# Vector Search ## Vector Search in ScyllaDB Vector Search is a powerful method for efficiently searching and retrieving high-dimensional data based on similarity rather than exact matches. It is particularly useful in AI and machine learning applications, where data is often represented as vectors — mathematical representations of objects such as text, images, audio, or video. In these applications, you typically need to retrieve data that is similar to a given query, rather than relying on keyword-based search or exact matches. ScyllaDB’s Vector Search feature allows you to store, index, and query high-dimensional vector data at scale. Vector Search is built to work within your existing ScyllaDB infrastructure, taking advantage of its high-performance and highly available architecture. ### Common Use Cases * **Semantic search** — Find documents or passages that match the *meaning* of a query, not just the keywords. * **Retrieval-Augmented Generation (RAG)** — Provide relevant context to an LLM by retrieving similar documents from a vector store. * **Recommendation systems** — Find items similar to those a user has interacted with. * **Image and audio search** — Find visually or acoustically similar media. * **Anomaly detection** — Identify outliers far from all clusters in vector space. * **Deduplication** — Find near-duplicate records by identifying vectors that are very close together. See [Common Use Cases](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-concepts.md#vs-concepts-use-cases) in the Concepts page for more details. ## Getting Started * [Quick Start Guide](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-quick-start.md)
Walk through setting up a vector-enabled table, inserting data, and running your first similarity search in minutes.
## Understanding Vector Search * [Vector Search Concepts](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-concepts.md)
Architecture overview, HNSW algorithm, CDC-based indexing, and data flow between storage and vector search nodes.
* [Glossary](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-glossary.md)
Definitions of key terms including ANN, HNSW, embeddings, similarity functions, quantization, filtering, and more.
## Deployment and Operations * [Vector Search Deployments](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-clusters.md)
Create, enable, resize, disable, and monitor Vector Search clusters in ScyllaDB Cloud via the UI or API.
* [Sizing and Capacity Planning](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-sizing.md)
Estimate memory requirements, understand quantization impact, and choose instance types for your workload.
* [Security](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-security.md)
Authentication, authorization, service-level isolation, and network security for vector search.
## Working with Vectors * [Working with Vector Search](https://cloud.docs.scylladb.com/stable/vector-search/work-with-vector-search.md)
CQL usage guide covering the vector data type, vector indexes, similarity functions, index tuning, ANN queries, and driver integration.
* [Vector Search with Alternator](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-alternator.md)
Use Vector Search through the DynamoDB-compatible Alternator API, with Python examples and a feature-availability comparison with CQL.
* [Filtering Vector Search Results](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-filtering.md)
Combine similarity search with metadata constraints using global and local vector indexes.
* [Quantization and Rescoring](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-quantization.md)
Reduce index memory usage with quantization (f16, i8, b1) and recover precision with oversampling and rescoring.
* [LangChain and CassIO Compatibility](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-langchain.md)
Use the LangChain Cassandra connector (CassIO) with ScyllaDB through Storage Attached Index (SAI) compatibility, including requirements, limitations, and a runnable RAG example.
## Troubleshooting and Reference * [Troubleshooting](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-troubleshooting.md)
Common issues and solutions for index creation, query results, data insertion, performance, and connectivity.
* [FAQ](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-faq.md)
Frequently asked questions about similarity functions, dimensions, latency, filtering, quantization, and more.
* [Reference](https://cloud.docs.scylladb.com/stable/vector-search/reference-vector-search.md)
Technical reference for instance types, CQL syntax, index options, and Cloud API endpoints.
## Examples * [Example Applications](https://vector-search.scylladb.com/)
Learn how to use ScyllaDB Vector Search to build RAG applications, semantic caching layers, and how it integrates with popular LLM libraries like LlamaIndex and LangChain.
* [LangChain and CassIO Compatibility](https://cloud.docs.scylladb.com/stable/vector-search/vector-search-langchain.md)
Run the LangChain Cassandra connector against ScyllaDB, with a complete Retrieval-Augmented Generation (RAG) example.