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# Change Proposal: BM25 + RRF Competence Matching
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- **Change ID**: `add-bm25-rrf-competence-matching`
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- **Status**: Proposed
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- **Target**: `teamlandkarte-mcp`
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- **Author**: Thomas Handke
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- **Date**: 2026-02-26
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## Summary
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Add an optional BM25-based competence matching strategy as an alternative to the existing
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embedding-based similarity approach. When enabled, the following pipeline replaces
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embedding-based competence similarity:
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1. **Auto-Tagging (LLM):** Before BM25 scoring, an LLM compares the required competences
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against each candidate's existing competences and suggests canonical additions
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(synonyms, abbreviations, cross-language equivalents). The expanded competence list
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is ephemeral — it is used only for the current scoring call and is never persisted.
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2. **BM25 Ranking:** The (expanded) candidate competences are indexed with BM25. Each
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required competence is used as a query to rank the candidates.
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3. **RRF Fusion:** Reciprocal Rank Fusion normalizes the BM25 ranked list into a score
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in [0, 1] per required competence.
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The feature is controlled by two new booleans in `config.toml`:
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- `use_bm25_search` (default `false`): enables BM25 + RRF.
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- `use_auto_tagging` (default `false`): enables LLM-based competence expansion before
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BM25 scoring. Strongly recommended when `use_bm25_search = true`.
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## Why
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The current embedding-based (`per_skill` / `aggregate`) strategy has a known weakness:
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it assigns non-trivial scores to candidates whose competences are only _semantically
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adjacent_ to the required skills but not actually matching.
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**Example (observed in production):**
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Searching for **Python** + **Machine Learning**, a candidate with only
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**JavaScript, TypeScript, Node.js, Vue.js, Web Architekturen** received a score of **0.392**
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because the embedding model considers all programming languages semantically related.
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BM25 (Best Match 25) is a classical lexical ranking algorithm that scores documents by
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**term overlap** between query and document. It is immune to the semantic drift problem
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because it only counts tokens that literally occur in the candidate text. BM25 will assign
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**0** to "JavaScript" when the query is "Python".
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### When BM25 outperforms embeddings
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| Scenario | Embeddings | BM25 |
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|----------|-----------|------|
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| Query "Python", candidate has "Python" | High ✓ | High ✓ |
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| Query "Python", candidate has "JavaScript" | Medium ✗ | Zero ✓ |
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| Query "Machine Learning", candidate has "ML" | High ✓ | Low ✗ |
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| Query "Softwarearchitektur", candidate has "software architecture" | High ✓ | Low ✗ |
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| Query "React", candidate has "React.js" | High ✓ | Medium ~ |
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BM25 excels at exact or near-exact skill names. Embeddings excel at synonyms and
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cross-language variants (e.g., German/English). The optional `use_auto_tagging` feature
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bridges BM25's synonym and cross-language gap by having an LLM pre-expand the candidate
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competence list — making the combination of BM25 + Auto-Tagging competitive with
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embeddings while still eliminating false positives from semantic drift.
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### Why RRF for fusion
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Reciprocal Rank Fusion merges multiple ranked lists into one without requiring score
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normalization. For each required competence query, we obtain a ranked list of candidate
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competences. RRF then assigns each candidate a fused score based on its (1-based) rank in
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one or more lists.
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RRF fused score formula (Cormack et al. 2009):
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```
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rrf_score(candidate) = Σ_i 1 / (k + rank_i(candidate))
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```
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RRF is robust to score scale differences and has been widely validated in information
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retrieval research as a high-quality fusion method.
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## What Changes
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### 1. New config parameter `matching.similarity.use_bm25_search` (boolean, default `false`)
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- When `false` (default): existing embedding-based strategy is used unchanged.
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- When `true`: BM25 + RRF replaces embedding-based competence similarity.
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Role similarity is **always** embedding-based (unchanged).
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### 2. New config parameter `matching.similarity.use_auto_tagging` (boolean, default `false`)
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- Only applies when `use_bm25_search = true`. When `false`, BM25 runs on the raw
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candidate competence list (no LLM expansion).
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- When `true`: before each BM25 scoring call, an LLM expands the candidate's competence
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list with canonical forms of any required competences that are already covered by the
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candidate's existing entries (via synonym, abbreviation, or cross-language equivalence).
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- The expanded list is ephemeral — it is not written back to the database and does not
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affect any other operation.
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### 3. New config parameter `azure_openai.chat_deployment` (string)
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- Azure deployment name of the chat model used for auto-tagging (e.g. `"gpt-4.1"`).
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- Required only when `use_auto_tagging = true`.
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- API key is read from environment variable `AZURE_OPENAI_LLM_API_KEY`.
