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chore: import upstream snapshot with attribution
2026-07-13 13:02:24 +08:00

52 lines
1.8 KiB
Python

import asyncio
from typing import List
from cognee.infrastructure.databases.vector.embeddings.LiteLLMEmbeddingEngine import (
LiteLLMEmbeddingEngine,
)
from cognee.shared.rate_limiting import embedding_rate_limiter_context_manager
class MockEmbeddingEngine(LiteLLMEmbeddingEngine):
"""
Mock version of LiteLLMEmbeddingEngine that returns fixed embeddings
and can be configured to simulate rate limiting and failures.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mock = True
self.fail_every_n_requests = 0
self.request_count = 0
self.add_delay = 0
def configure_mock(self, fail_every_n_requests=0, add_delay=0):
"""
Configure the mock's behavior
Args:
fail_every_n_requests: Raise an exception every n requests (0 = never fail)
add_delay: Add artificial delay in seconds to each request
"""
self.fail_every_n_requests = fail_every_n_requests
self.add_delay = add_delay
async def embed_text(self, text: List[str]) -> List[List[float]]:
"""
Mock implementation that returns fixed embeddings and can
simulate failures and delays based on configuration.
"""
self.request_count += 1
# Simulate processing delay if configured
if self.add_delay > 0:
await asyncio.sleep(self.add_delay)
# Simulate failures if configured
if self.fail_every_n_requests > 0 and self.request_count % self.fail_every_n_requests == 0:
raise Exception(f"Mock failure on request #{self.request_count}")
# Return mock embeddings of the correct dimension
async with embedding_rate_limiter_context_manager():
return [[0.1] * self.dimensions for _ in text]