Refactoring into ingest() and ask()

Where we left off

naive_rag Lesson 23 collapsed its whole pipeline into two functions: ingest() (run once, build the index) and ask() (run per question). This lesson does the same collapse here, and the result is this course's implementation of the RAG-architecture series' shared Strategy protocol (docs/RAG-SERIES-PLAN/README.md): any course in this series can be swapped in behind the same two-function shape, as long as it provides ingest(docs) -> State and ask(query, state, k) -> str. This lesson is what makes multimodal_rag compatible with that shape.

What State is, here

def ingest(notes_dir: Path, images_dir: Path, chroma_client) -> chromadb.Collection:
    ...

State for this course is a single chromadb.Collection, exactly like naive_rag's, holding both modalities together: every record's documents entry is a caption (for images) or a chunk (for text), and every record's metadatas entry carries modality ("text" or "image"), source (the original filename), and image_path (the absolute path to the original file, an empty string for text records, since chromadb metadata values must be scalars, None isn't representable). Nothing in State distinguishes "this collection is for multimodal_rag" from any other course's collection, structurally, that's the whole point: a caller consuming State through ask() alone doesn't need to know this course produced it, only that it's a chromadb.Collection shaped the way this protocol expects. This is exactly what adaptive_rag Lesson 21 (a later course in this series) needs to wire this course in as one of several retrieval strategies it routes between, without reading this course's full implementation, only this README's description of State.

The code, piece by piece

def ask(query: str, collection: chromadb.Collection, k: int = 2) -> str:
    query_vector = embed_texts([query])[0]
    results = collection.query(
        query_embeddings=[query_vector], n_results=k, include=["documents", "metadatas"]
    )
    ...
    for document, metadata in zip(documents, metadatas):
        if metadata["image_path"]:
            image_bytes = Path(metadata["image_path"]).read_bytes()
            parts.append(types.Part.from_bytes(data=image_bytes, mime_type="image/png"))
        else:
            parts.append(document)

Everything this course built, Lesson 8's re-attach-the-original-image idea, Lesson 14's modality-aware citation prompt, collapses into this one function. metadata["image_path"] (truthy or empty string) plays the exact role record["image_path"] is not None played back in Lesson 6, just expressed as chromadb-compatible metadata instead of a Python None.