Portfolio
ChatGPT-Based Knowledge Assistant
Industry:
Knowledge Management / Customer Support
Built for organizations to query massive PDF libraries and extract precise answers via a conversational interface without manual document searching.
Problem
The manual retrieval of information from dense document repositories created significant delays in decision-making and support response.
Unstructured Data:
Difficulty in navigating and searching thousands of pages across fragmented PDF files.
Search Limitations:
Traditional keyword search failed to capture context or answer complex, multi-part questions.
Response Latency:
Staff spent excessive time manually cross-referencing documents to find specific policy or product details.
Solution
The system implements a retrieval-augmented architecture to transform static documents into an interactive, searchable knowledge base.
NLP Preprocessing:
Automated cleaning and chunking of raw PDF text to prepare data for machine understanding.
Vector Storage:
Indexed document fragments in a vector database to enable high-speed semantic search.
Conversational Interface:
Integrated ChatGPT to synthesize retrieved data into natural, easy-to-understand answers.
Contextual Retrieval:
The system identifies the most relevant document sections before generating a response to ensure accuracy.
Tech Stack
LLM:
ChatGPT (OpenAI API).
Processing:
NLP libraries for text normalization and chunking.
Database:
Vector Database for semantic indexing and retrieval.
Results & Impact
Information Accessibility:
Streamlined the process of finding specific data within massive, unstructured datasets.
Support Efficiency:
Reduced the time required to resolve complex inquiries by providing instant, cited answers.
Operational Scalability:
Enabled teams to manage growing document libraries without increasing headcount for data retrieval.
Improved UX:
Replaced manual document browsing with an intuitive, interactive chat experience.
Generative AI & RAG solutions