Build and design a Mistral RAG agent
Overview
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.
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
Solution
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