Use an AI chatbot with website context retrieved from a vector store like LanceDB. LanceDB is lightweight and can be embedded directly into Next.js, with data stored on-prem.
Gen AI C4 L2 A11 Exercise 06 V2 - YouTube
Instructor: Chang She, founder/CEO of LanceDB
Chang She, a co-author of the Pandas Python library
MS MIT EECS
LanceDB is the AI-native Multimodal Lakehouse, the unified foundation to accelerate training dataset development.
How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors
The ultimate multimodal data platform for AI/ML applications.
Chang She, a co-author of the Pandas Python library
MS MIT EECS
LanceDB is the AI-native Multimodal Lakehouse, the unified foundation to accelerate training dataset development.
LanceDB @GitHub
Rust, Apache
The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
ChromaDB: Parquet based
AI Overview:
When choosing between vector databases, the best fit usually depends on your scale and how much infrastructure you want to manage. LanceDB is often the top choice for embedded, disk-based performance, while ChromaDB is favored for its simplicity during early-stage prototyping. [1, 2, 3, 4]
Comparison of Key Vector Databases
| Database [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] | Primary Strength | Architecture | Best Use Case |
|---|---|---|---|
| LanceDB | High-performance, disk-based search | Embedded (Serverless) | Large datasets, multimodal AI, edge apps |
| ChromaDB | Easiest to start for Python/JS | Embedded or Client/Server | Prototyping, small-to-medium RAG apps |
| Pinecone | Fully managed simplicity | Cloud-only | Teams wanting zero infra management |
| Milvus | Massive enterprise scale | Distributed (Clustered) | Billions of vectors, production systems |
| Qdrant | High performance & filtering | Standalone Server | Production RAG needing hybrid search |
| pgvector | Uses existing Postgres infra | SQL Extension | Teams already using PostgreSQL |
Detailed Highlights
- LanceDB: Built on the Lance data format, it uses an "embedded-first" design similar to SQLite. It excels at scanning massive amounts of data without needing a separate server running in the background. It is particularly strong for multimodal data like images and video.
- ChromaDB: Designed specifically for AI workflows, it abstracts away complex indexing (like HNSW) to let you get started in minutes. While traditionally for local development, Chroma Cloud now offers a managed solution for production scaling.
- Pinecone: A popular managed service that handles all infrastructure, scaling, and indexing for you. It is often the default choice for startups that want to move fast without managing database clusters.
- Milvus: The "heavyweight" option. It is a distributed, cloud-native database that can scale to billions of vectors. It is more complex to set up than LanceDB or Chroma but offers robust features for enterprise-grade security and high-throughput production.
- Qdrant: Known for its balance of high speed and advanced filtering capabilities. It is a favorite in the open-source community for being production-ready and easy to shard across nodes. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
Data Format: Parquet vs. Lance
ChromaDB: Parquet based
ChromaDB saves its vectors in the widely used Parquet format that is used for the data lakes at Uber and Netflix. Parquet is a column-oriented data format that is characterised by efficient compression and fast query performance.
LanceDB: Lance format
LanceDB uses the innovative Lance data format, a further development of the Parquet format. Lance offers:
- Faster scans: Data is divided into fragments, allowing for more targeted and efficient querying. Only the necessary fragments are loaded into memory, minimizing I/O overhead
- Multimodal support: Efficiently stores and retrieves unstructured data like images, audio files, and raw text, eliminating the need for separate storage solutions
- Zero-Copy Versioning: Creates new versions of datasets without duplicating unchanged data, significantly reducing storage overhead for iterative updates which means incremental updates without a full rewrite.
LanceDB uses the innovative Lance data format, a further development of the Parquet format. Lance offers:
- Faster scans: Data is divided into fragments, allowing for more targeted and efficient querying. Only the necessary fragments are loaded into memory, minimizing I/O overhead
- Multimodal support: Efficiently stores and retrieves unstructured data like images, audio files, and raw text, eliminating the need for separate storage solutions
- Zero-Copy Versioning: Creates new versions of datasets without duplicating unchanged data, significantly reducing storage overhead for iterative updates which means incremental updates without a full rewrite.
