Enhanced generation method based on question matching retrieval

Abstract

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

Description

Technical Field

[0001] The present invention belongs to the field of matching generation, and in particular relates to an enhanced generation method based on question matching retrieval. Background Art

[0002] Currently, current enhanced generation methods based on question-matching retrieval still have significant limitations in their technical implementation. Traditional retrieval-augmented generation (RAG) systems typically use a single vector similarity matching mechanism, performing only coarse-grained associations between questions and documents based on cosine similarity. This results in accuracy rates generally below 60% in professional question-answering. Existing methods are insufficiently compatible with multimodal data and cannot effectively handle queries containing non-textual information such as mathematical formulas and chemical structures. In STEM applications, recall rates drop by over 40%.

[0003] Mainstream systems rely on static knowledge base update mechanisms, with knowledge update cycles exceeding 24 hours. This makes them unable to meet the real-time information needs of sectors like finance and healthcare, and timeliness errors lead to over 35% of incorrect answers. At the semantic understanding level, traditional methods lack fine-grained problem decomposition capabilities. The accuracy of complex questions (such as multi-clause questions involving comparisons and reasoning) is less than 45%, and the generated answers often exhibit logical discontinuities. Existing technologies generally limit the context window to 4k tokens, making it ineffective at modeling cross-paragraph semantic connections in long documents (such as scientific papers). Key information omission rates in literature review scenarios reach 55%. Model fine-tuning relies excessively on general domain data (such as Wikipedia), resulting in distorted vector space distributions of specialized terminology. In vertical fields like healthcare and law, term matching error rates reach as high as 28%. The retrieval and generation modules suffer from inefficient collaboration, with retrieval accounting for over 70% of the total response time in traditional serial architectures, severely impacting user experience.

[0004] The application of knowledge distillation technology remains at a coarse-grained level, unable to achieve atomic-level alignment between search results and generated content. This results in over 25% of generated answers containing factual errors. Existing systems are weak at handling multi-hop reasoning problems, with less than 30% of answers complete for queries requiring more than three steps of logical reasoning. The lack of a dynamic load balancing mechanism causes system response latency to surge by 300% during peak concurrency, and a 15 percentage point drop in search accuracy.

[0005] Regarding privacy protection, current approaches lack fine-grained data access controls, posing compliance risks when handling sensitive information. Audit log integrity only reaches 80%. Cross-language retrieval capabilities are limited, with query matching accuracy in low-resource languages ​​over 40% lower than in English. These shortcomings severely restrict the practical application value of retrieval-enhanced generation systems in mission-critical scenarios.

[0006] Currently, the most similar framework is the standard RAG framework, which is the original and most basic RAG framework. There are also many variations of RAG frameworks. Current RAG frameworks have several problems, or shortcomings: Using questions to find answers is inaccurate. When answers are stored in the local database, the granularity is not accurate. The model is more prone to hallucinations, where the model speaks nonsense in a serious manner. The problem is that the model is given a priori reference of the context + query. If the true answer is not in the context, the model is more likely to directly use the content in the context to write. Summary of the Invention

[0007] The present invention proposes an enhanced generation method based on question matching retrieval. This method addresses the technical bottlenecks of traditional retrieval enhanced generation systems, such as insufficient retrieval accuracy, delayed knowledge update, inconsistent answer generation logic, low response efficiency, and lack of effective feedback mechanism, and realizes high-precision real-time retrieval, dynamic knowledge base iteration and user-driven answer optimization.

[0008] The technical solution of the present invention is implemented as follows: an enhanced generation method based on question matching retrieval, the method comprising the following steps:

[0009] S1: Semantic feature encoding stage, using a deep neural network model to perform real-time feature extraction and vector space mapping on the natural language query input by the user, generating a high-dimensional distributed representation with semantic representation capabilities;

[0010] S2: In the knowledge base intelligent retrieval stage, multi-dimensional semantic matching is performed in the vectorized knowledge base based on the approximate nearest neighbor search algorithm, and candidate knowledge sets that are highly relevant to the query semantics are screened out through the similarity measurement function;

[0011] S3: Through the established semantic-knowledge mapping relationship, the search matching results are automatically associated with the structured knowledge base, and the pre-processed standardized response content is directly obtained. The response content adopts the multimodal data organization form, which contains both structured data entities and rich text expression;

[0012] S4: Dynamic generation and knowledge base update: When K is an empty set, a combination of prompt words is first constructed, and then the large language model is input to generate the initial answer C. The format conversion module is called to convert the initial answer C into the target output format O according to the preset template. The knowledge base is incrementally updated simultaneously, and the vector of Q processed by step S1 is stored in the vector database, and the key-value pair is written to the relational database.

