unset unset Embedding介紹 unset unset
詞向量是 NLP 中的一種表示形式,其中詞匯表中的單詞或短語被對映到實數向量。它們用於捕獲高維空間中單詞之間的語意和句法相似性。
在詞嵌入的背景下,我們可以將單詞表示為高維空間中的向量,其中每個維度對應一個特定的特征,例如「生物」、「貓科動物」、「人類」、「性別」等。每個單詞在每個維度上都分配有一個數值,通常在 -1 到 1 之間,表示該詞與該特征的關聯程度。
例如,「貓」這個詞在「貓科動物」維度上可能具有較高的正值,而在「人類」維度上具有接近於零的值,這反映了它與貓科動物的緊密關聯性,而與人類的關聯性較低。 這種數值表示使我們能夠捕捉單詞之間的語意關系並對其執行數學運算,例如計算單詞之間的相似度或將其用作 NLP 任務中 ML 模型的輸入。LangChain 可容納來自不同來源的多種嵌入。
unset unset OpenAI unset unset
import os
os.environ["OPENAI_API_KEY"] = "your-key"
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "Text"
text_embedding = embeddings.embed_query(text)
print(text_embedding)
"""
[-0.0006077770551231004,
-0.02036312831034526,
0.0015661947077772864,
-0.0008398058726938265,
0.00801365303172794,
0.01648443640533639,
-0.015071485112588635,
-0.006794635682304868,
-0.009232670381151012,
-0.004512441507728793,
0.00296615975583046,
0.02781575545470095,
-0.004290802116650396,
0.009204965399058554,
-0.007286398183123463,
0.01896402857732122,
0.03457576177203527,
0.01469746878566298,
0.03812199202928964,
-0.033024282774857694,
-0.014143370075136358,
-0.0016640276929606461,
-0.00023289462736494386,
-0.009856030615586264,
-0.018867061139997622,
...
-0.0007159994667987885,
-0.024920590413974295,
0.009017956769934473,
0.005336663327995613,
...]
"""
print(len(text_embedding))
"""
1536
"""
unset unset HuggingFace unset unset
from langchain_community.embeddings import HuggingFaceEmbeddings
embedding_path = r'H:\pretrained_models\bert\english\paraphrase-multilingual-mpnet-base-v2'
embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
text = "This is a test document."
text_embedding = embeddings.embed_query(text)
print(len(text_embedding)) # 768
unset unset Google unset unset
from langchain_google_genai import GoogleGenerativeAIEmbeddings
os.environ["GOOGLE_API_KEY"] = "your-key"
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
text_embedding = embeddings.embed_query("hello, world!")
print(text_embedding) # 768
更多Embedding可以檢視https://python.langchain.com/v0.2/docs/integrations/text_embedding/
unset unset 計算相似性 unset unset
我們可以使用嵌入來計算文本的相似度。
word_list = ["Cat", "Dog", "Car""Truck","Computer","Laptop","Apple","Orange", "Music","Dance"]
embedding_model = OpenAIEmbeddings()
embeds = [embedding_model.embed_query(word) for word in word_list]
embeds
"""
[[-0.008174207879591734,
-0.007511803310590743,
-0.00995655437174355,
-0.024788951157780095,
-0.012790553094547429,
0.006654775143594856,
-0.0015151649503578363,
-0.03783217392596492,
-0.014422662356334227,
-0.026250339680779597,
0.017154227704543168,
0.046327340706031526,
0.0035646922858117093,
0.004240754467349556,
-0.032287098019987186,
-0.004592443287070655,
0.03955306057962428,
0.005261676778755394,
0.00789422251521935,
-0.015501631209043845,
-0.023723641081760536,
0.0053197228543978925,
0.014873371253461594,
-0.012141805905252653,
-0.006781109980413554,
...
0.00566348496318421,
0.01855802589283819,
0.00531267762533671,
0.02393075147421956,
...]]
"""
我們引入另一個單詞並計算相似度。
input_word = "Lion"
input_embed = embedding_model.embed_query(input_word)
import numpy as np
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
similarity = cosine_similarity(embeds[0], input_embed)
print(similarity) #0.8400893968591456
from sklearn.metrics.pairwise import cosine_similarity
similarity = cosine_similarity(np.array([embeds[0]]), np.array([input_embed]))
print(similarity) #array([[0.8400894]])
sims = [cosine_similarity(np.array([emb]), np.array([input_embed])) for emb in embeds]
"""
[array([[0.8400894]]),
array([[0.80272758]]),
array([[0.79536215]]),
array([[0.81627175]]),
array([[0.82762581]]),
array([[0.81705796]]),
array([[0.82609729]]),
array([[0.78917449]]),
array([[0.79970112]])]
"""
考慮文本儲存在 CSV 檔中,我們計劃將其用作評估輸入相似性的參考。
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='data.csv', csv_args={
'delimiter': ',',
'quotechar': '"',
'fieldnames': ['Words']
})
data = loader.load()
data
"""
[Document(page_content='Words: Words', metadata={'source': 'data.csv', 'row': 0}),
Document(page_content='Words: Cat', metadata={'source': 'data.csv', 'row': 1}),
Document(page_content='Words: Dog', metadata={'source': 'data.csv', 'row': 2}),
Document(page_content='Words: CarTruck', metadata={'source': 'data.csv', 'row': 3}),
Document(page_content='Words: Computer', metadata={'source': 'data.csv', 'row': 4}),
Document(page_content='Words: Laptop', metadata={'source': 'data.csv', 'row': 5}),
Document(page_content='Words: Apple', metadata={'source': 'data.csv', 'row': 6}),
Document(page_content='Words: Orange', metadata={'source': 'data.csv', 'row': 7}),
Document(page_content='Words: Music', metadata={'source': 'data.csv', 'row': 8}),
Document(page_content='Words: Dance', metadata={'source': 'data.csv', 'row': 9})]
"""
CSVLoader 類用於從 CSV 檔載入數據。我們將在系列後面介紹裝載機。我們可以利用FAISS結合LangChain來建立一個向量儲存。
embeddings = OpenAIEmbeddings()
from langchain_community.vectorstores import FAISS
db = FAISS.from_documents(data, embeddings)
user_input = "Lion"
results = db.similarity_search(user_input)
results
"""
[Document(page_content='Words: Cat', metadata={'source': 'data.csv', 'row': 1}),
Document(page_content='Words: Apple', metadata={'source': 'data.csv', 'row': 6}),
Document(page_content='Words: Dog', metadata={'source': 'data.csv', 'row': 2}),
Document(page_content='Words: Orange', metadata={'source': 'data.csv', 'row': 7})]
"""