A method for generating Cypher query statements based on thought chains in graph databases

Abstract

This invention discloses a method for generating Cypher query statements for graph databases based on thought chains, belonging to the field of graph database query technology. It includes data preprocessing, model training and query generation, model evaluation, security and privacy protection, system integration, and dynamic adaptive adjustment, transforming user natural language query requirements into executable Cypher query statements for graph databases. By defining meta-functions, the query problem is decomposed into combinations of basic operations, simulating the human thought chain and conforming to human logical problem-solving habits. This decomposition method makes the problem-solving process clearer, more organized, easier to understand and control. Whether it's developers constructing query logic or subsequent system maintenance and optimization, this intuitive problem decomposition method makes it more efficient and convenient.

Description

Technical Field

[0001] This invention relates to the field of graph database query technology, and in particular to a method for generating Cypher query statements for graph databases based on thought chains. Background Technology

[0002] With the continuous development of information technology, data storage and retrieval have become increasingly important. Graph databases are powerful tools for storing and analyzing complex relational data. Data in graph databases is represented in the form of nodes and edges, a data structure particularly suitable for representing complex relationships between entities, such as social networks, knowledge graphs, and recommender systems. Cypher is a query language for querying graph databases, possessing powerful capabilities for retrieving and manipulating data within them. However, writing Cypher queries requires a deep understanding of database structure and query syntax, which can be a complex task for non-technical personnel or beginners.

[0003] The rapid development of Natural Language Processing (NLP) technology, especially large language models, has undoubtedly provided excellent solutions to many text generation problems. NLP, based on deep learning and the Transformer architecture, enables computers to understand and generate natural language text through training on large-scale text data, thus performing well in various NLP tasks such as machine translation, sentiment analysis, and question answering. Large language models, typically based on the Transformer architecture, can understand and generate natural language text through pre-training on large-scale text data. They can handle the syntactic, semantic, and contextual information of text, enabling them to perform well in various NLP tasks such as text generation, question answering, sentiment analysis, and machine translation. However, in a specific domain, large language models often require training on a large amount of domain-specific data to achieve efficient solutions for that problem.

[0004] To address the aforementioned issues, this invention proposes a thought chain-based method that can train a large language model to automatically generate Cypher query statements for interacting with data in a graph database based on user-submitted questions. This method allows users to easily ask questions and obtain the data they need without requiring in-depth knowledge of Cypher query syntax. Summary of the Invention

[0005] The purpose of this invention is to provide a method for generating Cypher query statements based on thought chains, which solves the technical problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides the following technical solution: a method for generating Cypher query statements for graph databases based on thought chains, including data preprocessing, model training and query generation, model evaluation, security and privacy protection, system integration, and dynamic adaptive adjustment, which transforms user natural language query requirements into Cypher query statements executable by the graph database, as detailed below:

[0007] The data preprocessing includes: integrating the metafunction system of task prompts and operation details, the metadata information and relation pairs of the graph database, cleaning the natural language issues in the training data, removing noise characters and unifying the text format; performing consistency verification and normalization processing on the graph database metadata, using the AES encryption algorithm to ensure data transmission and storage security, building an access control system to manage query permissions, and adding noise to the training data according to the differential privacy mechanism;

[0008] The model training and query generation utilize a large-scale pre-trained model based on deep learning and Transformer architecture. The pre-processed user natural language query requirements are mapped to Python code composed of predefined metafunctions, and then converted into Cypher query statements by a Python interpreter to achieve data interaction with the graph database.

[0009] The model evaluation uses an independent test dataset. Accuracy is evaluated by calculating precision, recall, and F1 score. Response efficiency is evaluated by recording code execution and query response time. Professional personnel review query statements to evaluate executability and rationality.

[0010] The security and privacy protection measures employ AES encryption algorithm, a fine-grained access control system, and differential privacy mechanism.

[0011] The system integration design includes interfaces with other systems.

