Lecture Notes:
1. Concepts
What is a Vector Database?
- A vector database is designed to store, manage, and query high-dimensional vector embeddings efficiently.
- It enables similarity search and nearest neighbor queries, critical for working with embeddings generated by models like Ollama.
Key Features of Vector Databases:
- High-Dimensional Indexing: Stores embeddings (vectors) and enables fast searches.
- Similarity Search: Finds vectors closest to a given query vector using distance metrics like cosine similarity or Euclidean distance.
- Scalability: Handles large-scale data efficiently.
- Integration: Can work with other data structures (e.g., JSON metadata) for richer querying.
2. Key Aspects
Why Use a Vector Database?
- Efficiency: Optimized for querying large-scale vector data.
- Accuracy: Provides precise similarity results using advanced indexing algorithms like HNSW (Hierarchical Navigable Small World).
- Real-World Use Cases: Image retrieval, semantic search, recommendation systems, chatbots.
Common Vector Databases:
- Pinecone
- Weaviate
- Qdrant
- Milvus
Querying Techniques:
- K-Nearest Neighbors (KNN): Finds top K vectors closest to the query vector.
- Hybrid Search: Combines vector similarity with traditional keyword-based searches.
3. Implementation
Setting Up a Vector Database
- Install and configure the database. Most vector databases provide cloud-hosted and local setups.
- Store embeddings: Use the embeddings generated by
ollama embed. - Query embeddings: Perform similarity searches to find relevant results.
4. CLI Commands for Working with Vector Databases
| Command | Description | Example |
|---|---|---|
ollama embed |
Generate embeddings for text or documents. | ollama embed "example text" --format json |
ollama run |
Use embeddings as part of a model query. | ollama run mymodel --format json |
5. Real-Life Example
Scenario: Build a Semantic Search Engine with a Vector Database
Suppose we want to search through a collection of customer reviews to find those most relevant to a user query. The embeddings of the reviews will be stored in a vector database and queried for similarity.
6. Code Examples
Step 1: Install Qdrant Vector Database
Qdrant is an easy-to-use vector database with local and cloud options.
# Install Qdrant locally via Docker
docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant
Step 2: Store Embeddings in Qdrant
import qdrant_client
from qdrant_client.models import PointStruct
import json
# Initialize Qdrant client
client = qdrant_client.QdrantClient(url="http://localhost:6333")
# Create a collection for embeddings
client.recreate_collection(
collection_name="customer_reviews",
vector_size=512, # Dimension of embeddings
distance="Cosine"
)
# Load embedding generated by Ollama
with open("review1_embedding.json", "r") as file:
review1 = json.load(file)
with open("review2_embedding.json", "r") as file:
review2 = json.load(file)
# Insert embeddings into the database
points = [
PointStruct(id=1, vector=review1["embedding"], payload={"text": review1["text"]}),
PointStruct(id=2, vector=review2["embedding"], payload={"text": review2["text"]}),
]
client.upsert(collection_name="customer_reviews", points=points)
Step 3: Query the Vector Database
# Simulate a user query
query = "What do customers say about product quality?"
# Generate query embedding (replace with actual embedding generated by Ollama)
query_embedding = [0.12, 0.34, ...] # Placeholder example
# Perform similarity search
results = client.search(
collection_name="customer_reviews",
query_vector=query_embedding,
limit=2 # Retrieve top 2 matches
)
# Display results
for result in results:
print(f"Score: {result.score}")
print(f"Review: {result.payload['text']}")
7. Summary
- Concepts Covered: What vector databases are, why they're useful, and how they enable efficient similarity searches.
- Key Aspects: High-dimensional indexing, similarity measures, and practical use cases.
- CLI Commands: Use
ollama embedto generate embeddings for storing in the database. - Real-Life Example: Semantic search through customer reviews using Qdrant.
- Code Examples: Storing and querying embeddings in Qdrant.
8. Homework/Practice
- Install a vector database of your choice (e.g., Qdrant, Milvus).
- Generate embeddings for five text samples using
ollama embed. - Store these embeddings in the database.
- Implement a Python script to perform similarity searches and rank the results.
- Experiment with different distance metrics (e.g., Euclidean vs. Cosine).
This lecture introduces students to working with vector databases and includes a practical example with Qdrant, a widely-used vector database.