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### 4. `AzureOpenAIClient` extended: LLM chat capability restored
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- Add `chat_completion(system: str, user: str) -> str` method supporting structured JSON
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responses via Azure OpenAI chat completions API.
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- Add `chat_deployment` and `llm_api_key` constructor parameters.
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- This restores LLM capability removed in a previous change, now scoped exclusively
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to auto-tagging.
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### 5. New module `src/teamlandkarte_mcp/matching/auto_tagger.py`
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Implements:
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- `AutoTagger`: class that wraps the LLM client and executes the tagging prompt.
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- `expand_competences()`: given `required: list[str]` and `existing: list[str]`,
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returns `list[str]` — the combined list `existing + additions`, where `additions`
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are the canonical required-competence names that the LLM identifies as already
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covered by `existing` entries.
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- Structured JSON output schema enforced via the Azure OpenAI `response_format` parameter.
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- Google Docstrings on all public symbols.
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### 6. New module `src/teamlandkarte_mcp/matching/bm25.py`
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Implements:
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- `Bm25Index`: builds an in-memory BM25 index over a corpus of competence strings.
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Designed to be instantiated once over the global candidate pool (see item 8a below)
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so IDF values are meaningful. Raw scores are clamped to `max(0.0, raw)` — terms with
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negative IDF produce 0.0 rather than a spurious tiny positive value.
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- `bm25_rank_competences()`: for a single required competence query, returns a ranked list
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of `(candidate_text, score)` tuples. Convenience wrapper for standalone/test use.
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- Pure Python, no external runtime dependencies beyond `rank_bm25` from PyPI.
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### 7. New module `src/teamlandkarte_mcp/matching/rrf.py`
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Implements:
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- `reciprocal_rank_fusion()`: merges one or more ranked lists into a final score dict.
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- `k` constant (default `60`, standard RRF value from Cormack et al. 2009).
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- Returns `dict[str, float]` mapping candidate text → fused score in `(0, 1]`
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(top-ranked candidate receives exactly `1.0`).
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### 8. `SimilarityEngine.compute_competence_similarity()` extended
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- Reads `use_bm25_search` and `use_auto_tagging` flags.
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- Accepts an optional `global_index: Bm25Index | None` parameter (passed in by `Matcher`).
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- When `use_bm25_search=True`:
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1. If `use_auto_tagging=True`: calls `AutoTagger.expand_competences()` to build the
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expanded candidate list.
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2. Delegates to `_bm25_rrf_similarity()` with the (optionally expanded) candidate list
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and the global index.
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- `_bm25_rrf_similarity()` uses `global_index.rank(req)` filtered to the candidate's
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competence set when `global_index` is provided, otherwise falls back to
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`bm25_rank_competences(req, candidate)`.
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- Output shape is **identical** to existing strategies:
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`dict[required_competence → {score, best_match, rationale}]`
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- Preserves the `Matcher` and scorer contracts unchanged.
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### 8a. `Matcher.match()` builds a global `Bm25Index` before the per-candidate loop
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When `use_bm25_search=True` and the filtered candidate pool is non-empty, `Matcher.match()`
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collects the deduplicated union of all filtered candidates' competences and builds one
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`Bm25Index` over that global corpus before the loop. The same index is passed to every
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`compute_competence_similarity()` call.
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**Why this matters:** With a per-person corpus (N ≈ 5–30 entries), BM25 IDF is 0 or
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negative for every term — all raw scores collapse to ≤ 0, and `max(0.0, raw)` clamping
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makes every match appear as 0.0. A global corpus ensures N is large enough for IDF to
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carry real signal.
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### 9. `SimilarityConfig` dataclass extended
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New fields:
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- `use_bm25_search: bool = False`
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- `use_auto_tagging: bool = False`
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### 10. `AzureOpenAIConfig` dataclass extended
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New fields:
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- `chat_deployment: str = ""`
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- (API key via `AZURE_OPENAI_LLM_API_KEY` env var)
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### 11. `config.py` parser extended
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Reads `matching.similarity.use_bm25_search`, `matching.similarity.use_auto_tagging`,
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and `azure_openai.chat_deployment` from TOML. Reads `AZURE_OPENAI_LLM_API_KEY` from
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environment when `use_auto_tagging = true`.
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### 12. `config.toml.example` updated
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Documents all three new keys with inline comments.
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### 13. Server initialization in `mcp_server.py`
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- Passes `use_bm25_search` and `use_auto_tagging` flags when constructing `SimilarityEngine`.
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- When `use_auto_tagging=True`: constructs `AutoTagger` with the LLM-capable
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`AzureOpenAIClient` and passes it to `SimilarityEngine`.
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### 14. Documentation updated
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- `docs/architecture.md`: new section on BM25/RRF/Auto-Tagging strategy.
