The chunking part is where I spent most of my time second-guessing myself.
Start by clarifying requirements (file types, scale, latency, accuracy) and then walk through the end-to-end pipeline: ingestion, chunking, embedding, indexing, retrieval, and generation with Mistral. Emphasize trade-offs at each stage (e.g., chunk size, embedding model, retrieval strategy) and how you would evaluate and iterate.
Pro tip: Mention that you would start with a simple baseline (fixed-size chunks, cosine similarity, top-k retrieval) and then improve with techniques like overlap, metadata filtering, and re-ranking, showing you prioritize shipping a working system before optimizing.
Ask about document volume, file types, expected query types, latency/throughput needs, and whether the index must persist. This shapes design decisions like chunk size and retrieval strategy.
Outline parsing Markdown and PDF (e.g., using libraries like markdown-it and PyPDF2), then chunking with overlap. Discuss trade-offs between fixed-size, semantic, and recursive chunking.
Use Mistral's embedding API to vectorize chunks, then store in an in-memory index (e.g., FAISS or a simple numpy array with cosine similarity). Mention normalization and efficient similarity search.
For a query, embed it, retrieve top-k relevant chunks, and construct a prompt for Mistral's completion API. Discuss prompt design, context window limits, and handling of irrelevant retrievals.
Propose metrics (retrieval recall, answer accuracy) and a feedback loop. Mention logging, A/B testing, and potential improvements like re-ranking or hybrid search.
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Start by clarifying requirements and constraints (e.g., scale, latency, persistence) to scope the design. Then outline a modular architecture with separate components for indexing, querying, and serving, and describe the CLI commands and HTTP endpoint. Finally, discuss trade-offs and potential optimizations.
Pro tip: Emphasize idempotency and incremental indexing to handle large directories efficiently, and mention how you would monitor and log the system for production readiness.
Ask about expected data volume, query types, latency requirements, and whether the index should be persistent or in-memory. This ensures the design meets the actual needs.
Define the indexer (e.g., using inverted index or embeddings), the query engine, and the HTTP server. Specify how they interact and share data.
Outline commands: `index <path>` to build/update the index, `ask <question>` to query, and `serve` to start the HTTP endpoint. Include flags for configuration.
Design a RESTful endpoint (e.g., POST /chat) that accepts a question and returns an answer, with proper request/response schemas and error handling.
Address choices like index type, storage, concurrency, and how to scale (e.g., sharding, caching). Mention monitoring and logging.
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Streaming was actually the part I was least confident about.
Start by outlining the end-to-end architecture: retrieval, prompt construction, streaming, and citation extraction. Then dive into key implementation details for each component, emphasizing trade-offs and Meta-scale considerations. Conclude with how you would test and monitor the system.
Pro tip: Mention that you would stream the answer first and then append citations as a final chunk or via a separate metadata channel, to avoid blocking the user experience. Also highlight the importance of measuring time-to-first-token and ensuring citations are accurate and verifiable.
Ask about expected latency, throughput, model choice, and whether citations must be inline or can be appended. Confirm if top-k retrieval is from a vector DB or another source.
Explain how you would retrieve top-k chunks, format them into the prompt with clear delimiters, and include metadata for citations. Discuss chunk size, overlap, and ranking.
Describe using server-sent events (SSE) or WebSockets to stream tokens. Handle partial responses, errors, and reconnection. Ensure the client can render tokens incrementally.
Decide on a citation format (e.g., [1], [2]) and map them to retrieved chunks. Stream citations either inline or as a final metadata object. Ensure the model is instructed to cite sources.
Discuss trade-offs like latency vs. accuracy, cost of top-k, and citation overhead. Outline testing strategies (unit, integration, load) and monitoring (TTFT, citation accuracy, error rates).
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Start by clarifying the requirements and constraints, then outline a layered design that separates retry logic from error handling. Explain how you would implement exponential backoff with jitter, handle rate limit errors and timeouts, and ensure structured error propagation. Finally, discuss trade-offs and testing strategies to validate the solution.
Pro tip: Emphasize the importance of idempotency and jitter to avoid thundering herd problems, and mention that you would make retry policies configurable per endpoint or operation. This shows you understand real-world production concerns beyond just the algorithm.
Ask about the specific APIs, expected error types, latency budgets, and whether operations are idempotent. Confirm if retries should be applied globally or per endpoint.
Propose a retry mechanism that uses exponential backoff (e.g., base delay * 2^attempt) with randomized jitter to spread out retries. Specify max retries and max delay caps.
For rate limit errors (HTTP 429), respect the Retry-After header if present; for timeouts, use a timeout value and retry only on transient failures. Differentiate between retryable and non-retryable errors.
Define custom error types or use error codes to propagate context (e.g., retry count, original error). Ensure errors are logged with sufficient detail and that retries don't mask underlying issues.
Talk about trade-offs: increased latency vs. reliability, complexity vs. simplicity. Explain how you would test with unit tests (mocking failures) and integration tests (simulating rate limits).
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Straightforward, used python-dotenv and os.environ.
Start by clarifying the tool's configuration requirements and the priority of environment variables over other sources. Then, outline a secure and flexible implementation using a configuration library, covering API key, model selection, and port settings with validation and defaults. Finally, discuss how to document and test the configuration for reliability.
Pro tip: Emphasize security by never hardcoding secrets and using environment variables or secret managers; also mention that configuration should be validated at startup to fail fast with clear error messages.
Determine which settings need to be configurable via environment variables: API key, model selection, and port. Consider other relevant settings like timeouts or log levels.
Decide on a library or approach (e.g., dotenv, envconfig, or native process.env) to load and parse environment variables, ensuring precedence and type conversion.
Load variables, validate required ones (like API key), provide sensible defaults (e.g., port 8080), and handle errors gracefully with clear messages.
Create documentation (e.g., README or .env.example) listing all variables, and write tests to verify correct behavior under different configurations.
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The evaluation question was the most interesting part to write about.
Start by outlining a minimal but complete project structure: a README with clear setup steps, a few tests that validate core retrieval behavior, and a simple evaluation script. Then explain your retrieval algorithm choices by comparing trade-offs (e.g., BM25 vs. embeddings) and justify based on latency, cost, and accuracy. Finally, describe a lightweight evaluation method like creating a small labeled set and computing precision@k or MRR.
Pro tip: Emphasize that you prioritize reproducibility and fast iteration: the README should enable anyone to run the system in under 5 minutes, and the evaluation should be automated to catch regressions. This shows you think about team productivity and long-term maintainability, not just algorithmic elegance.
Describe the repository structure, dependencies, and exact commands to install and run the system. Include a minimal example to demonstrate usage.
Write a few unit tests that cover critical paths: indexing, querying, and edge cases (e.g., empty query). Use a lightweight framework like pytest.
Explain why you chose a particular retrieval method (e.g., BM25, dense embeddings, hybrid) based on requirements like latency, scalability, and accuracy. Mention trade-offs.
Propose a quick evaluation using a small labeled dataset (e.g., 50 queries with relevant docs) and metrics like precision@k, recall, or MRR. Automate it to run with tests.
Discuss how you would use evaluation results to iterate on the algorithm and set up monitoring for production to detect drift or degradation.
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