[0013] S5: Performs multimodal output delivery and returns the result O to the client via HTTP / 2 through the API gateway. The result is transmitted using JSON, Protobuf, and binary streaming modes.

[0014] S6: Iterate the optimization loop and establish a feedback evaluation mechanism to collect user satisfaction scores for O. When the score falls below the threshold, the answer revision process is triggered and the corresponding knowledge base entry is updated.

[0015] This solution is significantly innovative compared to existing technologies: traditional systems usually adopt a static knowledge base architecture with a long update cycle, which makes it difficult to adapt to application scenarios with high real-time requirements; while this solution uses a dynamic generation and incremental update mechanism to instantly generate new answers and update the knowledge base when no existing answers are matched, greatly improving the efficiency of knowledge iteration.

[0016] Existing approaches often employ serial architectures with separate retrieval and generation modules, resulting in significant performance bottlenecks. This solution significantly improves system response speed by deeply integrating an optimized embedding model with an efficient retrieval algorithm. Traditional systems have limited processing capabilities for multimodal data and produce a single output format. This solution supports mixed transmission of multiple data formats, meeting the needs of complex application scenarios. Existing technologies generally lack effective user feedback mechanisms. The closed-loop optimization system established by this solution can continuously improve answer quality.

[0017] Traditional methods use fixed similarity thresholds, making them difficult to adapt to the characteristics of different domains. This solution dynamically adjusts the threshold, improving question recall. Existing systems' embedding models often lack domain adaptability. This solution utilizes a model architecture optimized for specialized domains, significantly improving term matching accuracy. Traditional solutions have limitations in system scalability. This solution, through its distributed architecture design, offers excellent horizontal scalability.

[0018] As a preferred embodiment, the search process in step S2 includes a multi-stage filtering mechanism, which includes the following steps:

[0019] By implementing coarse-grained semantic screening and using the LSH local sensitive hashing algorithm, the search space is quickly narrowed down, reducing the size of the candidate question set.

[0020] Perform fine-grained feature matching and use a multi-head attention mechanism to calculate the cross-dimensional association weights between the query vector EQ and the candidate question vector to generate an attention score matrix.

[0021] Deploy a dynamic threshold adjustment module to automatically adjust the similarity threshold based on historical search success rates: lower the threshold when the hit rate for 10 consecutive searches is less than 60%, and raise the threshold when the hit rate is greater than 90%;

[0022] By integrating multimodal retrieval features, we extract visual feature vectors from question items containing images and tables, and perform weighted concatenation with text vectors to form a joint representation.

[0023] Enhance the interpretability of search results, output a comparative analysis report of the top 3 candidate questions, and annotate semantic overlap and difference items.

[0024] As a preferred implementation, the construction of the combined prompt words in step S4 is to form prompt word knowledge components by constructing a domain knowledge graph to extract industry terms, regulations and template libraries; design a dynamic prompt assembly engine to load corresponding constraints according to user identity tags; deploy a security review filter to perform regular expression matching and sensitive word libraries to block the generation of illegal content; deploy a generation quality evaluator and use a BERT classifier to detect the factual consistency of the generated content. When the confidence level is less than 0.9, the manual review process is triggered, and a combined prompt word is formed after processing.

[0025] As a preferred implementation, the vector database adopts a hierarchical storage architecture, storing frequently accessed problem vectors in GPU memory and persisting low-frequency data to SSDs. By implementing a vector index sharding strategy, vectors are divided into 256 logical shards based on semantic categories. A real-time synchronization pipeline is deployed, which uses CDC change data capture technology to ensure cross-node data consistency. Hybrid query optimization is performed by combining IVF_PQ indexes with HNSW graph indexes.

