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š Festival Dhamaka Sale ā Upto 80% Off on All Courses š
šINDIA'S NO. 1 INTERNSHIP PORTAL
Optimized for RAG, long-context reasoning, and enterprise-grade NLP tasks.
Build intelligent agents with memory, grounding, and fast response times.
Embed and retrieve documents with high relevance using Cohere embeddings.
Deploy models with low latency and privacy-first architecture.
Supports over 100 languages for global applications.
Create a Cohere account and access your API credentials.
Use Command R+ for chat, RAG, summarization, or classification.
Use Cohere embeddings to index and retrieve relevant documents.
Pass retrieved context and user query to the model for grounded output.
Integrate into apps and monitor usage with analytics and observability tools.
import cohere
co = cohere.Client("YOUR_API_KEY")
response = co.chat(
message="What is retrieval-augmented generation?",
connectors=[{"type": "web-search"}]
)
print(response.text)Deploy grounded assistants with document retrieval and long-context memory.
Embed and search documents with high precision across languages.
Generate concise summaries of articles, reports, and transcripts.
Label and organize content using custom classifiers.
Build retrieval-augmented generation systems with fast, relevant responses.
Explore Cohereās ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Cohereās capabilities, usage, and ecosystem.