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https://github.com/hwchase17/langchain.git
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ca7375ac20
- Fix `BaichuanTextEmbeddings` api url - Remove unused params in api doc - Fix word spelling
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
import hashlib
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from typing import List
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import numpy as np
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel
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class FakeEmbeddings(Embeddings, BaseModel):
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"""Fake embedding model."""
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size: int
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"""The size of the embedding vector."""
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def _get_embedding(self) -> List[float]:
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return list(np.random.normal(size=self.size))
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return [self._get_embedding() for _ in texts]
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def embed_query(self, text: str) -> List[float]:
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return self._get_embedding()
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class DeterministicFakeEmbedding(Embeddings, BaseModel):
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"""
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Fake embedding model that always returns
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the same embedding vector for the same text.
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"""
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size: int
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"""The size of the embedding vector."""
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def _get_embedding(self, seed: int) -> List[float]:
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# set the seed for the random generator
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np.random.seed(seed)
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return list(np.random.normal(size=self.size))
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@staticmethod
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def _get_seed(text: str) -> int:
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"""
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Get a seed for the random generator, using the hash of the text.
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"""
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return int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 10**8
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]
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def embed_query(self, text: str) -> List[float]:
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return self._get_embedding(seed=self._get_seed(text))
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