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graflo.hq.fuzzy_matcher

Fuzzy matching utilities for PostgreSQL schema analysis.

This module provides improved fuzzy matching strategies for identifying vertex names from table and column fragments.

FuzzyMatcher

Improved fuzzy matcher with multiple matching strategies and optional caching.

Uses a combination of matching techniques: 1. Exact matching (case-insensitive) 2. Substring matching (with length-based scoring) 3. Sequence similarity (difflib) 4. Prefix/suffix matching 5. Common pattern matching (handles id, fk, etc.)

Supports optional caching to improve performance when processing multiple fragments.

Source code in graflo/hq/fuzzy_matcher.py 
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class FuzzyMatcher:
    """Improved fuzzy matcher with multiple matching strategies and optional caching.

    Uses a combination of matching techniques:
    1. Exact matching (case-insensitive)
    2. Substring matching (with length-based scoring)
    3. Sequence similarity (difflib)
    4. Prefix/suffix matching
    5. Common pattern matching (handles id, fk, etc.)

    Supports optional caching to improve performance when processing multiple fragments.
    """

    def __init__(
        self, vertex_names: list[str], threshold: float = 0.8, enable_cache: bool = True
    ):
        """Initialize the fuzzy matcher.

        Args:
            vertex_names: List of vertex table names to match against
            threshold: Similarity threshold (0.0 to 1.0)
            enable_cache: Whether to enable result caching (default: True)
        """
        self.vertex_names = vertex_names
        self.threshold = threshold
        self._enable_cache = enable_cache
        # Pre-compute lowercase versions for efficiency
        self._vertex_lower_map = {vn.lower(): vn for vn in vertex_names}
        self._vertex_lower_list = list(self._vertex_lower_map.keys())
        # Initialize cache if enabled
        self._cache: dict[str, str | None] = {}
        if enable_cache:
            self._build_cache()

    def match(self, fragment: str) -> tuple[str | None, float]:
        """Match a fragment against vertex names using multiple strategies.

        Args:
            fragment: Fragment to match

        Returns:
            Tuple of (best_match, score) or (None, 0.0) if no match above threshold
        """
        if not self.vertex_names or not fragment:
            return (None, 0.0)

        fragment_lower = fragment.lower()

        # Check cache first if enabled
        if self._enable_cache and fragment_lower in self._cache:
            cached_match = self._cache[fragment_lower]
            if cached_match is not None:
                return (
                    cached_match,
                    1.0,
                )  # Cached matches are treated as high confidence
            return (None, 0.0)

        # Strategy 1: Exact match (highest priority, returns immediately)
        if fragment_lower in self._vertex_lower_map:
            result = (self._vertex_lower_map[fragment_lower], 1.0)
            if self._enable_cache:
                self._cache[fragment_lower] = result[0]
            return result

        best_match = None
        best_score = 0.0

        # Strategy 2: Substring matching with length-based scoring
        substring_score = self._substring_match(fragment_lower)
        if substring_score[1] > best_score:
            best_match, best_score = substring_score

        # Strategy 3: Sequence similarity (difflib)
        sequence_score = self._sequence_match(fragment_lower)
        if sequence_score[1] > best_score:
            best_match, best_score = sequence_score

        # Strategy 4: Prefix/suffix matching
        prefix_suffix_score = self._prefix_suffix_match(fragment_lower)
        if prefix_suffix_score[1] > best_score:
            best_match, best_score = prefix_suffix_score

        # Strategy 5: Common pattern matching (handles id, fk, etc.)
        pattern_score = self._pattern_match(fragment_lower)
        if pattern_score[1] > best_score:
            best_match, best_score = pattern_score

        # Return match only if above threshold
        if best_score >= self.threshold:
            result = (best_match, best_score)
            if self._enable_cache:
                self._cache[fragment_lower] = best_match
            return result

        # Cache miss result
        if self._enable_cache:
            self._cache[fragment_lower] = None
        return (None, 0.0)

    def _substring_match(self, fragment_lower: str) -> tuple[str | None, float]:
        """Match using substring containment with length-based scoring.

        Args:
            fragment_lower: Lowercase fragment to match

        Returns:
            Tuple of (best_match, score)
        """
        best_match = None
        best_score = 0.0

        for vertex_lower, vertex_name in self._vertex_lower_map.items():
            # Check if fragment is contained in vertex or vice versa
            if fragment_lower in vertex_lower:
                # Fragment is substring of vertex (e.g., "user" in "users")
                score = len(fragment_lower) / len(vertex_lower)
                # Boost score if fragment is significant portion
                if len(fragment_lower) >= 3:  # At least 3 chars
                    score = min(score * 1.2, 0.95)  # Cap at 0.95
                if score > best_score:
                    best_score = score
                    best_match = vertex_name
            elif vertex_lower in fragment_lower:
                # Vertex is substring of fragment (e.g., "user" in "user_id")
                score = len(vertex_lower) / len(fragment_lower)
                # Boost score if vertex is significant portion
                if len(vertex_lower) >= 3:
                    score = min(score * 1.2, 0.95)
                if score > best_score:
                    best_score = score
                    best_match = vertex_name

        return (best_match, best_score)

    def _sequence_match(self, fragment_lower: str) -> tuple[str | None, float]:
        """Match using sequence similarity (difflib).