[0012] The dynamic adaptive adjustment establishes a graph database change monitoring mechanism, which automatically triggers the model fine-tuning mechanism when the database changes, ensuring that the model adapts to the new environment;

[0013] The predefined metafunctions used to build Python code are the basic operational units that are decomposed and abstracted for graph database query tasks. The preprocessing stage standardizes the input and output data formats of metafunctions, checks data transmission compatibility, and performs single operations such as node query, relation retrieval, and data filtering. These metafunctions can be combined to build complex query logic. Task prompts provide macro-level guidance, while operational details explain the input and output specifications, parameter constraints, and internal logic. The contribution of metafunctions to the model generation results is evaluated, and the frequency of use and correct call rate are analyzed. Function definitions, parameter settings, or prompt messages are adjusted accordingly.

[0014] It has the capability to handle complex query requirements across all scenarios in graph databases. In the preprocessing stage, it deeply analyzes natural language query requirements, extracts key information and standardizes its representation, mines metadata to build a relational graph for query optimization, performs semantic parsing and logical reconstruction on the preprocessed query requirements, and transforms them into metafunction call sequences to generate Python code and Cypher query statements.

[0015] Preferably, in the data preprocessing stage, in addition to basic data cleaning, verification, and standardization operations, the model's own language understanding capabilities are used to deeply analyze natural language query requirements. This includes extracting key entities, relationships, and attribute information, labeling them according to specific classification standards, calibrating and standardizing the type and attribute information of graph database metadata, and deeply analyzing the performance differences of different types of query requirements based on the model's evaluation results on the test dataset. For query types with poor performance, the reasons are analyzed from the aspects of training data quality, model parameter rationality, and training algorithm effectiveness. Targeted optimization is then carried out by adjusting the training data distribution, optimizing model parameters, or improving the training algorithm. Visualization tools are developed to display the model's reasoning process, and interpretation generation algorithms are designed to generate natural language explanations for code and query statements, thereby enhancing the model's interpretability.

[0016] Preferably, the process of translating user natural language query requirements into Python code includes the following steps:

[0017] Precise design of meta-functions: Based on the underlying data model, topology, and common query patterns of graph databases, design functionally independent, interface-standard, and reusable meta-functions, and define the input and output of meta-functions;

[0018] The example data is carefully constructed: the examples are designed based on graph database metadata and business requirements, including natural language query requirements, metafunction call sequences and semantic interpretations, preprocessing query requirements, verifying the accuracy of call sequences, filtering and labeling examples to cover typical query scenarios and business logic, providing high-quality training data, regularly evaluating the completeness, accuracy and coverage of new scenarios of the example data, supplementing and updating data, incorporating system integration cases, and considering the impact of dynamic environmental data changes;

[0019] Model training and guidance: Input example data into a large pre-trained model, use supervised learning and reinforcement learning strategies to guide the model to learn the mapping relationship between natural language query requirements and Python code, use data preprocessing techniques to enhance and standardize input data, optimize model parameters and structure, monitor training indicators, adjust training parameters according to changes, consider model interpretability during training, and guide the model to learn interpretable decision-making processes.

[0020] Code generation and verification: After the model training reaches the target, Python code is generated according to the new natural language query requirements. The code undergoes syntax checking, semantic verification, and logical reasoning. It is optimized by combining preprocessing information. The code verification results are incorporated into the evaluation system. Error types and frequencies are analyzed to assess the reliability of code generation. The code's compatibility, correctness, and stability under dynamic database changes are verified in system integration.

[0021] Preferably, after constructing the Cypher query statement by executing code through the Python interpreter, the query statement is sent to the graph database query execution engine. The engine performs lexical analysis, syntax parsing, semantic checking, and query optimization, generates an execution plan to retrieve data and format the output results. The system monitors and analyzes the query execution performance, records time and resource consumption for query optimization and system tuning, preprocesses the query results, removes redundant information and converts it into an easy-to-understand format, evaluates the model based on the accuracy and performance indicators of the query results, and provides feedback to optimize the model.

