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Vespa Documentation | Vespa: The AI search platform Vespa basics Deploy an application Vespa applications Schemas Writing Querying Ranking Operations What's more Learn more Vespa overview Getting help from LLMs Features Tutorials and use cases Frequently asked questions Glossary Releases Tenants, apps and instances Migrating to Vespa Cloud Migrating from ElasticSearch About this documentation Contributing to Vespa Applications and components Developer guide IDE support Deployment .vespaignore files Containers Components Searchers Document processors Request handlers Result renderers Dependency injection Configuring components Chaining Inspecting structured data in a Searcher Developing web services Unit testing System testing The config system Request-response processing Bundles Using ZooKeeper Http servers and filters Using pluggable frameworks Java config API Schemas and documents Documents Inheritance in schemas Concrete document types Parent-child relationships Structs Predicate fields Exposing schema information Reading and writing Reads and writes /document/v1 Indexing Index bootstrap Visiting Document API Partial updates Batch delete Feed block Document routing Indexing paged vectors Querying The query api The YQL query language Grouping and aggregation Federation Query profiles An intro to vector search Nearest neighbor search Approximate nearest neighbor search Nearest neighbor search guide Text matching Searching multivalue fields Geo search Document summaries Result diversity Page templates Ranking and inference Ranking introduction Ranking expressions and features Multivalue query operators Tensor user guide Tensor examples Phased ranking Using TensorFlow models Using ONNX models Using XGBoost models Using LightGBM models Wand: Accelerated OR search The BM25 rank feature The nativeRank rank feature Cross-encoder transformer ranking Searcher re-ranking Significance model Stateless model evaluation RAG and embedding RAG in Vespa Working with chunks Embedding Binarizing vectors LLMs in Vespa Using local LLMs Using external LLMs Document enrichment with LLMs Model hub Linguistics and text processing Linguistics Linguistics Overview Default (OpenNLP) linguistics Lucene linguistics Custom linguistics Query rewriting Troubleshooting character encoding Content and elasticity Content clusters Content nodes and states Elasticity Document attributes Consistency Model Distribution algorithm Buckets Performance Performance overview Practical performance guide Serving sizing guide Feed sizing guide Node resources Instance types AWS instance types GCP instance types Azure instance types Topology and resizing Streaming search Benchmarking Benchmarking using Vespa Cloud Memory visualizer Profiling Container tuning Rate-limiting queries Graceful degradation Caches HTTP performance testing HTTP/2 Feature tuning Valgrind Operations Quota Environments Zones Availability Zones Production deployment Deployment variants Automated deployments Autoscaling Enclave: Bring your own cloud Enclave AWS getting started AWS architecture Azure getting started Azure architecture GCP getting started GCP architecture Log archive Operations Reindexing Reindexing on Vespa Cloud Data management and backup Cloning applications and data Monitoring Metrics Telemetry export Notifications Support Login Help Single Sign-On (SSO) Setup Deployment patterns Private endpoints Endpoint routing Access logging Artifact archive Archive guide Archive Guide AWS Archive Guide GCP Deleting applications Self-managed Admin procedures Multinode Systems Files, Processes, Ports, Environment Node Setup Build and install Monitoring Content node recovery Configuration
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