{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/v3ctron-data-retrieval-access-system-for","title":"V3CTRON | Data Retrieval & Access System For Flexible Semantic Search & Retrieval Of Proprietary Document Collections Using Natural Language Queries.","arxiv_id":null,"date":"2023-04-26","proceeding":"Social Science Research Network (SSRN) 2023 4","authors":["Devin Schumacher"],"abstract":"V3CTRON is an open source vector database that allows users to upload text based documents & document collections, which are automatically embedded for super-accurate semantic search & retrieval using natural language queries. V3CTRON supports multiple vector database providers, including Milvus, LlamaIndex and Qdrant, giving developers flexibility & customization - and end users the ability to leverage the benefits of LLMs that can directly access their use-case specific data. A notable feature of V3CTRON is its memory capability, which allows ChatGPT to remember and retrieve information from previous conversations by saving conversation snippets to the vector database. V3CTRON is a practical solution for enhancing document accessibility and enabling context-aware and memory-relevant chat experiences with effective neural search capabilities.","url_abs":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430463","url_pdf":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4430463","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"v3ctron-data-retrieval-access-system-for","repo_url":"https://dagshub.com/serpdotai/V3CTRON-vector-database-embedding-neural-search-retrieval-chatgpt-plugin","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"v3ctron-data-retrieval-access-system-for","repo_url":"https://github.com/serp-ai/V3CTRON-vector-database-embedding-neural-search-retrieval-chatgpt-plugin","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"v3ctron-data-retrieval-access-system-for","repo_url":"https://hub.serp.ai/serpdotai/V3CTRON-vector-database-embedding-neural-search-retrieval-chatgpt-plugin","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"conversational-search","task_name":"Conversational Search"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"natural-language-queries","task_name":"Natural Language Queries"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}