2041 results about "Index term" patented technology

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention belongs to the field of question and answer generation, and provides an interactive retrieval enhancement question and answer generation method and system based on a knowledge graph, and the method comprises the steps: carrying out the document partitioning based on an original document set, generating a global block set, carrying out the entity extraction of each text block in the global block set, and obtaining an entity set; performing relation extraction on entity subsets in each text block in the entity set to obtain a global relation set; generating a plurality of sub-knowledge maps based on the global block set, the entity set and the global relationship set, and performing entity fusion and relationship fusion on the sub-knowledge maps to obtain a knowledge map; performing keyword extraction and semantic embedding on the original problem to obtain a dense vector, performing semantic embedding based on the knowledge graph to obtain an embedded vector, and generating a candidate entity set according to the dense vector and the embedded vector; and based on the candidate entity set, utilizing a large language model calling tool to carry out extended search to generate a candidate information set, and utilizing a large language model to obtain an answer to the original question based on the candidate information set.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

The invention discloses an RAG intelligent retrieval question-answering system and method based on enhanced metadata, and relates to the technical field of information processing and intelligent retrieval, multi-source heterogeneous knowledge data is preprocessed to obtain unified knowledge data, and structured metadata is extracted from the unified knowledge data based on different text forms; vectorizing a document text in the structured metadata by combining with embedding of the knowledge graph to obtain document representation, and outputting the document representation, the structured metadata and the enhanced keyword set as an enhanced metadata object; labeling a display relationship between different enhanced metadata objects, and constructing to obtain a knowledge database; according to the intelligent knowledge service system and method, restrictive conditions and question intentions are extracted from user questions, mixed retrieval is performed from a knowledge database based on the restrictive conditions and the question intentions, a candidate literature semantic set is output, then structured statistical visualization reports and structured answers are output, and accurate, explainable and multifunctional intelligent knowledge services are achieved.

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 discloses a document knowledge base LLM intelligent question and answer method, device and equipment and a storage medium. The method comprises the steps that dynamic partitioning is conducted on a to-be-processed document, and vectorization and index storage are conducted on knowledge blocks; vector retrieval and keyword retrieval are carried out according to the natural language question, a vector retrieval result and a keyword retrieval result are fused, an answer is obtained through LLM, and the answer is fed back to a user after being safely filtered; when it is detected that the to-be-processed document is updated, the vector library and the index are synchronously updated, and the cue word template and the partitioning strategy are periodically optimized, so that the problem of form picture information loss can be solved, the integrity of document information analysis is guaranteed, the situation that the partitioning strategy is single is avoided, the multi-hop recall rate is increased, and the problem of semantic missing is avoided; a partitioning mechanism is reasonable, retrieval precision is improved, updating cost is reduced, data security is improved, implementation is convenient, universality is good, and the speed and efficiency of LLM intelligent question answering of the document knowledge base are improved.

The invention belongs to the technical field of text processing, and particularly relates to a method and system for retrieving DOCX document content based on keywords, which comprises the following steps: analyzing an Office Open XML structure of a DOCX document, combining with multi-dimensional features such as style names, and utilizing a title classification score model to accurately distinguish a title and a text, so that a semantic hierarchical structure of the document is effectively reserved; and secondly, a multi-level semantic extension mechanism is introduced, and a Sension-BERT, a HowNet knowledge base and a Word2Vec model are fused, so that intelligent extension of synonyms and synonyms of keywords is realized, and the recall rate and semantic understanding ability of retrieval are remarkably improved. And in addition, a BM25 model is combined with paragraph length normalization and structure position weight to calculate a correlation score, so that retrieval results are sorted more accurately and reasonably. The construction of the reverse index is combined with the position coding and compression optimization strategy, and the retrieval efficiency and the storage performance are both considered.

The invention discloses an AI intelligent marketing content publishing subject matching recommendation method, and relates to the technical field of marketing content publishing subject matching recommendation, and the method comprises the following steps: carrying out keyword density mutation detection, brand semantic coincidence detection and content publishing period anomaly detection on the basis of a behavior content fusion graph; determining whether the content publishing main body has a condition that cooperation information is lost and behavior characteristics show that cooperation history exists or not; and on the basis of a determination result, performing semantic co-occurrence path construction, propagation pattern similarity analysis and behavior residual discrimination operation, and obtaining a cooperation frequency recessive signal under the condition that the cooperation information of the content publishing subject is lost but the behavior characteristic shows that the cooperation history exists. According to the method, the problem that the real cooperation frequency cannot be identified and the exposure risk cannot be accurately judged under the condition that the cooperation information of the content publishing main body is lost but the behavior characteristic display has the cooperation history is solved, and the implicit cooperation frequency extraction and the risk level dynamic regulation and control based on the multi-source behavior characteristics are realized.

