Vector Search

ZeroDB provides vector storage and semantic search with free embeddings — no OpenAI key required.

💡Free Embeddings Included

ZeroDB includes a free embedding service powered by TEI (Text Embeddings Inference). No OpenAI key needed — just send text and get vectors back automatically.

Quick Start

npx zerodb-cli init

Store Vectors

POST/api/v1/public/zerodb/vectors🔒

curl -X POST https://api.ainative.studio/api/v1/public/zerodb/vectors \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": ["ZeroDB is fast", "Semantic search is powerful"],
    "metadata": [
      {"source": "docs", "category": "product"},
      {"source": "docs", "category": "feature"}
    ]
  }'

Embeddings are generated automatically using TEI (HuggingFace Text Embeddings Inference) at zero cost.

Parameters:

Parameter	Type	Required	Description
texts	string[]	Yes	Text content to embed and store
vector	float[]	No	Pre-computed embedding (if omitted, auto-generated)
metadata	object[]	No	Key-value metadata for filtering
namespace	string	No	Namespace isolation (default: "default")
ids	string[]	No	Custom IDs. Auto-generated if omitted.

Search by Meaning

POST/api/v1/public/zerodb/vectors/search🔒

curl -X POST https://api.ainative.studio/api/v1/public/zerodb/vectors/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "fast database for AI",
    "limit": 5,
    "min_score": 0.7,
    "filter_metadata": {"source": "docs"}
  }'

Response:

{
  "results": [
    {
      "id": "vec_abc...",
      "text": "ZeroDB is fast",
      "score": 0.94,
      "metadata": {"source": "docs", "category": "product"}
    }
  ]
}

Search Parameters:

Parameter	Type	Required	Description
query	string	Yes	Natural language search query
limit	integer	No	Max results (default: 10, max: 100)
min_score	float	No	Minimum similarity score (0.0–1.0)
namespace	string	No	Search within a specific namespace
filter_metadata	object	No	Filter by metadata key-value pairs

Python SDK

import requests

headers = {"Authorization": "Bearer YOUR_API_KEY"}
base = "https://api.ainative.studio/api/v1/public/zerodb"

# Store vectors
requests.post(f"{base}/vectors", headers=headers, json={
    "texts": ["AI agents need persistent memory"],
    "metadata": [{"source": "research", "year": 2026}],
    "namespace": "papers"
})

# Search
response = requests.post(f"{base}/vectors/search", headers=headers, json={
    "query": "How do AI agents use memory?",
    "limit": 5,
    "min_score": 0.7,
    "filter_metadata": {"source": "research"}
})
for result in response.json()["results"]:
    print(f"{result['score']:.3f} — {result['text'][:80]}")

LangChain Integration

pip install langchain-zerodb

from langchain_zerodb import ZeroDBVectorStore

store = ZeroDBVectorStore(
    api_key="your-api-key",
    project_id="your-project-id",
)

# Add documents (embeddings generated free)
store.add_texts(["ZeroDB is fast", "Semantic search"])

# Search by meaning
results = store.similarity_search("fast database", k=5)

LlamaIndex Integration

pip install llama-index-vector-stores-zerodb

from llama_index_zerodb import ZeroDBVectorStore
from llama_index.core import VectorStoreIndex

store = ZeroDBVectorStore(
    api_key="your-api-key",
    project_id="your-project-id",
)

index = VectorStoreIndex.from_vector_store(store)
response = index.as_query_engine().query("What is ZeroDB?")

Embeddings

ZeroDB generates embeddings automatically using TEI with BAAI/bge models:

BAAI/bge-base-en-v1.5

Default

768-dimension embeddings optimized for semantic search

16ms Inference

Fast

Average embedding latency with TEI backend

Free at All Tiers

No Cost

No OpenAI key needed — embeddings included

Bring Your Own

Flexible

Pass a vector field to use custom embeddings

Upsert Alias

POST /zerodb/vectors/upsert is an alias for POST /zerodb/vectors. Use either — both accept the same request body and return the same response.

curl -X POST https://api.ainative.studio/api/v1/public/zerodb/vectors/upsert \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"texts": ["ZeroDB is fast"], "namespace": "docs"}'

All Endpoints

Method	Path	Description
POST	/zerodb/vectors	Upsert vectors with text or raw embeddings
POST	/zerodb/vectors/upsert	Alias for POST /zerodb/vectors
POST	/zerodb/vectors/search	Semantic similarity search
GET	/zerodb/vectors	List vectors with pagination
DELETE	/zerodb/vectors/{id}	Delete a vector by ID
GET	/zerodb/vectors/stats	Vector count and storage stats
POST	/zerodb/embed	Generate embeddings from text (free)