Efficient data dialogue method based on LangChain

Abstract

The invention relates to the technical field of computers, and discloses an efficient data pairing method based on LangChain, which enhances context modeling capability through a vectorization technology, realizes precise conversion from a natural language to an SQL (Structured Query Language) in combination with LLM (Logical Language Model), and integrates a multi-modal output function. The method significantly reduces the technical threshold of data interaction, improves the processing efficiency, and is suitable for enterprise data analysis, BI tool intelligent upgrading and other scenes.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to an intelligent data interaction method based on a large language model (LLM) and vectorization technology, which is used to realize the integrated processing of natural language to structured query, data analysis, and visualization. Background Art

[0002] With the growth of enterprise data scale, users' requirements for data query, analysis, and visualization are becoming increasingly complex. Traditional data interaction methods rely on manually writing SQL statements or using fixed-template tools. Such traditional data interaction methods can meet certain usage requirements, but there are many defects, specifically manifested as problems such as high technical thresholds, low efficiency, and lack of flexibility.

[0003] In the prior art, although the SQL generation method based on natural language processing (NLP) can partially solve the problem of user input parsing, it still has the following defects: 1. Insufficient context understanding: The semantic information of the database table structure and business logic is not effectively utilized, resulting in low accuracy of the generated SQL; 2. Lack of multimodal output: There is a lack of in-depth parsing of user intentions, and it is impossible to dynamically judge whether charts or data analysis are required; 3. Disconnection between execution and display: The generated SQL has a high coupling with the execution and result visualization links, and it is difficult to achieve end-to-end automation.

[0004] Therefore, there is an urgent need for an efficient data interaction method that can deeply integrate semantic understanding, dynamic decision-making, and multimodal output, and the present case can perfectly achieve the above functions. Summary of the Invention

[0005] The purpose of the present invention is to provide an efficient data pair method based on the LangChain framework, which enhances the context modeling ability through vectorization technology, combines the LLM large language model to achieve accurate conversion from natural language to structured query, and integrates SQL execution, data analysis, and visualization functions to form an end-to-end intelligent data interaction system, significantly reducing the technical threshold and improving data processing efficiency.

[0006] To achieve the above technical objectives and reach the above technical effects, the present invention discloses an efficient data dialogue method based on LangChain, which is characterized in that it includes steps such as vectorized context modeling, natural language parsing and SQL generation, multimodal requirement decision-making, SQL execution and data acquisition, data visualization conversion, intelligent report generation, multimodal result integration, etc. The specific implementation steps are as follows: S1: Vectorized context modeling. First, load the business database table structure (including table names, field names, field types, primary and foreign key relationships, etc.) through a pre-trained vector model (such as BERT, Sentence-BERT) to generate a vectorized representation of the table structure and build a semantic context knowledge base. Then, combined with the chaining processing ability of the LangChain framework, dynamically associate the table structure vectors with the user's historical query records to optimize the context retrieval efficiency; S2: Natural language parsing and SQL generation. First, the natural language request input by the user is parsed by an LLM (such as GPT-4, ChatGLM) to extract the query intent, filtering conditions, and aggregation requirements. Then, based on the vectorized context knowledge base in step 1, the LLM generates a standardized SQL statement that matches the user's intent, supporting multi-table joins, complex condition nesting, and aggregate function calls; S3: Multi-modal requirement decision-making. During the SQL generation stage, the LLM synchronously analyzes the implicit requirements in the user's request (such as keywords like "trend analysis", "proportion", etc.), and through the combination of preset rules and probability models, dynamically determines whether it is necessary to generate charts or data analysis reports; S4: SQL execution and data acquisition. Design a lightweight SQL executor based on the Java language, supporting connection pool management, transaction control, and exception handling, and efficiently execute the generated SQL statement and return a structured data set; S5: Data visualization conversion. If chart display is required, convert the data set into a standardized Option configuration item of ECharts, and dynamically generate visualization components such as bar charts, line charts, and pie charts, supporting interactive operations; S6: Intelligent report generation. Through semantic analysis of the data set by the LLM, extract key metrics (such as maximum value, growth rate), and generate multi-language analysis reports in combination with the business scenario to highlight data insights; S7: Multi-modal result integration. The final result is presented in a unified interface, including the data set, visualization charts, and analysis reports, supporting export in formats such as JSON and PDF.

[0007] The present invention has the following beneficial effects: 1. Improvement in accuracy and efficiency: Through the collaboration of vectorized context modeling and the LLM, the accuracy of SQL generation is increased by more than 40%, reducing the manual correction cost; 2. End-to-end automation: Achieve full-process automation from natural language input to multi-modal output, and shorten the response time to the second level; 3. Flexible and scalable: Adopt a modular design, support custom rule engines and visualization templates, and adapt to different industry scenarios; 4. Low resource dependence: Through the optimized scheduling of the lightweight SQL executor and LangChain, reduce the dependence on high-performance computing resources. Brief Description of the Drawings

[0008] Figure 1 It is a schematic block diagram of the process framework of an embodiment proposed by the present invention.

[0009] Figure 2 It is an example diagram of the multi-modal result display interface of the present invention Detailed Embodiments

[0010] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments.

[0011] As Figure 1 shown, the present invention discloses an efficient data pair method based on the LangChain framework, which enhances the context modeling ability through vectorization technology, combines the LLM language large model to achieve accurate conversion from natural language to structured queries, and integrates SQL execution, data analysis and visualization functions to form an end-to-end intelligent data interaction system, significantly reducing the technical threshold and improving data processing efficiency.

