"""
Mock OpenAI client for testing.
"""
import json
import hashlib
from typing import List, Dict, Any
class MockEmbeddingResponse:
"""Mock embedding response object."""
def __init__(self, embeddings: List[List[float]]):
self.data = [MockEmbedding(embedding) for embedding in embeddings]
class MockEmbedding:
"""Mock embedding object."""
def __init__(self, embedding: List[float]):
self.embedding = embedding
class MockOpenAIClient:
"""Mock OpenAI client for testing."""
def __init__(self, dimension: int = 1536):
"""Initialize mock client."""
self.dimension = dimension
self.embeddings = MockEmbeddings()
def create_deterministic_embedding(self, text: str) -> List[float]:
"""Create a deterministic embedding based on text hash."""
# Create a hash of the text
text_hash = hashlib.md5(text.encode()).hexdigest()
# Convert hash to numbers and normalize to create embedding
embedding = []
for i in range(0, len(text_hash), 2):
# Take pairs of hex characters and convert to float
hex_pair = text_hash[i:i+2]
value = int(hex_pair, 16) / 255.0 # Normalize to 0-1
embedding.append(value - 0.5) # Center around 0
# Pad or truncate to desired dimension
while len(embedding) < self.dimension:
embedding.extend(embedding[:min(len(embedding), self.dimension - len(embedding))])
return embedding[:self.dimension]
class MockEmbeddings:
"""Mock embeddings API."""
def __init__(self):
self.client = None
def create(self, input: List[str], model: str = "text-embedding-3-small") -> MockEmbeddingResponse:
"""Create mock embeddings for input texts."""
mock_client = MockOpenAIClient()
embeddings = [mock_client.create_deterministic_embedding(text) for text in input]
return MockEmbeddingResponse(embeddings)