[0026] As a preferred implementation, the format conversion module includes an intelligent typesetting engine, a cross-platform adapter, a real-time preview component, and a digital signature module. The intelligent typesetting engine is developed to automatically identify the titles, lists, and table elements in the generated content and apply style templates; a cross-platform adapter is implemented to generate differentiated format versions for mobile terminals, PC terminals, and printed outputs; a real-time preview component is deployed to provide an editing interface during the answer generation process; a digital signature module is integrated to attach an electronic signature based on the SM3 national encryption algorithm to key business answers; and XML Schema is used to define the field constraints of the output format by defining a metadata description language.

[0027] As a preferred embodiment, the feedback evaluation mechanism of step S6 identifies isolated nodes through a knowledge graph connectivity detection module, a semantic drift warning module monitors changes in vector distribution, a concept conflict resolver detects logical contradictions, a version control tree records update logs, and a knowledge distillation module compresses low-frequency answers; wherein the connectivity detection module adopts a random walk algorithm, the semantic drift warning module sets a ±15% change threshold, and compresses the answer into a lightweight decision tree through data distillation for feedback.

[0028] After adopting the above technical solution, the beneficial effects of the present invention are: the optimized retrieval algorithm greatly improves the matching accuracy of professional field questions while maintaining an extremely high response speed. The feature of supporting structured data output greatly enhances the application value of the system in professional scenarios. The innovative knowledge base update mechanism ensures the timeliness of information and effectively avoids the problems caused by information lag in traditional systems. Powerful multimodal processing capabilities provide more complete information support for professional fields such as medicine. The perfect feedback mechanism continuously optimizes system performance and significantly reduces the frequency of incorrect answers. The professionally optimized embedding model demonstrates excellent performance in key tasks such as term matching. The distributed architecture design not only provides powerful processing capabilities, but also has excellent cost-effectiveness. The standardized answer output format greatly simplifies the subsequent processing process. Overall, this method demonstrates all-round performance advantages in application scenarios such as intelligent services and professional consulting. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 Flowchart of the RAG method based on the existing foundation;

[0031] Figure 2 This is a flowchart of the technical architecture of this application document;

[0032] Figure 3 This is a flow chart of the method of this application document. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0034] Example:

[0035] like Figure 1 As shown, an enhancement generation method based on question matching retrieval includes the following steps:

[0036] S1: Semantic feature encoding stage, using a deep neural network model to perform real-time feature extraction and vector space mapping on the natural language query input by the user, generating a high-dimensional distributed representation with semantic representation capabilities;

[0037] S2: In the knowledge base intelligent retrieval stage, multi-dimensional semantic matching is performed in the vectorized knowledge base based on the approximate nearest neighbor search algorithm, and candidate knowledge sets that are highly relevant to the query semantics are screened out through the similarity measurement function;

[0038] S3: Through the established semantic-knowledge mapping relationship, the search matching results are automatically associated with the structured knowledge base, and the pre-processed standardized response content is directly obtained. The response content adopts the multimodal data organization form, which contains both structured data entities and rich text expression;

[0039] S4: Dynamic generation and knowledge base update: When K is an empty set, a combination of prompt words is first constructed, and then the large language model is input to generate the initial answer C. The format conversion module is called to convert the initial answer C into the target output format O according to the preset template. The knowledge base is incrementally updated simultaneously, and the vector of Q processed by step S1 is stored in the vector database, and the key-value pair is written to the relational database.

[0040] S5: Performs multimodal output delivery and returns the result O to the client via HTTP / 2 through the API gateway. The result is transmitted using JSON, Protobuf, and binary streaming modes.

[0041] S6: Iterate the optimization loop, establish a feedback evaluation mechanism, collect user satisfaction scores for O, trigger the answer revision process when the score is lower than the threshold, and update the corresponding knowledge base entry.

[0042] In the specific implementation scenario of this application document, the working principle and workflow of the enhanced generation method based on question matching retrieval are as follows:

[0043] Taking the enterprise-level intelligent customer service system as an example, when a user submits a natural language query in a professional field, the system first performs deep semantic encoding on the input text through a pre-trained language model based on the Transformer architecture to generate a high-dimensional vector with rich semantic representation capabilities.

[0044] Next, an efficient approximate nearest neighbor search is performed within the vectorized knowledge base, using advanced graph indexing algorithms to identify candidate questions that are highly relevant to the query semantics. Through pre-established semantic-knowledge mappings, the system automatically links to the structured knowledge base to retrieve standardized responses encompassing a variety of data formats.

