search

Basic filtering:

# Search for documents with specific metadata
results = store.search(
    ("docs",),
    filter={"type": "article", "status": "published"}
)

Natural language search (requires vector store implementation):

# Initialize store with embedding configuration
store = YourStore( # e.g., InMemoryStore, AsyncPostgresStore
    index={
        "dims": 1536,  # embedding dimensions
        "embed": your_embedding_function,  # function to create embeddings
        "fields": ["text"]  # fields to embed. Defaults to ["$"]
    }
)

# Search for semantically similar documents

results = store.search(
    ("docs",),
    query="machine learning applications in healthcare",
    filter={"type": "research_paper"},
    limit=5
)