[0022] Preferably, the metafunction design follows the principles of modularity, hierarchy, and composability. Metafunctions are functionally cohesive, responsible for a single operation, and have standardized interfaces for easy combination and expansion. The hierarchical design enables operations at different abstraction levels to collaborate in constructing complex logic. Preprocessing verifies the input and output data of metafunctions to ensure accurate and stable transmission, taking into account the modularity. The metafunction design adapts to the development of graph databases and business changes, possessing scalability and adaptability. The scalability of the metafunction system is evaluated, and the adaptability of the model and the quality of the generated results are tested by simulating new scenarios and data changes. The architecture design is optimized, and the scalability, compatibility, stability, and adaptability of metafunctions in system integration, as well as under dynamic database changes, are evaluated.

[0023] Preferably, the relation pairs used to train the large language model undergo data mining, feature engineering, and quality assessment. During the mining and engineering process, natural language problem features are extracted and converted into vector representations. Key features and patterns are extracted by analyzing and optimizing function call sequences. In the quality assessment stage, relation pairs are cleaned and screened to remove noise and mislabeled data. The relation pairs cover common and complex query cases to ensure that the model learns comprehensive query knowledge and reasoning ability. The training data is regularly updated and expanded to adapt to dynamic changes in the database and business needs. The impact of training data on model generalization is evaluated, the training effects of different subsets are compared, data selection and expansion strategies are optimized, system integration cases are added, and the impact of dynamic environmental data changes is considered.

[0024] Preferably, before the generated Python code is input into the interpreter, static code analysis, symbolic execution, and model checking techniques are used for quality checks. Variables and function calls in the code are preprocessed to provide accurate verification information. Code syntax, data types, control flow, and data flow are analyzed to detect errors and provide repair suggestions. Code coverage tools are used to evaluate test coverage, improving code reliability and stability. The code quality check results are used as an evaluation indicator of model generation capability, analyzing their relationship with training parameters and data to optimize the training process and improve code quality. The code's compatibility, security, and stability under dynamic database changes in system integration are also checked.

[0025] Preferably, the generated Cypher query statements undergo multi-level performance optimization. During the construction phase, the query logic is transformed based on metadata and optimization rules analysis to eliminate redundant operations and subqueries. During the execution phase, graph database indexes, caching, and parallel computing technologies are used to optimize the query. Optimization strategies are formulated by combining preprocessed statistical information and historical data. The strategies are adjusted in real time based on monitoring and feedback. A performance evaluation model is established. The effectiveness of the optimization strategies is evaluated by comprehensively considering execution time, resource consumption, and result accuracy. The optimal combination is selected and continuously improved. The effectiveness, adaptability, and adjustment capability of the optimization strategies in system integration and under dynamic changes in the database are evaluated.

[0026] Compared with related technologies, the Cypher query statement generation method for graph databases based on thought chains provided by this invention has the following beneficial effects:

[0027] 1. This invention provides a method for generating Cypher query statements for graph databases based on thought chains. By defining meta-functions, the query problem is decomposed into a combination of basic operations, simulating the human thought chain and conforming to human logical problem-solving habits. This decomposition method makes the problem-solving process clearer, more organized, easier to understand and control. Whether it's developers constructing query logic or subsequent system maintenance and optimization, this intuitive problem decomposition method makes it more efficient and convenient.

[0028] 2. This invention provides a method for generating Cypher query statements for graph databases based on thought chains. Users do not need to deeply learn and master complex Cypher query syntax; they can interact with the graph database simply by asking questions using natural language. This significantly lowers the barrier to entry for graph databases, enabling beginners and non-technical personnel to easily obtain the information they need, expanding the user base of graph databases and increasing their application penetration in various fields.

