pramiti-merger-reconciler

Standalone data model reconciler for M&A integration. Matches, classifies, and suggests resolutions for conflicting business concepts across two workspaces. Zero dependencies -- inject an optional LLM adapter for semantic matching.

Install

pip install pramiti-merger-reconciler

Quick Start

from pramiti_merger_reconciler import ClassSpec, ClassConflict, match_classes, classify, suggest

classes_a = [ClassSpec(class_name="Revenue", definition="Net revenue from paid invoices")]
classes_b = [ClassSpec(class_name="Revenue", definition="Total gross revenue")]

matches = match_classes(classes_a, classes_b)
for m in matches:
    conflict_type = classify(m)
    proposal = suggest(ClassConflict(m, conflict_type))
    print(f"{m.match_type} (score={m.score}): {conflict_type} -> {proposal}")

4-Step Matching Algorithm

1. Exact name match (score=1.0) -- case-insensitive string comparison
2. Normalized name match (score=0.9) -- strips suffixes (-data, -record, -table, -entity, -type, -class, -model, -info, -detail), whitespace, and underscores
3. Semantic LLM similarity (score=0.5--0.85, threshold=0.7) -- capped at max_similarity_pairs (default: 200) to prevent O(N*M) LLM budget exhaustion. Pairs the cap drops are returned as MatchResult(match_type="skipped", skipped_by_cap=True) rather than disappearing into unmatched residuals.
4. Unmatched residuals (score=0.0) -- classes with no match in either workspace

Conflict Classification

from pramiti_merger_reconciler import classify

conflict_type = classify(match_result)

Resolution Suggestions

from pramiti_merger_reconciler import suggest, ClassConflict

# Without LLM -- canned responses for identical/compatible/missing
proposal = suggest(ClassConflict(match_result, "semantic_conflict"))

# With LLM -- richer proposals for semantic/structural conflicts
proposal = suggest(ClassConflict(match_result, "semantic_conflict"), llm=my_llm)

Custom LLM Adapter

Implement ILLMAdapter (structural subtyping via Protocol -- no inheritance required):

from pramiti_merger_reconciler import ILLMAdapter

class MyLLM:
    def compute_similarity(self, description_a: str, description_b: str) -> float:
        """Return semantic similarity 0.0-1.0."""
        ...

    def generate_resolution(
        self, name_a: str, name_b: str,
        definition_a: str, definition_b: str,
        tables_a: str, tables_b: str,
        conflict_type: str,
    ) -> str:
        """Return a resolution proposal string."""
        ...

matches = match_classes(classes_a, classes_b, llm=MyLLM(), max_similarity_pairs=200)

Without an LLM, NoOpLLMAdapter is used (returns 0.0 for all similarities, so step 3 produces no matches).

API

License

MIT -- Pramiti Labs