472 results about "Text retrieval" patented technology

The invention provides a traditional Chinese medicine knowledge question-answering system based on a fine-tuning large model and dual retrieval enhancement. The traditional Chinese medicine knowledge question-answering system comprises a large model fine-tuning module, a dual retrieval enhancement module, a prompt template module and an answer generation module. The large model fine tuning module constructs a high-quality corpus by using traditional Chinese medicine ancient books, clinical cases and the like, performs incremental pre-training and supervised fine tuning on a ChatGLM3-6B pre-training model, and adopts technologies such as low-rank adaptation (LoRA) and direct preference optimization (DPO) to improve the adaptability of the model to traditional Chinese medicine professional knowledge. The dual retrieval module enables the model to map user questions to related contents in traditional Chinese medicine classical literatures, guidelines and modern literatures through text retrieval based on a LangChain framework on one hand, and provides structured background knowledge through map retrieval on the other hand. The system effectively makes up for the knowledge blind area of a large-scale general model in the field of traditional Chinese medicine, realizes the improvement of the accuracy, continuity and speciality of question and answer results, and has wide application prospects and relatively high innovativeness.

The invention provides a retrieval method and device based on document segmentation and a document retrieval system. The method comprises the following steps: acquiring a to-be-segmented document; based on an NLP algorithm, calculating the semantic similarity between the partial texts of the to-be-segmented document to obtain a first semantic relevancy; according to the first semantic relevancy of all the partial texts, the document to be segmented is segmented, a plurality of semantic text blocks are obtained, and each semantic text block comprises at least one partial text; under the condition that a query request is received, calculating semantic similarity between a query text corresponding to the query request and each semantic text block based on an NLP algorithm to obtain a plurality of second semantic relevancy, and determining the semantic text block with the highest second semantic relevancy of the query text corresponding to the query request as a target semantic text block, and displaying the target semantic text block in a display interface. According to the scheme, the problem that in the prior art, the accuracy rate is low during text retrieval is solved.

The invention relates to an enhanced LLM-RAG multi-hop question and answer method based on logic tree reasoning, and belongs to the technical field of new-generation information, and the method comprises the following steps: inputting a multi-hop question and answer question into a computer system; the computer system calls a pre-training large language model LLM, the multi-hop question-answer question is decomposed into a hierarchical logic tree in a recursive mode, and each node of the logic tree comprises a sub-question and a corresponding hypothesis answer; performing image retrieval from a structured knowledge source Wikidata and performing text retrieval from an unstructured knowledge source Wikipedia on the basis of each node sub-question and the hypothesis answer to obtain corresponding evidence; traversing the logic tree, verifying the consistency between the hypothetical answer of each node and the evidence through LLM, if the contradiction exists, reconstructing the corresponding sub-tree, and dynamically correcting the reasoning path; and integrating the verified logic tree node information, and outputting an accurate answer to the multi-hop question and answer question.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention relates to the technical field of data processing, and discloses a multi-modal data alignment method and system based on an iterative Riemannian manifold, a terminal and a medium, and the method comprises the steps: obtaining a multi-modal data sample set for cross-modal image text retrieval, and creating a multi-modal data alignment network training model; performing iterative Riemannian manifold training on the multi-modal data alignment network model according to the multi-modal data sample set to obtain a multi-modal data alignment model; and obtaining to-be-aligned multi-modal data, inputting the to-be-aligned multi-modal data into the multi-modal data alignment model, and outputting a multi-modal data alignment result. According to the method, different modal data are embedded into the same Riemannian manifold, and the geometrical characteristics of the Riemannian manifold are kept by utilizing the Riemannian curvature regularization, so that the embedded representation can better reflect the internal structure of the data, cross-modal alignment is carried out based on the optimal transmission of the manifold geodesic distance, more accurate and deeper semantic correspondence is realized, and the accuracy and the reliability of the data are improved. Therefore, the multi-modal data alignment effect is obviously improved.

