Empowering Google Drive Search with AI-Powered Knowledge Graph QA

Kagen AI developed a Conversational AI-powered Knowledge Graph QA System that seamlessly integrates with Google Drive. This AI-driven solution enables enterprises to retrieve information using natural language queries while ensuring data security and integrity without requiring data migration.

40% Reduction in Search Time
98% Query Accuracy Rate
100% Real-time Sync Reliability

Business Requirements

Our large enterprise client faced challenges managing vast amounts of unstructured data stored across Google Drive. Traditional document search methods were inefficient, leading to lost productivity and difficulty retrieving relevant information. The company needed:

Direct access without requiring data migration.
Convert unstructured documents into a structured, searchable knowledge base.
Ensure the system reflects the latest document versions while maintaining history.
Implement strict access controls and authentication mechanisms.
Provide contextually relevant responses based on document relationships and previous interactions.

To overcome these challenges, they sought a Conversational AI solution capable of transforming unstructured documents into actionable knowledge, improving accessibility, and ensuring robust security measures.

Solution

Document Ingestion

Established a secure OAuth 2.0-based connection to Google Drive.
Monitored designated Drive folders for real-time updates, including new document additions and modifications.
Implemented priority-based document queuing for optimized processing.
Extracted metadata, including author, creation date, modification history, and document structure.

Processing Pipeline

Developed a multi-format parser to process various document types (PDF, DOCX, etc.).
Applied text-cleaning techniques to remove noise, normalize whitespace, and correct OCR artifacts.
Segmented content into meaningful chunks while preserving semantic relationships.
Generated high-dimensional vector embeddings using state-of-the-art AI models to capture semantic meaning.

Storage and Indexing

Utilized MongoDB’s vector database capabilities for efficient storage and retrieval.
Employed approximate nearest neighbor (ANN) search algorithms for rapid content discovery.
Maintained a chat history database to track user interactions and enhance query context.
Built a knowledge graph layer to map relationships between content pieces, improving information retrieval accuracy.

Query Processing

Converted natural language queries into vector representations for precise matching.
Conducted similarity searches within the vector database to fetch the most relevant content.
Integrated chat history to refine responses based on past user interactions.
Leveraged the knowledge graph to provide comprehensive answers from multiple related documents.

Story Highlights

Seamless Knowledge Access: Users retrieved relevant documents instantly via AI-driven natural language queries.

Context-aware Responses: The system leveraged prior interactions and documented relationships for nuanced, highly relevant answers.

Secure and Scalable Architecture: OAuth authentication and MongoDB’s optimized storage ensured enterprise-grade security and performance.

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