Review AtomDB

[andre-senna]

EPIC

• Sync das-query-engine with das-atom-db after refactoring work das-query-engine#304
• Refactor AtomDB.get_matched_links and related code das-atom-db#185
• Refactor AtomDB.get_atom method das-atom-db#182
• [AtomDB] Add missing and/or fix existing type annotations and docstrings das-atom-db#161
• [AtomDB] Replace Dict with dataclasses when possible das-atom-db#162
• Avoid making document copy in database.py->_replace_keys() das-atom-db#56
• Implement Abstract/Protocol classes to allow full type annotation das-query-engine#281
• Remove all instances of get_atom_as_dict() das-atom-db#126
• [QE] Replace Dict with dataclass when possible das-query-engine#283
• [QE] Revisit and fix all type annotations das-query-engine#282
• Bump up python version (from 3.8.5 to 3.10) das-atom-db#171
• Add pylint to das-atom-db repo das-atom-db#169
• PoC - Create a wrapper to HandleTrie in python #79

Goals:

• Review algorithms and data structures looking for optimization opportunities. The idea is to prioritize good design, code readability etc but without wasting time/memory unnecessarily.
• Measure performance (time/memory) against standard MeTTa interpreter spaces

Eventually, other downstream called classes may be refactored as well. Two known issue are

• Avoid making document copy in database.py->_replace_keys() das-atom-db#56
• Remove all instances of get_atom_as_dict() das-atom-db#126

but there may be others.

The test-cases to benchmark can be extracted from MeTTa examples provided in the MeTTa interpreter repo: https://github.com/trueagi-io/hyperon-experimental but it'd be interesting to collect more test MeTTa code from users. There's a channel in Mattermost for MeTTa user group ("Metta Coders" in public World server)