Why do I need to use a vector database in large models?
Understand the indispensability of vector databases in AI big models and master the key applications of vector technology in semantic understanding.
Core content:
1. The necessity and problem solving of vector databases in AI big models
2. The definition of vectors and their applications in the field of AI
3. The semantic value and conversion technology of vector representation of text
1. Introduction
In the application of large AI models (such as ChatGPT and LLaMA), we often hear the concept of "vector database". Why do large models need vector databases? What problems do they solve? This article will start from vectors, semantic understanding, and the distance between vectors to help you understand the core role of vector databases and explain their importance in the AI era.
2. What is a vector?
Vector is a concept in mathematics, which can be simply understood as a list of values with direction and size . In the field of AI, vectors are usually used to represent information such as text, pictures, and audio. For example, the following is an example of a 3D vector:
ounter (lineounter(lineounter(lineounter(lineimport numpy as npvector = np.array([ 0 . 5 , - 0 . 2 , 0 . 8 ])print (vector)
But in actual applications, the dimension of the vector may be 128, 512, or even 768. Such high-dimensional vectors can more accurately represent the characteristics of information.
3. Why do we need to use vectors to represent text?
Traditional computers understand text based on string matching. For example, when searching for the keyword "apple", it will only look for "apple" that matches exactly. But in reality, we hope that AI can understand semantic similarity , for example:
"Apple" and "iPhone" may be related (Apple makes mobile phones). "Cat" and "pet" also have a certain correlation.
Ordinary keyword search cannot do this, but vector representation allows AI to know which words, sentences, and paragraphs are similar. This is the value of semantic vectors .
How to convert text to vector
Usually, text can be converted into vectors through the word embedding technology. Common methods include Word2Vec, GloVe and the Embedding layer of the Transformer model. sentence-transformers library, we can easily vectorize text:
ounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(linefrom sentence_transformers import SentenceTransformermodel = SentenceTransformer( 'all-MiniLM-L6-v2' ) # Pre-trained text vector modelsentence = "Artificial intelligence is changing the world"vector = model.encode(sentence)print (vector[: 10 ]) # print the first 10 values of the vector
4. Distance between vectors - a way to measure similarity
Since the vector represents the semantics of the text, we need to calculate the "similarity" between different vectors. Common similarity calculation methods include:
4.1 Cosine Similarity
Measures whether the directions of two vectors are similar. The smaller the angle, the higher the similarity:
Python code example:
ounter (lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(linefrom sklearn.metrics.pairwise import cosine_similarityvec1 = np.array([ 0 . 5 , 0 . 1 , 0 . 3 ])vec2 = np.array([ 0 . 4 , 0 . 2 , 0 . 3 ])similarity = cosine_similarity([vec1],print(f"Cosine similarity: {similarity[0][0]:.4f}")
4.2 Euclidean Distance
Compute the actual distance between two vectors in space:
Python code example:
ounter(lineounter(lineounter(lineounter(linefrom scipy.spatial.distance import euclideandistance = euclidean(vec1, vec2)print ( f"Euclidean distance: {distance: .4 f} " )
5. The role of vector database
If AI needs to find content related to user questions in massive amounts of knowledge, it cannot rely on traditional databases (such as MySQL) because:
Text matching does not support semantic search . For example, "AI development" and "artificial intelligence trends" are related, but keyword matching cannot find them. When the amount of data is huge, the query speed is slow , and ordinary databases are not suitable for processing queries on millions of high-dimensional vectors.
Vector databases (such as FAISS, ChromaDB, and Pinecone) index a large number of high-dimensional vectors and use cosine similarity or Euclidean distance to quickly find the most similar content.
Vector search using FAISS
ounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineounter(lineimport faiss# Create a vector databasedimension = 3 # Assume it is a 3-dimensional vectorindex = faiss.IndexFlatL2(dimension)# Add vectorvectors = np.array([[ 0.5 , 0.1 , 0.3 ], [ 0.4 , 0.2 , 0.3 ], [ 0.1 , 0.3 , 0.7 ]], dtype= 'float32' )index.add(vectors)# Query the most similar vectorquery_vector = np.array([[ 0.45 , 0.15 , 0.3 ]], dtype= 'float32' )D, I = index.search(query_vector, k= 1 ) # k=1 returns the most similar vectorprint ( f "Nearest vector index: {I[ 0 ][ 0 ]} , distance: {D[ 0 ][ 0 ]: .4 f} " )
6. ChatGPT Advanced Tutorial
ChatGPT from entry to mastery]-https://pan.quark.cn/s/f4cc2f481cad
7. Conclusion
The vector database is an important component of the big model. It solves the problem that ordinary databases cannot handle semantic search, and enables AI to understand semantics, quickly match, and access local knowledge . In the AI era, mastering the basic concepts of vectors and vector databases will help us better understand and apply big model technology. In the future, the vector database will become the "knowledge base" of AI applications, making AI smarter and more personalized. Are you ready to use it to build your own AI knowledge assistant?
