initial commit

venv/Lib/site-packages/langchain_community/embeddings/fake.py

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+import hashlib
+from typing import List
+
+import numpy as np
+from langchain_core.embeddings import Embeddings
+from pydantic import BaseModel
+
+
+class FakeEmbeddings(Embeddings, BaseModel):
+    """Fake embedding model."""
+
+    size: int
+    """The size of the embedding vector."""
+
+    def _get_embedding(self) -> List[float]:
+        return list(np.random.normal(size=self.size))
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return [self._get_embedding() for _ in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._get_embedding()
+
+
+class DeterministicFakeEmbedding(Embeddings, BaseModel):
+    """
+    Fake embedding model that always returns
+    the same embedding vector for the same text.
+    """
+
+    size: int
+    """The size of the embedding vector."""
+
+    def _get_embedding(self, seed: int) -> List[float]:
+        # set the seed for the random generator
+        np.random.seed(seed)
+        return list(np.random.normal(size=self.size))
+
+    @staticmethod
+    def _get_seed(text: str) -> int:
+        """
+        Get a seed for the random generator, using the hash of the text.
+        """
+        return int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 10**8
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        return self._get_embedding(seed=self._get_seed(text))