Welcome to a tutorial on creating a Chat with Data application using Mistral 7B, Haystack, and Chainlit.
Mistral 7B:
Meet Mistral 7B, a high-performance language model with 7.3B parameters. It outperforms its predecessors, is easy to fine-tune, and can handle a wide range of tasks.
Haystack:
Haystack is your all-in-one LLM framework, offering tools for preprocessing, retrieval, and fine-tuning. It seamlessly scales to handle millions of documents and integrates with various databases.
Chainlit:
Chainlit is an open-source Python package that simplifies Chat GPT-like application development, allowing you to add your business logic and data.
Use Cases:
This tutorial explores various use cases for Chat with Data applications:
Question Answering: Leverage the power of Mistral 7B and Haystack to create question-answering systems that can provide accurate responses from vast datasets. Ideal for educational platforms, customer support, and information retrieval.
Chatbots: Develop advanced chatbots that can engage in meaningful conversations with users. Customize chatbot behavior and responses based on your specific use case, such as e-commerce, virtual assistants, or entertainment.
Conversational Interfaces: Design intuitive conversational interfaces for your applications, making them more user-friendly and accessible. Enhance user interactions by integrating natural language understanding and generation.
Information Retrieval: Build powerful tools for information retrieval from large databases, allowing users to search and access relevant data efficiently. Suitable for knowledge management, data analysis, and research.
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GitHub Repo: [ Ссылка ]
Mistral 7B Model: [ Ссылка ]
Mistral 7B Info: [ Ссылка ]
Haystack Docs: [ Ссылка ]
Chainlit Docs: [ Ссылка ]
LLM Playlist:
Haystack playlist:
Content Recommendation: Create personalized content recommendation engines by analyzing user interactions and preferences, and delivering tailored recommendations in real-time.
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