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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
This commit is contained in:
Katarina Supe
GitHub
316
libs/community/langchain_community/chains/graph_qa/memgraph.py
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316
libs/community/langchain_community/chains/graph_qa/memgraph.py
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"""Question answering over a graph."""
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from __future__ import annotations
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import re
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from typing import Any, Dict, List, Optional, Union
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from langchain.chains.base import Chain
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from langchain_core.callbacks import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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SystemMessage,
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ToolMessage,
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)
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import (
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BasePromptTemplate,
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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)
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from langchain_core.runnables import Runnable
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from pydantic import Field
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from langchain_community.chains.graph_qa.prompts import (
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MEMGRAPH_GENERATION_PROMPT,
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MEMGRAPH_QA_PROMPT,
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)
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from langchain_community.graphs.memgraph_graph import MemgraphGraph
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INTERMEDIATE_STEPS_KEY = "intermediate_steps"
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FUNCTION_RESPONSE_SYSTEM = """You are an assistant that helps to form nice and human
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understandable answers based on the provided information from tools.
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Do not add any other information that wasn't present in the tools, and use
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very concise style in interpreting results!
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"""
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def extract_cypher(text: str) -> str:
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"""Extract Cypher code from a text.
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Args:
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text: Text to extract Cypher code from.
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Returns:
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Cypher code extracted from the text.
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"""
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# The pattern to find Cypher code enclosed in triple backticks
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pattern = r"```(.*?)```"
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# Find all matches in the input text
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matches = re.findall(pattern, text, re.DOTALL)
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return matches[0] if matches else text
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def get_function_response(
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question: str, context: List[Dict[str, Any]]
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) -> List[BaseMessage]:
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TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
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messages = [
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [
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{
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"id": TOOL_ID,
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"function": {
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"arguments": '{"question":"' + question + '"}',
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"name": "GetInformation",
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},
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"type": "function",
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}
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]
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},
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),
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ToolMessage(content=str(context), tool_call_id=TOOL_ID),
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]
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return messages
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class MemgraphQAChain(Chain):
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"""Chain for question-answering against a graph by generating Cypher statements.
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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graph: MemgraphGraph = Field(exclude=True)
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cypher_generation_chain: Runnable
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qa_chain: Runnable
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graph_schema: str
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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top_k: int = 10
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"""Number of results to return from the query"""
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return_intermediate_steps: bool = False
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"""Whether or not to return the intermediate steps along with the final answer."""
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return_direct: bool = False
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"""Optional cypher validation tool"""
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use_function_response: bool = False
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"""Whether to wrap the database context as tool/function response"""
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allow_dangerous_requests: bool = False
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"""Forced user opt-in to acknowledge that the chain can make dangerous requests.
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*Security note*: Make sure that the database connection uses credentials
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that are narrowly-scoped to only include necessary permissions.
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Failure to do so may result in data corruption or loss, since the calling
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code may attempt commands that would result in deletion, mutation
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of data if appropriately prompted or reading sensitive data if such
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data is present in the database.
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The best way to guard against such negative outcomes is to (as appropriate)
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limit the permissions granted to the credentials used with this tool.
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See https://python.langchain.com/docs/security for more information.
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"""
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def __init__(self, **kwargs: Any) -> None:
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"""Initialize the chain."""
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super().__init__(**kwargs)
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if self.allow_dangerous_requests is not True:
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raise ValueError(
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"In order to use this chain, you must acknowledge that it can make "
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"dangerous requests by setting `allow_dangerous_requests` to `True`."
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"You must narrowly scope the permissions of the database connection "
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"to only include necessary permissions. Failure to do so may result "
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"in data corruption or loss or reading sensitive data if such data is "
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"present in the database."
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"Only use this chain if you understand the risks and have taken the "
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"necessary precautions. "
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"See https://python.langchain.com/docs/security for more information."
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)
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return the output keys.
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:meta private:
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"""
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_output_keys = [self.output_key]
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return _output_keys
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@property
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def _chain_type(self) -> str:
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return "graph_cypher_chain"
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@classmethod
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def from_llm(
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cls,
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llm: Optional[BaseLanguageModel] = None,
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*,
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qa_prompt: Optional[BasePromptTemplate] = None,
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cypher_prompt: Optional[BasePromptTemplate] = None,
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cypher_llm: Optional[BaseLanguageModel] = None,
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qa_llm: Optional[Union[BaseLanguageModel, Any]] = None,
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qa_llm_kwargs: Optional[Dict[str, Any]] = None,
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cypher_llm_kwargs: Optional[Dict[str, Any]] = None,
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use_function_response: bool = False,
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function_response_system: str = FUNCTION_RESPONSE_SYSTEM,
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**kwargs: Any,
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) -> MemgraphQAChain:
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"""Initialize from LLM."""
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if not cypher_llm and not llm:
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raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
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if not qa_llm and not llm:
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raise ValueError("Either `llm` or `qa_llm` parameters must be provided")
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if cypher_llm and qa_llm and llm:
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raise ValueError(
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"You can specify up to two of 'cypher_llm', 'qa_llm'"
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", and 'llm', but not all three simultaneously."
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)
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if cypher_prompt and cypher_llm_kwargs:
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raise ValueError(
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"Specifying cypher_prompt and cypher_llm_kwargs together is"
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" not allowed. Please pass prompt via cypher_llm_kwargs."
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)
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if qa_prompt and qa_llm_kwargs:
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raise ValueError(
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"Specifying qa_prompt and qa_llm_kwargs together is"
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" not allowed. Please pass prompt via qa_llm_kwargs."