The invention relates to the field of artificial intelligence, in particular to an intelligent standard knowledge retrieval system based on an AI large model, which comprises a standard knowledge management database, a multi-source acquisition preprocessing module, a semantic understanding and indexing module, an intelligent retrieval engine module, a knowledge enhancement module, a user interaction feedback module, a security control module and a deployment expansion module. The standard knowledge management database is used for storing standard knowledge design data, real-time retrieval data and feedback data and constructing a dynamically updated standard knowledge resource pool; through the multi-source standard knowledge acquisition and preprocessing module, multi-channel objective standard data can be integrated and dynamically updated, and the problem of knowledge fragmentation is solved; and through the semantic understanding and indexing module, industry professional semantic accurate matching is realized, the limitation of traditional keyword retrieval is broken through, and the accuracy and efficiency of standard knowledge retrieval are remarkably improved.

Systems and methods disclosed herein are enabled to dynamically generate structured documents using one or more artificial intelligence models. A computing device receives an output generation request and uses a first AI model to retrieve data chunks from source documents and applicable templates. A second AI model ranks the retrieved chunks based on one or more metrics, such as vector similarity, keyword density, and temporal relevance. A third AI model subsequently generates a response using the ranked chunks, templates, and predefined operational boundaries for each chunk. The generated response is tagged with source identifiers to enable the traceability of the response back to corresponding chunks. The system transmits, via the computing device, the response, the retrieved chunks, and / or the source identifiers.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention relates to the technical field of artificial intelligence and information processing, and particularly discloses a multi-modal file intelligent classification and label generation method and system. The method comprises the steps of obtaining text paragraphs, image segments and video key frame data in a file, performing format recognition and region separation, and generating multi-modal content structure data; semantic features are extracted based on the data, and fused semantic representation is constructed; generating a main label, a sub-label and a keyword set by using the fused semantic vector, and constructing a multi-level label structure; and further performing label redundancy elimination and structure optimization to generate a label atlas, and performing consistency analysis and feedback optimization through the label atlas and the semantic representation structure. Compared with the prior art, the method has the advantages that various modal information can be effectively fused, the accuracy, hierarchical structure and semantic consistency of archive label generation are improved, and the method has high intelligence, self-adaption and sustainable optimization capabilities and is suitable for various scenes such as archive management, content auditing and semantic archiving.

The invention belongs to the technical field of artificial intelligence and information retrieval, and provides a multi-modal retrieval method and system based on a lightweight knowledge graph and an index table, and the method comprises the steps: obtaining multi-modal source data, and extracting a structured semantic tag set; constructing a lightweight knowledge graph and a metadata index table; analyzing a natural language query input by a user to obtain a semantic query vector and a keyword set, executing semantic retrieval in the knowledge graph to obtain a text candidate result, and executing keyword matching in the metadata index table to obtain a non-text candidate result; for each non-text meta record, fusing the cross-modal similarity between the non-text meta record and the text candidate result, performing index matching on an original score and a graph semantic evidence score, calculating a comprehensive correlation score, and performing reordering; and generating a natural language answer containing a non-text record link according to a reordering result. The method is suitable for efficient cross-modal knowledge retrieval in a high-security and low-resource scene.

The invention relates to the technical field of information retrieval, and provides a multi-dimensional heterogeneous knowledge recall enhanced hybrid retrieval method and system.The method comprises the steps that texts recalled through keyword retrieval, sparse vector retrieval and dense vector retrieval are screened through a reciprocal sorting fusion algorithm, and a text type candidate knowledge list is obtained; based on user query, generating and checking a query statement through a large language model, and retrieving an entity-relationship-attribute triple from the knowledge graph library; based on user query, generating enhanced knowledge through a knowledge graph enhanced retrieval method fusing keyword retrieval, vector retrieval and community retrieval; and carrying out format alignment and duplicate removal on the text type candidate knowledge list, the triple result and the enhanced knowledge to form a multi-modal candidate pool, and carrying out reordering score calculation and ordering on each piece of recall knowledge in the multi-modal candidate pool through a reordering model and a business rule to obtain a final retrieval result. And the coverage blind area of single retrieval on heterogeneous knowledge is solved.

