Vector Databases for AI/ML Infrastructure
Master the specialized databases powering AI applications, from semantic search to recommendation systems. Learn how to build, scale, and optimize vector databases for production AI workloads.
📚 Essential Resources
📖 Must-Read Papers & Articles
- Faiss: A Library for Efficient Similarity Search - Facebook Research
- Billion-scale similarity search with GPUs - Johnson et al.
- HNSW: Hierarchical Navigable Small World - Malkov & Yashunin
- Product Quantization - Jegou et al.
- Vector Database Benchmarks - ANN comparison
🎥 Video Resources
- Vector Databases Explained - Fireship
- Building RAG Applications - DeepLearning.AI
- Pinecone Vector Database Course - James Briggs
- Embeddings Workshop - Hugging Face
- Vector Search at Scale - Weaviate
🎓 Courses & Training
- Vector Databases & Embeddings - DeepLearning.AI
- LangChain for LLM Apps - Harrison Chase
- Building AI Applications - Coursera
- Semantic Search Course - Pinecone Learning Center
- Vector Database Fundamentals - Milvus tutorials
📰 Blogs & Articles
- Pinecone Blog - Vector DB insights
- Weaviate Blog - Vector search articles
- Qdrant Blog - Technical deep dives
- Milvus Blog - Scalability focus
- Chroma Blog - Embedding database
🔧 Essential Tools & Platforms
- Pinecone - Managed vector database
- Weaviate - Open source vector DB
- Qdrant - High-performance vector search
- Milvus - Scalable vector database
- Chroma - Embedding database
💬 Communities & Forums
- Vector Database Community - Discord
- r/VectorDatabases - Reddit
- Pinecone Community - Forum
- Weaviate Slack - Slack community
- AI Stack Exchange - Q&A
🏆 RAG & LLM Resources
- LangChain Docs - LLM framework
- LlamaIndex Guide - Data framework
- OpenAI Embeddings - API guide
- Semantic Kernel - Microsoft's SDK
- Haystack - NLP framework
Understanding Vector Databases
Why Vector Databases Matter
Traditional Database vs Vector Database:
Traditional DB: WHERE name = 'John' AND age > 25
Vector DB: Find nearest neighbors to [0.2, -0.5, 0.8, ...]
Use Cases:
- Semantic search ("find similar documents")
- Recommendation systems
- Image/video similarity search
- RAG (Retrieval Augmented Generation) for LLMs
- Anomaly detection
- Clustering and classification
Vector Embeddings Fundamentals
# Understanding embeddings
import numpy as np
from sentence_transformers import SentenceTransformer
import torch
class EmbeddingPipeline:
def __init__(self, model_name='all-MiniLM-L6-v2'):
self.model = SentenceTransformer(model_name)
self.dimension = self.model.get_sentence_embedding_dimension()
def encode_text(self, texts):
"""Convert text to vector embeddings"""
# Batch encoding for efficiency
embeddings = self.model.encode(
texts,
batch_size=32,
show_progress_bar=True,
convert_to_numpy=True,
normalize_embeddings=True # L2 normalization
)
return embeddings
def encode_multimodal(self, data):
"""Encode different data types"""
if data['type'] == 'text':
return self.encode_text([data['content']])[0]
elif data['type'] == 'image':
# Use CLIP or similar model
return self.encode_image(data['content'])
elif data['type'] == 'audio':
# Use audio embedding model
return self.encode_audio(data['content'])
def compute_similarity(self, vec1, vec2):
"""Compute cosine similarity"""
return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
Production Vector Database Implementations
Pinecone
# Pinecone production implementation
import pinecone
from typing import List, Dict, Optional
import asyncio
from tenacity import retry, stop_after_attempt, wait_exponential
class PineconeVectorStore:
def __init__(self, api_key: str, environment: str):
pinecone.init(api_key=api_key, environment=environment)
self.index_name = None
self.index = None
def create_index(self, name: str, dimension: int, metric: str = 'cosine'):
"""Create a new Pinecone index with optimal settings"""
if name not in pinecone.list_indexes():
pinecone.create_index(
name=name,
dimension=dimension,
metric=metric,
pod_type='p2.x1', # Performance-optimized
replicas=2, # High availability
shards=1,
metadata_config={