[0029] 3. This invention provides a method for generating Cypher query statements for graph databases based on thought chains. It uses natural language interaction, as natural and fluent as conversation between people, conforming to users' daily communication habits and making user interaction with graph databases more comfortable and convenient. Compared to traditional methods that require memorizing and writing specific syntax, this natural language interaction method reduces the user's learning cost and operational difficulty, improving user experience and satisfaction. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the implementation of the present invention;

[0031] Figure 2 This is a flowchart of the present invention;

[0032] Figure 3 This is an extended flowchart of the data preprocessing process of the present invention;

[0033] Figure 4 This is an extended flowchart for model training and querying in this invention;

[0034] Figure 5 This is an extended flowchart of the model evaluation of the present invention;

[0035] Figure 6 This is an extended flowchart illustrating the security and privacy protection features of this invention.

[0036] Figure 7 This is an extended flowchart of the system integration of the present invention;

[0037] Figure 8 This is an extended flowchart of the dynamic adaptive adjustment of the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] Example 1:

[0040] Please see Figures 1-8 This invention provides a technical solution: a method for generating Cypher query statements for graph databases based on thought chains, including data preprocessing, model training and query generation, model evaluation, security and privacy protection, system integration, and dynamic adaptive adjustment, which transforms users' natural language query requirements into Cypher query statements executable by the graph database, as detailed below:

[0041] The data preprocessing includes: integrating the metafunction system of task prompts and operation details, the metadata information and relation pairs of the graph database, cleaning the natural language issues in the training data, removing noise characters and unifying the text format; performing consistency verification and normalization processing on the graph database metadata, using the AES encryption algorithm to ensure data transmission and storage security, building an access control system to manage query permissions, and adding noise to the training data according to the differential privacy mechanism;

[0042] The model training and query generation utilize a large-scale pre-trained model based on deep learning and Transformer architecture. The pre-processed user natural language query requirements are mapped to Python code composed of predefined metafunctions, and then converted into Cypher query statements by a Python interpreter to achieve data interaction with the graph database.

[0043] The model evaluation uses an independent test dataset. Accuracy is evaluated by calculating precision, recall, and F1 score. Response efficiency is evaluated by recording code execution and query response time. Professional personnel review query statements to evaluate executability and rationality.

[0044] The security and privacy protection measures employ AES encryption algorithm, a fine-grained access control system, and differential privacy mechanism.

[0045] The system integration design includes interfaces with other systems.

[0046] The dynamic adaptive adjustment establishes a graph database change monitoring mechanism, which automatically triggers the model fine-tuning mechanism when the database changes, ensuring that the model adapts to the new environment.

[0047] In the data preprocessing stage, in addition to basic data cleaning, verification, and standardization, the model's own language understanding capabilities are leveraged to perform in-depth analysis of natural language query requirements. This includes extracting key entities, relationships, and attribute information, and labeling them according to specific classification standards. Simultaneously, the types and attribute information of graph database metadata are calibrated and standardized. Based on the model's evaluation results on the test dataset, the performance differences of different types of query requirements are analyzed in depth. For query types with poor performance, the reasons are analyzed from the perspectives of training data quality, model parameter rationality, and training algorithm effectiveness. Targeted optimization is then carried out by adjusting the training data distribution, optimizing model parameters, or improving training algorithms. Visualization tools are developed to demonstrate the model's reasoning process, and interpretation generation algorithms are designed to generate natural language explanations for code and query statements, enhancing the model's interpretability.

[0048] Predefined metafunctions in Python code are basic operational units that decompose and abstract graph database query tasks. The preprocessing stage standardizes the input and output data formats of metafunctions and checks data transmission compatibility. Metafunctions perform single operations such as node query, relation retrieval, and data filtering, and can be combined to construct complex query logic. Task prompts provide macro-level guidance, while operational details explain the input and output specifications, parameter constraints, and internal logic. The contribution of metafunctions to the model generation results is evaluated, usage frequency and correct call rate are analyzed, and function definitions, parameter settings, or prompt messages are adjusted. The design of metafunctions considers system integration compatibility and scalability to adapt to the dynamic changes of graph databases.

