How do I handle errors and exceptions in LlamaIndex workflows?

Handling errors and exceptions in LlamaIndex workflows involves a combination of Python’s native error-handling techniques, understanding LlamaIndex’s specific components, and addressing common failure points. Start by using try-except blocks around operations like data loading, indexing, and query execution. LlamaIndex may raise exceptions related to API connectivity (e.g., APIError), invalid data formats, or failed retrieval steps. For example, when using an LLM provider like OpenAI, network issues or rate limits can cause APIConnectionError or RateLimitError. Wrapping API calls in retry logic (e.g., using the tenacity library) helps manage transient errors. Logging errors with details like timestamps and stack traces aids debugging.

Focus on error-prone stages like data ingestion and external API interactions. During data loading, validate inputs upfront: check file existence, permissions, or URL accessibility before processing. If a document parser fails (e.g., due to corrupted files), catch ValueError or custom parser exceptions and skip invalid files. When querying indexes, handle QueryEngineError to manage issues like invalid query syntax or missing context. For LLM API calls, implement retries with exponential backoff. For instance, a retry decorator can retry a failed GPT-4 call three times with 2-second delays. Use libraries like backoff or tenacity to simplify this.

Customize error handling by extending LlamaIndex’s components. Override methods in data connectors or query engines to add validation or fallback logic. For example, if a vector database call fails, fall back to a local cache. Use logging frameworks like logging or structlog to track errors and audit workflows. For async operations, ensure exceptions are caught in async context (e.g., async with and try). Unit tests with mocked failures (e.g., unittest.mock to simulate API outages) validate your error-handling logic. By combining defensive coding, structured logging, and retries, you can build robust LlamaIndex workflows that gracefully handle failures.