3018 results about "Question answering" patented technology

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on a large language model technology. The method comprises the following steps: receiving a multi-source heterogeneous data stream, and completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; performing incremental optimization on the skeleton, and performing entity relationship disambiguation and conflict detection; and iteratively updating the knowledge representation, and outputting a target knowledge graph meeting semantic consistency. The system comprises a data receiving module, a semantic fusion module, a skeleton construction module, an optimization module and a knowledge updating module. According to the method, multi-source heterogeneous data is effectively processed, the accuracy, the dynamic updating capability and the semantic consistency of the knowledge graph are improved, and the method has wide application prospects in the fields of intelligent question answering, information retrieval and the like.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention relates to the technical field of computer systems for natural language processing or semantic processing, and discloses an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering system and an enterprise-level schedule planning and knowledge base-oriented intelligent collaborative question-answering method. The system comprises a vectorization processing module, an RAG knowledge base module and the like. The system converts natural language input of a user and enterprise knowledge data into semantic vectors, and retrieves related enterprise knowledge fragments from a vector database based on semantic similarity. The large language model core module analyzes the user intention, generates a preliminary answer and identifies whether a schedule type operation request is included or not; and if the schedule operation request exists, the system splits the request through the multi-agent cooperation module and distributes the request to the corresponding agent to obtain a task processing result. And finally, semantic consistency fusion is carried out on the preliminary answer and a task processing result through a context fusion module, a comprehensive answer is generated by a large language model and is returned to a user side, and unified intelligent response of enterprise knowledge and schedule service is realized.

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

A ground visual question-answering method based on dynamic dual-level visual information fusion includes using a dual-level multiscale network, which is divided into language-guided pixel-level features and region-level features. These two scale branches are combined to predict the final textual answer and ground answer. Furthermore, a question-guided dynamic region-level feature localization network is proposed to locate visual information guided by the question and adaptively assign masks of different sizes to ground answers, thereby enhancing the accuracy of locating and segmenting small targets. Additionally, a cross-modal aggregation module is designed to fuse features from both levels, enhancing the fusion of pixel-level and region-level features to improve the segmentation effect of ground answer masks' edges. The ground visual question-answering system built by the language-guided adaptive dual-level feature fusion network in this invention can effectively improve the accuracy of the entire model while answering questions and generating answer ground masks simultaneously.

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

Provided in the embodiments of the present disclosure are a question answering processing method and system, a device and a storage medium. The method comprises: on the basis of a user question, a first prompt template containing first tool set information, and an LLM, a first agent acquires, successively generated by the LLM, a plurality of pieces of solving task information that are required to be executed by invoking a second agent and summary task information that is required to be executed by the first agent, and successively transmits the plurality of pieces of solving task information to the second agent; on the basis of received current solving task information, a second prompt template containing second tool set information, and the LLM, the second agent acquires task execution information that is generated by the LLM and corresponds to the current solving task information, and accordingly acquires from an application server a task execution result of the current solving task information; and the first agent summarizes the task execution results of the plurality of pieces of solving task information, so as to determine reply information corresponding to the user question. By means of the cooperation of different agents, user questions are gradually and accurately solved.

The invention discloses an intelligent question answering system construction method and system based on an LLM large language model, and the method comprises the steps: carrying out the corpus aggregation from a plurality of heterogeneous data sources, and dynamically capturing the text data related to a target domain; preliminarily screening the text data, and based on semantic similarity and a text quality scoring rule, removing redundant and low-quality text data or text data irrelevant to a target field; performing diversity analysis on the text data, and generating diversified text data with high similarity with real data through an adversarial generation technology; performing multi-level semantic annotation on the text data, and generating a training sample by analyzing context information in the annotated corpus; and converting the training sample into a standard format sample, and training an LLM large language model by using the standard format sample to serve as an intelligent question-answering system of a target domain. By utilizing the embodiment of the invention, the intelligent level of the intelligent question-answering system in various application scenes can be effectively improved, and further development of the intelligent question-answering technology is promoted.

The invention discloses a vertical field document question-answering method and system based on a knowledge graph enhanced large model, and the method comprises the following steps: S1, construction of a knowledge graph: collecting related data of a vertical field, and cleaning and sorting the related data; performing knowledge extraction on the processed data, and designing a domain ontology according to a knowledge extraction result to construct and store a knowledge graph; s2, questioning and answering based on the knowledge graph: extracting key entities and relationships in the input text, and querying related information from the knowledge graph according to the key entities and the relationships; and dynamically constructing a prompt based on the knowledge graph query result and the question, inputting the prompt into the large model, and generating an answer. The method has the advantages that data of professional fields are structured by constructing the vertical domain knowledge graph, authoritative and reliable background information support is provided during question answering, and it is ensured that model answering is more scientific and accurate.