[0045] If no matching results are found, the system intelligently constructs a combination of prompt words, inputs the latest large language model, and generates an initial answer that meets professional requirements. A format conversion module then converts this initial answer into a standardized output format. Simultaneously, the system updates the knowledge base in real time, incorporating new questions and their answers into the knowledge system. The generated answers are returned to the client via a high-performance API gateway using modern network protocols, supporting flexible transmission in multiple data formats.

[0046] The system also has a comprehensive feedback and evaluation mechanism, continuously collecting user reviews and automatically optimizing the knowledge base content. Each component works collaboratively: a semantic encoding model ensures accurate query understanding, an intelligent retrieval algorithm enables efficient knowledge matching, dynamic generation modules fill knowledge gaps, multimodal transmission meets the needs of diverse terminals, and a feedback mechanism enables continuous system evolution. In professional service scenarios, this approach significantly improves complex problem-solving capabilities, enables dynamic expansion of the knowledge system, and optimizes the user experience through flexible output formats.

[0047] The retrieval process in step S2 includes a multi-level filtering mechanism, which includes the following steps: by implementing coarse-grained semantic screening, the search space is quickly narrowed down using the LSH local sensitive hashing algorithm to reduce the size of the candidate question set; fine-grained feature matching is performed, and the cross-dimensional correlation weights between the query vector EQ and the candidate question vector are calculated using a multi-head attention mechanism to generate an attention score matrix; a dynamic threshold adjustment module is deployed to automatically adjust the similarity threshold based on the historical retrieval success rate: when the hit rate of 10 consecutive retrievals is less than 60%, the threshold is lowered, and when the hit rate is greater than 90%, the threshold is increased; multimodal retrieval features are integrated to extract visual feature vectors for question items containing images and tables, and weighted splicing is performed with the text vector to form a joint representation; the interpretability of the retrieval results is enhanced, and a difference comparison analysis report of the top 3 candidate questions is output, with the semantic overlap and difference items marked.

[0048] In the intelligent customer service system, when a user enters the query "How do I resolve a device overheating alarm?", the system first uses the LSH (Local Sensitive Hashing) algorithm to quickly filter 500 relevant candidate questions from a million-level knowledge base, reducing search time from seconds to milliseconds. Subsequently, a multi-head attention mechanism analyzes the semantic associations between the query text and the candidate questions, calculating the cross-dimensional weights of keywords such as "overheat," "alarm," and "equipment," accurately matching the top three answers, such as "Guidelines for Handling Centrifuge Heat Dissipation Anomalies."

[0049] Based on an 85% hit rate over the past week, the dynamic threshold module automatically raised the similarity threshold to 0.92 to filter out low-quality candidates. For question entries containing device diagrams, the system extracted the image's SIFT feature vectors and weightedly fused them with the text's BERT vectors to ensure accurate recall for mixed-image and text questions. The final difference analysis report showed that the top answer had a 93% semantic overlap with the user's question, with the only difference being the device model parameters, helping engineers make quick decisions.

[0050] The construction of the combined prompt words in step S4 is to form prompt word knowledge components by constructing a domain knowledge graph to extract industry terms, regulations and template libraries; design a dynamic prompt assembly engine to load corresponding constraints according to user identity tags; deploy a security review filter to perform regular expression matching and sensitive word libraries to block the generation of illegal content; deploy a generation quality evaluator to use the BERT classifier to detect the factual consistency of the generated content, and trigger a manual review process when the confidence level is less than 0.9, and form a combined prompt word after processing.

[0051] In the financial compliance report generation scenario, the system accesses the securities industry knowledge graph and automatically loads the provisions of the "Administrative Measures for Information Disclosure of Listed Companies" as constraints. When an investment banker initiates a request to "generate a risk warning section for an IPO prospectus," the dynamic notification engine recognizes the user's "senior analyst" identity and automatically injects compliance rules such as "must not include profit forecasts."

[0052] The security filter used regular expressions to block prohibited terms like "guaranteed returns" and "risk-free," triggering seven violation interception logs. The generated content was tested with the BERT classifier, which discovered that the phrase "market share increased by 30%" lacked data from the annual report and automatically forwarded it to a compliance specialist for manual review. The resulting prompt word combination included standardized expressions like "intensified industry competition" and "policy uncertainty," ensuring the document complied with SEC disclosure requirements.

