RAG Implementation Medical Chatbot with Mistral 7B LLM LlamaIndex GTE Colab Demo

Create Medical Chatbot with Mistral 7B LLM LlamaIndex Colab Demo Custom embeddings and Custom LLM
In this video I explain how you can create a prototype medical chatbot/converse with medical data using LlamaIndex and Mistral 7B LLM. I also explain how you can use custom embeddings ( General Text Embeddings (GTE) model )
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Colab Notebook:
colab.research...
Relevant Links:
huggingface.co...
mistral.ai/new...
huggingface.co...
gpt-index.read...
python.langcha...
gpt-index.read...
www.versusarth...
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.
LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools:
• Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)
• Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
• Provides an advanced retrieval/query interface over your data: Feed in any LLM input prompt, get back retrieved context and knowledge-augmented output.
• Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else).
LlamaIndex provides tools for both beginner users and advanced users. Our high-level API allows beginner users to use LlamaIndex to ingest and query their data in 5 lines of code. Our lower-level APIs allow advanced users to customize and extend any module (data connectors, indices, retrievers, query engines, reranking modules), to fit their needs.
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