The invention provides an intelligent text retrieval method and system, a storage medium and a program product, and relates to the field of intelligent text retrieval, the method comprises the following steps: segmenting each original document into a plurality of semantic segments according to semantic similarity; extracting a text feature of each semantic fragment to generate a text fragment vector; selecting a related fragment from each text fragment vector, wherein the first similarity between the related fragment and the retrieval vector is greater than a preset first threshold value; calculating a second similarity between each related fragment and the adjacent fragment; combining the adjacent segments with the second similarity greater than a preset second threshold value with the corresponding related segments to obtain stitching segments; based on a retrieval result corresponding to a historical retrieval request of a user, determining a retrieval granularity which is firstly displayed; and according to the retrieval granularity, adjusting the display content of the stitching fragment, and returning a text retrieval result. By implementing the method, the semantic integrity and the result relevancy of the text retrieval result can be improved.

The invention discloses a text retrieval method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining a question input by a user and at least one candidate text; for each candidate text, determining semantic similarity and keyword similarity between the question and the candidate text; sentence features of the questions and document features of the candidate texts are extracted respectively, and the weight of semantic similarity and the weight of keyword similarity are determined according to the sentence features and the document features; based on the weight of the semantic similarity and the weight of the keyword similarity, according to the semantic similarity and the keyword similarity of the question and each candidate text, calculating the comprehensive similarity of the question and each candidate text; at least one candidate text with the maximum comprehensive similarity is screened out, and an answer candidate set is obtained; and inputting the answer candidate set and the question into a preset large language model, and outputting a retrieval result. According to the method, the accuracy and efficiency of long text retrieval can be improved.

The invention discloses a water conservancy design archive retrieval system and method based on a local lightweight large model, and the method comprises the steps: S1, constructing a Python automatic preprocessing assembly line, extracting texts for PDF and Word multi-format archives, correcting metadata, and outputting standardized data; s2, constructing a full-text retrieval and semantic retrieval dual-mode cross-document retrieval service by relying on a Weavi ate local vector database and a lightweight text embedding model; s3, analyzing a user query intention through a local large model, synchronously triggering metadata accurate retrieval and content semantic retrieval, and generating a structured result; and S4, integrating the core module into a local area network Web platform, adopting Docker containerization deployment, and combining an RBAC permission model and JWT authentication to guarantee security. The system comprises a preprocessing module, a cross-document retrieval module, an intelligent agent module and a background management module, and collaboration is achieved through a standardized API. According to the method, the problem of archive fragmentation is solved, multi-mode retrieval breaks through keyword limitation, an intelligent agent reduces manual intervention, a localized architecture prevents secret-related leakage, background management adapts to an existing I T environment, and full-process intelligent archive service is provided for water conservancy design.

The invention relates to a retrieval method and device based on static word embedding, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of performing word segmentation on an original text to obtain a first word segmentation result, training a static word embedding model by utilizing the first word segmentation result to obtain word vectors, and generating a synonym word library; the method comprises the following steps: establishing a full-text inverted index by utilizing an original text, and expanding query words by utilizing a synonym library in a retrieval stage; encoding the original text into a semantic vector by using a semantic generation model, and constructing a vector index based on the semantic vector; based on a to-be-queried text in a user query request, performing retrieval by using the full-text inverted index to obtain a first candidate document, and performing retrieval by using the vector index to obtain a second candidate document; performing fusion processing on the first candidate document and the second candidate document to obtain a target candidate document; and inputting the target candidate document into the text generation model to obtain a retrieval result. By adopting the method, the accuracy of text retrieval can be improved.

The invention discloses a file retrieval method, device and equipment based on multi-modal AI and a storage medium, relates to the technical field of data retrieval, and aims to solve the problem that traditional file retrieval is low in efficiency and precision. The method comprises the following steps: performing content analysis on a multi-modal file including a picture file, a video file and a document file of text and / or visual information, and extracting a text semantic feature, a picture visual feature and a video time sequence feature; mapping the text semantic feature, the picture visual feature and the video time sequence feature to a cross-modal semantic space used for representing a high-dimensional vector associated with different modal features, and generating a cross-modal association vector; according to the modal type of the retrieval information, a corresponding retrieval module in a mixed retrieval engine is called to conduct retrieval in a cross-modal semantic space, an initial retrieval result is obtained, and the mixed retrieval engine comprises a text retrieval module, a visual retrieval module and a cross-modal fusion module; and performing dynamic weight distribution sorting on the initial retrieval result, and outputting a target matching result.

