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| **Day 1: Ingestion & Structuring (The Foundation)** | |
| * **Goal:** Turn a PDF into a clean, multi-modal dataset with UUIDs. | |
| * **Tasks:** | |
| * Set up a Python script with Docling to parse the provided test PDFs. | |
| * Write a function to intercept any extracted Image or Table, send it to the VLM API, and get a text description. | |
| * Format the final output into a flat list of dictionaries, where every element has a chunk\_id, type (text/table/image), content, and parent\_header\_id. | |
| * **Checkpoint:** You can print a clean JSON payload representing the entire document layout. | |
| **Day 2: Hybrid Memory & Graph Mapping (The Brain)** | |
| * **Goal:** Build the search and relationship engine. | |
| * **Tasks:** | |
| * Initialize ChromaDB. Embed the content of every chunk and store it with its chunk\_id in the metadata. | |
| * Initialize a NetworkX directed graph. | |
| * Write the logic to iterate through your parsed JSON and add nodes and edges (e.g., Header\_1 $\\rightarrow$ Paragraph\_A, Paragraph\_A $\\rightarrow$ Table\_1). | |
| * Wrap this ingestion process in a FastAPI /upload endpoint that stores the resulting NetworkX graph in app.state. | |
| * **Checkpoint:** You can query ChromaDB for a keyword, get a chunk\_id, and successfully print its neighboring nodes from NetworkX. | |
| **Day 3: Agentic UI & Demo Polish (The Wow Factor)** | |
| * **Goal:** Connect the brain to the user and make it fast. | |
| * **Tasks:** | |
| * Build the LangGraph agent: Query -> Vector DB (Find ID) -> NetworkX (Expand Context up to depth=1) -> LLM Prompt -> Answer. | |
| * Implement StreamingResponse in FastAPI so the answer types out instantly. | |
| * Build the React frontend. Implement the chat window on the left, and use React Flow on the right to visualize the nodes the AI traversed. | |
| * **Checkpoint:** End-to-end testing with sample Dell documents. Practice the pitch. | |
| **Day 1: Ingestion & Structuring (The Foundation)** | |
| * **Goal:** Turn a PDF into a clean, multi-modal dataset with UUIDs. | |
| * **Tasks:** | |
| * Set up a Python script with Docling to parse the provided test PDFs. | |
| * Write a function to intercept any extracted Image or Table, send it to the VLM API, and get a text description. | |
| * Format the final output into a flat list of dictionaries, where every element has a chunk\_id, type (text/table/image), content, and parent\_header\_id. | |
| * **Checkpoint:** You can print a clean JSON payload representing the entire document layout. | |
| **Day 2: Hybrid Memory & Graph Mapping (The Brain)** | |
| * **Goal:** Build the search and relationship engine. | |
| * **Tasks:** | |
| * Initialize ChromaDB. Embed the content of every chunk and store it with its chunk\_id in the metadata. | |
| * Initialize a NetworkX directed graph. | |
| * Write the logic to iterate through your parsed JSON and add nodes and edges (e.g., Header\_1 $\\rightarrow$ Paragraph\_A, Paragraph\_A $\\rightarrow$ Table\_1). | |
| * Wrap this ingestion process in a FastAPI /upload endpoint that stores the resulting NetworkX graph in app.state. | |
| * **Checkpoint:** You can query ChromaDB for a keyword, get a chunk\_id, and successfully print its neighboring nodes from NetworkX. | |
| **Day 3: Agentic UI & Demo Polish (The Wow Factor)** | |
| * **Goal:** Connect the brain to the user and make it fast. | |
| * **Tasks:** | |
| * Build the LangGraph agent: Query -> Vector DB (Find ID) -> NetworkX (Expand Context up to depth=1) -> LLM Prompt -> Answer. | |
| * Implement StreamingResponse in FastAPI so the answer types out instantly. | |
| * Build the React frontend. Implement the chat window on the left, and use React Flow on the right to visualize the nodes the AI traversed. | |
| * **Checkpoint:** End-to-end testing with sample Dell documents. Practice the pitch. |