Mistral AI Agents | Full Walkthrough
Mistral AI is one of the big AI labs providing various models including SotA LLM and embedding models, making them an ideal sole provider for retrieval tasks (such as RAG) that require both generation via LLMs and retrieval via embedding models. The Aurelio Platform provides several utility services to help AI engineers build RAG and GenAI applications faster. In this example we're going to use both of these together to create a "chat-with-video" AI pipeline. We'll see how to: 1. Take any YouTube video and transcribe it to text using Aurelio's video-to-text endpoint. 2. Use Mistral LLMs to chat with our transcribed video content. 3. Add chat history to make our AI conversational. 4. Integrate async and streaming for a better UX and improved scalability. 5. See how we can optimize response latency and costs by reducing overall token count using semantic similarity, using Aurelio's chunking endpoint and Mistral's embedding models. 📌 Code: https://github.com/aurelio-labs/cookbook/blob/main/gen-ai/agents/video-agent.ipynb 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #aiagents #mistralai #ai #coding 00:00 Mistral AI Agent 00:47 Python Setup 01:44 Video Transcription 03:25 Agent Overview 07:03 Using Mistral 09:26 Adding Agent Chat History 11:23 Async and Streaming 17:53 Agent Token Usage 19:17 Building a Retrieval Agent 25:52 Creating a Tool for Mistral 33:40 Tool Execution Logic