[0049] It possesses the capability to handle complex query requirements across all scenarios in graph databases. During the preprocessing stage, it deeply analyzes natural language query requirements, extracts key information and standardizes its representation, mines metadata to construct a relationship graph for query optimization, performs semantic parsing and logical reconstruction on the preprocessed query requirements, transforms them into metafunction call sequences to generate Python code and Cypher query statements, optimizes queries by combining metadata information, and utilizes graph database indexing, storage, and query mechanisms to achieve accurate retrieval and efficient analysis. It evaluates the model's ability to handle complex queries, compares it with expert query statements, and optimizes complex query generation strategies based on the differences. The metafunction system and query generation strategies adaptively adjust with database changes, ensuring accurate transmission and processing of complex query results during integration.

[0050] The process of translating user natural language queries into Python code includes the following steps:

[0051] Precise design of meta-functions: Based on the underlying data model, topology, and common query patterns of graph databases, design functionally independent, interface-standard, and reusable meta-functions, and define the input and output of meta-functions;

[0052] The example data is carefully constructed: the examples are designed based on graph database metadata and business requirements, including natural language query requirements, metafunction call sequences and semantic interpretations, preprocessing query requirements, verifying the accuracy of call sequences, filtering and labeling examples to cover typical query scenarios and business logic, providing high-quality training data, regularly evaluating the completeness, accuracy and coverage of new scenarios of the example data, supplementing and updating data, incorporating system integration cases, and considering the impact of dynamic environmental data changes;

[0053] Model training and guidance: Input example data into a large pre-trained model, use supervised learning and reinforcement learning strategies to guide the model to learn the mapping relationship between natural language query requirements and Python code, use data preprocessing techniques to enhance and standardize input data, optimize model parameters and structure, monitor training indicators, adjust training parameters according to changes, consider model interpretability during training, and guide the model to learn interpretable decision-making processes.

[0054] Code generation and verification: After the model training reaches the target, Python code is generated according to the new natural language query requirements. The code undergoes syntax checking, semantic verification, and logical reasoning. It is optimized by combining preprocessing information. The code verification results are incorporated into the evaluation system. Error types and frequencies are analyzed to assess the reliability of code generation. The code's compatibility, correctness, and stability under dynamic database changes are verified in system integration.

[0055] After constructing the Cypher query statement by executing code through the Python interpreter, the query statement is sent to the graph database query execution engine. The engine performs lexical analysis, syntax parsing, semantic checking, and query optimization, generates an execution plan, retrieves data, and formats the output results. The system monitors and analyzes the query execution performance, records time and resource consumption for query optimization and system tuning, preprocesses the query results, removes redundant information and converts it into an easy-to-understand format, evaluates the model based on the accuracy and performance indicators of the query results, and provides feedback to optimize the model.

[0056] Metafunction design follows the principles of modularity, hierarchy, and composability. Metafunctions are cohesive, responsible for a single operation, and have standardized interfaces for easy combination and expansion. The hierarchical design enables operations at different levels of abstraction to collaborate in constructing complex logic. Preprocessing verifies the input and output data of metafunctions to ensure accurate and stable transmission, taking into account the modularity. The metafunction design adapts to the development of graph databases and business changes, possessing scalability and adaptability. The scalability of the metafunction system is evaluated, and the adaptability of the model and the quality of the generated results are tested by simulating new scenarios and data changes. The architecture design is optimized, and the scalability, compatibility, stability, and adaptability of metafunctions in system integration, as well as under dynamic database changes, are evaluated.

