Chat with Data App: RAG using Mistral 7B, Haystack, and Chainlit

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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
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Excellent job! While many channels prioritize commercial models, yours is dedicated solely to open source. Kudos to you for that focus.

shikharmishra
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Great work, whereas most of the channels focus on commercial models. Yours is the one dedicated to open source . Kudos

manugnair
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Hey!!Great Content. What are the system specs to run the LLM model(mistral) that you've used in this video? By using quantized ones(TheBlokes Mistral) can we run the same on a system with 8gb RAM and get proper instant response?

sushmithakamalakannan
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Thank you for creating such great quality content. I'm learning so much from this channel. Please keep up the good work!

shivamroy
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Best Generative AI channel on YouTube.... Thank you bro

iuidehx
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your content is very helpful ❤ 😊Love from Pakistan ❤

hamadkhanofficial
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how to use gguf model after downloading it from the huggingface modelhub in vscode by uploading that model in vscode?

rakeshkumarrout
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Good videos, very informative, can you make video like how to extend function calling in mistral or zephyr models.

amangrover
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I tried as you showed but it shows the following error and response in empty for my query

Token indices sequence length is longer than the specified maximum sequence length for this model (1154 > 1024). Running this sequence through the model will result in indexing errors
2023-12-02 10:07:25 - The prompt has been truncated from 1154 tokens to 924 tokens so that the prompt length and answer length (100 tokens) fit within the max token limit (1024 tokens). Shorten the prompt to prevent it from being cut off.

vimkuqd
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I love your videos and have an idea perhaps you could try..what if (once these prompt techniques are assembled) the LLM could ask for context clarification before determining the final ranked results? is that possible? can you make a video for this step? please please with Python

RedCloudServices
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amazing tutorial! can you make a video using Zep with Langchain?

NagibDev
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i have my project related to the summarization and question answering from ducuments using the LLMs, so i need to know that from where i will start learning from your videos, there are a lot of videos and i don't find the starting node to learn, please help me in that,
conclusion:
there are 56 videos,
if any prerequisite is required mention it
thanks for your supporting content, uptodated and easy to understand,
A subscriber from Lahore, Pakistan

junaidiqbal
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What is

Do I need to need to download this 9.94 GB of data to use this??

vishnusureshperumbavoor
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Sir error - Error

Exception while running node 'retriever': 'Message' object has no attribute 'lower'

Enable debug logging to see the data that was passed when the pipeline failed.

sidindian
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Amazing tutorial! Can you make a video on analysing the java source code with langchain

mohan
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Hi, Can you help me in streaming the output generated by mistral by using callbackmanagers in langchain or any other efficient ways in gradio or chainlit UI. As I am producing large outputs its take sometime so if I stream it as it is generated I can overcome the issue. Thank you

ashvathnarayananns
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Do you have a preference between chainlit, streamlit, and gradio?

joshbane
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sir, please make a video for deployment on cloud for these chatbots
🙂🙂

parwezalam
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Can we do this offline, as a local or private one ?

kishoretvk
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The bot shows error message object has no attribute lower

harisudhan.s