The invention discloses a retrieval enhancement question and answer generation method and system based on a large language model in combination with a multi-agent cooperation mechanism. The method comprises the steps of S1, text preprocessing and semantic difference enhancement and amplification; s2, checking and complementing user questions; s3, evaluating problem complexity and selecting a generator; s4, text retrieval and answer generation; the system comprises an information enhancement processing module, a query understanding optimization module, a question complexity intelligent evaluation module and a collaborative answer generation module, and is used for realizing the method. According to the method, a multi-agent collaborative RAG framework of an information enhancement agent, an interaction analysis agent and a problem complexity evaluation agent is introduced, and semantic difference enhancement and amplification, query integrity verification and complementation and a dynamic generation strategy based on problem complexity are performed after hierarchical document partitioning are combined; and the performance of the system in the aspects of semantic distinguishing capability, query understanding precision, generation efficiency and reliability is comprehensively improved.

The invention discloses a large model retrieval enhancement generation method and system, and the method comprises the steps: obtaining the comprehensive document data of a power grid field, carrying out the partitioning processing of the comprehensive document data, and obtaining the partitioned text data; performing deep analysis on the block text data to generate a thinking chain enhanced text; encoding the thinking chain enhanced text based on a hierarchical weighted encoding method to generate a thinking chain enhanced semantic vector; inputting the block text data into a pre-trained large language model to obtain a generated task perception representation vector; splicing the thinking chain enhanced semantic vector and the generated task perception representation vector to obtain a fusion vector, and constructing a vector database in the power grid field based on the fusion vector; responding to the text retrieval signal, obtaining question text data of the target user, analyzing the question text data to obtain a question fusion vector, comparing the question fusion vector with a vector database, and obtaining retrieval data of the question text data based on a comparison result.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

A system for extracting a number of data elements from one or more data sources. The system may separate the text from the tables in a document, such that only the table data may be sent to the large language model (LLM), when the LLM only needs to review the table data. The system may include converting a PDF to text, and separating the tables form the document text using markdown language from converting the PDF. The system may form table chunks and text chunks, index the chunks using a vector embedding and store a chunk identifier, document identifier, and or a page identifier with the chunk to provide result traceability. The system may, in response to a prompt, retrieve and send the targeted table chunks or text chunks to the LLM to extract the data elements. The system populates an ontological data store based on the LLM response.

The invention relates to the technical field of remote sensing image analysis and cross-modal retrieval. The invention discloses a remote sensing image text retrieval method based on a remote sensing multi-modal basic model, which applies the large-scale pre-training capability of a CLIP large model to semantic alignment of a remote sensing image and a text by finely adjusting the CLIP large model. By introducing the visual saliency calculation module and the visual block fine-grained selection integration module, the problems of multi-scale targets and redundant information in the remote sensing image are effectively solved, fine-grained semantic alignment between the image and the text is realized, and the retrieval accuracy is improved. Particularly, under the condition that the image contains a plurality of salient targets and redundant regions, the cross-modal semantic alignment fine-grained filtering method provided by the invention can accurately identify key information blocks in the image and perform fine matching with text description.

The invention discloses an image-text cross-modal retrieval method, which comprises the following steps of: constructing an image-text data pair set, and respectively carrying out feature extraction on data of an image modal and a text modal; extracting hierarchical features of the image and the text through a multi-level attention distillation network, and performing inter-modal fine-grained alignment by adopting a cross attention mechanism and knowledge distillation to obtain fusion features; a dynamic batch clustering strategy is adopted to optimize feature alignment, a positive intra-class distance and a positive and negative inter-class distance are calculated, a distance correlation coefficient is adopted to calculate feature similarity, a joint loss function is constructed, and the loss function is minimized, intra-class distance correlation of similar samples is minimized, and inter-class distance correlation of heterogeneous samples is maximized; and mapping the fusion features to a public feature space, calculating the similarity between the fusion features and target modal features, and outputting image-text cross-modal and single-modal retrieval results. The technical problem that the retrieval precision and efficiency are limited due to the fact that modal heterogeneity and semantic heterogeneity are difficult to guarantee in an existing method is solved.

