8/27/2024

Integrating Ollama with ChainLit Framework: Unlocking the Power of Local LLMs

The integration of Ollama with ChainLit offers a revolutionary way to deploy local large language models (LLMs) to enhance user experience across a variety of digital platforms. This blog post guides you through the steps to effectively leverage these powerful tools to create interactive, responsive, conversational AI products that engage your audience like never before.

What is Ollama?

Ollama is a cutting-edge tool that enables you to run open-source large language models locally, including popular ones like Llama 2 and Mistral. With Ollama, you can seamlessly fetch models and utilize GPU acceleration without undergoing extensive setup processes. The beauty of Ollama comes from its user-friendly interface that allows customization without requiring prior expertise in machine learning. Whether you're developing chatbots or conducting research, Ollama offers versatile functionalities to fulfill diverse needs. You can check their GitHub repository for further insights.

What is ChainLit?

ChainLit is an open-source Python framework designed to simplify the building of conversational AI applications. This platform allows rapid development of ChatGPT-like applications with minimal coding. Its features include:

Rapid Construction: Get started in minutes by integrating existing codebases.
Data Continuity: Utilize user-generated feedback to improve performance.
Visualize Reasoning Steps: Gain insights into how outputs are generated.
Prompt Refinement: Iterate on prompts within a playground interface to pinpoint areas for improvement.

For detailed documentation on ChainLit, refer to their official site.

Why Integrate Ollama with ChainLit?

Integrating Ollama with ChainLit provides numerous benefits, making your workflow more efficient:

Leverage Local Models: Use your powerful local LLMs to reduce latency in responses, making the app snappy and responsive.
Enhanced Interactivity: Combining the capabilities of both tools allows for engaging interactions and a seamless user experience.
Customization: You can tailor model behaviors and chat experiences according to your brand’s requirements.
Cost Efficiency: Avoid API usage fees associated with external LLM services since Ollama runs models locally.

Getting Started: Setup Requirements

Before diving into integration, let’s lay down the setup requirements:

Install WSL2 (if using Windows): Ollama is readily available for MacOS and Linux, whereas Windows users need to set up WSL2. You can find instructions to create a WSL development environment here.
Installation Commands: Run the following commands to install Ollama:
1 2 3bash curl https://ollama.ai/install.sh | sh ollama serve
The commands above utilize the
1curl
functionality to fetch and install Ollama.
Setup ChainLit: Use pip to install ChainLit in Python environment:
1 2bash pip install chainlit
If you haven't already, see the ChainLit installation guide.

Creating Your First Chat Application

To showcase the integration of Ollama and ChainLit, let’s create a simple chatbot. This will involve:

Setting up the environment.
Writing a basic chatbot app.
Running and interacting with the chatbot.

Step 1: Environment Setup

Create a New Directory:
1 2 3bash mkdir my_chatbot cd my_chatbot
Virtual Environment (Optional): It’s always a good practice to create a virtual environment:
1 2 3bash python3 -m venv env source env/bin/activate
Install Required Packages: You need to install several libraries, including ChainLit, LangChain, etc. Here’s a sample
1requirements.txt
file you can use:
1 2 3 4 5plaintext chainlit langchain ollama chromadb
Finally, install the packages using:
1 2bash pip install -r requirements.txt

Step 2: Writing the Chatbot Application

Here’s a simplified chatbot setup integrating Ollama with ChainLit: ```python import chainlit as cl from langchain.llms import Ollama from langchain.chains import RetrievalQA

@cl.on_chat_start def on_chat_start(): cl.Message(content="Welcome to your Ollama chatbot! What do you want to know?").send()

@cl.on_message async def main(message: cl.Message): llm = Ollama(model="mistral") # Initialize Ollama with desired LLM response = llm(message.content) # Get a response from Ollama await cl.Message(content=response).send() # Send response to the chat ``` In this basic setup, we’ve defined how the chatbot will behave when a user sends a message. When a message is received, the model processes it and returns an insightful response.

Step 3: Running Your Chatbot Application

To run your chatbot app, execute the following command in your terminal:

1
2

bash
chainlit run my_chatbot.py

Once you get that running, navigating to

http://localhost:8000

will showcase your chatbot in action!

Using Retrieval Augmented Generation (RAG) with Ollama and ChainLit

One of the standout features of integrating these two systems is the ability to utilize Retrieval Augmented Generation (RAG). RAG enhances the model’s response by incorporating specific context or data, thereby increasing response accuracy and relevance. To implement RAG in your project:

Read and load data sources (PDFs, CSVs, etc.).
Create vector embeddings using a vector database like Chromadb for your documents, allowing for efficient retrieval.
Set RAG pipeline: Using
1RetrievalQA
, fetch the most relevant context based on a user’s query & then use Ollama for generating the final output.

Here’s a high-level view of how you might set this up: ```python from langchain.vectorstores import Chroma from langchain.embeddings import OllamaEmbeddings from langchain.document_loaders import PDFLoader

Load your document

loader = PDFLoader("path/to/file.pdf") documents = loader.load()

Create embeddings & vector store

embedding_model = OllamaEmbeddings() vectorstore = Chroma.from_documents(documents, embedding_model)

Setup RAG

rag = RetrievalQA.from_chain_type(llm=Ollama(model="mistral"), retriever=vectorstore.as_retriever()) ``` In this setup, whenever a user sends a query, the bot will fetch context from your documents and generate relevant responses accordingly.

Conclusion

Integrating Ollama with ChainLit opens the door to developing powerful, local conversational agents that empower businesses & individuals alike. By leveraging local models and rich contextual data, you can create unique and tailored chatbot experiences. Have fun experimenting with this amazing functionality, and feel free to explore various combinations to find what works best for your use case!

Explore Arsturn for Effortless AI Solutions

If you’re looking to boost engagement & conversions further, consider checking Arsturn. With Arsturn's no-code AI chatbot builder, effortlessly create custom chatbots tailored to your needs. Not only can you engage your audience with unique conversational AI, but you also get insightful analytics, ensuring you understand your audience better than ever!

Join numerous enterprises and startups enhancing their engagement through conversational AI. Create meaningful connections with your customers before they even reach out! Claim your chatbot on Arsturn today, it's free to start!

Diversify your AI tools by integrating Ollama with ChainLit and Arsturn for a comprehensive solution that meets all your logistical and customer engagement needs.