The help.py module#

Summary#

build_help_map

Return {leaf_python_name: docstring} for the active PyFluent.

get_default_help_map

Process-wide cached help map, which falls back to an empty dictionary.

reset_default_help_map

Test hook for clearing the in-process help map cache.

Description#

Docstring extractor for PyFluent’s generated settings_.py.

The bundled api_objects.json carries only (path, kind) tuples, which is fine for exact-path lookup but useless for free-text queries such as “temperature of incoming gas”. The relevant leaf is spelled velocity_inlet.thermal.t, and the token t does not match any English word.

PyFluent does, however, ship a one-paragraph class docstring with every generated settings class. Indexing those docstrings into the lexical fallback closes most of the recall gap to an embedding-based retriever without adding a vector store.

This module extracts leaf_python_name -> docstring from the generated module by regex. Importing the 3.4 MB module is slow and has side effects unwanted at retrieval time. The parsed map is cached to disk so the regex is paid once per (PyFluent version, ansys-fluent-mcp version) combination.

Multiple classes can share a leaf name. For example, temperature appears as a child of wall, mass_flow_inlet, radiator, and more. The map stores the deduplicated union of all docstrings for a given leaf, joined by \\n\\n. The lexical scorer then tokenizes that blob and uses it as additional bag-of-words evidence for any path whose leaf matches.

This is approximate. A query that hits via the wall-specific docstring may also boost radiator-rooted paths sharing the leaf. That’s an acceptable trade for the cost (~50 LOC, no extra dependency) because the eventual ranker still prefers paths whose path tokens overlap the query. The docstring evidence is additive, not decisive.

Module detail#

help.build_help_map(*, settings_path: pathlib.Path | None = None, use_cache: bool = True) → dict[str, str]#

Return {leaf_python_name: docstring} for the active PyFluent.

The parsed map is memoized to $CACHE_DIR/api_help_.json keyed on the absolute path of the settings file plus its mtime, so a PyFluent upgrade transparently rebuilds the cache while normal process starts read it back in less than 10 ms.

Parameters:
settings_pathOptional[Path]

Settings path to supply to the function.

use_cachebool

Use cache to supply to the function.

Returns:
dict[str, str]

Mapping containing the operation result.

help.get_default_help_map() → dict[str, str]#

Process-wide cached help map, which falls back to an empty dictionary.

Returns:
dict[str, str]

Mapping containing the operation result.

help.reset_default_help_map() → None#

Test hook for clearing the in-process help map cache.

Returns:
None

The function completes through its side effects.