[0057] The training of the large language model involves data mining, feature engineering, and quality assessment. During mining and engineering, natural language problem features are extracted and converted into vector representations. Key features and patterns are extracted by analyzing and optimizing function call sequences. The quality assessment stage cleans and filters relation pairs, removing noise and mislabeled data. The relation pairs cover both common and complex query cases, ensuring the model learns comprehensive query knowledge and reasoning abilities. Training data is regularly updated and expanded to adapt to dynamic database changes and business needs. The impact of training data on model generalization is evaluated, and the training effects of different subsets are compared to optimize data selection and expansion strategies. System integration cases are added, and the impact of dynamic environmental data changes is considered.

[0058] Before the generated Python code is input into the interpreter, static code analysis, symbolic execution, and model checking techniques are used for quality checks. Variables and function calls in the code are preprocessed to provide accurate verification information. Code syntax, data types, control flow, and data flow are analyzed to detect errors and provide repair suggestions. Code coverage tools are used to evaluate test coverage, improving code reliability and stability. The code quality check results are used as an evaluation indicator of model generation capability, analyzing their relationship with training parameters and data to optimize the training process and improve code quality. The code's compatibility, security, and stability under dynamic database changes in system integration are also checked.

[0059] The generated Cypher query statements undergo multi-level performance optimization. During the construction phase, the query logic is transformed based on metadata and optimization rules analysis to eliminate redundant operations and subqueries. During the execution phase, graph database indexes, caching, and parallel computing technologies are used to optimize the query. Optimization strategies are formulated by combining preprocessed statistical information and historical data. The strategies are adjusted in real time based on monitoring and feedback. A performance evaluation model is established, and the effectiveness of the optimization strategies is evaluated by comprehensively considering execution time, resource consumption, and result accuracy. The optimal combination is selected and continuously improved. The effectiveness, adaptability, and adjustment capability of the optimization strategies in system integration and under dynamic database changes are evaluated.

[0060] This invention defines a series of metafunctions that break down complex query problems into combinations of basic operations, simulating the human problem-solving thought process. When processing a query in a social network graph database, to find "Alice's friends who are over 30 years old," the system decomposes this query into multiple basic operations represented by metafunctions. First, the NODE metafunction locates the user node "Alice." Then, the RELATION metafunction finds nodes with a "friend" relationship to "Alice." Finally, the FILTER metafunction filters out nodes older than 30. This decomposition method makes the query logic clear and concise. Developers can more intuitively organize the various operations when constructing queries, like building blocks, combining metafunctions with different functions. Subsequent system maintenance and optimization also allow for quick identification and adjustment of specific operational steps, greatly improving development and maintenance efficiency. Users do not need to... By mastering the complex Cypher query syntax, users only need to submit their query requests in natural language, and the system can automatically convert the natural language into Cypher query statements. This is thanks to the system's data preprocessing, model training, and query generation stages. In the data preprocessing stage, the system cleans and standardizes natural language issues and extracts key information. In the model training and query generation stages, a large-scale pre-trained model, trained on a large scale of multi-source heterogeneous text data, learns the mapping relationship between natural language and metafunction codes. The system interacts in a natural language manner, aligning with users' daily communication habits, reducing the cost and difficulty of learning specific syntax for users, and improving the comfort and satisfaction of users using graph databases.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for generating Cypher query statements for graph databases based on thought chains, characterized in that: This includes data preprocessing, model training and query generation, model evaluation, security and privacy protection, system integration, and dynamic adaptive adjustment, transforming users' natural language query requirements into Cypher query statements executable by the graph database, as detailed below: The data preprocessing includes: integrating the metafunction system of task prompts and operation details, the metadata information and relation pairs of the graph database, cleaning the natural language issues in the training data, removing noise characters and unifying the text format; performing consistency verification and normalization processing on the graph database metadata, using the AES encryption algorithm to ensure data transmission and storage security, building an access control system to manage query permissions, and adding noise to the training data according to the differential privacy mechanism; The model training and query generation utilize a large-scale pre-trained model based on deep learning and Transformer architecture. The pre-processed user natural language query requirements are mapped to Python code composed of predefined metafunctions, and then converted into Cypher query statements by a Python interpreter to achieve data interaction with the graph database. The model evaluation uses an independent test dataset. Accuracy is evaluated by calculating precision, recall, and F1 score. Response efficiency is evaluated by recording code execution and query response time. Professional personnel review query statements to evaluate executability and rationality. The security and privacy protection measures employ AES encryption algorithm, a fine-grained access control system, and differential privacy mechanism. The system integration design includes interfaces with other systems. The dynamic adaptive adjustment establishes a graph database change monitoring mechanism, which automatically triggers the model fine-tuning mechanism when the database changes, ensuring that the model adapts to the new environment; The predefined metafunctions used to build Python code are the basic operational units that are decomposed and abstracted for graph database query tasks. The preprocessing stage standardizes the input and output data formats of metafunctions, checks data transmission compatibility, and performs single operations such as node query, relation retrieval, and data filtering. These metafunctions can be combined to build complex query logic. Task prompts provide macro-level guidance, while operational details explain the input and output specifications, parameter constraints, and internal logic. The contribution of metafunctions to the model generation results is evaluated, and the frequency of use and correct call rate are analyzed. Function definitions, parameter settings, or prompt messages are adjusted accordingly. It has the capability to handle complex query requirements across all scenarios in graph databases. In the preprocessing stage, it deeply analyzes natural language query requirements, extracts key information and standardizes its representation, mines metadata to build a relational graph for query optimization, performs semantic parsing and logical reconstruction on the preprocessed query requirements, and transforms them into metafunction call sequences to generate Python code and Cypher query statements.

