Vector Databases

Vector databases are essential components in Retrieval-Augmented Generation (RAG) systems, enabling efficient storage, retrieval, and querying of high-dimensional embeddings. These embeddings represent text, images, and other data types in a numerical format that preserves semantic relationships, allowing for precise information retrieval in AI-driven applications.

Why Use Vector Databases in RAG?

1. Efficient Semantic Search: Vector databases facilitate fast and scalable similarity searches.

2. Handling Large-Scale Data: They store millions of vector embeddings efficiently.

3. Real-Time Retrieval: Enables rapid querying, improving response times in AI applications.

4. Integration with LLMs: Helps enhance context retrieval in RAG pipelines.

Popular Vector Databases

Several vector databases can be used in RAG systems, including:

• FAISS (Facebook AI Similarity Search): Optimized for large-scale similarity searches.

• Pinecone: Fully managed vector database with real-time indexing.

• Weaviate: Offers built-in semantic search and hybrid search capabilities.

• ChromaDB: Lightweight, open-source vector database designed for AI applications.

• Milvus: Scalable and designed for handling billions of vectors efficiently.

Implementing a Vector Database in RAG

To demonstrate how vector databases integrate with RAG, let's implement a simple example using FAISS and Pinecone with OpenAI embeddings.

Example 1: Using FAISS for Vector Storage

import faiss
import numpy as np
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(api_key="your-api-key")

# Sample documents
documents = ["AgentScope AI enhances security audits.", "Vector databases optimize RAG pipelines."]

# Generate embeddings using OpenAI
embeddings = [client.embeddings.create(input=doc, model="text-embedding-ada-002").data[0].embedding for doc in documents]
embeddings = np.array(embeddings, dtype=np.float32)

# Create FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)

# Perform a search
query = "How does AgentScope AI improve security?"
query_embedding = client.embeddings.create(input=query, model="text-embedding-ada-002").data[0].embedding
query_embedding = np.array([query_embedding], dtype=np.float32)

D, I = index.search(query_embedding, k=1)  # Retrieve the closest match
print(f"Best match: {documents[I[0][0]]}")

Example 2: Using Pinecone for Scalable Vector Storage

import pinecone
from openai import OpenAI

# Initialize Pinecone
pinecone.init(api_key="your-pinecone-api-key", environment="us-west1-gcp")
index = pinecone.Index("agentscope-ai")

# Initialize OpenAI client
client = OpenAI(api_key="your-api-key")

# Sample documents
documents = ["AI-driven security enhances audits.", "Vector databases store high-dimensional data."]

# Generate and store embeddings
for i, doc in enumerate(documents):
    embedding = client.embeddings.create(input=doc, model="text-embedding-ada-002").data[0].embedding
    index.upsert(vectors=[(str(i), embedding)])

# Query the index
query = "What is the role of AI in audits?"
query_embedding = client.embeddings.create(input=query, model="text-embedding-ada-002").data[0].embedding
result = index.query(queries=[query_embedding], top_k=1, include_metadata=True)

print(f"Best match: {documents[int(result['matches'][0]['id'])]}")

Choosing the Right Vector Database

Feature	FAISS	Pinecone	Weaviate	ChromaDB	Milvus
Scalability	✅	✅	✅	✅	✅
Cloud-Based	❌	✅	✅	✅	✅
Open Source	✅	❌	✅	✅	✅
Real-Time	❌	✅	✅	✅	✅