The invention provides a cross-modal joint contrast learning method and device and electronic equipment, and the method comprises the steps: constructing a sample data set which covers a plurality of task types, such as a text retrieval image, an image retrieval text, a text retrieval text, an image retrieval image, an image-text joint retrieval image and an image-text joint retrieval text; and generating prompt words for identifying task types for each task sample, splicing the prompt words with sample data to form task input, and determining modal types of a retrieval object and a retrieval target. A retrieval object and a retrieval target are respectively input into encoders of corresponding modes to extract features, the features are mapped to the same semantic space through a unified projection layer to obtain an embedded vector, and the parameters of the encoders and the projection layer are optimized by utilizing a contrast learning loss function based on the similarity of the retrieval object and the retrieval target, so that multi-task unified training is realized. Various cross-modal retrieval tasks can be supported in a unified semantic space at the same time, and the overall retrieval effect is improved on the premise of ensuring multi-task performance balance.

The invention discloses a cross-modal image-text retrieval method based on pulse fusion, and belongs to the technical field of image-text retrieval. The method comprises the following steps: inputting a target image set into a target detection network to obtain an image floating point code, and inputting a target text set into a word segmentation device to obtain a word floating point code; inputting the image floating point code and the word floating point code into a pulse encoder to obtain a first image pulse code and a first text pulse code; inputting the first image pulse code and the first text pulse code into a pulse cross attention fusion module to obtain a second image pulse code and a second text pulse code; respectively carrying out weighted accumulation and average pooling on the second image pulse code and the second text pulse code to obtain an image floating point feature vector set and a text floating point feature vector set, and calculating cosine similarity to obtain an image-text alignment result; and the text retrieval result of each image in the target image set is obtained based on the image-text alignment result, so that the accuracy and efficiency of image-text retrieval are improved.

The invention discloses a server hardware testing terminal and method, and relates to the technical field of server hardware testing, and the method comprises the steps: obtaining server hardware configuration parameters and storage equipment specification information, collecting the number of processor cores, memory capacity and storage equipment read-write speed reference data through a system monitoring interface, building a hardware performance baseline file, and storing the baseline file in a server; obtaining a complete hardware resource list and a performance index range; the method comprises the following steps: constructing a multi-dimensional workload model according to information retrieval scene features, creating query request sets of different scales by adopting a random number generator, simulating a mixed load mode of full-text retrieval and database query, and determining resource consumption weights and execution time distribution of various query operations; and analyzing query request distribution characteristics in the workload model through a dynamic load balancing algorithm, if query requests are concentrated in a specific time period, adjusting a load distribution strategy, obtaining a uniformly distributed test load sequence, and judging the impact degree of a load peak value on hardware resources.

The invention discloses a multi-modal data processing method and device based on a large model, equipment and a medium. Performing feature extraction and association mining on the multi-modal data to obtain multi-modal data features; according to a retrieval sequence of the multiple text matching methods, sequentially using the multiple text matching methods to perform text retrieval on the multi-modal data features in a pre-constructed knowledge base to obtain a first associated sub-graph; based on the semantic similarity between the multi-modal data features and multi-modal data in a pre-constructed knowledge base and a similarity threshold value, determining a second associated sub-graph; performing standard evaluation on the plurality of candidate results in the first associated sub-graph and the second associated sub-graph, and sorting the plurality of candidate results according to an evaluation result to obtain a retrieval result; inputting the retrieval result and the multi-modal data features into a large model for information analysis to obtain prompt information and a target tool; and sending the prompt information and the target tool to the terminal, and receiving a data processing result of the terminal.

