Use names that accurately reflect meaning, scope (internal vs external), and responsibility—and keep them consistent with existing/base APIs.

Apply these rules: 1) Internal fields vs user-visible concepts: If a property is implicit/internal, name it consistently with an underscore prefix and ensure all relevant types expose it.

• Example: store internal vectors on the graph vertex as _embedding (not embedding) and ensure ParagraphChunk provides _embedding.

2) Match the data type/role to the identifier: Don’t call vectors “keywords” or “text” if the value is embeddings.

• Rename patterns: keywords: List[List[float]] → subs or embeddings.

3) Responsibility-revealing function names: The function name must describe what it actually does.

• Example: if the function constructs and persists a document graph, prefer something like def _load_doc_graph(self, chunks: List[Chunk]): over _parse_chunks.

4) Avoid duplicate/conflicting base names: Don’t introduce new variants of fields already defined by base classes.

• Example: remove redundant _metadata if Knowledge already owns the metadata concept.

5) Avoid ambiguous configuration parameter names: Prefer names that won’t be mistaken for other common concepts.

• Example: use model_alias instead of version when the field is an alias used for registration.

6) Use pythonic module/file naming: Ensure filenames follow standard Python conventions (lowercase with underscores, no camel-case file names).

If you’re unsure, ask: “Can another developer infer the value’s meaning and the function’s side effects from the name alone?” If not, rename it.