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Vector Store → Upsert Documents

AI-generated

Summary

Upsert documents into a vector store backend.

Inputs

  • documentsJson (required) — Array of documents: [{
  • vectorBackend — Choose where vectors are persisted between executions
  • vectorDimension — Deterministic embedding dimension used for upsert and search
  • cacheNamespace — Namespace key for local persistent cache
  • qdrantUrl — Qdrant server URL
  • qdrantCollection — Qdrant collection name
  • qdrantApiKey — Optional for local/self-hosted Qdrant
  • pineconeHost — Example: your-index-xxxx.svc.region.pinecone.io
  • pineconeApiKey — Pinecone API key
  • pineconeNamespace — Pinecone namespace

Output shape

An array of paired execution results, each containing a json payload and pairedItem index.

The returned json object varies by vectorBackend setting. All outputs include resource, operation, and backend fields.

Examples

Example 1: Local cache backend

vectorBackend=localCache, documentsJson=[{content:'Hello',metadata:{source:'demo'}}] with default vectorDimension=64 and cacheNamespace='myNamespace' ;nReturns: {json:{resource:'Vector Store',operation:'Upsert Documents',backend:'localCache',namespace:'myNamespace',upsertedCount:1,storedCount:1},pairedItem:{item:0}}

Example 2: Qdrant backend

vectorBackend=qdrant, documentsJson=[{content:'Hello',metadata:{source:'demo'}}] with default qdrantUrl='http://localhost:6333', qdrantCollection='mycollection' ;nReturns: {json:{resource:'Vector Store',operation:'Upsert Documents',backend:'qdrant',collection:'mycollection',upsertedCount:1,status:'ok',operationId:null},pairedItem:{item:0}}

Example 3: Pinecone backend

vectorBackend=pinecone, documentsJson=[{content:'Hello',metadata:{source:'demo'}}] with pineconeHost='my-index-xyz.svc.us-west1.gcp.cloud.pinecone.io', pineconeNamespace='myNamespace' ;nReturns: {json:{resource:'Vector Store',operation:'Upsert Documents',backend:'pinecone',namespace:'myNamespace',upsertedCount:1},pairedItem:{item:0}}

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