Feat/keyword retriever

src/lang2sql/components/retrieval/__init__.py

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+from .keyword import KeywordRetriever
+
+__all__ = ["KeywordRetriever"]

src/lang2sql/components/retrieval/_bm25.py

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+"""
+Internal BM25 index — stdlib only (math, collections).
+
+BM25 parameters:
+  k1 = 1.5  (term frequency saturation)
+  b  = 0.75 (document length normalization)
+
+Tokenization: text.lower().split()  (whitespace, no external deps)
+"""
+
+from __future__ import annotations
+
+import math
+from collections import Counter
+from typing import Any
+
+_K1 = 1.5
+_B = 0.75
+
+
+def _tokenize(text: str) -> list[str]:
+    return text.lower().split()
+
+
+def _extract_text(value: Any) -> list[str]:
+    """Recursively extract text tokens from any value (str, list, dict, other)."""
+    if isinstance(value, str):
+        return [value]
+    if isinstance(value, list):
+        result: list[str] = []
+        for item in value:
+            result.extend(_extract_text(item))
+        return result
+    if isinstance(value, dict):
+        result = []
+        for k, v in value.items():
+            result.append(str(k))
+            result.extend(_extract_text(v))
+        return result
+    return [str(value)]
+
+
+def _entry_to_text(entry: dict[str, Any], index_fields: list[str]) -> str:
+    """
+    Convert a catalog dict entry into a single text string for indexing.
+
+    Handles:
+    - str fields  → joined as-is
+    - dict fields → "key value key value ..." (for columns: {col_name: col_desc})
+    - list fields → each element extracted recursively
+    - other types → str(value)
+    """
+    parts: list[str] = []
+    for field in index_fields:
+        value = entry.get(field)
+        if value is None:
+            continue
+        parts.extend(_extract_text(value))
+    return " ".join(parts)
+
+
+class _BM25Index:
+    """
+    In-memory BM25 index over a list[dict] catalog.
+
+    Usage:
+        index = _BM25Index(catalog, index_fields=["name", "description", "columns"])
+        scores = index.score("주문 테이블")  # list[float], one per catalog entry
+    """
+
+    def __init__(
+        self,
+        catalog: list[dict[str, Any]],
+        index_fields: list[str],
+    ) -> None:
+        self._catalog = catalog
+        self._n = len(catalog)
+
+        # Tokenize each document
+        self._docs: list[list[str]] = [
+            _tokenize(_entry_to_text(entry, index_fields)) for entry in catalog
+        ]
+
+        # Term frequencies per document
+        self._tfs: list[Counter[str]] = [Counter(doc) for doc in self._docs]
+
+        # Document lengths
+        doc_lengths = [len(doc) for doc in self._docs]
+        self._avgdl: float = sum(doc_lengths) / self._n if self._n > 0 else 0.0
+
+        # Inverted index: term → set of doc indices that contain it
+        self._df: Counter[str] = Counter()
+        for tf in self._tfs:
+            for term in tf:
+                self._df[term] += 1
+
+    def score(self, query: str) -> list[float]:
+        """
+        Return a BM25 score for each catalog entry.
+
+        Args:
+            query: Natural language query string.
+
+        Returns:
+            List of float scores, one per catalog entry, in original order.
+        """
+        if self._n == 0:
+            return []
+
+        query_terms = _tokenize(query)
+        scores = [0.0] * self._n
+
+        for term in query_terms:
+            df_t = self._df.get(term, 0)
+            if df_t == 0:
+                continue
+
+            # IDF — smoothed to avoid log(0)
+            idf = math.log((self._n - df_t + 0.5) / (df_t + 0.5) + 1)
+
+            for i, tf in enumerate(self._tfs):
+                tf_t = tf.get(term, 0)
+                if tf_t == 0:
+                    continue
+
+                dl = len(self._docs[i])
+                denom = tf_t + _K1 * (1 - _B + _B * dl / self._avgdl)
+                scores[i] += idf * (tf_t * (_K1 + 1)) / denom
+
+        return scores

src/lang2sql/components/retrieval/keyword.py

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+from __future__ import annotations
+
+from typing import Any, Optional
+
+from ...core.base import BaseComponent
+from ...core.context import RunContext
+from ...core.hooks import TraceHook
+from ._bm25 import _BM25Index
+
+_DEFAULT_INDEX_FIELDS = ["name", "description", "columns"]
+
+
+class KeywordRetriever(BaseComponent):
+    """
+    BM25-based keyword retriever over a table catalog.
+
+    Indexes catalog entries at init time (in-memory).
+    On each call, reads ``run.query`` and writes top-N matches
+    into ``run.schema_selected``.
+
+    Args:
+        catalog:       List of table dicts. Each dict should have at minimum
+                       ``name`` (str) and ``description`` (str).
+                       Optional keys: ``columns`` (dict[str, str]), ``meta`` (dict).
+        top_n:         Maximum number of results to return. Defaults to 5.
+        index_fields:  Fields to index. Defaults to ["name", "description", "columns"].
+                       Pass a custom list to replace the default (complete override).
+        name:          Component name for tracing. Defaults to "KeywordRetriever".
+        hook:          Optional TraceHook for observability.
+
+    Example::
+
+        retriever = KeywordRetriever(catalog=[
+            {"name": "orders", "description": "주문 정보 테이블"},
+        ])
+        run = retriever(RunContext(query="주문 조회"))
+        print(run.schema_selected)  # [{"name": "orders", ...}]
+    """
+
+    def __init__(
+        self,
+        *,
+        catalog: list[dict[str, Any]],
+        top_n: int = 5,
+        index_fields: Optional[list[str]] = None,
+        name: Optional[str] = None,
+        hook: Optional[TraceHook] = None,
+    ) -> None:
+        super().__init__(name=name or "KeywordRetriever", hook=hook)
+        self._catalog = catalog
+        self._top_n = top_n
+        self._index_fields = (
+            index_fields if index_fields is not None else _DEFAULT_INDEX_FIELDS
+        )
+        self._index = _BM25Index(catalog, self._index_fields)
+
+    def run(self, run: RunContext) -> RunContext:
+        """
+        Search the catalog with BM25 and store results in ``run.schema_selected``.
+
+        Args:
+            run: Current RunContext. Reads ``run.query``.
+
+        Returns:
+            The same RunContext with ``run.schema_selected`` set to a
+            ranked list[dict] (BM25 score descending). Empty list if no match.
+        """
+        if not self._catalog:
+            run.schema_selected = []
+            return run
+
+        scores = self._index.score(run.query)
+
+        # Pair each catalog entry with its score, sort descending
+        ranked = sorted(
+            zip(scores, self._catalog),
+            key=lambda x: x[0],
+            reverse=True,
+        )
+
+        # Return up to top_n entries that have a positive score
+        results = [entry for score, entry in ranked[: self._top_n] if score > 0.0]
+
+        run.schema_selected = results
+        return run

src/lang2sql/core/ports.py

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+from __future__ import annotations
+
+from typing import Protocol
+
+
+class EmbeddingPort(Protocol):
+    """
+    Placeholder — will be implemented in OQ-2 (VectorRetriever).
+
+    Abstracts embedding backends (OpenAI, Azure, Bedrock, etc.)
+    so VectorRetriever is not coupled to any specific provider.
+    """
+
+    def embed_query(self, text: str) -> list[float]: ...
+
+    def embed_texts(self, texts: list[str]) -> list[list[float]]: ...