Cohere Command R : A Complete Step-by-Step Tutorial

This tutorial explores Cohere Command R , a cutting-edge large language model (LLM), demonstrating its use online, locally, and via the Cohere Python API. We'll build an AI agent utilizing LangChain and Tavily to accomplish multi-step tasks.

For those familiar with Cohere models, jump to the project section. Beginners can explore the AI Fundamentals track to learn about ChatGPT, LLMs, and generative AI.

What is Cohere Command R ?

Command R is Cohere's advanced LLM, excelling in conversational interactions and long-context tasks. Its optimization for complex Retrieval Augmented Generation (RAG) workflows and multi-step tool use makes it ideal for enterprise applications.

Key Features of Cohere Command R :

• Extended Context: Handles up to 128k tokens, exceeding previous models in quality and reliability (4k token output limit).
• Multilingual Support: Trained on diverse multilingual corpora, optimized for English, French, Spanish, Italian, German, and more.
• Cross-lingual Capabilities: Performs translation and cross-lingual question answering.
• Built-in RAG: Facilitates RAG without external frameworks; simply provide documents for context-aware responses with citations.
• Multi-step Tool Use: Integrates with search engines and custom tools (databases, APIs) for up-to-date responses.

For a comprehensive understanding of Cohere models, refer to the Cohere API Tutorial: Getting Started With Cohere Models.

Accessing Cohere Command R :

Several methods exist, many free of charge:

Online Access (HuggingChat):

1. Visit https://www.php.cn/link/217ffec3caf17a44bf340fc11d93e8ab.
2. Select "Models," then "c4ai-command-r-plus."
3. Enter your query; expect fast, accurate responses. Six tools (image generation, internet search, etc.) are also integrated.

Local Access (Jan):

1. Download and install Jan from https://www.php.cn/link/1d72d067ad71fc47c245e249dc16cb7f.
2. Access the model hub (left panel).
3. Search for "pmysl/c4ai-command-r-plus-GGUF" (requires >30GB RAM).
4. Download and use the "Q4_K_M version" (~31.24GB).

API Access (Jan with Cohere API):

1. Sign in to https://www.php.cn/link/19c81ddc9575bacf2a6f73b428065821.
2. Go to "Dashboard," then "API keys," and create a trial key.
3. Paste the key into Jan's "Model Provider" (Settings > Cohere).
4. Select Command R in Jan's "Thread" menu. Use "Stream" for real-time responses.

Cohere Python API:

1. Install: pip install cohere
2. Obtain a Cohere API key (see API Access above).
3. Set up environment variables (DataCamp's DataLab recommended).
4. Initialize the Cohere client:

import os
import cohere

cohere_api_key = os.environ["COHERE_API_KEY"]
co = cohere.Client(api_key=cohere_api_key)

5. Generate responses using .chat():

response = co.chat(model="command-r-plus", message="Your query here")
print(response.text)

Exploring Cohere Python API Features:

• Text Generation: Use preamble, chat_history, max_tokens, and temperature for customized responses.
• Streaming: Use .chat_stream() for real-time token generation.
• Predictable Output: Set the seed argument for reproducible results.
• RAG: Use the documents argument in .chat() for context-aware responses.
• Embeddings: Use .embed() for semantic text representation.
• Fine-tuning: Upload custom datasets using .datasets.create() and fine-tune with .finetuning.create_finetuned_model().

AI Project: Multi-step Agent with LangChain and Tavily:

This project creates an AI agent that searches the web (Tavily) and generates/executes Python code (Python REPL) to produce visualizations.

1. Install packages: %pip install --quiet langchain langchain_cohere langchain_experimental
2. Set up Cohere chat model:

import os
import cohere

cohere_api_key = os.environ["COHERE_API_KEY"]
co = cohere.Client(api_key=cohere_api_key)

3. Set up Tavily search tool.
4. Set up Python REPL tool.
5. Create and execute the AI agent using create_cohere_react_agent and AgentExecutor.
6. Test the agent (e.g., generate a pie chart).

Conclusion:

This tutorial provides a comprehensive guide to using Cohere Command R , highlighting its capabilities and demonstrating its application in building sophisticated AI agents. The combination of powerful LLMs and tools like LangChain and Tavily simplifies the development of advanced AI systems.

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