Part 11/14:
Data Ingestion: Extract unstructured data from PDFs, documents, audio, or video.
Chunking & Embedding: Split large files into manageable chunks; convert them into embeddings with domain-specific models.
Storage: Store vectors efficiently in vector databases, optimized with compression and indexing.
Query Handling: When a user poses a query, convert it into an embedding, perform a nearest neighbor search, retrieve similar vectors, and generate contextual responses via the LLM.
Application Layer: Use APIs, low-code platforms, or AI workbenches to build user interfaces and workflows.
Tools like LangChain facilitate orchestrating these components, simplifying integration and deployment.