The embodiment of the invention discloses a question answering method and device based on retrieval enhancement generation, equipment and a medium, and relates to the technical field of large language model question answering. The method comprises the steps of obtaining a target question input by a user; on the basis of keywords in the target question, keyword retrieval is carried out on each text in a pre-established knowledge base; each text in the knowledge base corresponds to a text vector; retrieving a text vector similar to the target vector in a pre-established knowledge base, and determining a text corresponding to the retrieved text vector; the target vector is a vector generated based on a target problem; and processing the target question and a retrieval result obtained in the knowledge base based on the large language model to obtain an answer corresponding to the target question. According to the technical scheme, text retrieval and vector retrieval are carried out in the knowledge base, the retrieval result with higher timeliness and higher accuracy is obtained, and then the answer output by the large language model can be obtained based on the retrieval result and the target question.

The invention relates to the technical field of intelligent information retrieval, in particular to an image-text retrieval generation method and device based on reference semantics and electronic equipment. The method comprises the steps of performing semantic segmentation on a document in a document knowledge base to obtain a plurality of paragraphs, performing semantic similarity judgment, and merging similar paragraphs; extracting semantic features of the merged paragraphs to form semantic features of lower-layer paragraphs; abstract description is carried out on the merged paragraphs, semantic features are extracted from abstract description, upper layer description semantic features are formed, and text level semantic features are constructed; image semantic features in the image knowledge base are extracted, normalization processing is carried out on the text level semantic features and the image semantic features, an inverted product quantitative index is constructed, and construction of an image-text reference semantic feature index is achieved; and processing the input text based on the image-text reference semantic feature index to obtain a retrieval result. According to the method, the problems of lack of previous knowledge, insufficient detail information and poor image-text relevance in image-text retrieval are solved.

The invention discloses an unsupervised image-text retrieval method for deep joint semantic alignment based on CLIP, and relates to the technical field of image-text retrieval methods, comprising the following steps: S1, CJSAH extracts features of images and texts from a pre-trained CLIP backbone network; s2, respectively calculating feature similarity matrixes in the image and text modals in batches; s3, the feature similarity matrix is enhanced and fused into a joint modal similarity matrix which is used for supervising learning of hash codes; s4, after the features are spliced, semantic interaction is carried out through a Transf ormer encoder, and then comparison loss is calculated by disassembling the features subjected to modal fusion; when the comparison loss is calculated in the step S4, the CJSAH further introduces a momentum comparison learning module which comprises a momentum encoder and a dynamic queue so as to mine negative sample information in a larger range; experiments carried out on three widely used data sets show that the CJSAH provided by the invention obtains a satisfactory result in the aspect of total retrieval accuracy.

The invention discloses a natural language and machine vision-based alignment method, which comprises the following steps of: providing a three-level alignment architecture, and respectively extracting local-global features of a visual scene and grammar-semantic features of a natural language by adopting a double-flow feature extraction network; through a space-time attention enhanced cross-modal alignment module, a dynamic gating mechanism is adopted to complete feature space adaptive projection; a joint optimization strategy is constructed based on comparative learning, and vision and language embedding space consistency is optimized by using a multi-granularity comparative loss function. And meanwhile, semantic topology constraint and visual causal reasoning are introduced, so that the calculation complexity is reduced, and the task robustness is improved. Through experimental test data, the Top-1 accuracy rate of the method in image-text retrieval reaches 92.3%, the visual question and answer F1 value is 83.4%, the model parameter quantity is reduced by 40%, the method can be widely applied to the fields of intelligent interaction systems, automatic driving scene understanding, industrial quality inspection knowledge base construction and the like, and the semantic perception and reasoning ability of a multi-modal system is remarkably improved.

The invention discloses an intelligent document analysis and question-answering method and system based on an RAG and a multi-modal knowledge graph, and aims to solve the problems that the existing GraphRAG mainly depends on text retrieval and generation, and lacks utilization of images or other modal information, so that the comprehensiveness and accuracy of answers are limited in a complex application scene. Analyzing the multi-modal document to obtain text modal information and image modal information; converting the text modal information into a text knowledge graph, and converting the image modal information into an image knowledge graph; fusing the text mapping knowledge domain and the image mapping knowledge domain to obtain a multi-modal mapping knowledge domain; and sequentially inputting the multi-modal knowledge graph into the embedded model and the vector database to obtain an entity vector database. And sequentially inputting a to-be-queried question of a user into the embedded model and the vector database to obtain information corresponding to the to-be-queried question, inputting the information corresponding to the to-be-queried question into the large language model, and outputting an answer to the to-be-queried question. The invention belongs to the field of data processing.

