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 ```typescript 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 ```python 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 | Code | Meaning | |------|---------| | 400 | Invalid request — bad JSON, wrong vector dimension, missing field | | 401 | Missing or invalid API key | | 402 | Vector capacity exceeded — upgrade plan | | 429 | Rate limit exceeded — back off and retry | | 500 | Server error — retry with backoff | ## Rate Limits | Plan | Limit | |------|-------| | Free | 60 req/min | | Launch | 300 req/min | | Scale | 600 req/min | ## Common Pattern: OpenAI Embeddings ```python 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.