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- `docs/semantic_similarity_tradeoffs.md`: comparison table updated with BM25+AutoTag entry.
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- `docs/assistant_system_prompt.md`: notes on score interpretation with BM25 and auto-tagging.
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- `README.md`: all new config parameters documented.
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### 15. Legacy/deprecated code removed
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- Remove `debug_similarity_analysis.py`
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- Remove `debug_categorization.py`
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- Remove `SEMANTIC_SIMILARITY_ANALYSIS.md`
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- Remove `SCORE_BUG_FIX.md`
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### 16. Tests
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- `tests/test_bm25.py`: unit tests for `Bm25Index` and `bm25_rank_competences()`.
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- `tests/test_rrf.py`: unit tests for `reciprocal_rank_fusion()`.
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- `tests/test_auto_tagger.py`: unit tests for `AutoTagger.expand_competences()` (mocked LLM).
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- `tests/test_similarity.py`: extend with BM25/RRF and auto-tagging strategy tests.
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- `tests/test_matcher_integration.py`: integration test for the full pipeline
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(auto-tag → BM25 → RRF) in both matching directions.
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## Impact
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### Affected specs
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- `matching-tools` (competence scoring path)
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### Affected code
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| File | Change |
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|------|--------|
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| `src/teamlandkarte_mcp/matching/similarity.py` | Add `_bm25_rrf_similarity()`, `global_index` param, auto-tag branch, read flags |
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| `src/teamlandkarte_mcp/matching/matcher.py` | Build global `Bm25Index` before per-candidate loop; pass as `global_index` |
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| `src/teamlandkarte_mcp/matching/bm25.py` | **New** |
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| `src/teamlandkarte_mcp/matching/rrf.py` | **New** |
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| `src/teamlandkarte_mcp/matching/auto_tagger.py` | **New** |
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| `src/teamlandkarte_mcp/azure/openai_client.py` | Restore `chat_completion()`; add `chat_deployment` + `llm_api_key` params |
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| `src/teamlandkarte_mcp/config.py` | `SimilarityConfig.use_bm25_search`, `use_auto_tagging`; `AzureOpenAIConfig.chat_deployment` |
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| `src/teamlandkarte_mcp/mcp_server.py` | Pass flags + `AutoTagger` to `SimilarityEngine` |
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| `config.toml.example` | Document all new keys |
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| `docs/architecture.md` | BM25/RRF/Auto-Tagging section |
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| `docs/semantic_similarity_tradeoffs.md` | Updated comparison with BM25+AutoTag entry |
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| `docs/assistant_system_prompt.md` | Score notes for BM25 and auto-tagging modes |
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| `README.md` | All new config parameters |
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| `tests/test_bm25.py` | **New** |
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| `tests/test_rrf.py` | **New** |
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| `tests/test_auto_tagger.py` | **New** |
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| `tests/test_similarity.py` | Extended |
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| `tests/test_matcher_integration.py` | Extended |
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| `debug_similarity_analysis.py` | **Removed** |
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| `debug_categorization.py` | **Removed** |
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| `SEMANTIC_SIMILARITY_ANALYSIS.md` | **Removed** |
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| `SCORE_BUG_FIX.md` | **Removed** |
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### Breaking changes
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None. The `use_bm25_search = false` and `use_auto_tagging = false` defaults preserve all
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existing behavior. The `overall_score` computation (weighted mean of `role_score` and
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`competence_score`) is unchanged.
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### New environment variables
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| Variable | Required when | Description |
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|----------|--------------|-------------|
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| `AZURE_OPENAI_LLM_API_KEY` | `use_auto_tagging = true` | Azure OpenAI API key for the chat model used in auto-tagging. The server raises a configuration error at startup if this key is absent and `use_auto_tagging = true`. |
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### New dependencies
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- `rank-bm25` (PyPI): pure-Python BM25 implementation, no native code, no external services.
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Adds to `pyproject.toml` `dependencies`.
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## Alternatives Considered
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### A. Hard-code exact-match bonus in per_skill
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Simpler but not principled: would require special-casing the scorer instead of a clean
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strategy abstraction.
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### B. Hybrid embedding + BM25 in a single `_bm25_rrf_similarity()` call
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Combining both signals via RRF is architecturally clean and naturally extensible. However,
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to keep this change focused and reviewable, the initial implementation uses BM25-only RRF.
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A hybrid mode can be added as a follow-up change.
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### C. Replace embeddings entirely
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Not desirable. Embeddings are essential for role inference and handle multilingual /
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synonymous skill names (e.g., "Softwarearchitektur" ↔ "Software Architecture").
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## Open Questions
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None. The design is sufficiently clear to proceed.
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