Built for RAG and Agent
Hybrid Search on a Flexible Document Model
Perform multi-field vector search across text and images simultaneously — without flattening your schema.
Store vectors, keywords, text, and complex nested objects in a single document, and search your data exactly as it is.
query = {
"rrf": [
# Keyword search on raw text
{"queryString": {"query": user_query, "defaultField": "text"}},
# Semantic search on text embeddings
{"knn": {"field": "text_vector", "queryVector": q_vec, "k": 5}},
# Semantic search on image embeddings
{"knn": {"field": "image_vector", "queryVector": q_vec, "k": 5}}
]
}
results = lambda_db.collections.query(
collection_name="assets",
query=query
)
Serverless Elasticity for Agent Storms
Compute, memory, and storage scale independently — with automatic shard scaling. No manual sharding. No capacity planning.
Whether it’s a single RAG query or a swarm of recursive agents, our disaggregated architecture instantly adapts using virtual sharding to maintain stable performance. Your ingestion pipeline never blocks your search.
Zero-Waste Scoped Retrieval
Don't search the whole library to find a single page. Retrieve only specific partitions based on tenant or category. Pay only for what you read, not for idle infrastructure.