'indexed': ['category', 'timestamp', 'user_id']
}
)
self.index_name = name
self.index = pinecone.Index(name)
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
async def upsert_batch(self, vectors: List[Dict]):
"""Batch upsert with retry logic"""
batch_size = 100 # Pinecone recommended batch size
for i in range(0, len(vectors), batch_size):
batch = vectors[i:i + batch_size]
# Format for Pinecone
upsert_data = [
(
vec['id'],
vec['embedding'],
vec.get('metadata', {})
)
for vec in batch
]
self.index.upsert(vectors=upsert_data)
async def hybrid_search(self,
query_vector: List[float],
filter_dict: Optional[Dict] = None,
top_k: int = 10,
include_metadata: bool = True):
"""Hybrid search with metadata filtering"""
results = self.index.query(
vector=query_vector,
top_k=top_k,
include_metadata=include_metadata,
filter=filter_dict, # e.g., {"category": "documentation"}
)
return self._process_results(results)
def _process_results(self, results):
"""Process and enrich search results"""
processed = []
for match in results['matches']:
processed.append({
'id': match['id'],
'score': match['score'],
'metadata': match.get('metadata', {}),
'distance': 1 - match['score'] # For cosine similarity
})
return processed
Weaviate
# Weaviate with GraphQL and modules
import weaviate
from weaviate.embedded import EmbeddedOptions
import json
class WeaviateVectorStore:
def __init__(self, url: str = None):
if url:
self.client = weaviate.Client(url)
else:
# Embedded Weaviate for development
self.client = weaviate.Client(
embedded_options=EmbeddedOptions()
)
def create_schema(self):
"""Create schema with vectorizer and modules"""
schema = {
"classes": [{
"class": "Document",
"description": "A document with semantic search",
"vectorizer": "text2vec-transformers",
"moduleConfig": {
"text2vec-transformers": {
"model": "sentence-transformers/all-MiniLM-L6-v2",
"options": {
"waitForModel": True,
"useCache": True,
"useGPU": True
}
},
"qna-transformers": {
"model": "deepset/bert-base-cased-squad2"
}
},
"properties": [
{
"name": "content",
"dataType": ["text"],
"description": "Document content",
"moduleConfig": {
"text2vec-transformers": {
"skip": False,
"vectorizePropertyName": False
}
}
},
{
"name": "title",
"dataType": ["string"],
"description": "Document title"
},
{
"name": "category",
"dataType": ["string"],
"description": "Document category",
"moduleConfig": {
"text2vec-transformers": {
"skip": True
}
}
},
{
"name": "timestamp",
"dataType": ["date"],
"description": "Creation timestamp"
}
],
"invertedIndexConfig": {
"bm25": {
"b": 0.75,
"k1": 1.2
},
"cleanupIntervalSeconds": 60,
"stopwords": {
"preset": "en"
}
}
}]
}
self.client.schema.create(schema)
def semantic_search_with_qa(self, query: str, limit: int = 5):
"""Semantic search with Q&A capability"""
# Semantic search
search_results = (
self.client.query
.get("Document", ["content", "title", "_additional {distance}"])
.with_near_text({"concepts": [query]})
.with_limit(limit)
.do()
)
# Q&A on results
qa_results = (
self.client.query
.get("Document", ["content", "title"])
.with_ask({
"question": query,
"properties": ["content"]
})
.with_limit(1)
.do()
)
return {
'semantic_results': search_results,
'answer': qa_results
}
Qdrant
# Qdrant with advanced features
from qdrant_client import QdrantClient
from qdrant_client.models import (
Distance, VectorParams, PointStruct,
Filter, FieldCondition, MatchValue,
SearchRequest, SearchParams
)
class QdrantVectorStore:
def __init__(self, host="localhost", port=6333):
self.client = QdrantClient(host=host, port=port)
def create_collection(self, name: str, vector_size: int):
"""Create collection with custom configuration"""
self.client.recreate_collection(
collection_name=name,
vectors_config=VectorParams(
size=vector_size,
distance=Distance.COSINE,
on_disk=True # For large collections
),
optimizers_config={
"indexing_threshold": 20000,
"memmap_threshold": 50000,
"default_segment_number": 5
},
hnsw_config={
"m": 16,
"ef_construct": 100,
"full_scan_threshold": 10000
}
)
async def insert_with_payloads(self, collection: str, documents: List[Dict]):
