Developed a private LLM-powered search tool using Retrieval-Augmented Generation with OpenAI embeddings and Pinecone vector database to make internal documentation instantly accessible through context-aware search. Integrated with structured and unstructured repositories and maintained full compliance with privacy and security requirements. Reduced search time by 60% and increased accuracy and adoption of internal knowledge resources.
Project Overview
Challenges & Solutions
Challenges
Employees had difficulty finding information in large internal documentation repositories
Existing search was keyword-based and returned incomplete or irrelevant results
Knowledge access was inconsistent, with valuable information locked in static files and unstructured formats
Needed a secure, private AI solution to ensure sensitive organizational information stayed internal
Required full compliance with privacy and cybersecurity standards
Solutions
Designed and implemented a private LLM-powered search tool using Retrieval-Augmented Generation for context-aware, semantic search results
Used OpenAI embeddings to transform document content into vector representations and stored them in Pinecone vector database for high-performance semantic retrieval
Connected the system to internal repositories containing both structured and unstructured data, enabling users to query and retrieve relevant information instantly
Worked with privacy and cybersecurity teams to ensure the architecture met internal security policies and compliance requirements
Conducted user testing to refine prompts, improve result accuracy, and optimize the retrieval process