Upsert data - LambdaDB Documentation

This page shows you how to upsert documents into a collection.

If the collection contains managed embedding vector fields, send only the configured source text fields. Do not send direct vector values for managed embedding fields in an upsert request.

from lambdadb import LambdaDB

with LambdaDB(
    project_api_key="YOUR_API_KEY",
    base_url="YOUR_BASE_URL",
    project_name="YOUR_PROJECT_NAME",
) as client:
    coll = client.collection("my_collection")
    docs = [
        {"id": "33201222", "url": "https://en.wikipedia.org/wiki/LambdaDB", "title": "LambdaDB", "text": "LambdaDB is an AI-native database ... ", "dense_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], "sparse_vector": {"LambdaDB": 0.83, "is": 0.1, "a": 0.1, "AI": 0.7}},
        {"url": "https://en.wikipedia.org/wiki/Winamp", "title": "Winamp", "text": "Winamp is a media player for Windows, macOS and Android ...", "dense_vector": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0], "sparse_vector": {"0": 0.8, "4": 0.1, "11": 0.1, "63": 0.54}},
    ]
    coll.docs.upsert(docs=docs)

Python: LambdaDB supports context manager usage. __enter__ returns the client, and __exit__ calls client.close() (closing the SDK-owned HTTP client) and makes the client unusable after the with block. If you don’t use with, call client.close() when you’re done. If you pass a custom client=/async_client=, you own closing it.

import { LambdaDBClient } from "@functional-systems/lambdadb";

const client = new LambdaDBClient({
  projectApiKey: "YOUR_API_KEY",
  baseUrl: "YOUR_BASE_URL",
  projectName: "YOUR_PROJECT_NAME",
});
const collection = client.collection("my_collection");
await collection.docs.upsert({
  docs: [
    { id: "33201222", url: "https://en.wikipedia.org/wiki/LambdaDB", title: "LambdaDB", text: "LambdaDB is an AI-native database ... ", denseVector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], sparseVector: { LambdaDB: 0.83, is: 0.1, a: 0.1, AI: 0.7 } },
    { url: "https://en.wikipedia.org/wiki/Winamp", title: "Winamp", text: "Winamp is a media player for Windows, macOS and Android ...", denseVector: [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0], sparseVector: { "0": 0.8, "4": 0.1, "11": 0.1, "63": 0.54 } },
  ],
});

package main

import (
  "context"
  "log"
  lambdadb "github.com/lambdadb/go-lambdadb"
)

func main() {
  ctx := context.Background()
  client := lambdadb.New(
    lambdadb.WithBaseURL("YOUR_BASE_URL"),
    lambdadb.WithProjectName("YOUR_PROJECT_NAME"),
    lambdadb.WithAPIKey("YOUR_API_KEY"),
  )
  coll := client.Collection("my_collection")
  myDocs := []map[string]interface{}{
    {"id": "33201222", "url": "https://en.wikipedia.org/wiki/LambdaDB", "title": "LambdaDB", "text": "LambdaDB is an AI-native database ... ", "dense_vector": []float64{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, "sparse_vector": map[string]float64{"LambdaDB": 0.83, "is": 0.1, "a": 0.1, "AI": 0.7}},
    {"url": "https://en.wikipedia.org/wiki/Winamp", "title": "Winamp", "text": "Winamp is a media player for Windows, macOS and Android ...", "dense_vector": []float64{1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0}},
  }
  _, err := coll.Docs().Upsert(ctx, lambdadb.UpsertDocsInput{Docs: myDocs})
  if err != nil {
    log.Fatal(err)
  }
}

LAMBDADB_PROJECT_API_KEY="YOUR_API_KEY"

curl -X POST \
  "$BASE_URL/projects/$PROJECT_NAME/collections/{collectionName}/docs/upsert" \
  -H 'content-type: application/json' \
  -H 'x-api-key: $LAMBDADB_PROJECT_API_KEY' \
  -d '{
    "docs": [
      {
        "id": "33201222",
        "url": "https://en.wikipedia.org/wiki/LambdaDB",
        "title": "LambdaDB",
        "text": "LambdaDB is an AI-native database ... ",
        "dense_vector" : [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
        "sparse_vector" : {"LambdaDB": 0.83, "is": 0.1, "a": 0.1, "AI": 0.7}
      },
      {
        "url": "https://en.wikipedia.org/wiki/Winamp",
        "title": "Winamp",
        "text": "Winamp is a media player for Windows, macOS and Android ...",
        "dense_vector" : [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0],
        "sparse_vector" : {"0": 0.8, "4": 0.1, "11": 0.1, "63": 0.54}
      }
    ]
  }'

Response

{
  "message": "Upsert request is accepted"
}

Each document implicitly contains an id field in order to uniquely identify a document. A unique string value is auto-generated by the system if id field is not provided in an upsert request. If you want to overwrite the entire document, you can do so by providing the id field in the document.

For a partitioned collection, documents are upserted into the __default__ partition if the partition field is not provided.

Upsert limits

Metric	Limit
Max payload size	6MB
Max length for a document ID	512 characters
Max vector dimensions	4,096
Max document size	5MB

When upserting larger amounts of data, it is recommended to use bulk-upsert operation.