filmov
tv
Chat with Data App: RAG using Mistral 7B, Haystack, and Chainlit
Показать описание
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.
Don't forget to like, comment, and subscribe to stay updated on the latest advancements in Generative AI and data technology. Let's embark on this exciting adventure together!
LLM Playlist:
Haystack playlist:
Content Recommendation: Create personalized content recommendation engines by analyzing user interactions and preferences, and delivering tailored recommendations in real-time.
#generativeai #ai #llm
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.
Don't forget to like, comment, and subscribe to stay updated on the latest advancements in Generative AI and data technology. Let's embark on this exciting adventure together!
LLM Playlist:
Haystack playlist:
Content Recommendation: Create personalized content recommendation engines by analyzing user interactions and preferences, and delivering tailored recommendations in real-time.
#generativeai #ai #llm
0:30:17
Chat with Data App: RAG using Mistral 7B, Haystack, and Chainlit
0:16:42
RAG + Langchain Python Project: Easy AI/Chat For Your Docs
0:07:35
Llama 3 RAG: Create Chat with PDF App using PhiData, Here is how..
1:24:56
RAG-GPT: Chat with any documents and summarize long PDF files with Langchain | Gradio App
1:07:30
Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings)
1:23:34
Chat and RAG with Tabular Databases Using Knowledge Graph and LLM Agents
0:24:03
Build a RAG Based LLM App in 20 Minutes! | Full Langflow Tutorial
0:21:33
Python RAG Tutorial (with Local LLMs): AI For Your PDFs
1:04:56
End to End Gen AI Project using LLAMA | Custom Rag Chat | Research Paper based Chatbot | LLAMA
0:24:36
6-Building Advanced RAG Q&A Project With Multiple Data Sources With Langchain
0:05:32
Llama3 local RAG | Step by step chat with websites and PDFs
0:53:15
Building a RAG application using open-source models (Asking questions from a PDF using Llama2)
0:20:14
Chatbot with RAG, using LangChain, OpenAI, and Groq
0:16:29
Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)
0:09:15
Create Your Own ChatGPT with PDF Data in 5 Minutes (LangChain Tutorial)
0:29:29
Building a RAG-powered AI chat app with Python and VS Code
0:21:14
Building a RAG Based LLM App And Deploying It In 20 Minutes
0:16:06
Multi-Vector Retriever for RAG on Tables + Texts Using LANGCHAIN & UNSTRUCTURED
1:11:47
Vector Search RAG Tutorial – Combine Your Data with LLMs with Advanced Search
0:14:01
OpenAI RAG Chatbot | Chat with PDF locally
0:35:53
Chatbots with RAG: LangChain Full Walkthrough
0:40:59
ADVANCED Python AI Agent Tutorial - Using RAG
0:32:59
End To End Document Q&A RAG App With Gemma And Groq API
0:33:34
Build an AI RAG Application with LangChain & Next.js
Комментарии