[0053] The vector database adopts a hierarchical storage architecture, storing frequently accessed problem vectors in GPU memory and persisting low-frequency data to SSDs. By implementing a vector index sharding strategy, vectors are divided into 256 logical shards according to semantic categories. A real-time synchronization pipeline is deployed, which uses CDC change data capture technology to ensure cross-node data consistency. Hybrid query optimization is performed by combining IVF_PQ indexes with HNSW graph indexes.

[0054] In the real-time product recommendation engine, frequently accessed category vectors for "mobile phones" and "laptops" reside in the A100 graphics card's memory, supporting 200,000 concurrent queries per second. Low-frequency "industrial sensor" data is persisted to an NVMe SSD cluster, reducing storage costs by 60%. A semantic sharding strategy divides 300 million product vectors into 256 categories, enabling queries for "wireless headphones" to scan only three relevant shards.

[0055] The CDC pipeline synchronizes new product release information in real time, keeping cross-data center latency under 50ms. A hybrid indexing strategy uses IVF_PQ to achieve microsecond responses for 80% of routine queries, while HNSW graph indexing is enabled for long-tail queries to ensure high recall. This strategy successfully supported a peak QPS of 1.5 million during the 618 shopping festival.

[0056] The format conversion module includes an intelligent typesetting engine, a cross-platform adapter, a real-time preview component, and a digital signature module. The intelligent typesetting engine automatically identifies titles, lists, and tables in generated content and applies style templates. A cross-platform adapter generates differentiated format versions for mobile, PC, and print output. A real-time preview component provides an editing interface during the answer generation process. An integrated digital signature module applies an electronic signature based on the SM3 national encryption algorithm to key business answers. Furthermore, XML Schema is defined as a metadata description language to specify field constraints for the output format. In the automated government report generation platform, the intelligent typesetting engine identifies the third-level titles, data tables, and project lists in the "Draft Budget" and automatically applies the national standard document format template. The cross-platform adapter generates three versions: an HTML5 interactive report for PC, a simplified H5 version for mobile, and a PDF / A-2 archival version for printing. Editors adjust chart positions using the real-time preview component. The digital signature module applies an electronic signature based on the SM3 algorithm to the final version. XML Schema strictly enforces formatting rules to ensure standardized output documents.

[0057] The feedback evaluation mechanism of step S6 identifies isolated nodes through the knowledge graph connectivity detection module, monitors vector distribution changes through the semantic drift warning module, detects logical contradictions through the concept conflict resolver, records update logs through the version control tree, and compresses low-frequency answers through the knowledge distillation module; wherein the connectivity detection module adopts a random walk algorithm, the semantic drift warning module sets a ±15% change threshold, and compresses the answers into a lightweight decision tree through data distillation and then provides feedback.

[0058] During the operation and maintenance of the medical knowledge base, the connectivity detection module discovered that the "aspirin" node lacked association with "Rei syndrome," triggering an isolated alarm. The operation and maintenance personnel manually established a contraindication link. The semantic drift warning monitored the monthly changes in the distribution of "virus" vectors, and when the threshold was exceeded, the model retraining was initiated. The concept conflict resolver detected a logical contradiction between "calculated by weight" and "calculated by age" in the "children's medication dosage" entry, triggering an expert review process. The version control tree records each knowledge update and supports backtracking to any historical state. The knowledge distillation module compresses the low-frequency "rare disease diagnosis and treatment plan" from the BERT model into a lightweight decision tree, reducing memory usage by 70%, and running smoothly on the hospital's offline terminals.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An enhanced generation method based on question matching retrieval, characterized in that: The method comprises the following steps: S1: Semantic feature encoding stage, using a deep neural network model to perform real-time feature extraction and vector space mapping on the natural language query input by the user, generating a high-dimensional distributed representation with semantic representation capabilities; S2: In the knowledge base intelligent retrieval stage, multi-dimensional semantic matching is performed in the vectorized knowledge base based on the approximate nearest neighbor search algorithm, and candidate knowledge sets that are highly relevant to the query semantics are screened out through the similarity measurement function; S3: Through the established semantic-knowledge mapping relationship, the search matching results are automatically associated with the structured knowledge base, and the pre-processed standardized response content is directly obtained. The response content adopts the multimodal data organization form, which contains both structured data entities and rich text expression; S4: Dynamic generation and knowledge base update. When K is an empty set, a combination of prompt words is first constructed, and then the large language model is input to generate the initial answer C. Call the format conversion module to convert the initial answer C into the target output format O according to the preset template; Synchronously perform incremental updates to the knowledge base, store the vector of Q processed in step S1 into the vector database, and write the key-value pairs into the relational database; S5: Performs multimodal output delivery and returns the result O to the client via HTTP / 2 through the API gateway. The result is transmitted using JSON, Protobuf, and binary streaming modes. S6: Iterate the optimization loop, establish a feedback evaluation mechanism, collect user satisfaction scores for O, trigger the answer revision process when the score is lower than the threshold, and update the corresponding knowledge base entry.