2. The method for generating Cypher query statements for graph databases based on thought chains according to claim 1, characterized in that: In the data preprocessing stage, in addition to basic data cleaning, verification, and standardization, the model's own language understanding capabilities are leveraged to perform in-depth analysis of natural language query requirements. This includes extracting key entities, relationships, and attribute information, and labeling them according to specific classification standards. Simultaneously, the types and attribute information of graph database metadata are calibrated and standardized. Based on the model's evaluation results on the test dataset, the performance differences of different types of query requirements are analyzed in depth. For query types with poor performance, the reasons are analyzed from the perspectives of training data quality, model parameter rationality, and training algorithm effectiveness. Targeted optimization is then carried out by adjusting the training data distribution, optimizing model parameters, or improving training algorithms. Visualization tools are developed to demonstrate the model's reasoning process, and interpretation generation algorithms are designed to generate natural language explanations for code and query statements, enhancing the model's interpretability.

3. The method for generating Cypher query statements for graph databases based on thought chains according to claim 1, characterized in that: The process of translating user natural language queries into Python code includes the following steps: Precise design of meta-functions: Based on the underlying data model, topology, and common query patterns of graph databases, design functionally independent, interface-standard, and reusable meta-functions, and define the input and output of meta-functions; The example data is carefully constructed: the examples are designed based on graph database metadata and business requirements, including natural language query requirements, metafunction call sequences and semantic interpretations, preprocessing query requirements, verifying the accuracy of call sequences, filtering and labeling examples to cover typical query scenarios and business logic, providing high-quality training data, regularly evaluating the completeness, accuracy and coverage of new scenarios of the example data, supplementing and updating data, incorporating system integration cases, and considering the impact of dynamic environmental data changes; Model training and guidance: Input example data into a large pre-trained model, use supervised learning and reinforcement learning strategies to guide the model to learn the mapping relationship between natural language query requirements and Python code, use data preprocessing techniques to enhance and standardize input data, optimize model parameters and structure, monitor training indicators, adjust training parameters according to changes, consider model interpretability during training, and guide the model to learn interpretable decision-making processes. Code generation and verification: After the model training reaches the target, Python code is generated according to the new natural language query requirements. The code undergoes syntax checking, semantic verification, and logical reasoning. It is optimized by combining preprocessing information. The code verification results are incorporated into the evaluation system. Error types and frequencies are analyzed to assess the reliability of code generation. The code's compatibility, correctness, and stability under dynamic database changes are verified in system integration.

