Cohere

AI & ML

텍스트를 생성하고, 결과를 재순위 매기고, 엔터프라이즈 검색을 구축하세요 — Neotask이 OpenClaw을 통해 Cohere의 NLP 스위트를 활용합니다.

할 수 있는 것

텍스트 생성 및 채팅

Call Cohere's Command models for text generation, summarization, and chat. Control temperature, stop sequences, and output format — Neotask handles parameter tuning based on your task description.

Semantic Embeddings

Generate high-quality embeddings for documents, queries, or code using Cohere Embed. Specify the input type and model, and pipe output directly into your vector store.

Reranking for Search Quality

Improve retrieval precision by passing your initial search results through Cohere Rerank. Describe the query and candidate documents — Neotask builds the rerank call and returns the reordered list with relevance scores.

Text Classification

Fine-tune or use Cohere's few-shot classification to categorize support tickets, emails, or documents. Define your labels in natural language and let Neotask handle the API formatting.

Usage and Billing Monitoring

Get a clear view of your Cohere token consumption by endpoint, model, and date. Spot expensive classification jobs or runaway generation loops before they exhaust your quota.

이렇게 물어보세요

  • "Summarize this contract using Cohere Command and highlight key obligations"
  • "Generate embeddings for these 300 customer reviews using cohere embed-english-v3.0"
  • "Rerank these 20 search results for the query 'enterprise data encryption'"
  • "Classify these support tickets into: billing, technical, account, and other"
  • "How many tokens have I used on Cohere this month?"
  • "Generate three variations of this product description in a professional tone"
  • "What's the difference between Cohere's embed-english and embed-multilingual models?"
  • "Run a chat completion with Command R+ and return the response in JSON format"

    • 프로 팁

  • Use Cohere Rerank as a second-stage retriever on top of any vector search — ask Neotask to wire it into your existing pipeline
  • Specify input_type when calling Embed (search_document vs search_query) for significantly better retrieval quality
  • Cohere's multilingual embed model handles 100+ languages — use it for international content without separate embedding pipelines
  • Batch classification jobs in groups of 96 examples to hit Cohere's optimal throughput window
  • Combine Command R+ with your Pinecone or Weaviate index for a fully managed RAG pipeline without infrastructure overhead

    • Works Well With