Automate Document Contextual Summarization and Storage with Kimi-K2, Gemini Embeddings, and Qdrant

The workflow begins by downloading a large document and extracting its text. Each page is processed to generate a contextual summary using the Kimi-K2 model via Featherless.ai. These summaries are then converted into embeddings using the Gemini-embedding-001 model and stored in a Qdrant collection. This setup is ideal for creating a retrieval-augmented generation (RAG) system that can efficiently handle large volumes of text data.