Intro to Vector Databases

What is a Vector Database?

A vector database is a type of database that stores data in the form of vectors, enabling efficient storage and retrieval of high-dimensional data such as embeddings used in machine learning and natural language processing applications.

Key Concepts

• **Vectors**: An ordered array of numbers representing data points in a high-dimensional space.
• **Embeddings**: Representations of objects (like words, images, etc.) in a vector space.
• **Similarity Search**: A search for data points that are similar to a given query vector, often using metrics like cosine similarity or Euclidean distance.
• **Indexing**: Techniques used to optimize the search and retrieval speed of vectors.

How It Works

Vector databases use specialized indexing structures to facilitate fast retrieval of vectors. The general workflow involves the following steps:

        graph TD;
            A[Data Ingestion] --> B[Vectorization];
            B --> C[Storage in Vector Database];
            C --> D[Querying];
            D --> E[Similarity Search];
            E --> F[Results Return];
        

Code Example

Here’s a basic example of using a vector database with Python and the FAISS library for similarity search:

import numpy as np
import faiss

# Create a random dataset of vectors
d = 64                          # dimension
nb = 1000                       # number of vectors
np.random.seed(1234)            # reproducibility
data = np.random.random((nb, d)).astype('float32')

# Build the index
index = faiss.IndexFlatL2(d)    # L2 distance
index.add(data)                 # add vectors to the index

# Query with a new random vector
nq = 10                         # number of queries
query = np.random.random((nq, d)).astype('float32')

# Perform the search
k = 5                           # number of nearest neighbors
D, I = index.search(query, k)   # D: distances, I: indices

print("Distances:\n", D)
print("Indices:\n", I)
        

Best Practices

1. **Choose the Right Index**: Select an index type based on your data size and query requirements.
2. **Normalize Your Vectors**: Ensure vectors are normalized for accurate similarity measurements.
3. **Batch Processing**: Process data in batches to improve performance and reduce memory usage.
4. **Regularly Update Your Database**: Update embeddings as your data evolves to maintain relevance.

FAQ