The invention discloses an enterprise knowledge base retrieval and intelligent answering method and system based on a large language model. The method comprises the following steps: performing clause-level segmentation on an enterprise knowledge base document, associating document metadata to form structured knowledge entries, and establishing a keyword reverse index and a semantic vector index for the structured knowledge entries; analyzing the natural language query of the user, and performing multi-strategy expansion to generate an enhanced query expression and a query semantic vector; performing dual-channel mixed retrieval, performing duplicate removal, version filtering and weighted fusion sorting on a result, and generating a final candidate knowledge item list; and based on the candidate list and a predefined instruction, calling a large language model to generate a structured answer with complete traceability information. The method is compatible with an existing retrieval framework, precise understanding, knowledge point-level positioning, cross-document content integration and version consistency control of natural language problems are achieved, and the retrieval accuracy, answer availability and service intelligence level of an enterprise knowledge base are remarkably improved.

The invention relates to an information retrieval method and device for hierarchical planning reinforcement learning based on retrieval enhancement. The method comprises the following steps: acquiring a natural language query request of a user; calling a large language model of hierarchical planning reinforcement learning training based on retrieval enhancement to generate semantic keywords; the high-level strategy splits the natural language query request into a series of sub-query requests; the low-level strategy generates a retrieval query according to the context of the current sub-query request, and obtains a result verification condition from an external knowledge base through an RAG model; calling a search engine to execute multiple rounds of fine-grained information retrieval to obtain a plurality of candidate retrieval results; and performing deep semantic matching analysis on the candidate retrieval results and the result verification conditions by utilizing the post-trained large language model to calculate a matching degree score, and screening the candidate results according to the matching degree to obtain a final information retrieval result. Compared with the prior art, the method has the advantages that high-quality content retrieval and result display of complex query problems of users in various scenes can be realized.

The invention discloses a human resource intelligent management method and system based on man-post matching, and the method comprises the steps: receiving an unstructured post description text, extracting key information through a natural language processing technology, generating a structured multi-dimensional post portrait, and classifying the structured multi-dimensional post portrait; on the basis of historical recruitment data, predicting the number of future post demands by means of a time sequence analysis model; for the candidate resumes and the target post portraits, keyword correlation scores, depth semantic similarity scores and predictive stability scores are calculated in parallel; according to the post portrait classification application dynamic weight, performing weighted summation on the scores to generate a comprehensive matching score; and sorting the candidates according to the comprehensive matching scores, and outputting a sorted candidate list. According to the method and the system provided by the invention, the man-post matching accuracy and the recruitment efficiency are effectively improved, the core pain points of intelligent recruitment, man-post matching and demand prediction in human resource management are solved, and the method and the system are particularly suitable for the demands of human resource outsourcing and labor dispatching industries for stable service selection.

The invention discloses a product data question and answer method based on a large model and mixed retrieval and related equipment. The method comprises the following steps: constructing a double-track knowledge base based on multi-source heterogeneous product data; performing multi-dimensional analysis on the current query statement in combination with the large model to obtain a multi-dimensional analysis result; the multi-dimensional analysis result comprises a query vector, a structured query instruction and a keyword; inputting the query vector into a vector database for retrieval to obtain a vector retrieval result; inputting the structured query instruction into a graph database for retrieval to obtain an entity relationship retrieval result; inputting the keyword into the text knowledge fragment for keyword matching to obtain a keyword retrieval result; and combining the vector retrieval result, the entity relationship retrieval result and the keyword retrieval result, and sorting and screening the combined mixed retrieval result through a large language model to obtain target answer data. The method can significantly improve the accuracy and reliability of the retrieval result, and can be widely applied to the technical field of artificial intelligence.

The invention discloses a scene and compliance fused dynamic hierarchical data encryption method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the preprocessing of the multi-source data, and obtaining to-be-encrypted data; key words in the to-be-encrypted data are extracted, and weight coefficients are distributed to the key words according to semantic roles and / or industry risk coefficients; according to the data type, determining a characteristic value corresponding to each keyword; determining a scene coefficient and a compliance coefficient; based on the weight coefficient, the feature value, the scene coefficient and the compliance coefficient, determining a data sensitivity score and a data sensitivity level corresponding to the to-be-encrypted data; and based on the data sensitivity level, encrypting the to-be-encrypted data by adopting the encryption strategy of the corresponding level. According to the method provided by the embodiment of the invention, the security of the high-sensitivity data is ensured, the resource waste caused by excessive encryption is avoided, the whole process is automatic, the labor cost and the overall cost of security protection are greatly reduced, and the comprehensive efficiency of data processing and security protection is improved.

