Docxplorer API

Having quick and precise access to comprehensive documentation for the api is essential foraccelerating software development and ensuring top-notch code quality. However, traditional retrievalaugmented generation (rag) systems often struggle to deliver contextually accurate technical answers,especially when handling the combination of structured code and unstructured explanatory text. Toovercome these limitations, docxplorer api introduces an upgraded rag architecture that is specificallydesigned to enhance the retrieval of api documentation. Central to this innovation is a dual-vector databasethat independently stores and intelligently interlinks code snippets with explanatory content, allowing formore granular and relevant retrieval.Key innovations include a dual-vector database that separates andinterlinks code snippets and explanatory content, and a two-step query optimization process using largelanguage models (llms).

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-02
DOI
https://doi.org/10.5281/zenodo.15578047
Primary Topic
Advanced Database Systems and Queries
Type
article
Field-Weighted Citation Impact
0.00
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article

Docxplorer API

Zenodo (CERN European Organization for Nuclear Research)
Advanced Database Systems and Queries
article

Docxplorer API

article en

Abstract

Having quick and precise access to comprehensive documentation for the api is essential foraccelerating software development and ensuring top-notch code quality. However, traditional retrievalaugmented generation (rag) systems often struggle to deliver contextually accurate technical answers,especially when handling the combination of structured code and unstructured explanatory text. Toovercome these limitations, docxplorer api introduces an upgraded rag architecture that is specificallydesigned to enhance the retrieval of api documentation. Central to this innovation is a dual-vector databasethat independently stores and intelligently interlinks code snippets with explanatory content, allowing formore granular and relevant retrieval.Key innovations include a dual-vector database that separates andinterlinks code snippets and explanatory content, and a two-step query optimization process using largelanguage models (llms).

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 52%
Advanced Database Systems and Queries
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