Emdb Insert

Insert Vectors into EmergentDB

Help the user insert vectors into EmergentDB using the official SDKs.

Prerequisites

• An EmergentDB API key (starts with emdb_)
• Vector embeddings (default dimension: 1536 for OpenAI text-embedding-3-small)

TypeScript SDK

import { EmergentDB } from "emergentdb";

const db = new EmergentDB("emdb_your_api_key");

// Single insert
const result = await db.insert(1, embedding, { title: "My doc" }, "production");
// result: { success: true, id: 1, namespace: "production", upserted: false }

// Batch insert (max 1000 per call)
const batch = await db.batchInsert([
  { id: 1, vector: [...], metadata: { title: "Doc 1" } },
  { id: 2, vector: [...], metadata: { title: "Doc 2" } },
], "production");

// Unlimited batch (auto-chunks into 1000s)
const all = await db.batchInsertAll(vectors, "production");

Python SDK

from emergentdb import EmergentDB

db = EmergentDB("emdb_your_api_key")

# Single insert
result = db.insert(1, embedding, metadata={"title": "My doc"}, namespace="production")

# Batch insert
result = db.batch_insert([
    {"id": 1, "vector": [...], "metadata": {"title": "Doc 1"}},
    {"id": 2, "vector": [...], "metadata": {"title": "Doc 2"}},
], namespace="production")

# Unlimited batch
result = db.batch_insert_all(vectors, namespace="production")

Key Behaviors

• Upsert: Re-inserting an existing ID in the same namespace replaces it. Response shows upserted: true.
• Namespaces: Optional, defaults to "default". Created automatically on first use.
• Metadata fields: title (str), content (str), source_url (str), tags (str[])
• Batch limit: Max 1,000 vectors per batchInsert / batch_insert call. Use batchInsertAll / batch_insert_all for larger sets.
• Dimensions: Must match the index dimension (1536 by default). Mismatched dimensions return HTTP 400.

Error Codes

Rate Limits

Common Pattern: OpenAI Embeddings

import openai
from emergentdb import EmergentDB

client = openai.OpenAI()
db = EmergentDB("emdb_your_key")

# Generate + store
resp = client.embeddings.create(model="text-embedding-3-small", input="My document text")
db.insert(1, resp.data[0].embedding, metadata={"title": "My document"})

When helping the user, determine which SDK they prefer and guide them through the insert flow. If they have raw text, suggest using OpenAI or another embedding model first.