Vector Search

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

Store 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.

Search by Meaning

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
  }'

Response:

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

LangChain Integration

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

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:

• Model: BAAI/bge-base-en-v1.5
• Dimensions: 768
• Latency: ~16ms per embedding
• Cost: Free at all tiers

You can also bring your own embeddings by passing a vector field instead of text.

Endpoints

Method	Path	Description
POST	/zerodb/vectors	Upsert vectors with text or raw embeddings
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