The invention discloses a business-adaptive RAG knowledge base rapid question and answer implementation method, relates to the technical field of natural language processing and knowledge base retrieval, and realizes deep fusion and accurate context extraction of fragmented and structured business knowledge by constructing a business enhanced knowledge base and executing multi-stage dynamic retrieval. When an original business document is subjected to intelligent structured analysis and segmentation, a link business entity is identified, rich business attribute metadata is marked, in the process, an unstructured text is converted into an enhanced knowledge fragment carrying clear business semantics, a semantic vector is generated through an embedded model, and on the basis, the semantic vector is subjected to semantic segmentation; according to the method, rapid preliminary screening based on keywords and metadata, fine arrangement based on semantic vectors and association expansion based on a business knowledge graph are sequentially executed, the progressive retrieval strategy ensures that retrieval results are highly related to business contexts, and the accuracy of generated answers is improved.

The invention discloses a question answering method and device based on retrieval enhancement generation, a medium and equipment, and relates to the technical field of computers. According to the method, related candidate sub-graphs are matched in an existing structured knowledge graph according to the query problem of a user, and the candidate sub-graphs are further judged to be insufficient to deal with the query problem through logical reasoning; according to the method, sparse keyword vectors of query questions based on surface vocabularies and dense question vectors based on context deep dependency are further extracted; matching the query question with a sparse semantic vector of each text block of the unstructured text based on surface vocabularies and a dense semantic vector of each text block based on context deep dependency, which are acquired in advance, so as to determine the text block related to the query question from the unstructured text; the candidate sub-graphs are further converted into graph structures to supplement the candidate sub-graphs, answers corresponding to the query questions are generated based on the graph structures, and the performance of questions and answers in multi-hop reasoning and information integration retrieval recall is improved.

The invention relates to an intelligent document information real-time retrieval method and system based on the RAG technology, and the method comprises the steps: analyzing documents of various formats, extracting a text, maintaining the content continuity through adaptive semantic partitioning processing, and building a character-level position index at the same time; after vectorizing the text blocks, generating a plurality of rewriting queries for the original query; respectively carrying out mixed retrieval (combining keywords and semantic retrieval) for each rewriting query, and fusing and reordering results to obtain candidate text blocks; after correlation filtering, inputting a large language model according to correlation to generate an answer, and if the result is negative, triggering secondary retrieval and reordering; and finally, outputting a structured answer containing position information and supporting front-end visualization. According to the method, the semantic integrity maintenance, the multi-format document processing efficiency and the key information retrieval accuracy are effectively improved.

The invention discloses a form category determination method and device for a power system, and the method comprises the following steps: obtaining different categories of form sample images, and extracting keywords and the positions of the keywords in the form sample images; constructing a relation direction matrix of the category form; obtaining a text block set of the to-be-classified form; constructing a relation direction matrix of the to-be-classified form based on the relative position relation between the text blocks; based on a similarity calculation scheme, calculating a similarity score between the relation direction matrix of the to-be-classified form and the relation direction matrix of each category of form; and thus, the category of the to-be-classified form is judged. According to the method, high robustness can be still kept when the forms shift or the fields change, and the classification accuracy of the forms with complex structures or sparse keywords is remarkably improved; meanwhile, the calculation amount is greatly reduced, a large-scale form library can be adapted, and the application requirement of an actual service scene of a power system is met.

The invention discloses a method for generating statements from natural languages to SQL (Structured Query Language) based on a large language model agent. The method comprises the following steps of: constructing an LLM (Logical Language Model) guide table mode index for a target database; obtaining a user natural language question and extracting a keyword; searching in an LLM guidance table mode index according to the keyword, and screening related data tables; splicing the table mode information and the keywords of the related data table into a user natural language problem, and constructing an enhanced cue word; based on the large language model intelligent agent, generating SQL statements according to the enhanced cue words and verifying the SQL statements, if the SQL statements succeed in verification, obtaining the required SQL statements, and if the SQL statements fail in verification, performing iterative closed-loop correction on the generated SQL statements through an SQL statement closed-loop correction workflow; if the corrected SQL statement is successfully verified, the required SQL statement is obtained, and if the corrected SQL statement fails and the number of times of correction reaches a threshold value, the suggestions that the natural language problem of the user has defects and is modified are output. According to the method, the accuracy and success rate of SQL statement generation can be improved.