4. The method for generating Cypher query statements for graph databases based on thought chains according to claim 3, characterized in that: After constructing the Cypher query statement by executing code through the Python interpreter, the query statement is sent to the graph database query execution engine. The engine performs lexical analysis, syntax parsing, semantic checking, and query optimization, generates an execution plan, retrieves data, and formats the output results. The system monitors and analyzes the query execution performance, records time and resource consumption for query optimization and system tuning, preprocesses the query results, removes redundant information and converts it into an easy-to-understand format, evaluates the model based on the accuracy and performance indicators of the query results, and provides feedback to optimize the model.

5. The method for generating Cypher query statements for graph databases based on thought chains according to claim 4, characterized in that: Metafunction design follows the principles of modularity, hierarchy, and composability. Metafunctions are cohesive, responsible for a single operation, and have standardized interfaces for easy combination and expansion. The hierarchical design enables operations at different levels of abstraction to collaborate in constructing complex logic. Preprocessing verifies the input and output data of metafunctions to ensure accurate and stable transmission, taking into account the modularity. The metafunction design adapts to the development of graph databases and business changes, possessing scalability and adaptability. The scalability of the metafunction system is evaluated, and the adaptability of the model and the quality of the generated results are tested by simulating new scenarios and data changes. The architecture design is optimized, and the scalability, compatibility, stability, and adaptability of metafunctions in system integration, as well as under dynamic database changes, are evaluated.

6. The method for generating Cypher query statements for graph databases based on thought chains according to claim 5, characterized in that: The training of the large language model involves data mining, feature engineering, and quality assessment. During mining and engineering, natural language problem features are extracted and converted into vector representations. Key features and patterns are extracted by analyzing and optimizing function call sequences. The quality assessment stage cleans and filters relation pairs, removing noise and mislabeled data. The relation pairs cover both common and complex query cases, ensuring the model learns comprehensive query knowledge and reasoning abilities. Training data is regularly updated and expanded to adapt to dynamic database changes and business needs. The impact of training data on model generalization is evaluated, and the training effects of different subsets are compared to optimize data selection and expansion strategies. System integration cases are added, and the impact of dynamic environmental data changes is considered.

7. The method for generating Cypher query statements for graph databases based on thought chains according to claim 6, characterized in that: Before the generated Python code is input into the interpreter, static code analysis, symbolic execution, and model checking techniques are used for quality checks. Variables and function calls in the code are preprocessed to provide accurate verification information. Code syntax, data types, control flow, and data flow are analyzed to detect errors and provide repair suggestions. Code coverage tools are used to evaluate test coverage, improving code reliability and stability. The code quality check results are used as an evaluation indicator of model generation capability, analyzing their relationship with training parameters and data to optimize the training process and improve code quality. The code's compatibility, security, and stability under dynamic database changes in system integration are also checked.

8. The method for generating Cypher query statements for graph databases based on thought chains according to claim 7, characterized in that: The generated Cypher query statements undergo multi-level performance optimization. During the construction phase, the query logic is transformed based on metadata and optimization rules analysis to eliminate redundant operations and subqueries. During the execution phase, graph database indexes, caching, and parallel computing technologies are used to optimize the query. Optimization strategies are formulated by combining preprocessed statistical information and historical data. The strategies are adjusted in real time based on monitoring and feedback. A performance evaluation model is established, and the effectiveness of the optimization strategies is evaluated by comprehensively considering execution time, resource consumption, and result accuracy. The optimal combination is selected and continuously improved. The effectiveness, adaptability, and adjustment capability of the optimization strategies in system integration and under dynamic database changes are evaluated.

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