"""Insert vectors with rich metadata"""
points = []
for idx, doc in enumerate(documents):
point = PointStruct(
id=doc.get('id', idx),
vector=doc['embedding'],
payload={
"text": doc['text'],
"metadata": doc.get('metadata', {}),
"timestamp": doc.get('timestamp', datetime.utcnow()),
"tags": doc.get('tags', [])
}
)
points.append(point)
# Batch upload with progress
batch_size = 1000
for i in range(0, len(points), batch_size):
batch = points[i:i + batch_size]
self.client.upsert(
collection_name=collection,
points=batch,
wait=True
)
def filtered_search(self,
collection: str,
query_vector: List[float],
filters: Dict,
limit: int = 10):
"""Advanced filtered search"""
# Build filter conditions
must_conditions = []
if 'category' in filters:
must_conditions.append(
FieldCondition(
key="metadata.category",
match=MatchValue(value=filters['category'])
)
)
if 'date_range' in filters:
must_conditions.append(
FieldCondition(
key="timestamp",
range={
"gte": filters['date_range']['start'],
"lte": filters['date_range']['end']
}
)
)
search_result = self.client.search(
collection_name=collection,
query_vector=query_vector,
limit=limit,
query_filter=Filter(must=must_conditions) if must_conditions else None,
search_params=SearchParams(
hnsw_ef=128,
exact=False
)
)
return search_result
Milvus
# Milvus for large-scale deployments
from pymilvus import (
connections, Collection, CollectionSchema,
FieldSchema, DataType, utility
)
class MilvusVectorStore:
def __init__(self, host='localhost', port='19530'):
connections.connect(host=host, port=port)
def create_collection(self, name: str, dim: int):
"""Create collection with optimized settings"""
fields = [
FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=dim),
FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
FieldSchema(name="metadata", dtype=DataType.JSON)
]
schema = CollectionSchema(
fields=fields,
description="Document embeddings with metadata"
)
collection = Collection(
name=name,
schema=schema,
using='default',
shards_num=2 # Distributed storage
)
# Create indexes for performance
index_params = {
"metric_type": "IP", # Inner product
"index_type": "IVF_FLAT",
"params": {"nlist": 2048}
}
collection.create_index(
field_name="embedding",
index_params=index_params
)
# Load collection to memory
collection.load()
return collection
def hybrid_search(self, collection_name: str, query: Dict):
"""Hybrid vector + keyword search"""
collection = Collection(collection_name)
# Vector search
vector_results = collection.search(
data=[query['vector']],
anns_field="embedding",
param={"metric_type": "IP", "params": {"nprobe": 64}},
limit=query.get('limit', 10),
expr=query.get('filter'), # e.g., "metadata['category'] == 'tech'"
output_fields=["text", "metadata"]
)
# Combine with keyword search if needed
if 'keywords' in query:
keyword_expr = " || ".join([
f"text like '%{keyword}%'"
for keyword in query['keywords']
])
keyword_results = collection.query(
expr=keyword_expr,
limit=query.get('limit', 10),
output_fields=["id", "text", "metadata"]
)
# Merge and rank results
return self._merge_results(vector_results, keyword_results)
return vector_results
Vector Database Optimization
Indexing Strategies
# Advanced indexing for performance
class VectorIndexOptimizer:
def __init__(self):
self.index_types = {
'flat': self.create_flat_index,
'hnsw': self.create_hnsw_index,
'ivf': self.create_ivf_index,
'scann': self.create_scann_index
}
def select_index_type(self, num_vectors: int, dimensions: int,
accuracy_requirement: float = 0.95):
"""Select optimal index type based on dataset"""
if num_vectors < 10000:
return 'flat' # Exact search for small datasets
elif num_vectors < 1000000 and accuracy_requirement > 0.9:
return 'hnsw' # High accuracy, moderate scale
elif num_vectors < 10000000:
return 'ivf' # Good balance
else:
return 'scann' # Large scale
def create_hnsw_index(self, vectors, config=None):
"""Hierarchical Navigable Small World index"""
import hnswlib
config = config or {
'M': 48, # Number of connections
'ef_construction': 200, # Build time accuracy
'ef': 100, # Query time accuracy
