RAG Implementation using Mistral 7B, Haystack, Weaviate, and FastAPI
In this article, we will discuss the implementation of the RAG (Red, Amber, Green) rating system using Mistral 7B, Haystack, Weaviate, and FastAPI. This system is commonly used in project management to quickly assess the status of various tasks or projects.
Mistral 7B
Mistral 7B is a powerful workflow automation platform that can be used to create and manage complex workflows. It provides a visual interface for designing workflows and supports integration with various tools and services. In the context of RAG implementation, Mistral 7B can be used to define the criteria for assigning Red, Amber, or Green statuses to tasks or projects based on predefined rules.
Haystack
Haystack is an AI-powered search and text extraction tool that can be used to analyze large amounts of text data. In the context of RAG implementation, Haystack can be used to extract relevant information from project updates, reports, or other documents to automatically assign RAG statuses to tasks or projects. This can help streamline the process of updating and maintaining the RAG status of various tasks or projects.
Weaviate
Weaviate is a semantic search engine that can be used to analyze and search structured and unstructured data. In the context of RAG implementation, Weaviate can be used to search for relevant data points that can be used to determine the RAG status of tasks or projects. This can help improve the accuracy and efficiency of the RAG rating system by providing more detailed and context-rich information.
FastAPI
FastAPI is a modern web framework for building APIs with Python. In the context of RAG implementation, FastAPI can be used to create a RESTful API that allows users to interact with the RAG rating system. This API can be used to retrieve and update the RAG status of tasks or projects, integrate with other tools and services, and generate reports and visualizations based on the RAG data.
Overall, the combination of Mistral 7B, Haystack, Weaviate, and FastAPI can provide a robust and efficient solution for implementing the RAG rating system in project management. By leveraging the capabilities of these tools and services, organizations can improve their ability to quickly assess and monitor the status of tasks or projects, identify potential risks and issues, and make informed decisions to ensure successful project outcomes.
thanks for the informative video,can you upload a same tutriol video for different types of document summarization implementing RAG and open source LLM not OpenAI asap…it would be greatful need help in that and not found any related video for summarization .
can you explain why this all is good thing?
people talk about it as awesome stuff. but why?
installing on python is nightmare. i tried 3.11, 3.10, 3.8 but still same error. could someone help. ImportError: cannot import name 'send_event' from 'haystack.telemetry' (/usr/local/lib/python3.8/site-packages/haystack/telemetry/__init__.py). is there is ready to use docker?
Hi , thank you for the tutorial , when i'm install the requirements.txt , I got this error message : ERROR: Could not find a version that satisfies the requirement torch (from versions: none)
ERROR: No matching distribution found for torch . Can you please advise and help? Thank you
It seems like there's a mismatch that I can't figure out. The model has dimensions 384 and the datastore 768. So it won't update any dimensions:
RuntimeError: Embedding dimensions of the model (384) don't match the embedding dimensions of the document store (768). Initiate WeaviateDocumentStore again with arg embedding_dim=384.
What am I missing?
Which sentence transformer are we using exactly? And why not any top model from mteb ? What are the drawbacks? Would be helpful if you can clarify.
Great video tho❤
excellent !
Hi. I am building a chatbot with my pdf document. I created an endpoint in Fastapi. If i send 1 request the answer is really fast, but if i try to send 100 requests in the same time the response time is so much bigger. How can i run those requests paralell using gpu?
bhai thoda mike ki quality sai krr lo yaar
aawaz chubh rai h kasam se
Baaki great knowledge
keep up the good work
At 1:40 you said you are going to use "Vector Database" not "Vector Store". Can somebody please tell me the difference between them.
Hi,
The embedding dimension specified is 768 and you are using a 384 model. It throws error, can you help?
Hey did you try fine-tuning mistral on custom domain data? Can you recommended GPU for fine tuning 7B models such as Mistral, Llama etc..?
i try finishing this pending video, and i have a question. today i see your video of jina v2. in this video you says about model of incrustation in huggingface that name is multilingüe. Is possible to fusion? you can make a video for best implementations cases that jina 2 or ideas for other cases of language spanish or catalan jeje 😛 thanks for your content! is increible!
Hello! can i add multiple pdfs and can it read images in pdfs?
thankyou sir sir i want use sqldatabase inplace of pdf and etc
so please make video for mysql
i am facing an issue with embedding dimension. this is the error: Embedding dimensions of the model (384) don't match the embedding dimensions of the document store (768). Initiate WeaviateDocumentStore again with arg embedding_dim=384
fire
awesome thank you!
Dependency management in Python is a pain. Which exact package version to install? Nobody knows 😂
check your mic or audio settings. totally over-driving the sound 🤗