Engage in Conversations with Files using ChainLit, LangChain, Ollama & Mistral 🧠

Unlocking the power of ChainLit, LangChain, Ollama & Mistral 🧠: Chat with documents like a pro! Create your own original applications with first-hand experience and jaw-dropping insights. AMA just got a major upgrade! No more boring chats, only pure excitement and unique perspectives. Let’s go! 🚀

Introduction and Overview of the AMA Series

In this AMA series, we have created a chat UI that allows us to use GPT and AMA models to create applications. We have discussed using Rag, Lang chain, Mistal and Ollama to create simple applications. These open source models can be utilized to create different applications based on your needs.

Key Takeaways:

Creating Applications with ChainLit and LangChain

Using LangChain, we have successfully created a chat UI that can be used for local LMS development. With ChainLit, we are able to view and work with PDF files directly on the UI itself. This provides an easy and convenient way to interact with documents and datasets.

Setting Up Virtual Environments

When working with such applications, it is important to create a virtual environment to ensure that there are no conflicts with existing systems. By installing the required packages using the requirements.txt file and setting up a virtual environment in your terminal, you can seamlessly integrate the necessary components to run the chain applications.

Example:

$ python3.11.0 -m venv myenv
$ source myenv/bin/activate
$ pip install -r requirements.txt

Usage of Rag Prompt and AMA Embeddings

Utilizing the features provided by AMA and Rag Prompt models, we can create prompts that lead to the efficient retrieval of information from documents and datasets. By embedding the information and using queries, we can generate accurate and relevant responses based on the user’s input.

Creating and Running Chain Applications

Once all the necessary configurations are in place, it is time to run the Chain main application and provide input files, questions, and prompts. This allows for seamless processing of queries, retrieval of information, and generating relevant responses based on the input provided.

Conclusion

Creating applications using ChainLit, LangChain, Ollama and Mistral provides an efficient solution for working with documents and datasets. Leveraging the features of AMA, GPT models, and Rag prompt enables efficient and accurate retrieval of information. By setting up virtual environments and utilizing the functionalities provided by these models, developers can create effective applications for various use cases.

Key Takeaways:

• ChainLit and LangChain enable easy integration of trace applications and chat UI for local LMS development.
• Utilizing Rag Prompt and AMA embeddings allows for accurate information retrieval and modeling responses.
• Virtual environments are vital for avoiding conflicts and ensuring seamless integration of packages and components.