'seed': 42
}
dim = vectors.shape[1]
num_elements = vectors.shape[0]
# Initialize index
index = hnswlib.Index(space='l2', dim=dim)
index.init_index(
max_elements=num_elements,
ef_construction=config['ef_construction'],
M=config['M'],
random_seed=config['seed']
)
# Add vectors with progress tracking
index.add_items(vectors, np.arange(num_elements))
# Set query time parameters
index.set_ef(config['ef'])
return index
def optimize_for_production(self, index, expected_qps: int):
"""Optimize index for production workload"""
optimizations = {
'cache_size': self._calculate_cache_size(expected_qps),
'num_threads': self._calculate_thread_count(expected_qps),
'batch_size': self._calculate_batch_size(expected_qps),
'prefetch': expected_qps > 1000
}
return optimizations
Sharding and Distribution
# Distributed vector database implementation
class DistributedVectorStore:
def __init__(self, nodes: List[str], replication_factor: int = 2):
self.nodes = nodes
self.replication_factor = replication_factor
self.consistent_hash = ConsistentHash(nodes)
self.shards = self._initialize_shards()
def _initialize_shards(self):
"""Initialize sharded vector stores"""
shards = {}
for node in self.nodes:
shards[node] = self._create_node_client(node)
return shards
async def distributed_insert(self, vector_id: str, vector: np.ndarray,
metadata: Dict):
"""Insert with replication across shards"""
# Determine primary and replica nodes
primary_node = self.consistent_hash.get_node(vector_id)
replica_nodes = self.consistent_hash.get_replicas(
vector_id,
self.replication_factor - 1
)
# Async insert to all nodes
tasks = []
for node in [primary_node] + replica_nodes:
task = self.shards[node].insert(vector_id, vector, metadata)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
# Verify successful replication
successful = sum(1 for r in results if not isinstance(r, Exception))
if successful < self.replication_factor // 2 + 1:
raise ReplicationError(f"Only {successful} replicas succeeded")
return results
async def distributed_search(self, query_vector: np.ndarray,
top_k: int = 10):
"""Distributed search across all shards"""
# Fan out search to all nodes
tasks = []
for node, client in self.shards.items():
task = client.search(query_vector, top_k * 2) # Over-fetch
tasks.append((node, task))
# Gather results
all_results = []
for node, task in tasks:
try:
results = await task
all_results.extend([
{**r, 'node': node} for r in results
])
except Exception as e:
print(f"Search failed on node {node}: {e}")
# Merge and rank results
all_results.sort(key=lambda x: x['score'], reverse=True)
return all_results[:top_k]
RAG (Retrieval Augmented Generation) Implementation
Production RAG Pipeline
# RAG system for LLM applications
class RAGPipeline:
def __init__(self, vector_store, llm_client, embedding_model):
self.vector_store = vector_store
self.llm = llm_client
self.embedder = embedding_model
self.reranker = CrossEncoderReranker()
async def process_query(self, query: str, context_config: Dict = None):
"""End-to-end RAG pipeline"""
config = context_config or {
'num_candidates': 20,
'num_contexts': 5,
'max_tokens': 2000,
'temperature': 0.7
}
# 1. Query understanding and expansion
expanded_queries = await self.expand_query(query)
# 2. Retrieve candidates
candidates = await self.retrieve_candidates(
expanded_queries,
config['num_candidates']
)
# 3. Rerank candidates
reranked = await self.reranker.rerank(
query,
candidates,
top_k=config['num_contexts']
)
# 4. Build context
context = self.build_context(reranked, config['max_tokens'])
# 5. Generate response
response = await self.generate_response(
query,
context,
config['temperature']
)
# 6. Add citations
response_with_citations = self.add_citations(response, reranked)
return {
'answer': response_with_citations,
'sources': reranked,
'confidence': self.calculate_confidence(reranked)
}
async def expand_query(self, query: str):
"""Query expansion for better retrieval"""
# Use LLM to generate alternative phrasings
prompt = f"""Generate 3 alternative ways to ask this question:
Original: {query}
Alternatives:
1."""
