Extensible large model knowledge base construction method based on Python

Abstract

The invention relates to a Python-based extensible large model knowledge base construction method. The method comprises the following steps of S1, receiving original document input; s2, partitioning the original document according to a partitioning strategy configured by a user, and preprocessing the original document to generate structured / semi-structured data; s3, performing embedded coding on the processed data by using a plug-in type vectorization model to generate vector representation; s4, adaptively storing the generated vector representation to a database of a corresponding type through an abstract data access layer; and S5, realizing interaction with an intelligent agent platform through a standardized interface. According to the method, cross-platform knowledge reuse can be effectively supported, the development and maintenance cost is reduced, the construction of an open and intercommunicated agent ecological system is promoted, and the urgent demand of the AI Agent for efficient management and flexible scheduling of knowledge in actual deployment is met.

Description

Technical Field

[0001] This invention relates to the field of software technology development technology, specifically to a method for constructing a scalable large model knowledge base based on Python. Background Technology

[0002] With the rapid development of artificial intelligence (AI) technology and the continuous maturation of AI agent technology, AI systems have demonstrated unprecedented capabilities in handling complex tasks and achieving autonomous decision-making. From speech recognition and natural language processing to image understanding and recommendation systems, the application scenarios of AI are becoming increasingly diverse and penetrating into various industries. In this process, how to vectorize knowledge and enable large models to understand knowledge from different domains has become an important issue.

[0003] Intelligent agent platforms, as a crucial medium for developing and implementing AI scenarios across various industries, are exhibiting diversified development trends. The market is seeing the emergence of various technical frameworks or platforms with their own unique characteristics, such as LangChain, RAG, Dify, and Coze, each aiming to meet diverse business needs through different architectural designs and technological advantages. Vector knowledge bases, as a mature technology for accessing large models, effectively help large models understand industry knowledge after knowledge is vectorized. They play a vital role in supporting efficient semantic retrieval, information reasoning, and knowledge management.

[0004] Behind the proliferation of intelligent agent platforms lies a series of pressing problems and challenges. The significant differences in underlying storage engines and database types supported by different knowledge bases complicate data migration, limit deployment environments, and hinder flexible expansion and multi-platform reuse. Secondly, existing vector knowledge bases generally feature simplistic vectorization model designs, lacking flexibility and making model updates and replacements difficult, thus failing to meet the personalized needs of different application scenarios (e.g., the need for domestically developed vector libraries, high-performance vector libraries, etc.). More critically, the knowledge base systems of most platforms are highly coupled with their ecosystems, creating strong platform dependencies and further increasing the cost and difficulty of cross-platform migration and integration.

[0005] The root cause of these problems lies in the lack of unified standard interfaces and interoperability specifications between knowledge bases and intelligent agent platforms across the industry. This lack of standardization not only limits the interoperability between products from different vendors but also hinders the development and collaborative innovation of the entire ecosystem. When building their own AI systems, enterprises often face the awkward situation of "one platform, one solution," making it difficult to achieve efficient resource reuse and maximize value. Therefore, how to build a reusable, easily transferable, and loosely coupled vector knowledge base system among diverse intelligent agent platforms has become a core issue in the current process of AI engineering implementation. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for constructing a scalable large model knowledge base based on Python.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for constructing a scalable large-model knowledge base based on Python, implemented in Python, includes the following steps: S1. Receive the original document input; S2. Divide the original document into blocks according to the user-configured block division strategy, and preprocess the original document to generate structured / semi-structured data; S3. Use a plug-in vectorization model to perform embedding encoding on the processed data to generate a vector representation; S4. Adapt and store the generated vector representation to the corresponding type of database through the abstract data access layer; S5. Interact with the intelligent agent platform through standardized interfaces.

[0008] Furthermore, the original document supports multiple formats, including text, tables, PDF, or web page content.

[0009] Furthermore, the preprocessing involves cleaning the original document and converting it into a unified Markdown format.

[0010] Furthermore, the segmentation strategy includes the data segmentation mechanism of the knowledge base system and the configuration and usage scheme of the vectorized model.

[0011] Furthermore, the plug-in vectorized model supports dynamic loading and replacement, including BERT, Sentence-BERT, OpenAI Embeddings, or HuggingFace Transformer models, and all models follow a unified calling interface standard.

[0012] Furthermore, the database includes relational databases, NoSQL databases, and vector databases.

[0013] Furthermore, the database is specifically at least one of PostgreSQL, MySQL, MongoDB, Faiss, Chroma, and Elasticsearch.

[0014] Furthermore, the standardized interface is a RESTful API, supporting calls from LangChain, RAG, and Dify intelligent agent platforms.

[0015] Furthermore, this method supports user-defined configuration of pluggable vectorized model versions, database indexing strategies, and knowledge base update frequencies.

[0016] By adopting the above technical solution, the beneficial effects of the present invention are as follows: 1. Effectively reduce cross-platform development costs and repetitive workload: This invention achieves compatibility support for multiple mainstream intelligent agent platforms (such as LangChain, RAG, Dify, etc.) by designing a unified knowledge base access interface and a standardized calling protocol. This significantly reduces the workload of developers in repeatedly implementing knowledge base adaptation logic on different platforms and improves development efficiency.

