic, with various sectors facing challenges and uncertainties.

CypherSyntaxError Traceback (most recent call last) in <cell line: 2>() 1 # Query the knowledge graph ----> 2 response = query_engine.query( 3 "tell me what government did to prevent it" 4 ) 5 print(response)

11 frames /usr/local/lib/python3.10/dist-packages/neo4j/_sync/io/_common.py in on_failure(self, metadata) 243 handler = self.handlers.get("on_summary") 244 Util.callback(handler) --> 245 raise Neo4jError.hydrate(**metadata) 246 247 def on_ignored(self, metadata=None):

CypherSyntaxError: {code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input 'The': expected "ALTER" "CALL" "CREATE" "DEALLOCATE" "DELETE" "DENY" "DETACH" "DROP" "DRYRUN" "ENABLE" "FINISH" "FOREACH" "GRANT" "INSERT" "LOAD" "MATCH" "MERGE" "NODETACH" "OPTIONAL" "REALLOCATE" "REMOVE" "RENAME" "RETURN" "REVOKE" "SET" "SHOW" "START" "STOP" "TERMINATE" "UNWIND" "USE" "USING" "WITH" (line 1, column 1 (offset: 0)) "The query is related to the impact of COVID-19 on the global economy. The key points include a significant decline in global GDP growth, increased unemployment rates, disruptions in supply chains, and a shift towards remote work and digitalization. The pandemic has also led to increased government spending and stimulus packages to support businesses and individuals affected by the crisis. Overall, the global economy has been severely impacted by the pandemic, with various sectors facing challenges and uncertainties." ^}

I think it generated the response but something else happened

dosubot[bot] commented 5 months ago

The error you're encountering indicates that the generated response is not a valid Cypher query. To address this, you need to ensure that the language model generates a valid Cypher query instead of a natural language response. You can achieve this by refining the prompt templates and using the CypherQueryCorrector to validate and correct the generated queries.

Here's how you can modify your code to include these improvements:

  1. Refine the Prompt Templates: Ensure that the prompt templates are designed to generate Cypher queries.
  2. Use CypherQueryCorrector: Validate and correct the generated Cypher queries.

Here is the updated code:

from llama_index.core.prompts.base import PromptTemplate, PromptType
from llama_index.core import StorageContext, ServiceContext
from llama_index.core.query_engine import KnowledgeGraphQueryEngine
from llama_index.graph_stores.neo4j import Neo4jGraphStore
from llama_index.tools.neo4j.query_validator import CypherQueryCorrector, Schema
from neo4j.exceptions import CypherSyntaxError

# Define your custom prompt templates
graph_query_synthesis_prompt = PromptTemplate(
    "Generate a Cypher query to retrieve information based on the following question: {query_str}",
    prompt_type=PromptType.QUESTION_ANSWER,
)

DEFAULT_KG_RESPONSE_ANSWER_PROMPT = PromptTemplate(
    "Provide a detailed and comprehensive answer to the query, including any relevant context and background information.",
    prompt_type=PromptType.QUESTION_ANSWER,
)

# Initialize the LLM and ServiceContext
llm = your_llm_instance  # Replace with your actual LLM instance
service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)

# Initialize the Neo4jGraphStore
username = "neo4j"
password = "your_password"  # Replace with your Neo4j password
url = "your_neo4j_url"  # Replace with your Neo4j URL
database = "neo4j"

graph_store = Neo4jGraphStore(
    username=username,
    password=password,
    url=url,
    database=database
)

# Create a StorageContext with your GraphStore
storage_context = StorageContext.from_defaults(graph_store=graph_store)

# Initialize the KnowledgeGraphQueryEngine with the required prompts
query_engine = KnowledgeGraphQueryEngine(
    storage_context=storage_context,
    service_context=service_context,
    llm=llm,
    graph_query_synthesis_prompt=graph_query_synthesis_prompt,
    graph_response_answer_prompt=DEFAULT_KG_RESPONSE_ANSWER_PROMPT,  # or your custom prompt
    verbose=True,
    refresh_schema=True
)

# Define the CypherQueryCorrector
schemas = [
    Schema("Person", "KNOWS", "Person"),
    Schema("Person", "LIKES", "Movie"),
    # Add more schemas as needed
]
cypher_query_corrector = CypherQueryCorrector(schemas)

# Function to run the query with self-healing flow
def run_request(query_engine, question, history=None, retry=True):
    cypher = query_engine.generate_query(question)
    cypher = cypher_query_corrector(cypher)
    print("Generated Cypher Query:", cypher)
    try:
        return query_engine.query_graph_db(cypher)
    except CypherSyntaxError as e:
        if not retry:
            return "Invalid Cypher syntax"
        print("Retrying with corrected query")
        return run_request(
            query_engine,
            question,
            [
                ChatMessage(role=MessageRole.ASSISTANT, content=cypher),
                ChatMessage(
                    role=MessageRole.SYSTEM,
                    content=f"This query returns an error: {e!s}\n"
                    "Give me an improved query that works without any explanations or apologies",
                ),
            ],
            retry=False,
        )

# Query the knowledge graph
response = run_request(query_engine, "tell me what government did to prevent it")
print(response)

Key Changes:

  1. Prompt Template: The graph_query_synthesis_prompt is refined to explicitly ask for a Cypher query.
  2. CypherQueryCorrector: Added to validate and correct the generated Cypher queries.
  3. Self-Healing Flow: Implemented in the run_request function to handle and correct CypherSyntaxError.

This setup should help in generating valid Cypher queries and handling any syntax errors that occur during the query execution [1][2].

#### Details **About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

data-scientist-shivam799 commented 5 months ago

The problem was fixed by defining 

from llama_index.core.prompts.base import PromptTemplate, PromptType
graph_query_synthesis_prompt = PromptTemplate(
    """You are a Graph database expert. You are given a question that requires information retrieval from a Neo4j knowledge graph. \n
    Generate a precise Cypher query that retrieves the relevant information needed to answer the question. 
    """,
    prompt_type=PromptType.QUESTION_ANSWER,
)

DEFAULT_KG_RESPONSE_ANSWER_PROMPT = PromptTemplate(
    """Based on the information retrieved, provide a detailed and comprehensive answer to the following question.
    """,
    prompt_type=PromptType.QUESTION_ANSWER,
)
luisenriqueramos1977 commented 5 months ago

'KnowledgeGraphQueryEngine' object has no attribute 'query_graph_db', so the solution proposed by the bot does not work. The code indicated by data-scientist-shivam799 is the only we have to add.