Pinecone
Vector databasepinecone
Managed serverless vector database with namespaces, metadata filtering and hybrid dense-sparse search.
- Provider
- default
- Kind
- Vector database
- Category
- Data
| property | type | values | required | kind default | this component |
|---|---|---|---|---|---|
| deletionProtection | boolean | — | no | false | inherited |
| deploymentMode | enum | serverless | pod-based | no | serverless | inherited |
| dimensions | integer | — | no | — | inherited |
| distanceMetric | enum | cosine | euclidean | dot-product | no | — | inherited |
| hybridSearch | boolean | — | no | false | ▸ true |
| indexType | enum | flat | hnsw | ivf | disk-ann | lsh | no | — | inherited |
| managed | boolean | — | no | false | ▸ true |
| maxDimensions | integer | — | no | 20000 | inherited |
| metadataFiltering | boolean | — | no | false | ▸ true |
| namespaces | boolean | — | no | false | ▸ true |
| quantization | enum | none | scalar | product | binary | no | none | inherited |
| replicas | integer | — | no | — | inherited |
Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.
No rule reads this kind yet. That is a gap, not a guarantee.
nodes:
- { id: "pinecone", component: "pinecone" }Alibaba Cloud Milvusalibaba-milvusAmazon S3 Vectorsaws-s3-vectorsAgent Memorycloudflare-agent-memoryCloudflare Vectorizecloudflare-vectorizeChromachromaDeep LakedeeplakeEpsillaepsillaKDB.AIkdb-aiLanceDBlancedbMarqomarqoMilvusmilvusMyScalemyscalepgvectorpgvectorpgvectorscalepgvectorscaleQdrantqdrantturbopufferturbopufferValdvaldGeneric vector databasevector-databaseVespa Cloudvespa-cloudWeaviateweaviateZilliz Cloudzilliz-cloudDigitalOcean Vector Databasesdigitalocean-vector-databaseVertex AI Vector Searchgoogle-vertex-ai-vector-search
# yaml-language-server: $schema=../../schema/component.schema.json # Pinecone: fully managed serverless vector database. "Indexing overview": records are partitioned # into namespaces created automatically on upsert, metadata filtering is built in and dense, sparse # and BM25 search can be combined in one index. "Create an index": serverless indexes, dimensions 1-20000. schemaVersion: "1" type: "component" id: "pinecone" kind: "vector-database" provider: "default" docs: "https://docs.pinecone.io/guides/index-data/indexing-overview" defaults: metadataFiltering: true hybridSearch: true namespaces: true managed: true properties: deploymentMode: type: "enum" values: ["serverless", "pod-based"] default: "serverless" maxDimensions: type: "integer" default: 20000 deletionProtection: type: "boolean" default: false