Google Colab: A Powerful AI Development Environment with Ollama and LangChain

Google Colab, a cloud-based Jupyter Notebook environment, simplifies Python coding and execution, eliminating the need for local environment setup. This makes it ideal for data science, machine learning, and general Python scripting. However, direct shell command execution is sometimes necessary for tasks like package installation or file management. While Colab offers in-notebook shell command execution, a full terminal environment provides greater flexibility. This guide demonstrates accessing the Colab terminal, installing and utilizing Ollama to access machine learning models, and performing inference using LangChain.

Table of Contents

• Step 1: Accessing the Colab Terminal with colab-xterm
• Step 2: Model Acquisition with Ollama
• Step 3: Installing Necessary Libraries
• Step 4: Inference with LangChain and Ollama
• Conclusion
• Frequently Asked Questions

Step 1: Accessing the Colab Terminal with colab-xterm

To access the Colab terminal, install and activate the colab-xterm extension. Execute these commands in a Colab code cell:

<code>!pip install colab-xterm
%load_ext colabxterm
%xterm</code>

This launches a terminal window within your Colab session. Install Ollama via the terminal using the Linux command:

curl -fsSL https://ollama.com/install.sh | sh

Step 2: Model Acquisition with Ollama

Now, download and prepare machine learning models. Use the terminal to pull models like deepseek-r1:7b or llama3 with Ollama:

ollama pull deepseek-r1:7b

or

ollama pull llama3

Step 3: Installing Necessary Libraries

Install the required Python libraries for model interaction within a new Colab code cell:

!pip install langchain langchain-core langchain-community

These libraries facilitate structured large language model interaction.

Step 4: Inference with LangChain and Ollama

With dependencies installed, use LangChain to interact with your model. Add this code to a Colab cell:

from langchain_community.llms import Ollama

# Load the model
llm = Ollama(model="llama3")

# Make a request
response = llm.invoke("Tell me about Analytics Vidhya.")
print(response)

This loads the llama3 model and generates a response to the prompt.

Conclusion

This guide demonstrates leveraging Colab's terminal for enhanced functionality, enabling seamless model installation with Ollama and interaction via LangChain. This approach transforms Colab into a versatile AI development platform, ideal for experimenting with advanced models and streamlining machine learning workflows.

Frequently Asked Questions

Q1: How do I access the Colab terminal?

A1: Install colab-xterm using !pip install colab-xterm and launch it with %xterm in a Colab code cell.

Q2: How do I install and use Ollama in Colab?

A2: Install Ollama in the terminal using curl -fsSL https://ollama.com/install.sh | sh and pull models with ollama pull <model_name></model_name>.

Q3: Can I run inference with LangChain and Ollama on any model?

A3: Yes, after installing LangChain and downloading a model via Ollama, you can use it for inference with llm.invoke("your prompt").

Q4: Can I use Google Colab for deep learning with large datasets?

A4: Yes, Colab supports deep learning and large datasets, particularly with GPUs/TPUs. Colab Pro provides increased resources for handling larger models and datasets.

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