expansions = await self.llm.generate(prompt, max_tokens=100)
# Also use semantic expansion
query_embedding = self.embedder.encode(query)
return {
'original': query,
'llm_expansions': self.parse_expansions(expansions),
'embedding': query_embedding
}
def build_context(self, documents: List[Dict], max_tokens: int):
"""Build optimized context for LLM"""
context_parts = []
total_tokens = 0
for doc in documents:
# Estimate tokens (rough approximation)
doc_tokens = len(doc['text'].split()) * 1.3
if total_tokens + doc_tokens > max_tokens:
# Truncate if necessary
remaining_tokens = max_tokens - total_tokens
truncated_text = self.truncate_to_tokens(
doc['text'],
int(remaining_tokens / 1.3)
)
context_parts.append(truncated_text)
break
context_parts.append(doc['text'])
total_tokens += doc_tokens
return "\n\n---\n\n".join(context_parts)
Hybrid Search Implementation
# Combining vector and keyword search
class HybridSearchEngine:
def __init__(self, vector_store, text_search_engine):
self.vector_store = vector_store
self.text_search = text_search_engine
self.fusion_weights = {'vector': 0.7, 'keyword': 0.3}
async def hybrid_search(self, query: str, filters: Dict = None):
"""Combine vector and keyword search results"""
# Parallel search
vector_task = self.vector_search(query, filters)
keyword_task = self.keyword_search(query, filters)
vector_results, keyword_results = await asyncio.gather(
vector_task, keyword_task
)
# Reciprocal Rank Fusion
fused_results = self.reciprocal_rank_fusion(
[vector_results, keyword_results],
weights=[self.fusion_weights['vector'],
self.fusion_weights['keyword']]
)
return fused_results
def reciprocal_rank_fusion(self, result_lists: List[List],
weights: List[float], k: int = 60):
"""RRF algorithm for result fusion"""
scores = {}
for results, weight in zip(result_lists, weights):
for rank, result in enumerate(results):
doc_id = result['id']
if doc_id not in scores:
scores[doc_id] = 0
# RRF score
scores[doc_id] += weight * (1 / (k + rank + 1))
# Sort by fused score
sorted_docs = sorted(
scores.items(),
key=lambda x: x[1],
reverse=True
)
# Retrieve full documents
return self.retrieve_documents([doc_id for doc_id, _ in sorted_docs])
Performance and Scalability
Benchmarking Vector Databases
# Comprehensive benchmarking suite
class VectorDBBenchmark:
def __init__(self):
self.metrics = {
'insert_throughput': [],
'query_latency': [],
'query_throughput': [],
'recall': [],
'memory_usage': []
}
async def benchmark_database(self, db_client, dataset, queries):
"""Run comprehensive benchmark"""
print(f"Benchmarking {db_client.__class__.__name__}")
# 1. Insertion benchmark
insert_metrics = await self.benchmark_insertion(db_client, dataset)
# 2. Query benchmark
query_metrics = await self.benchmark_queries(db_client, queries)
# 3. Recall benchmark
recall_metrics = await self.benchmark_recall(db_client, queries)
# 4. Resource usage
resource_metrics = self.measure_resources(db_client)
return {
'insertion': insert_metrics,
'query': query_metrics,
'recall': recall_metrics,
'resources': resource_metrics
}
async def benchmark_insertion(self, db_client, dataset):
"""Measure insertion performance"""
batch_sizes = [100, 1000, 10000]
results = {}
for batch_size in batch_sizes:
start_time = time.time()
for i in range(0, len(dataset), batch_size):
batch = dataset[i:i + batch_size]
await db_client.insert_batch(batch)
elapsed = time.time() - start_time
throughput = len(dataset) / elapsed
results[f'batch_{batch_size}'] = {
'throughput': throughput,
'latency_per_item': elapsed / len(dataset)
}
return results
Caching Strategies
# Multi-level caching for vector search
class VectorSearchCache:
def __init__(self, vector_store, cache_size_mb=1024):
self.vector_store = vector_store
self.cache_size = cache_size_mb * 1024 * 1024
self.query_cache = LRUCache(maxsize=10000)
self.vector_cache = {}
self.similarity_cache = SimilarityCache()
async def cached_search(self, query_vector, top_k=10):
"""Multi-level cached search"""
