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d184dfc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import numpy as np
from typing import List, Tuple
from aimakerspace.openai_utils.embedding import EmbeddingModel
class VectorDatabase:
def __init__(self):
print("Initializing VectorDatabase...") # Debug log
try:
self.embedding_model = EmbeddingModel()
print("EmbeddingModel initialized") # Debug log
self.embeddings = []
self.texts = []
except Exception as e:
print(f"Error initializing VectorDatabase: {str(e)}") # Debug log
raise
async def abuild_from_list(self, texts: List[str]):
try:
print(f"Building vector database from {len(texts)} texts") # Debug log
self.texts = texts
print("Generating embeddings...") # Debug log
self.embeddings = await self.embedding_model.async_get_embeddings(texts)
print(f"Generated {len(self.embeddings)} embeddings") # Debug log
return self
except Exception as e:
print(f"Error building vector database: {str(e)}") # Debug log
raise
def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
a = np.array(a)
b = np.array(b)
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
async def search_by_text(self, query: str, k: int = 4) -> List[Tuple[str, float]]:
try:
print(f"Searching for query: {query}") # Debug log
query_embedding = await self.embedding_model.embed_query(query)
print("Generated query embedding") # Debug log
# Calculate similarities
similarities = []
for i, embedding in enumerate(self.embeddings):
similarity = self._cosine_similarity(query_embedding, embedding)
similarities.append((self.texts[i], similarity))
# Sort by similarity and return top k
similarities.sort(key=lambda x: x[1], reverse=True)
print(f"Found {len(similarities)} matches") # Debug log
return similarities[:k]
except Exception as e:
print(f"Error in search_by_text: {str(e)}") # Debug log
raise |