Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain

• Hindi-Aesthetics-Corpus

  1 11 -

To create a knowledge base for our chatbot, I'll be using the Hindi Aesthetic Corpus dataset. This dataset contains a large number of Hindi texts, more than 1000 text files. You can replace this dataset with your business-related data. It can be a collection of FAQs, product manuals, or any other information that you want your chatbot to have.

• llama3

  17 20,272 8.9 Python

  The official Meta Llama 3 GitHub site

  

Now, let's start building the next part of the chatbot. In this part, we will be using the LLM from Ollama and integrating it with the chatbot. More particularly, we will be using the Llama-3 model. Llama-3 is Meta's latest and most advanced open-source large language model (LLM). It is the successor to the previous Llama 2 model and represents a significant improvement in performance across a variety of benchmarks and tasks. Llama 3 comes in two main versions - an 8 billion parameter model and a 70 billion parameter model. Llama 3 supports longer context lengths of up to 8,000 tokens.

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• Hindi-Language-AI-Chatbot-for-Enterprises-using-Qdrant-LangChain-Ollama-Llama3-FastText-and-MLFlow

  1 1 7.9 Jupyter Notebook

  RAG powered AI chatbot for Indian Language (Hindi) using LangChain, Ollama, Qdrant, and MLFlow

You can find the code related to this blog at the below-mentioned GitHub link: https://github.com/quamernasim/Hindi-Language-AI-Chatbot-for-Enterprises-using-Qdrant-MLFlow-and-LangChain

• qdrant

  142 18,129 9.9 Rust

  Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

  

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.

• ollama

  212 66,540 9.9 Go

  Get up and running with Llama 3, Mistral, Gemma, and other large language models.

  

# install the Ollama curl -fsSL https://ollama.com/install.sh | sh # get the llama3 model ollama pull llama2 # install the MLFlow pip install mlflow

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