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
)
