Build and design a Mistral RAG agent

Q: Build and design a Mistral RAG agent

This question evaluates a candidate's skills in building a retrieval-augmented generation (RAG) system, covering document ingestion, chunking, embedding generation, vector indexing, retrieval, LLM API-driven streaming completions, short-term conversation memory, and operational reliability features.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Technical Screen rounds at Mistral AI.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Mistral AI during technical interviews.

Question

Design and Implement a Minimal LLM-Powered RAG Agent (Python, Mistral API)

Context

You are asked to build a minimal, but production-minded, retrieval-augmented generation (RAG) agent in Python that uses the Mistral API for embeddings and chat completions. The agent should work over a small local corpus (e.g., Markdown and PDF files), support streaming responses, maintain a short conversation memory, and include basic reliability features.

Assume Python 3.10+ and that external dependencies can be installed. The Mistral API token is provided via an environment variable.

Deliverables

Implement a minimal Python tool that:

Ingests a small local corpus (Markdown/PDF), chunks text, generates embeddings, and builds an index.
Retrieves top-k passages for a user query.
Composes a prompt and invokes Mistral chat/completions with streaming.
Keeps short-term conversation memory.
Reads the API token from an environment variable.
Handles errors, retries, and rate limits.
Includes a brief README and smoke tests.

Then, explain your system design choices, covering:

Chunking strategy
Embedding model choice
Vector index selection
Latency and cost budgeting
Caching
Prompt templating
Safety/PII handling
Logging/monitoring
Offline RAG evaluation
Fallbacks when retrieval fails

Build and design a Mistral RAG agent

Quick Overview

Build and design a Mistral RAG agent

Design and Implement a Minimal LLM-Powered RAG Agent (Python, Mistral API)

Context

Deliverables

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