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An Amazon cargo plane crashed at Miami International Airport

A plane bearing an Amazon logo overran the runway at Miami International Airport on Sunday during landing, crashing into vehicles and resulting in multiple injuries. The extent of the damage or the seriousness of the injuries was not clear at the time of publication. In the aftermath of the incident, the FAA released a statement […]

2026-09-07 原文 →
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Using SynapCores as a LlamaIndex Vector Store + Property Graph Store

Most LlamaIndex setups end up with two separate backends once you go beyond plain vector search: a vector store for VectorStoreIndex , and a separate graph database for PropertyGraphIndex when you need relationship-aware retrieval (GraphRAG). Two services, two connection strings, two things to keep in sync. This is a walkthrough of backing both index types with SynapCores instead — one engine, one connection, both index types. Setup docker run -d --name synapcores -p 8080:8080 \ -e AIDB_ACCEPT_LICENSE = 1 \ -v synapcores-data:/var/lib/synapcores \ ghcr.io/synapcores/community:latest pip install llama-index llama-index-vector-stores-synapcores llama-index-graph-stores-synapcores Both integration packages are independently published on PyPI: llama-index-vector-stores-synapcores llama-index-graph-stores-synapcores Vector store — standard RAG from llama_index.core import VectorStoreIndex , StorageContext , Document from llama_index.vector_stores.synapcores import SynapCoresVectorStore vector_store = SynapCoresVectorStore ( uri = " http://localhost:8080 " , embedding_dim = 1536 ) storage_context = StorageContext . from_defaults ( vector_store = vector_store ) docs = [ Document ( text = " SynapCores runs vector search, graph traversal, and SQL in one engine. " )] index = VectorStoreIndex . from_documents ( docs , storage_context = storage_context ) query_engine = index . as_query_engine () response = query_engine . query ( " What does SynapCores combine into one engine? " ) print ( response ) The vector store implements the full BasePydanticVectorStore ABC — add , delete , query , delete_nodes , clear , plus the async surface. Metadata filtering supports the full MetadataFilters grammar: all 12 operators ( EQ , NE , GT / GTE / LT / LTE , IN , NIN , TEXT_MATCH , TEXT_MATCH_INSENSITIVE , CONTAINS , IS_EMPTY ) with AND / OR / NOT and nested groups — so you're not giving up filtering power by moving off a dedicated vector DB. If you already have data in SynapCores from a prev

2026-08-27 原文 →