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 provides a power grid intelligent operation and maintenance multi-mode knowledge base question answering method and system. The method comprises the steps of obtaining natural language query of a user and historical dialogue information of the user in a power grid intelligent operation and maintenance scene; on the basis of the historical dialogue information, keyword preprocessing is conducted on the natural language query, and multi-query input information is obtained; based on a pre-constructed multi-modal knowledge base, carrying out mixed search on the multi-query input information to obtain a core knowledge block set; according to the core knowledge block set, the natural language query and the historical dialogue information, generating question and answer prompt words, inputting the question and answer prompt words into a preset large language model, and outputting target output corresponding to the natural language query; through the method provided by the invention, the multi-modal heterogeneous technical document in the intelligent operation and maintenance scene of the power grid can be effectively processed and utilized, so that the generated answer can be ensured to accurately match the semantic requirement of user query and has high semantic quality.

The invention provides a search processing method and device for a query statement, equipment, a medium and a program product. The method comprises the steps of obtaining a query statement and a multi-layer index; wherein the multi-layer index comprises a text structure-based index, a text semantic-based index and a text unit-based index; performing semantic similarity retrieval and keyword matching retrieval according to the query statement and the multi-layer index to obtain a semantic retrieval result set and a keyword retrieval result set; wherein each of the semantic retrieval result set and the keyword retrieval result set comprises a plurality of document blocks; performing repeated document block filtering processing on the semantic retrieval result set and the keyword retrieval result set to obtain a filtered retrieval result set; wherein the filtered retrieval result set comprises a plurality of filtered document blocks; and ranking the filtered document blocks in the filtered retrieval result set to obtain a target document block candidate set, and providing a context prompt for generation of an answer statement of the query statement. Therefore, the retrieval accuracy is improved.

The invention discloses a synthetic data set construction method and electronic equipment, and relates to the technical field of artificial intelligence, and the synthetic data set construction method comprises the following steps: dividing an original multi-source document of a target field into a plurality of word segmentation units by using a word segmentation device; obtaining representative scores of the plurality of word segmentation units on the original multi-source document; based on the representative scores, determining the word segmentation units with the representative scores higher than a first score threshold as candidate keywords; determining importance degree scores of the candidate keywords based on the representative scores of the candidate keywords; based on the importance score, determining the candidate keyword of which the importance score is higher than a second score threshold as a target keyword; and calling a pre-training language model, and based on the target keyword, generating a question and answer pair corresponding to the target keyword to obtain a synthetic data set of the target field. The technical problem that the data coverage rate and the field correlation of the generated synthetic data set are low in the prior art is solved, and the technical effect of improving the data coverage rate and the field correlation of the generated synthetic data set is achieved.

The embodiment of the invention provides a published content recommendation method and device, equipment, a medium and a product, and the scheme can comprise the following steps: filling a search keyword used for searching a target advertisement and candidate published content matched with the search keyword into a cue word template containing thinking chain guide information; and generating prompt information for being input into the large language model. Wherein the thinking chain guiding information is guiding information of a thinking chain of a recommendation reason generated for the candidate published content; the recommendation reason is a specific reason basis for putting the target advertisement by utilizing the candidate release content. And inputting the cue word information into a large language model to obtain recommendation information at least comprising the recommendation reason and a thinking chain corresponding to the recommendation reason. According to the scheme, the advertisement publisher can fully understand the recommendation basis of the recommendation system, the recognition degree and the satisfaction degree of the recommendation content are improved, and the overall user experience and the advertisement putting effect of the recommendation system can be improved.

The invention discloses a document retrieval method and device, equipment, a medium and a program product, and relates to the technical field of document processing. The method comprises the steps of obtaining a query statement of a document retrieval party, at least two candidate documents and candidate abstract vectors and candidate full-text vectors of the candidate documents; performing keyword extraction on the query statement to obtain a query keyword, and performing vectorization on the query statement to obtain a query statement vector; according to the query keyword, the query statement vector, each candidate document and a candidate abstract vector of each candidate document, performing coarse screening on each candidate document to obtain at least two coarse screening documents; and according to the query statement vector and the candidate full-text vector of each coarse screening document, performing fine arrangement on each coarse screening document to obtain a target retrieval document. According to the technical scheme provided by the embodiment of the invention, the accuracy of document retrieval is improved.