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)
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use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {}
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use_cypher_llm_kwargs = (
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cypher_llm_kwargs if cypher_llm_kwargs is not None else {}
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)
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if "prompt" not in use_qa_llm_kwargs:
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use_qa_llm_kwargs["prompt"] = (
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qa_prompt if qa_prompt is not None else MEMGRAPH_QA_PROMPT
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)
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if "prompt" not in use_cypher_llm_kwargs:
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use_cypher_llm_kwargs["prompt"] = (
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cypher_prompt
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if cypher_prompt is not None
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else MEMGRAPH_GENERATION_PROMPT
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)
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qa_llm = qa_llm or llm
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if use_function_response:
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try:
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qa_llm.bind_tools({}) # type: ignore[union-attr]
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response_prompt = ChatPromptTemplate.from_messages(
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[
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SystemMessage(content=function_response_system),
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HumanMessagePromptTemplate.from_template("{question}"),
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MessagesPlaceholder(variable_name="function_response"),
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]
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)
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qa_chain = response_prompt | qa_llm | StrOutputParser() # type: ignore
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except (NotImplementedError, AttributeError):
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raise ValueError("Provided LLM does not support native tools/functions")
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else:
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qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser() # type: ignore
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prompt = use_cypher_llm_kwargs["prompt"]
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llm_to_use = cypher_llm if cypher_llm is not None else llm
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if prompt is not None and llm_to_use is not None:
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cypher_generation_chain = prompt | llm_to_use | StrOutputParser() # type: ignore[arg-type]
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else:
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raise ValueError(
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"Missing required components for the cypher generation chain: "
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"'prompt' or 'llm'"
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)
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graph_schema = kwargs["graph"].get_schema
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return cls(
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graph_schema=graph_schema,
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qa_chain=qa_chain,
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cypher_generation_chain=cypher_generation_chain,
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use_function_response=use_function_response,
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**kwargs,
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)
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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"""Generate Cypher statement, use it to look up in db and answer question."""
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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callbacks = _run_manager.get_child()
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question = inputs[self.input_key]
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args = {
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"question": question,
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"schema": self.graph_schema,
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}
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args.update(inputs)
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intermediate_steps: List = []
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generated_cypher = self.cypher_generation_chain.invoke(
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args, callbacks=callbacks
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)
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# Extract Cypher code if it is wrapped in backticks
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generated_cypher = extract_cypher(generated_cypher)
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_run_manager.on_text("Generated Cypher:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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generated_cypher, color="green", end="\n", verbose=self.verbose
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)
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intermediate_steps.append({"query": generated_cypher})
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# Retrieve and limit the number of results
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# Generated Cypher be null if query corrector identifies invalid schema
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if generated_cypher:
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context = self.graph.query(generated_cypher)[: self.top_k]
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else:
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context = []
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if self.return_direct:
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result = context
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else:
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_run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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str(context), color="green", end="\n", verbose=self.verbose
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)
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intermediate_steps.append({"context": context})
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if self.use_function_response:
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function_response = get_function_response(question, context)
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result = self.qa_chain.invoke( # type: ignore
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{"question": question, "function_response": function_response},
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)
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else:
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result = self.qa_chain.invoke( # type: ignore
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{"question": question, "context": context},
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callbacks=callbacks,
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)
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chain_result: Dict[str, Any] = {"result": result}
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if self.return_intermediate_steps:
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chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
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return chain_result
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@@ -411,3 +411,58 @@ NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_PROMPT = PromptTemplate(
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input_variables=["schema", "question", "extra_instructions"],
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template=NEPTUNE_OPENCYPHER_GENERATION_SIMPLE_TEMPLATE,
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)
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MEMGRAPH_GENERATION_TEMPLATE = """Your task is to directly translate natural language inquiry into precise and executable Cypher query for Memgraph database.
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You will utilize a provided database schema to understand the structure, nodes and relationships within the Memgraph database.
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Instructions:
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- Use provided node and relationship labels and property names from the
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schema which describes the database's structure. Upon receiving a user
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question, synthesize the schema to craft a precise Cypher query that
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directly corresponds to the user's intent.
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- Generate valid executable Cypher queries on top of Memgraph database.
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Any explanation, context, or additional information that is not a part
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of the Cypher query syntax should be omitted entirely.
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- Use Memgraph MAGE procedures instead of Neo4j APOC procedures.
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- Do not include any explanations or apologies in your responses.
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- Do not include any text except the generated Cypher statement.
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- For queries that ask for information or functionalities outside the direct
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generation of Cypher queries, use the Cypher query format to communicate
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limitations or capabilities. For example: RETURN "I am designed to generate
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Cypher queries based on the provided schema only."
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Schema:
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{schema}
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With all the above information and instructions, generate Cypher query for the
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user question.
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The question is:
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{question}"""
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MEMGRAPH_GENERATION_PROMPT = PromptTemplate(
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input_variables=["schema", "question"], template=MEMGRAPH_GENERATION_TEMPLATE
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)
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MEMGRAPH_QA_TEMPLATE = """Your task is to form nice and human
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understandable answers. The information part contains the provided
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information that you must use to construct an answer.
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The provided information is authoritative, you must never doubt it or try to
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use your internal knowledge to correct it. Make the answer sound as a
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response to the question. Do not mention that you based the result on the
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given information. Here is an example:
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Question: Which managers own Neo4j stocks?
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Context:[manager:CTL LLC, manager:JANE STREET GROUP LLC]
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Helpful Answer: CTL LLC, JANE STREET GROUP LLC owns Neo4j stocks.
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Follow this example when generating answers. If the provided information is
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empty, say that you don't know the answer.
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Information:
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{context}
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Question: {question}
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Helpful Answer:"""
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MEMGRAPH_QA_PROMPT = PromptTemplate(
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input_variables=["context", "question"], template=MEMGRAPH_QA_TEMPLATE
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)
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Reference in New Issue
Block a user