This Langflow flow implements a Google Drive → vector database ingestion pipeline for RAG.

The workflow starts by connecting to Google Drive and enumerating files within a target folder (or set of folders). Document loader components download file contents and extract text from common formats. The pipeline then applies chunking (split text) to break documents into semantically coherent segments with configurable chunk size and overlap.

Next, embedding components generate vector representations for each chunk. The flow attaches metadata such as file name, folder path, source URL/id, and chunk identifiers so downstream retrieval can cite and filter by source.

Finally, the pipeline writes chunks, embeddings, and metadata to AstraDB, creating or updating a vector collection that can be queried by similarity search. This indexed store becomes the foundation for RAG chatbots and agents that need fast, relevant retrieval across an organization’s Google Drive knowledge.

Because ingestion is automated and structured, teams can re-run the flow to keep the vector index fresh and ensure retrieval reflects the latest documentation.

The flow can be extended to support production-grade indexing and governance. Add incremental sync (only ingest new/changed files) to reduce costs, and store file hashes or modified timestamps in metadata. You can implement folder-level access controls by writing ACL metadata and filtering retrieval results per user. Add a webhook or scheduler trigger to re-index on a cadence, and integrate with Drive change notifications for near real-time updates. For higher quality retrieval, incorporate document-type specific parsing, semantic chunking, and deduplication. Finally, connect this ingestion pipeline to downstream RAG answer flows so indexing and retrieval run as a cohesive, maintainable knowledge system.