# Level 1: Exact query cache
cache_key = self._hash_vector(query_vector)
if cache_key in self.query_cache:
return self.query_cache[cache_key]
# Level 2: Similar query cache
similar_results = self.similarity_cache.find_similar(
query_vector,
threshold=0.95
)
if similar_results:
return similar_results
# Level 3: Perform actual search
results = await self.vector_store.search(query_vector, top_k * 2)
# Update caches
self.query_cache[cache_key] = results[:top_k]
self.similarity_cache.add(query_vector, results)
return results[:top_k]
def _hash_vector(self, vector):
"""Create hash key for vector"""
# Quantize vector for better cache hit rate
quantized = np.round(vector * 1000).astype(int)
return hash(quantized.tobytes())
Production Deployment
Kubernetes Deployment
# Vector database on Kubernetes
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: vector-db-cluster
spec:
serviceName: vector-db
replicas: 3
selector:
matchLabels:
app: vector-db
template:
metadata:
labels:
app: vector-db
spec:
containers:
- name: vector-db
image: qdrant/qdrant:latest
ports:
- containerPort: 6333
name: api
- containerPort: 6334
name: grpc
env:
- name: QDRANT__CLUSTER__ENABLED
value: "true"
- name: QDRANT__CLUSTER__P2P__PORT
value: "6335"
volumeMounts:
- name: storage
mountPath: /qdrant/storage
resources:
requests:
memory: "4Gi"
cpu: "2"
limits:
memory: "8Gi"
cpu: "4"
livenessProbe:
httpGet:
path: /health
port: 6333
initialDelaySeconds: 30
periodSeconds: 10
volumeClaimTemplates:
- metadata:
name: storage
spec:
accessModes: ["ReadWriteOnce"]
storageClassName: fast-ssd
resources:
requests:
storage: 100Gi
---
apiVersion: v1
kind: Service
metadata:
name: vector-db
spec:
clusterIP: None
selector:
app: vector-db
ports:
- name: api
port: 6333
- name: grpc
port: 6334
Monitoring and Observability
# Vector database monitoring
class VectorDBMonitoring:
def __init__(self):
self.prometheus = PrometheusClient()
self.metrics = {
'search_latency': Histogram(
'vector_search_duration_seconds',
'Vector search latency'
),
'index_size': Gauge(
'vector_index_size_bytes',
'Size of vector index'
),
'query_recall': Gauge(
'vector_search_recall',
'Search recall metric'
),
'insertion_rate': Counter(
'vector_insertions_total',
'Total vector insertions'
)
}
def record_search(self, duration, recall_score):
"""Record search metrics"""
self.metrics['search_latency'].observe(duration)
self.metrics['query_recall'].set(recall_score)
def create_dashboard_config(self):
"""Grafana dashboard configuration"""
return {
"dashboard": {
"title": "Vector Database Metrics",
"panels": [
{
"title": "Search Latency",
"targets": [{
"expr": "histogram_quantile(0.95, vector_search_duration_seconds_bucket)"
}]
},
{
"title": "Insertion Rate",
"targets": [{
"expr": "rate(vector_insertions_total[5m])"
}]
},
{
"title": "Recall Score",
"targets": [{
"expr": "vector_search_recall"
}]
}
]
}
}
Interview Questions
System Design
- Design a distributed vector database for 1 billion embeddings
- Build a real-time recommendation system using vectors
- Create a semantic search engine for code
- Design a multi-modal search system (text + images)
Implementation
- Implement approximate nearest neighbor search
- Build a vector database shard rebalancer
- Create a query optimizer for hybrid search
- Implement vector quantization for compression
Optimization
- How to handle hot partitions in vector databases?
- Optimize vector search for mobile devices
- Reduce memory usage for large-scale deployment
- Implement progressive vector indexing
Essential Resources
Documentation
Papers
- 📄 Faiss: A Library for Efficient Similarity Search
- 📄 Billion-scale similarity search with GPUs
- 📄 HNSW: Hierarchical Navigable Small World
Tools
- 🔧 VectorDBBench - Benchmarking
- 🔧 ANN Benchmarks - Algorithm comparison
- 🔧 Faiss - Vector similarity library
Courses
Remember: Vector databases are the backbone of modern AI applications. Understanding their internals, optimization techniques, and deployment patterns is crucial for building production AI systems.