        Args:
            fragment_lower: Lowercase fragment to match

        Returns:
            Tuple of (best_match, score)
        """
        best_match = None
        best_score = 0.0

        for vertex_lower, vertex_name in self._vertex_lower_map.items():
            similarity = SequenceMatcher(None, fragment_lower, vertex_lower).ratio()
            if similarity > best_score:
                best_score = similarity
                best_match = vertex_name

        return (best_match, best_score)

    def _prefix_suffix_match(self, fragment_lower: str) -> tuple[str | None, float]:
        """Match using prefix or suffix patterns.

        Args:
            fragment_lower: Lowercase fragment to match

        Returns:
            Tuple of (best_match, score)
        """
        best_match = None
        best_score = 0.0

        for vertex_lower, vertex_name in self._vertex_lower_map.items():
            # Check prefix match
            if fragment_lower.startswith(vertex_lower):
                score = len(vertex_lower) / len(fragment_lower)
                if score > best_score:
                    best_score = score
                    best_match = vertex_name
            # Check suffix match
            elif fragment_lower.endswith(vertex_lower):
                score = len(vertex_lower) / len(fragment_lower)
                if score > best_score:
                    best_score = score
                    best_match = vertex_name
            # Check if vertex starts with fragment
            elif vertex_lower.startswith(fragment_lower):
                score = len(fragment_lower) / len(vertex_lower)
                if score > best_score:
                    best_score = score
                    best_match = vertex_name

        return (best_match, best_score)

    def _pattern_match(self, fragment_lower: str) -> tuple[str | None, float]:
        """Match using common patterns (id, fk, etc.).

        Args:
            fragment_lower: Lowercase fragment to match

        Returns:
            Tuple of (best_match, score)
        """
        # Common suffixes/prefixes to remove
        common_patterns = [
            ("_id", ""),
            ("_fk", ""),
            ("_key", ""),
            ("_pk", ""),
            ("_ref", ""),
            ("_reference", ""),
            ("id_", ""),
            ("fk_", ""),
            ("key_", ""),
            ("pk_", ""),
            ("ref_", ""),
            ("reference_", ""),
        ]

        best_match = None
        best_score = 0.0

        # Try removing common patterns and matching
        for pattern, replacement in common_patterns:
            if fragment_lower.endswith(pattern):
                base = fragment_lower[: -len(pattern)]
                if base in self._vertex_lower_map:
                    # High score for pattern-based matches
                    score = 0.9
                    if score > best_score:
                        best_score = score
                        best_match = self._vertex_lower_map[base]
            elif fragment_lower.startswith(pattern):
                base = fragment_lower[len(pattern) :]
                if base in self._vertex_lower_map:
                    score = 0.9
                    if score > best_score:
                        best_score = score
                        best_match = self._vertex_lower_map[base]

        return (best_match, best_score)

    def _build_cache(self) -> None:
        """Pre-compute fuzzy matches for common patterns."""
        # Pre-compute exact matches (case-insensitive)
        for vertex_name in self.vertex_names:
            vertex_lower = vertex_name.lower()
            self._cache[vertex_lower] = vertex_name
            # Also cache common variations
            for suffix in ["id", "fk", "key", "pk", "ref", "reference"]:
                self._cache[f"{vertex_lower}_{suffix}"] = vertex_name
                self._cache[f"{suffix}_{vertex_lower}"] = vertex_name

    def get_match(self, fragment: str) -> str | None:
        """Get cached fuzzy match for a fragment, computing if not cached.

        Convenience method that returns only the match (not the score).
        Uses caching when enabled.

        Args:
            fragment: Fragment to match

        Returns:
            Best matching vertex name or None if no match above threshold
        """
        match, _ = self.match(fragment)
        return match

    def batch_match(self, fragments: list[str]) -> dict[str, str | None]:
        """Match multiple fragments in batch, using cache when possible.

        Args:
            fragments: List of fragments to match

        Returns:
            Dictionary mapping fragments to their matched vertex names (or None)
        """
        results = {}
        for fragment in fragments:
            results[fragment] = self.get_match(fragment)
        return results

__init__(vertex_names, threshold=0.8, enable_cache=True)

Initialize the fuzzy matcher.