The present invention relates to the technical field of artificial intelligence, and provides a diversity-enhanced text retrieval-augmented generation method and system. The method comprises: using knowledge data to construct a local knowledge base; acquiring a user question and the desired number of retrieval results; in a retrieval stage, using a large language model to paraphrase the user question to obtain a paraphrased question, then expanding the retrieval range, expanding the number of retrieval results, and performing vector retrieval in the knowledge base on the user question and the paraphrased question to obtain retrieval results; calculating the similarity among the retrieval results, and screening for diverse retrieval results on the basis of the similarity calculation result; and integrating the screened retrieval results and the user question by means of prompt engineering, inputting the integrated result into the large language model, and taking a model generation result as an answer to be returned to the user, thereby providing the user with richer and more informative search results, and improving the richness and accuracy of content generated therefrom.

The invention relates to the technical field of knowledge maps, and particularly discloses a fabric cross-modal image-text retrieval method based on a knowledge map and a storage medium, and the method comprises the following steps: constructing a textile fabric structured knowledge map; performing semantic modeling on the structured knowledge graph of the textile fabric according to a TransR model; semantic structure extraction is carried out on the structured semantic model of the textile fabric according to the graph attention network; extracting deep semantic features of the textile fabric image; constructing an image and entity bidirectional contrast loss function according to the textile fabric entity semantic structure and the image deep semantic features; constructing a multi-task joint loss function according to the bidirectional comparison loss function and the structured semantic model; and training the spatial alignment matching degree of the entity semantic structure and the deep semantic features of the image according to a multi-task joint loss function to obtain a fabric cross-modal image-text retrieval result. The fabric cross-modal image-text retrieval method based on the knowledge graph can be suitable for fabric cross-modal image-text retrieval.

Provided is a computer-implemented method for training a machine-learned video representation model, some example implementations of which can be referred to as "VideoPrism." This model can be used for a variety of video understanding tasks, such as action recognition, spatiotemporal localization, video-text retrieval, video captioning, and video question answering. The model can be trained using a hybrid dataset containing high-quality video-caption pairs, videos with noisy parallel text, and / or training examples containing only video.

The invention discloses an image-text retrieval method based on feature collaboration and adaptive attention adjustment, which comprises the following steps of: firstly, generating a regional feature set of an image and a text feature set corresponding to a sentence, and generating an enhanced regional feature set by adopting a global-local feature collaboration enhancement module; performing interactive matching on word features in the text feature set and the enhanced regional feature set to obtain a comprehensive image feature vector concerned by each word, and performing similarity calculation to obtain a similarity score between an image and a sentence; and meanwhile, an adaptive cross-modal attention regulator module is adopted to update a comprehensive image feature vector concerned by each word, and triple loss based on the most difficult negative sample is applied to training of a target function. According to the method, through cooperation of local and global features of the image, region features are enhanced, channel weights and attention distribution of image regions and word pairs are optimized, and then the cross-modal semantic alignment capability between the image and the text is improved.

The invention provides a large language model-based finance and taxation data intelligent dialogue and query system, and relates to the technical field of finance and taxation data processing and natural language processing.The method comprises the following steps of: firstly, constructing a middle adaptation layer for connecting a large language model and a finance and taxation service database, and converting database information into a form which can be recognized by the large language model; and performing finance and taxation field special training on the large language model, so that the large language model can process finance and taxation problems of a user and generate a query instruction. And executing data query through the middle adaptation layer to obtain an original numerical result. The model is called to analyze the result to generate preliminary interpretation, and a finance and tax rule text retrieval library is combined for correction to form a final result and push the final result to the user. The finance and taxation data query efficiency and accuracy are improved, and comprehensive and professional finance and taxation information is provided for users.