Key Features
Command R+ Model
Optimized for RAG, long-context reasoning, and enterprise-grade NLP tasks.
Chat & Assistants
Build intelligent agents with memory, grounding, and fast response times.
Semantic Search
Embed and retrieve documents with high relevance using Cohere embeddings.
Fast & Private Inference
Deploy models with low latency and privacy-first architecture.
Multi-language Support
Supports over 100 languages for global applications.
How It Works
Sign Up & Get API Key
Create a Cohere account and access your API credentials.
Choose a Model
Use Command R+ for chat, RAG, summarization, or classification.
Embed & Retrieve
Use Cohere embeddings to index and retrieve relevant documents.
Generate Response
Pass retrieved context and user query to the model for grounded output.
Deploy & Monitor
Integrate into apps and monitor usage with analytics and observability tools.
Code Example
import cohere
co = cohere.Client("YOUR_API_KEY")
response = co.chat(
message="What is retrieval-augmented generation?",
connectors=[{"type": "web-search"}]
)
print(response.text)Use Cases
Enterprise Chatbots
Deploy grounded assistants with document retrieval and long-context memory.
Semantic Search
Embed and search documents with high precision across languages.
Summarization
Generate concise summaries of articles, reports, and transcripts.
Classification & Tagging
Label and organize content using custom classifiers.
RAG Pipelines
Build retrieval-augmented generation systems with fast, relevant responses.
Integrations & Resources
Explore Cohere’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- LangChain for agentic workflows
- Haystack for RAG pipelines
- Slack and Notion for assistant deployment
- Python SDK via `cohere` package
- Vector DBs like Pinecone, Weaviate, and Qdrant
- Streamlit and Gradio for UI demos
Helpful Resources
FAQ
Common questions about Cohere’s capabilities, usage, and ecosystem.