[0017] 2. Enhance the deployment flexibility and migration capability of the knowledge base in different environments: This invention supports a variety of common database backends (such as PostgreSQL, Elasticsearch, MySQL, MongoDB, Faiss, Chroma, etc.) and shields the underlying differences through an abstract data access layer, enabling the vector knowledge base to be flexibly migrated and expanded in different deployment environments, thereby improving the system's adaptability and stability.

[0018] 3. Improve the configurability and replaceability of vectorized models: This invention adopts a modular design, managing vectorized models as independent plug-ins. Users can dynamically select, replace or expand model components according to actual needs, thereby meeting the semantic understanding accuracy and computational performance requirements of different business scenarios.

[0019] 4. Reduce platform dependence and achieve loosely coupled architecture design: This invention decouples the core functions of the knowledge base from the intelligent agent platform's operating environment, enabling the knowledge base system to have good cross-platform portability, avoiding the risk of system reconstruction due to platform changes, and improving the overall system's sustainable evolution capability. Attached Figure Description

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Figure 1 This is a simplified flowchart of the present invention; Figure 2 A schematic diagram illustrating the configuration of the vectorized model; Figure 3 A diagram illustrating an example of code for the abstract data access layer; Figure 4 A diagram illustrating the standardization of interface specifications. Detailed Implementation

[0021] like Figure 1-4As shown, this invention presents a method for constructing a scalable large-model knowledge base based on Python. Implemented in Python, it possesses excellent cross-platform compatibility and secondary development extensibility. The core of this invention lies in achieving the construction, flexible expansion, and cross-platform reuse of the knowledge base system through layered architecture design, modular component encapsulation, and standardized interface definition. Specifically, it includes the following steps: S1. Receive raw document input; the raw document supports multiple formats, including but not limited to text, tables, PDF or web page content.

[0022] S2. The original document is divided into chunks according to the user-configured chunking strategy, and preprocessed to generate structured / semi-structured data. The chunking strategy includes the data chunking mechanism of the knowledge base system and the configuration and usage scheme of the vectorization model. Preprocessing involves cleaning the original document and converting it into a unified Markdown format.

[0023] S3. Use a plug-in vectorization model to perform embedding encoding on the processed data to generate vector representations. The plug-in vectorization model supports dynamic loading and replacement, including BERT, Sentence-BERT, OpenAI Embeddings, or HuggingFace Transformer models. All models follow a unified calling interface standard, which facilitates dynamic replacement and upgrades.

[0024] S4. The generated vector representation is adapted and stored in the corresponding type of database through the abstract data access layer. The database includes relational databases (such as PostgreSQL and MySQL), NoSQL databases (such as MongoDB), and vector databases (such as Faiss, Chroma, and Elasticsearch).

[0025] S5. Interaction with the intelligent agent platform is achieved through standardized interfaces. These standardized interfaces are manifested at the platform access and API call levels, encapsulating standardized API call protocols for interfacing and interacting with different intelligent agent platforms.

[0026] As one embodiment, the standardized interface of this invention adopts a RESTful API, supporting calls from intelligent agent platforms such as LangChain, RAG, and Dify. Developers can complete knowledge base integration, querying, and control operations through a unified interface, effectively reducing platform coupling and migration costs. Furthermore, the method of this invention also supports user-defined configuration of parameters such as pluggable vectorized model version, database indexing strategy, and knowledge base update frequency.

[0027] Through the above implementation process, this invention can effectively solve the pain points of current vector knowledge bases in terms of multi-platform adaptation, model flexibility, environment migration and ecosystem interoperability.

[0028] The specific embodiments of the present invention have been described above. However, those skilled in the art should understand that this is merely an example. Those skilled in the art can make various changes or modifications to this embodiment without departing from the principles and essence of the present invention, but all such changes and modifications fall within the protection scope of the present invention.

Claims

1. A method for constructing a scalable large-scale model knowledge base based on Python, characterized by: Implemented using the Python language, the steps include: S1. Receive the original document input; S2. Divide the original document into blocks according to the user-configured block division strategy, and preprocess the original document to generate structured / semi-structured data; S3. Use a plug-in vectorization model to perform embedding encoding on the processed data to generate a vector representation; S4. Adapt and store the generated vector representation to the corresponding type of database through the abstract data access layer; S5. Interact with the intelligent agent platform through standardized interfaces.

2. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The original document supports multiple formats, including text, tables, PDF, or web page content.

3. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The preprocessing involves cleaning the original document and converting it into a unified Markdown format.

4. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The segmentation strategy includes the data segmentation mechanism of the knowledge base system and the configuration and usage scheme of the vectorized model.

5. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The pluggable vectorized models support dynamic loading and replacement, including BERT, Sentence-BERT, OpenAIembeddings, or HuggingFace Transformer models, and all models follow a unified calling interface standard.

6. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The databases include relational databases, NoSQL databases, and vector databases.

7. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 6, characterized in that: The database is specifically at least one of PostgreSQL, MySQL, MongoDB, Faiss, Chroma, and Elasticsearch.

8. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: The standardized interface is a RESTful API, which supports calls from LangChain, RAG, and Dify intelligent agent platforms.

9. The method for constructing a scalable large-scale model knowledge base based on Python according to claim 1, characterized in that: This method allows users to customize the pluggable vectorized model version, database indexing strategy, and knowledge base update frequency.

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