An AI-Driven PDF Query System Leveraging OpenAI LLM and LangChain for Enhanced Data Retrieval

Abstract

One of the most important issues in knowledge mining is the problem of how to extract correct and useful information from the unprejudiced PDFs. Introducing OpenAI LLM with LangChain for contextual understanding of PDF, this paper proposes a new PDF querying system. The system operates in multiple stages: extracting text, generating the embedding, and storing the embeddings in a vector database. From a business user perspective, they can ask natural language queries, which the Conversational Chain of LangChain processes to obtain text chunks, context, and prompt optimization. The input provided by the user is processed by OpenAI’s highly developed LLM to produce factual and suitable output. The efficiency of the developed system has been tested through experiments on different PDF materials with higher accuracy, relevance of the search results and users’ satisfaction compared to conventional keyword-based search. LangChain helps to enriched text meaning from OpenAI, and its contextual reasoning helps to efficiently extract structured information from texts. This approach has innovative use cases in science, law, and finance by allowing easy access to large amounts of information available in PDFs. Through implementation of NLP, the proposed system enables effective search and enhanced learning from data that is less likely to be managed structurally.

Keywords

• PDF Querying
• NLP
• Language Models
• Contextual AI
• LangChain
• Document Understanding
• Semantic Search

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