chore: import upstream snapshot with attribution

libs/checkpoint-postgres/tests/embed_test_utils.py

@@ -0,0 +1,55 @@
+"""Embedding utilities for testing."""
+
+import math
+import random
+from collections import Counter, defaultdict
+from typing import Any
+
+from langchain_core.embeddings import Embeddings
+
+
+class CharacterEmbeddings(Embeddings):
+    """Simple character-frequency based embeddings using random projections."""
+
+    def __init__(self, dims: int = 50, seed: int = 42):
+        """Initialize with embedding dimensions and random seed."""
+        self._rng = random.Random(seed)
+        self.dims = dims
+        # Create projection vector for each character lazily
+        self._char_projections: defaultdict[str, list[float]] = defaultdict(
+            lambda: [
+                self._rng.gauss(0, 1 / math.sqrt(self.dims)) for _ in range(self.dims)
+            ]
+        )
+
+    def _embed_one(self, text: str) -> list[float]:
+        """Embed a single text."""
+        counts = Counter(text)
+        total = sum(counts.values())
+
+        if total == 0:
+            return [0.0] * self.dims
+
+        embedding = [0.0] * self.dims
+        for char, count in counts.items():
+            weight = count / total
+            char_proj = self._char_projections[char]
+            for i, proj in enumerate(char_proj):
+                embedding[i] += weight * proj
+
+        norm = math.sqrt(sum(x * x for x in embedding))
+        if norm > 0:
+            embedding = [x / norm for x in embedding]
+
+        return embedding
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of documents."""
+        return [self._embed_one(text) for text in texts]
+
+    def embed_query(self, text: str) -> list[float]:
+        """Embed a query string."""
+        return self._embed_one(text)
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, CharacterEmbeddings) and self.dims == other.dims