The invention discloses a question answering method for graph-driven fusion retrieval in the field of computer networks, and relates to technologies such as intention recognition, multi-channel fusion retrieval, hierarchical semantic matching and answer traceable generation. Aiming at the problems of professional term matching, incomplete knowledge, single retrieval and low answer credibility of an existing question-answering system, a dual-channel fusion retrieval and hierarchical semantic matching mechanism based on a knowledge graph is provided, a retrieval strategy is dynamically controlled through intention recognition, accurate matching of structured knowledge is realized by adopting phrase preliminary screening and triple fine matching, and the accuracy of the structured knowledge is improved. In combination with graph subgraph dynamic construction and Prompt generation, a large language model is guided to output high-quality answers, traceable knowledge sources are attached, and the accuracy and credibility of the answers are improved. The method is widely applicable to computer network professional question and answer scenes such as network protocols, configuration management and troubleshooting.

The invention relates to an aviation standard question and answer optimization method and system based on atlas and document data, and belongs to the field of natural language processing in the aviation field, and the method comprises the steps: S1, recognizing the intention of a user question based on a BERT model; s2, constructing an aviation enterprise standard attribute knowledge graph; s3, constructing an aviation enterprise standard text knowledge base; s4, recalling aviation enterprise standard attribute question knowledge; s5, recalling aviation enterprise standard content question knowledge; and S6, performing big model intelligent question answering based on RAG, and inputting a recall result and an original question of the user into the pre-trained big model through a retrieval enhancement generation technology to serve as knowledge reference to assist in generating answers. According to the method, the knowledge graph of the aviation enterprise standard is constructed, the large model RAG technology based on the knowledge graph is researched, the problem that entities and relations in user questions cannot be intelligently understood in the existing standard attribute information retrieval process is solved, and the accuracy and efficiency of standard attribute information question answering can be improved.

The invention discloses a knowledge base generation and retrieval method based on the RAG technology and an intelligent question answering system, and belongs to the technical field of natural language processing, and the knowledge base generation and retrieval method based on the RAG technology and the intelligent question answering system based on the RAG technology have the beneficial effects that the accuracy and the real-time performance of information processing are improved through efficient combination of retrieval and generation. High-dimensional vector representation is generated through text slicing and vectorization, a vocabulary index is constructed, and hierarchical retrieval and keyword matching are achieved; meanwhile, a question generation and question-question retrieval method is utilized, a result is optimized and generated in combination with a large language model (LLM), the reasoning ability of the large model is further improved through chain reasoning (COT) and a cue word optimization module, and it is ensured that the generated answer has high accuracy and logicality; the method can be widely applied to the fields of intelligent question answering, knowledge base construction, real-time information retrieval and the like.

The invention provides a knowledge graph construction and intelligent question and answer method and device based on deep learning. The method includes; obtaining target text data; information extraction is conducted on the target text data, entity relation data is generated, and the entity relation data comprises key data, entity data and relation data used for indicating the relation between entities; according to the entity relationship data, importing the target text data into a graph database to form a knowledge graph; obtaining an input question statement; the question sentence classification model is used for classifying the question sentences, question sentence features in the question sentences are determined, and the question sentence features comprise question sentence types and question sentence entity data; and retrieving text data matched with the question features in the knowledge graph as answer data of the question statements. According to the method, the public text information retrieval efficiency is improved, and the accuracy of intelligent question answering is improved.

The invention discloses a knowledge graph-based large language model intelligent teaching aid question-answering system and an implementation method thereof. The knowledge graph-based large language model intelligent teaching aid question-answering system comprises a knowledge base module, a question-answering module and a graph visualization module. And the knowledge base module divides the subject resources into documents to be stored as a local knowledge base of a large language model, and performs related vector base construction for the local knowledge base, so that subsequent retrieval enhancement generation tasks are facilitated. The question and answer module converts user questions into embedded vectors, retrieves most relevant documents in a local knowledge base through semantic similarity to serve as context filling of a large language model, and through prompt word engineering construction, the large language model generation effect is enhanced, the professional degree is enhanced, and the illusion problem is reduced. And the graph visualization module returns the context generated by interaction between the user and the large language model to the large language model again, knowledge nodes in the context are extracted through prompt word engineering, so that a context knowledge graph is constructed and visually presented to the user, an inquiry node and an inquiry relationship are set for interaction, and the interaction experience of the user is improved. According to the method, the knowledge graph and the big language model application are combined and landing in the education field, the text generation and semantic understanding ability of the big language model and knowledge graph visualization are utilized, the question and answer enthusiasm of the user is enhanced, and the learning effect of the user is improved. Meanwhile, a novel man-machine collaborative normal form is provided for an education mode, and a new insight is provided for the technical field of education.