Parameters:

Name Type Description Default
vertex_names list[str]

List of vertex table names to match against

 required
threshold float

Similarity threshold (0.0 to 1.0)

 0.8
enable_cache bool

Whether to enable result caching (default: True)

 True
Source code in graflo/hq/fuzzy_matcher.py 
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def __init__(
    self, vertex_names: list[str], threshold: float = 0.8, enable_cache: bool = True
):
    """Initialize the fuzzy matcher.

    Args:
        vertex_names: List of vertex table names to match against
        threshold: Similarity threshold (0.0 to 1.0)
        enable_cache: Whether to enable result caching (default: True)
    """
    self.vertex_names = vertex_names
    self.threshold = threshold
    self._enable_cache = enable_cache
    # Pre-compute lowercase versions for efficiency
    self._vertex_lower_map = {vn.lower(): vn for vn in vertex_names}
    self._vertex_lower_list = list(self._vertex_lower_map.keys())
    # Initialize cache if enabled
    self._cache: dict[str, str | None] = {}
    if enable_cache:
        self._build_cache()

batch_match(fragments)

Match multiple fragments in batch, using cache when possible.

Parameters:

Name Type Description Default
fragments list[str]

List of fragments to match

 required

Returns:

Type Description
dict[str, str | None]

Dictionary mapping fragments to their matched vertex names (or None)
Source code in graflo/hq/fuzzy_matcher.py 
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def batch_match(self, fragments: list[str]) -> dict[str, str | None]:
    """Match multiple fragments in batch, using cache when possible.

    Args:
        fragments: List of fragments to match

    Returns:
        Dictionary mapping fragments to their matched vertex names (or None)
    """
    results = {}
    for fragment in fragments:
        results[fragment] = self.get_match(fragment)
    return results

get_match(fragment)

Get cached fuzzy match for a fragment, computing if not cached.

Convenience method that returns only the match (not the score). Uses caching when enabled.

Parameters:

Name Type Description Default
fragment str

Fragment to match

 required

Returns:

Type Description
str | None

Best matching vertex name or None if no match above threshold
Source code in graflo/hq/fuzzy_matcher.py 
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def get_match(self, fragment: str) -> str | None:
    """Get cached fuzzy match for a fragment, computing if not cached.

    Convenience method that returns only the match (not the score).
    Uses caching when enabled.

    Args:
        fragment: Fragment to match

    Returns:
        Best matching vertex name or None if no match above threshold
    """
    match, _ = self.match(fragment)
    return match

match(fragment)

Match a fragment against vertex names using multiple strategies.

Parameters:

Name Type Description Default
fragment str

Fragment to match

 required

Returns:

Type Description
tuple[str | None, float]

Tuple of (best_match, score) or (None, 0.0) if no match above threshold
Source code in graflo/hq/fuzzy_matcher.py 
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def match(self, fragment: str) -> tuple[str | None, float]:
    """Match a fragment against vertex names using multiple strategies.

    Args:
        fragment: Fragment to match

    Returns:
        Tuple of (best_match, score) or (None, 0.0) if no match above threshold
    """
    if not self.vertex_names or not fragment:
        return (None, 0.0)

    fragment_lower = fragment.lower()

    # Check cache first if enabled
    if self._enable_cache and fragment_lower in self._cache:
        cached_match = self._cache[fragment_lower]
        if cached_match is not None:
            return (
                cached_match,
                1.0,
            )  # Cached matches are treated as high confidence
        return (None, 0.0)

    # Strategy 1: Exact match (highest priority, returns immediately)
    if fragment_lower in self._vertex_lower_map:
        result = (self._vertex_lower_map[fragment_lower], 1.0)
        if self._enable_cache:
            self._cache[fragment_lower] = result[0]
        return result

    best_match = None
    best_score = 0.0

    # Strategy 2: Substring matching with length-based scoring
    substring_score = self._substring_match(fragment_lower)
    if substring_score[1] > best_score:
        best_match, best_score = substring_score

    # Strategy 3: Sequence similarity (difflib)
    sequence_score = self._sequence_match(fragment_lower)
    if sequence_score[1] > best_score:
        best_match, best_score = sequence_score

    # Strategy 4: Prefix/suffix matching
    prefix_suffix_score = self._prefix_suffix_match(fragment_lower)
    if prefix_suffix_score[1] > best_score:
        best_match, best_score = prefix_suffix_score

    # Strategy 5: Common pattern matching (handles id, fk, etc.)
    pattern_score = self._pattern_match(fragment_lower)
    if pattern_score[1] > best_score:
        best_match, best_score = pattern_score

    # Return match only if above threshold
    if best_score >= self.threshold:
        result = (best_match, best_score)
        if self._enable_cache:
            self._cache[fragment_lower] = best_match
        return result

    # Cache miss result
    if self._enable_cache:
        self._cache[fragment_lower] = None
    return (None, 0.0)