我正在尝试遵循官方 MongoDB 文档中的示例。
它提到搜索向量是基于使用 OpenAI
text-embedding-ada-002为什么？
curl https://api.openai.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-xxxxx" \
-d '{
"input": "historical heist",
"model": "text-embedding-ada-002",
"encoding_format": "float"
}'
我的结果如下：
"data": [
{
108 "object": "embedding",
"index": 0,
19 "embedding": [
8 -0.02273331,
97 -0.027646419,
0.0030520316,
3 -0.033631727,
1 -0.026207773,
4 0:00:01 --:- 0.0016854883,
-:-- 0:0 -0.017983066,
0:0 0.006311038,
1 -0.0013012275,
201 -0.024823416,
54 0.015227924,
0.005751188,
0.0004003777,
-0.008095773,
示例结果如下：
'queryVector': [-0.020156775, -0.024996493, 0.010778184, ....
官方OpenAI论坛有讨论。人们说他们为相同输入获得了不同的嵌入向量。
来自 Curt Kennedy 的第一个回复表明，同一模型的 OpenAI API 端点和 Azure API 端点之间似乎存在差异。
嵌入仅返回向量。对于相同的向量是相同的 输入、相同的模型和相同的 API 端点。但我们已经看到 OpenAI 端点和 Azure 端点之间的差异 相同型号。因此，选择一个端点并坚持下去以避免任何 差异。
后来，Curt Kennedy 补充道，预计会出现一些差异。
由于 GPU 的随机时序，预计会出现一些变化， 并且浮点数不具有关联性，并且他们可能会采取 最后一个隐藏层并扩展到单位超球体，其中 会放大接近原点的隐藏状态的误差。
我编写了以下Python代码进行测试：
# Imports
import os
import numpy as np
from openai import OpenAI
# Initialize OpenAI client
client = OpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
)
# Vector from MongoDB documentation
vector_from_mongodb = np.array([-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, -0.013106481, -0.005385822, -0.018652538, 0.012642129, -0.005189617, 0.018835662, -0.0048102876, -0.0261214, -0.016167276, -0.007972456, 0.0023381072, -0.010058766, -0.009012341, 0.008358325, 0.018665617, 0.02163485, -0.012975678, -0.010745483, -0.002571918, -0.014479915, 0.007226877, 0.015003128, 0.013165343, -0.028279653, 0.0053727417, -0.020588424, -0.017383745, 0.023518417, 0.01262905, -0.011922712, 0.007638907, -0.0073249796, -0.014859244, -0.00001101736, 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-0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783])
# Loop to generate embeddings and calculate dot products
for _ in range(3):
# Generate an embedding
response = client.embeddings.create(
model="text-embedding-ada-002",
input="historical heist",
encoding_format="float",
)
# Extract the embedding vector from the response
vector = response.data[0].embedding
# Calculate the dot product between the generated embedding and the vector from MongoDB
dot_product = np.dot(vector, vector_from_mongodb)
# Print the first 5 elements of the embedding
print(f"\nEmbedding vector (first 5 elements): {vector[:5]}")
# Print the dot product value
print(f"Dot product: {dot_product}\n")
print("------------------------------------")
运行后，我得到以下输出：
Embedding vector (first 5 elements): [-0.02273331, -0.027646419, 0.0030520316, -0.033631727, -0.026207773]
Dot product: 0.9853400963855683
------------------------------------
Embedding vector (first 5 elements): [-0.02273331, -0.027646419, 0.0030520316, -0.033631727, -0.026207773]
Dot product: 0.9853400963855683
------------------------------------
Embedding vector (first 5 elements): [-0.02273331, -0.027646419, 0.0030520316, -0.033631727, -0.026207773]
Dot product: 0.9853400963855683
------------------------------------
如您所见：
论坛上继续讨论“相同”输入的嵌入向量之间的这种微小差异是否有问题。换句话说，您是否应该关心差异？我认为你不应该，只要你使用它们得到有意义的解决方案。 此外，如果给定的 API 端点、模型和输入具有相同的嵌入向量，我就更不关心了。不要比较使用 OpenAI API 端点和 MongoDB API 端点时获得的嵌入向量。比较多次使用 OpenAI API 端点时获得的嵌入向量。比较多次使用 MongoDB API 时获得的嵌入向量。 OpenAI API 端点和 MongoDB API 端点之间嵌入向量的差异可能具有技术性质，正如
Curt Kennedy