The Smarter Vector Database for AI


### User's Query
query = 'Summarize the Q1 2024 AAA Earnings Statement'

### Create the dense query vector
dense_query = [embedding_model.encode(query).tolist()]

### Create the sparse query vector
sparse_query = [dict(Counter(y)) for y in token([query], 

### Hybrid search
Python (
		      ("=", "sym", "AAA"),
                      (">", "price", "100.00"),

### Time Series Window Query Vector
q = AAA_df['price'][100].tolist()

### Search for Similar Time Series Windows[q], n=10)

### Outlier Search for Anomaly Detection[q], n=-10)[0]

### Multi Search over Multiple Temporal Windows Simultaneously[q, q1, q2], n=10)

Why Build With KDB.AI

Multimodal RAG

Ability to handle GenAI complexities for modeling unstructured data such as text, video, audio, and images.

Mixed Search

Hybrid, semantic, keyword and temporal search align with the market’s move towards scalable and fast rendering of vectors.

Behavioral Analytics

Spot trends, patterns, and anomalies in your time-oriented data, covering sensors, market-related insights, and more.  

Zero Embedding

Perform search 17x faster with 12x less memory than HNSW without the need for embeddings for fast changing temporal data.

Killer Compression

Reduce memory and on-disk storage by 100x for slow changing time-based data sets and accelerate search by 10x.

Dynamic Hybrid Search

Combines similarity, exact, and literal search in a single query where query results remain relevant with content changes.

YouTube Channel

Learn from our AI Experts with helpful videos on vector databases.

GitHub Repository

Get hands on with our code repositories and try out sample projects.

Vector Wireframe Brain

New to vector databases? Master the basics and explore key use cases like semantic search, recommendation systems, and anomaly detection. 

Compress and search over massive, fast-flowing time series datasets.

Combine semantic and keyword vector searches for increased search relevance.

Filter vectors based on unlimited metadata to refine search accuracy with structured data.

Understand how to optimize your retrieval of vectors in a variety of scenarios.

Combine vector retrieval with LLM orchestration to power contextual experiences.

Chunk documents and inject relevant data into LLMs to reduce hallucination.