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| # KnowFlow AI Architecture | |
| ## High-Level Flow | |
| User uploads documents | |
| -> Document loader extracts text | |
| -> Text cleaner normalizes content | |
| -> Chunker splits text into retrieval units | |
| -> Embedding model converts chunks into vectors | |
| -> ChromaDB stores vectors locally | |
| -> Retriever finds relevant chunks | |
| -> Prompt builder creates RAG prompt | |
| -> Cloud LLM generates grounded answer | |
| -> Streamlit displays answer and sources | |
| ## Main Components | |
| - Streamlit UI | |
| - Document ingestion layer | |
| - Chunking layer | |
| - Embedding layer | |
| - Vector database layer | |
| - Retrieval layer | |
| - Cloud LLM client | |
| - RAG orchestration pipeline | |
| - Logging and observability layer |