[0012] To achieve the above technical objectives and effects, the present invention discloses an efficient data dialogue method based on LangChain, which is characterized in that it includes steps such as vectorized context modeling, natural language parsing and SQL generation, multi-modal requirement decision-making, SQL execution and data acquisition, data visualization conversion, intelligent report generation, multi-modal result integration, etc. The specific implementation steps are as follows: S1: Vectorized context modeling. First, load the business database table structure (including table names, field names, field types, primary and foreign key relationships, etc.) through a pre-trained vector model (such as BERT, Sentence-BERT) to generate a vectorized representation of the table structure, construct a semantic context knowledge base, and then combine the chain processing ability of the LangChain framework to dynamically associate the table structure vectors with the user's historical query records to optimize the context retrieval efficiency; S2: Natural language parsing and SQL generation. First, the natural language request input by the user is parsed by an LLM (such as GPT-4, ChatGLM) to extract the query intention, filtering conditions and aggregation requirements, and then based on the vectorized context knowledge base in step 1, the LLM generates a standardized SQL statement that matches the user's intention, supporting multi-table association, complex condition nesting and aggregation function calls; S3: Multi-modal requirement decision-making. During the SQL generation stage, the LLM synchronously analyzes the implicit requirements in the user's request (such as keywords like "trend analysis", "proportion", etc.), and dynamically determines whether to generate a chart or a data analysis report by combining preset rules and probability models; S4: SQL Execution and Data Retrieval. Design a lightweight SQL executor based on the Java language, which supports connection pool management, transaction control, and exception handling. Efficiently execute the generated SQL statements and return a structured data set; S5: Data Visualization Transformation. If chart display is required, convert the data set into the standardized Option configuration items of ECharts, dynamically generate visual components such as bar charts, line charts, and pie charts, and support interactive operations; S6: Intelligent Report Generation. Perform semantic analysis on the data set through the LLM, extract key metrics (such as maximum value, growth rate), and generate multi-language analysis reports in combination with the business scenario to highlight data insights; S7: Multi-modal Result Integration. The final result is presented in a unified interface, including the data set, visual charts, and analysis reports, and supports export in formats such as JSON and PDF.

[0013] The following is a detailed description with a specific example: Taking the enterprise sales data analysis scenario as an example, the user inputs "Show the sales trends of each quarter in East China in 2023 and analyze the year-on-year growth situation", 1. Context Loading: The vector model loads the structures of the sales table (Sales) and the region table (Region) to build semantic associations; ⦁ SQL Generation: The LLM generates the following SQL after parsing: SELECT quarter, SUM(amount) AS total_sales FROM Sales JOIN Region ON Sales.region_id = Region.id WHERE year = 2023 AND Region.name = 'East China Region' GROUP BY quarter ORDER BY quarter; 3. Requirement Decision: The LLM identifies the keywords "trend" and "year-on-year growth", triggering the generation of charts and reports; 4. Execution and Transformation: The SQL executor returns the quarterly sales data and converts it into the ECharts line chart Option; 5. Report Generation: The LLM extracts the year-on-year growth rate and generates conclusions such as "The sales in Q2 of the East China Region increased by 15% quarter-on-quarter"; Result Presentation: The user interface synchronously displays the data table, trend chart, and analysis report As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An efficient data dialogue method based on LangChain, characterized by: It includes vectorized context modeling, natural language parsing and SQL generation, multimodal demand decision-making, SQL execution and data acquisition, data visualization conversion, intelligent report generation, multimodal result integration and other steps. The specific implementation steps are as follows: S1: Vectorized context modeling: first load the business database table structure (including table name, field name, field type, primary and foreign key relationships, etc.) through a pre-trained vector model (such as BERT, Sentence-BERT), generate a vectorized representation of the table structure, build a semantic context knowledge base, and then combine the chain processing capabilities of the LangChain framework to dynamically associate the table structure vector with the user's historical query records to optimize context retrieval efficiency; S2: Natural language parsing and SQL generation. First, the natural language request input by the user is parsed by LLM (such as GPT-4, ChatGLM) to extract the query intent, screening conditions and aggregation requirements. Then, based on the vectorized context knowledge base in step 1, LLM generates standardized SQL statements that match the user intent, supporting multi-table association, complex condition nesting and aggregation function calls. S3: Multimodal demand decision-making. During the SQL generation phase, LLM simultaneously analyzes the implicit requirements in the user's request (such as keywords such as "trend analysis" and "proportion"), and dynamically determines whether it is necessary to generate a chart or data analysis report by combining preset rules with the probability model. S4: SQL execution and data acquisition, a lightweight SQL executor designed based on Java language, supports connection pool management, transaction control and exception handling, efficiently executes generated SQL statements and returns structured data sets; S5: Data visualization conversion. If chart display is required, the data set is converted into the standardized Option configuration item of ECharts, and visualization components such as bar charts, line charts, and pie charts are dynamically generated to support interactive operations. S6: Intelligent report generation: Through LLM, semantic analysis of data sets is performed to extract key indicators (such as maximum value and growth rate), and multilingual analysis reports are generated based on business scenarios to highlight data insights; S7: Multimodal result integration. The final results are presented in a unified interface, including data sets, visualization charts and analysis reports, and can be exported to JSON, PDF and other formats.

2. The LangChain-based efficient data dialogue method according to claim 1, characterized in that: The vector model uses a pre-trained language model and combines metadata of the table structure with business logic to generate a context vector.

3. The LangChain-based efficient data dialogue method according to claim 1, characterized in that: The rule engine is introduced into the LLM parsing process to dynamically determine charts and report requirements through keyword matching and probability models.

4. The LangChain-based efficient data dialogue method according to claim 1, characterized in that: The SQL executor supports connection pool reuse and transaction rollback mechanisms to ensure stability in high-concurrency scenarios.

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