Automated Google Drive Document Chunking and Pinecone Indexing via Gemini AI

Overview

This workflow automates the extraction, intelligent chunking, and semantic indexing of documents from Google Drive into Pinecone using advanced AI models. It leverages Google Gemini for embeddings and OpenRouter for context-aware processing.

Key Features

• Automated Document Retrieval: Fetches documents directly from Google Drive.
• Text Extraction & Chunking: Extracts text and splits it into context-aware sections using recursive character splitting.
• AI-Powered Embeddings: Utilizes Google Gemini to generate high-quality vector embeddings.
• Semantic Indexing: Stores processed vectors in Pinecone for efficient retrieval and search.
• Batch Processing: Handles multiple documents or large files in manageable batches.

Benefits

• Saves Time: Eliminates manual document processing and indexing.
• Enhances Searchability: Enables semantic search and retrieval across large document sets.
• Scalable Automation: Easily adapts to growing document volumes and business needs.
• Seamless Integration: Connects Google Drive, AI models, and vector databases in a unified workflow.

Use Cases

• Knowledge base creation and enrichment
• Enterprise document management
• AI-powered search and retrieval for support or research teams
• Automated content ingestion for analytics or training datasets