Weaviate 벡터 데이터베이스를 검색하고, 저장하고, 관리하세요 - Neotask이 Neotask을 통해 모든 작업을 처리합니다.
자연어로 객체를 가져오고 시맨틱, 키워드, 하이브리드 검색 실행
GraphQL 없이 스키마, 클래스, 크로스 레퍼런스 설계 및 관리
검색과 LLM 생성을 단일 단계로 결합하는 생성 검색 워크플로우 구축
할 수 있는 것
시맨틱 및 하이브리드 검색
Ask Neotask to find objects in Weaviate using natural language. It will build the nearText, nearVector, or hybrid query, execute it, and return results in a readable format.
Schema Design and Management
Describe your data model in plain language and Neotask will create Weaviate classes with the right properties, data types, and vectorizer configuration. Modify schemas without downtime risk.
Object Import and Updates
Batch-import objects from JSON, CSV, or plain description. Update individual properties or run bulk patches across a class - Neotask handles the Weaviate client calls.
Generative Search
Use Weaviate's generate module to retrieve relevant objects AND generate a response in a single query. Describe the task and Neotask constructs the generative search call end-to-end.
Cross-Reference Management
Create and query cross-references between Weaviate classes. Model complex relationships - articles linked to authors, products linked to categories - without writing BeaconIDs by hand.
이렇게 물어보세요
"Search my Weaviate 'Articles' class for content about climate policy using semantic search"
"Add this JSON array of 200 products to the 'Products' class in Weaviate"
"Create a Weaviate schema for a movie database with Title, Genre, Year, and Director"
"Run a hybrid search for 'machine learning optimization' and return the top 5 results with scores"
"Generate a summary of the most relevant support articles for 'password reset issues'"
"How many objects are in each class in my Weaviate instance?"
"Update the 'status' property to 'archived' for all Articles published before 2022"
"Show me the schema for the 'CustomerFeedback' class"
프로 팁
Use hybrid search (BM25 + vector) for enterprise search use cases - Neotask will tune the alpha parameter based on your description of the tradeoff you want
Describe your vectorizer requirements (OpenAI, Cohere, or local) and Neotask will configure the module during schema creation
Request object counts by class weekly to monitor database growth and plan capacity upgrades
Use generative search for customer-facing Q&A - one query retrieves and answers simultaneously, reducing latency
Always include a certainty or distance threshold when querying - ask Neotask to filter out low-confidence results automatically