Build a Searchable Audio Knowledge Base with Gemini Embedding 2 and LlamaParse

Gemini Embedding 2 is a cutting-edge model that supports 3072-dimensional vectors and excels in semantic quality for multimodal data, making it one of the most advanced embedding models currently available. The audio-kb tool, developed within the LlamaIndex ecosystem, exemplifies its application by allowing users to record or upload audio notes from a terminal, transcribe them using LlamaParse, and index these transcriptions for semantic search. The process involves uploading audio files to LlamaParse for transcription, chunking the resulting text, and embedding it using GoogleGenAIEmbedding, with the data stored in a SurrealDB instance equipped with an HNSW index. This setup facilitates efficient cosine-similarity searches by embedding query strings using the same model, with LlamaAgent Workflows coordinating both the ingestion and retrieval processes. The combination of LlamaParse, Gemini Embedding models, and LlamaIndex Workflows provides a robust platform for developing various applications, from personal knowledge management to enterprise-level document searches, emphasizing the composability and power of the LlamaIndex stack enhanced by Gemini Embedding 2.