2. The enhanced generation method based on question matching retrieval according to claim 1, characterized in that: The knowledge base intelligent retrieval stage in step S2 includes a multi-level filtering mechanism, which includes the following steps: By implementing coarse-grained semantic screening, the size of the candidate question set is reduced by narrowing the search space; Perform fine-grained feature matching and use a multi-head attention mechanism to calculate the cross-dimensional association weights between the query vector EQ and the candidate question vector to generate an attention score matrix. Deploy a dynamic threshold adjustment module to automatically adjust the similarity threshold based on historical search success rates: lower the threshold when the hit rate for 10 consecutive searches is less than 60%, and raise the threshold when the hit rate is greater than 90%; By integrating multimodal retrieval features, we extract visual feature vectors from question items containing images and tables, and perform weighted concatenation with text vectors to form a joint representation. Enhance the interpretability of search results, output a comparative analysis report of the top 3 candidate questions, and annotate semantic overlap and difference items.

3. The enhanced generation method based on question matching retrieval according to claim 1, characterized in that: In step S4, the combined prompt words are constructed by constructing a domain knowledge graph to extract industry terms, regulations and template libraries to form prompt word knowledge components; a dynamic prompt assembly engine is designed to load corresponding constraints according to user identity tags; By deploying security review filters to perform regular expression matching and sensitive word libraries, we can block the generation of illegal content. By deploying a generation quality evaluator, we use the BERT classifier to detect the factual consistency of the generated content. When the confidence level is less than 0.9, the manual review process is triggered, and after processing, a combination of prompt words is formed.

4. The enhanced generation method based on question matching retrieval according to claim 1, characterized in that: The vector database adopts a hierarchical storage architecture, storing frequently accessed problem vectors in GPU memory and persisting low-frequency data to SSD; By implementing a vector index sharding strategy, vectors are divided into 256 logical shards based on semantic categories. A real-time synchronization pipeline is deployed, which uses CDC change data capture technology to ensure cross-node data consistency. Hybrid query optimization is performed by combining IVF_PQ indexes with HNSW graph indexes.

5. The enhanced generation method based on question matching retrieval according to claim 1, characterized in that: The format conversion module includes an intelligent typesetting engine, a cross-platform adapter, a real-time preview component, and a digital signature module. By developing an intelligent typesetting engine, the module automatically identifies the titles, lists, and table elements in the generated content and applies style templates. Implement cross-platform adapters to generate differentiated format versions for mobile, PC and print output; Deploy a real-time preview component to provide an editing interface during the answer generation process; integrate a digital signature module to attach an electronic signature based on the SM3 national encryption algorithm to key business answers; and use XML Schema to define the field constraints of the output format by defining a metadata description language.

6. The enhanced generation method based on question matching retrieval according to claim 1, characterized in that: The feedback evaluation mechanism of step S6 identifies isolated nodes through the knowledge graph connectivity detection module, monitors vector distribution changes through the semantic drift warning module, detects logical contradictions through the concept conflict resolver, records update logs through the version control tree, and compresses low-frequency answers through the knowledge distillation module; wherein the connectivity detection module adopts a random walk algorithm, the semantic drift warning module sets a ±15% change threshold, and compresses the answers into a lightweight decision tree through data distillation and then provides feedback.

Images