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Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
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| name | RAG Pipeline |
| description | Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search. |
backend/ingest.pydocs/.models/text-embedding-004.physical_ai_book.python backend/ingest.pyqdrant-clientphysical_ai_bookbackend/utils/helpers.py.backend/personalization.py creates system instructions based on software_background and hardware_background of the user.We use a custom Agent class (backend/agents.py) that wraps the LLM calls, allowing for future expansion into multi-agent workflows.
Documentation for the FastAPI backend, endpoints, and dependency injection.
Overview of the project's tech stack, directory structure, and architectural patterns.
Guide to using Better Auth for client and server-side authentication.
Validates and analyzes Docusaurus MDX chapters for structure, pedagogical quality, and component usage.
Details of the RAG Chatbot, including UI and backend logic.
Interaction with NeonDB Postgres using Drizzle ORM.