The invention discloses an intelligent question answering system based on a large language model, and relates to the technical field of natural language processing and information retrieval. According to the system, a retrieval enhancement generation (RAG) and knowledge reordering (Re-rankig) technology is utilized, and the knowledge processing capacity for mechanical manufacturing process design, industrial standards and related specifications is improved. The system is mainly composed of four parts, wherein the first part is used for constructing a knowledge base covering machine manufacturing knowledge; in the second part, a LoRA + fine-tuned Qwen2-7B model is used for guaranteeing high-precision requirements such as process parameter calculation; in the third part, the RAG technology is integrated to effectively retrieve and process domain knowledge; and 4, introducing a reordering model to optimize answers. The system is remarkably superior to a traditional large language model LLM in BLEU-4 and ROUGE text matching evaluation indexes, the reliability and speciality of application of the system in the mechanical manufacturing field are verified, an effective scheme is provided for intelligent question answering in the field, and the prospect is wide.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention discloses a self-adaptive learning question answering system and method based on multi-modal interaction, and particularly relates to the technical field of self-adaptive learning question answering. By constructing a modal recognition and preprocessing module, standardization and structuralization of multi-modal input of texts, voices, images and the like are realized; establishing a cross-round modal memory map through time sequence coding and map modeling; dynamically updating a user portrait in combination with user interaction history and current modal characteristics; in the question and answer generation process, current input, a user portrait and a historical graph are fused, intermediate semantic representation is generated through a context enhancement module, and the intermediate semantic representation is combined with a knowledge base to generate answers; meanwhile, a modal confidence degree dynamic evaluation mechanism is introduced, and weights are distributed according to the input quality, the user adaptation degree and the context correlation; and finally, incremental optimization is carried out on the graph structure, the user portrait and the question and answer strategy through a user feedback driving system, and multi-round, multi-mode and self-adaptive intelligent question and answer interaction is realized.

The technology discloses a system for validation of generative AI model operators using preloaded query context connected to independent AI models in generative AI-driven trivia gameplay. The system includes a user interface connected to a generative AI model that generates trivia responses based on user input. A model superstructure between the user interface, the generative AI model, and independent validation model(s) facilitates the evaluation of the user input or trivia response by the validation model(s). The validation model(s) are provided with a pre-loaded query context to detect adherence to predetermined conditions. A consensus module receives the validation model's output and delivers the user input or trivia response to the generative AI model or user interface, respectively, based on the evaluation.

The invention relates to an intelligent operation and maintenance question-answering system for cable manufacturing equipment, and belongs to the technical field of computer systems based on specific calculation models. The system comprises an edge data acquisition module, a predictive map construction module, a semantic perception module, a question-oriented reasoning module and a question and answer generation module. According to the system, multi-dimensional real-time data in the operation process of equipment is collected and structurally processed, a process knowledge graph is constructed in combination with industry knowledge, and the causal relationship and reasoning parameters in the graph are dynamically updated according to the data trend. A semantic perception module is used for recognizing the problem intention of a user, a semantic weight vector is formed to guide the reasoning process, and a problem-oriented reasoning module is made to execute joint reasoning on the basis of combining real-time data and a knowledge graph and generate an explanatory conclusion. Finally, operation and maintenance suggestions with high readability are output through a question and answer generation module, and the targets of equipment fault intelligent diagnosis, process optimization and man-machine efficient interaction are achieved.

The invention discloses a double-engine government affair question and answer method based on large model fine tuning and RAG retrieval, belongs to the field of government affair digitization and natural language processing, and combines large model language understanding generation ability, retrieval enhancement generation technology and a structured reasoning mode. The defects of a traditional government affair question and answer method in the aspects of dynamic policy response, complex semantic understanding and compliance control are overcome. Government affair field knowledge is adapted through large-model fine adjustment, and high-precision and timeliness answering of government affair consultation is realized in combination with vector retrieval and a dynamic updating mechanism. The core innovation of the method lies in deep fusion of a double-engine architecture and dynamic knowledge management, the accuracy and response efficiency of government affair questions and answers are improved on the premise of ensuring policy compliance, and the method is suitable for intelligent upgrading of scenes such as government affair service halls and online consultation platforms.

The invention provides a multi-modal intelligent question-answering system based on a large model and a construction method and device, and the method comprises the steps: carrying out the region recognition of a PDF document, and obtaining a table region picture, a text region picture and / or an image region picture; the Word document is analyzed, paragraph content of the Word document is obtained, and the paragraph content is composed of the first text, table data and / or Word pictures; constructing a knowledge base; wherein the knowledge base comprises vector representation of document slices; the document slice is determined based on the table area picture, the character area picture and / or the image area picture, or the document slice is determined based on the paragraph content and the corresponding chapter information thereof. Visibly, according to the method, layout recognition can be carried out on the PDF document, catalog chapter recognition can be carried out on the Word document, the knowledge base is constructed based on the layout recognition, the semantic integrity and coherence of multi-modal text question and answer recall can be improved, and the answer accuracy is improved.

