System for big language model to manage cache data in dialogue history

Abstract

The invention discloses a system for caching data in big language model management dialogue history, which comprises a data check module, a data extraction module, a data merging module and a data storage module, and is characterized in that data processing and analysis of the system cover but are not limited to statistics, analysis, mining and prediction operations; according to the method, the accuracy, the integrity and the real-time performance of the data are ensured by adopting multiple technical means, the data are persistently stored by using a storage medium, the data are prevented from being lost, and meanwhile, the data are subjected to memory loading during system operation, so that the retrieval speed of the data is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a system for managing cached data in a conversation history using a large language model. Background Art

[0002] With the progress of big language models, various applications based on big language models that use general natural language as input are becoming increasingly abundant. However, the various data involved in the conversation process are constantly accumulated and superimposed during the conversation, making it impossible for the big language model itself to load and unload such complex data.

[0003] Therefore, it is necessary to design a system for large language models to manage cached data in conversation history. Summary of the invention

[0004] The object of the present invention is to provide a system for managing cached data in a conversation history for a large language model, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A system for managing cached data in a conversation history using a large language model, including a data checking module, a data extraction module, a data merging module, and a data storage module. The data processing and analysis of the system include but are not limited to statistical, analytical, mining, and predictive operations. A variety of technical means are used to ensure the accuracy, integrity, and real-time performance of the data. A storage medium is used to persist the data to avoid loss. At the same time, the data is loaded into memory during system operation to improve the data retrieval speed.

[0007] Data checking module: analyzes and judges the input information, and chooses whether to load the existing stored data based on the judgment results;

[0008] Data extraction module: extracts data from the storage medium according to the keyword information obtained by the data inspection module, and the storage medium includes the memory and physical medium when the system is running;

[0009] Data merging module: merges the extracted data and the system input data according to semantics to generate a result statement with clear and accurate semantics;

[0010] Data storage module: stores the generated result statements according to the data dimensions contained in the input information, and the data is stored in the storage medium.

[0011] According to the above technical solution, the data checking module also decomposes the input sentence into components and recognizes the intent of the input sentence.

[0012] According to the above technical solution, the data merging module also performs conversion processing on the data while keeping the dimension unchanged, and performs up-down processing on the dimension of the data.

[0013] According to the above technical solution, the data storage module also performs data disassembly, data storage and data consistency management.

[0014] According to the above technical solution, the data extraction module can extract the data stored in the physical medium according to the multi-dimensional parameters obtained by semantic analysis.

[0015] According to the above technical solution, the memory when the system is running includes but is not limited to the part of the computer memory occupied when the system is running;

[0016] The physical media include, but are not limited to, computer mechanical hard disks, solid-state hard disks, and optical disks.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0018] The present invention adopts a variety of technical means to ensure that the data already existing in the historical conversation can be quickly loaded, and the generated data is used for subsequent conversations; at the same time, memory loading technology is also used to load the data into the runtime memory to provide data retrieval performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, the present invention provides a technical solution: a system for managing cached data in a conversation history with a large language model, a data inspection module, a data extraction module, a data merging module and a data storage module. The data processing and analysis of the system include but are not limited to statistics, analysis, mining and prediction operations, and a variety of technical means are used to ensure the accuracy, integrity and real-time performance of the data. Among them, a storage medium is used to persist the data to avoid loss. At the same time, the data is loaded into the memory when the system is running to improve the data retrieval speed;

[0022] Data checking module: analyzes and judges the input information, and chooses whether to load the existing stored data based on the judgment results;

[0023] Data extraction module: extracts data from the storage medium according to the keyword information obtained by the data inspection module, and the storage medium includes the memory and physical medium when the system is running;

[0024] Data merging module: merges the extracted data and the system input data according to semantics to generate a result statement with clear and accurate semantics;

[0025] Data storage module: stores the generated result statements according to the data dimensions contained in the input information, and the data is stored in the storage medium.

[0026] Specifically, the data checking module also decomposes the input sentence into components and recognizes the intent of the input sentence.

[0027] Specifically, the data merging module also performs conversion processing on the data while keeping the dimension unchanged, and performs up-down processing on the dimension of the data.

[0028] Specifically, the data storage module also performs data disassembly, data storage and data consistency management.

[0029] Specifically, the data extraction module can extract data stored in the physical medium according to the multi-dimensional parameters obtained by semantic analysis.

[0030] Specifically, the memory during the operation of the system includes, but is not limited to, the portion of the computer memory occupied during the operation of the system;

[0031] The physical media include, but are not limited to, computer mechanical hard disks, solid-state hard disks, and optical disks.

[0032] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0033] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A system for managing cached data in a conversation history with a large language model, comprising but not limited to a data checking module, a data extraction module, a data merging module and a data storage module, characterized in that: Data checking module: analyzes and judges the input information, and chooses whether to load the existing stored data based on the judgment results; Data extraction module: extracts data from the storage medium according to the keyword information obtained by the data inspection module, and the storage medium includes the memory and physical medium when the system is running; Data merging module: merges the extracted data and the system input data according to semantics to generate a result statement with clear and accurate semantics; Data storage module: stores the generated result statements according to the data dimensions contained in the input information, and the data is stored in the storage medium.

2. A system for managing cached data in a conversation history using a large language model according to claim 1, characterized in that: The data checking module also decomposes the input sentence into components and recognizes the intent of the input sentence.

3. A system for managing cached data in a conversation history using a large language model according to claim 1, characterized in that: The data merging module also performs conversion processing on the data while keeping the dimension unchanged, and performs up-down processing on the dimension of the data.

4. A system for managing cached data in a conversation history using a large language model according to claim 1, characterized in that: The data storage module also performs data disassembly, data storage and data consistency management.

5. A system for managing cached data in a conversation history using a large language model according to claim 1, characterized in that: The data extraction module can extract the data stored in the physical medium according to the multi-dimensional parameters obtained by semantic analysis.

6. A system for managing cached data in a conversation history using a large language model according to claim 1, characterized in that: The memory of the system when it is running includes but is not limited to the portion of the computer memory occupied when the system is running; The physical media include, but are not limited to, computer mechanical hard disks, solid-state hard disks, and optical disks.

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