What is Ollama and LangChain? How to Install Models & Libraries

🤖 What is Ollama and LangChain? (Beginner Guide)

What is Ollama and LangChain? How to Install Models & Libraries

Before building AI projects, it’s important to understand the tools behind them. This post explains Ollama, LangChain, how to download Ollama models, and which LangChain libraries you actually need.

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🧠 What is Ollama?

Ollama is a tool that lets you run large language models (LLMs) locally on your system without using cloud APIs.

With Ollama, you can:

• ✔ Run LLaMA, Mistral, Gemma models locally
• ✔ Build AI apps without internet
• ✔ Keep data private and secure
• ✔ Avoid API costs

Ollama works as a local model server that your Python code can talk to.

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🧩 What is LangChain?

LangChain is a Python framework used to build applications powered by language models.

It helps you:

• 🔗 Connect LLMs to your code
• 📄 Load and process documents
• 🧠 Add memory and context
• 🔍 Build RAG (Retrieval-Augmented Generation) systems

When combined with Ollama, LangChain allows you to build fully local AI applications.

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⬇ How to Install Ollama

Download Ollama from the official website:

🔗 https://ollama.com

After installation, verify it using:

ollama --version

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📦 How to Download Ollama Models

To download Ollama models, you just need to run a simple command in your command prompt or terminal

ollama pull llama3.2

Some commonly used models:

• llama3.2 – General purpose LLM
• mistral – Fast and lightweight
• gemma – Google open model
• llama3.2-vision – Image understanding

To run a model directly:

ollama run llama3.2

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📚 How to Install LangChain (Required Libraries)

For most Ollama-based projects, you only need these libraries:

pip install langchain langchain-community

Optional but commonly used libraries:

• faiss-cpu – Vector search for RAG
• pypdf – PDF document loading
• sentence-transformers – Embeddings
• python-dotenv – Environment variables

pip install faiss-cpu pypdf sentence-transformers python-dotenv

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🔗 How Ollama and LangChain Work Together

In simple terms:

• 🧠 Ollama runs the LLM locally
• 🔗 LangChain sends prompts to Ollama
• 📄 LangChain manages documents, memory, and logic

This combination is widely used for:

• ✔ Chatbots
• ✔ RAG systems
• ✔ AI assistants
• ✔ Offline AI tools

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🚀 What’s Next?

Now that you understand Ollama and LangChain, the next step is building real projects such as:

• 💬 Ollama-based chatbots
• 📄 Document Q&A (RAG)
• 🖼 Vision-based AI apps

These projects will be covered in upcoming posts.

Happy building with local AI 🚀