Disclosed is an industrial question answering model training method based on reinforcement learning and knowledge base matching, comprising the following steps: S1, collecting professional knowledge questions and answers in an industrial field to construct an industrial knowledge base, training a reward model, carrying out, for industrial knowledge questions and answers, matching comparison on outputs of an industrial question answering model and content of the industrial knowledge base, and obtaining reward values on the basis of similarities; S2, sorting the reward values, and using a sorting loss function to train and update parameters of a reward model network; and S3, carrying out industrial question answering model training, incorporating a penalty term for the reward values, and using a reinforcement learning algorithm to train the industrial question answering model multiple times to obtain an optimal strategy. According to the industrial question answering model training method based on reinforcement learning and knowledge base matching of the present invention, the reinforcement learning algorithm is used, and iterative training is carried out multiple times, thereby helping the industrial question answering model to learn and understand industrial professional knowledge and improving the question answering accuracy of the industrial question answering model.

The invention provides a multi-modal intelligent question-answering and recommendation system supporting emotional speech output, and relates to the technical field of visual question-answering, and the system specifically comprises a multi-modal question-answering module, a multi-modal recommendation module, a speech recognition module, and a speech synthesis module. Wherein the multi-mode question answering module converts an image input by a user into a natural language form, and final question answering is completed in combination with a comet knowledge base COMET; the voice recognition module is used for recognizing voice data input into the multi-mode intelligent question answering and recommending system and processing the voice data into a text form; the multi-modal recommendation module identifies whether modal missing exists in input data or not, generates representation of the missing modal, obtains vector representation of the user and the article through a graph convolutional neural network and learning according to an interaction matrix of the user and the article, carries out reliability calculation, and finally sends a multi-modal recommendation result to the user and the article. Sending back to the multi-mode question and answer module; and the voice synthesis module outputs the voice to the user.

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention discloses a knowledge graph analysis and question answering method and device based on a large language model. The method comprises the steps of obtaining original data, performing data processing based on a large language model to obtain target data, constructing a knowledge graph according to the target data, and storing the knowledge graph into a graph database; acquiring an open source data set, and processing the open source data set by using the large language model to obtain a training data set; according to the graph database, constructing a dictionary which is used for reflecting a graph structure of the graph database; utilizing the training data set, the dictionary and a preset prompt instruction to finely adjust the large language model to obtain an optimized large model; generating a query statement by utilizing the graph database and the optimization large model according to a query problem of a user, and determining a query result in the graph database according to the query statement; and based on the optimization large model, carrying out integration processing on the query question and the query result to obtain a final answer. According to the method, the construction process of the knowledge graph is simplified, and the accuracy of analysis and question answering is improved.

The invention discloses a reverse question guide question answering implementation method and system, and belongs to the technical field of artificial intelligence and natural language processing. Context-aware intention dynamic correction is realized through a three-level intention classification system, and cross-modal knowledge matching is realized by adopting a distributed semantic index technology; based on the reinforcement learning strategy, optimizing a cooperative work mechanism of the dialogue strategy and the knowledge base; comprising the steps of intention recognition: analyzing a session of a user by using an intention recognition model, and constructing a three-level intention classification system based on deep semantic understanding, including main class recognition, fine-grained analysis and context perception; question rewriting: constructing a dynamic rewriting engine to rewrite the user question; recalling and cleaning multi-source item knowledge; generating a reverse question; locking items and acquiring item data; generating questions and answers. According to the method, the robustness, the real-time performance and the scene adaptation capability of a professional question answering system can be improved, and the government affair service question answering accuracy, the intention recognition precision and the cross-region recommendation adoption rate are improved.

The invention discloses a question and answer method and system based on tool calling, electronic equipment and a storage medium. The method comprises the steps that a user question and answer interface obtains question information of a user and sends the question information to a service layer; the tool retrieval module screens out IDs of candidate tools from existing tools according to the question information to obtain a candidate tool list; the tool calling module calls a large language model based on the candidate tool list and the question information; the big language model screens out an ID of a target tool associated with the question information from the candidate tool list, and generates a tool calling instruction based on the ID of the target tool; the tool calling module calls a target tool according to the tool calling instruction and obtains output parameters of the target tool; and the tool calling module calls a large language model to generate reply content based on the output parameters and the question information. The candidate tools are screened out through the tool retrieval model, then the large language model is called based on the ID of the candidate tools and the question information to determine the target tool, and the accuracy and comprehensiveness of reply content can be improved.