Improve LLM-based Applications with Fallback Mechanisms [PyCon DE & PyData Berlin 2024]

Haystack is an open-source framework for building production-ready LLM applications with flexible pipeline architecture allowing loops, branches, and merges

Fallback mechanisms can serve as safety nets when LLMs can’t find answers in the primary knowledge base, with web search being a common fallback option

The conditional router component in Haystack enables routing queries through different paths based on specified conditions, helping manage fallback scenarios

Prompt engineering is crucial for RAG pipelines - proper instructions should be included to handle cases where answers aren’t found in provided documents

Pipeline components are modular and can be customized:

• Retriever for document fetching
• Text embedder for query embedding
• Prompt builder for formatting prompts
• Generator for LLM interaction

Haystack supports multiple model integrations including:

• OpenAI models (GPT-3.5, GPT-4)
• Hugging Face models
• Self-hosted options (Ollama, Llama CPP)
• Cloud providers (Azure, Vertex AI)

Loop limits and duration controls can be implemented to prevent infinite loops and manage resource usage

Validation mechanisms can be added to check output quality and trigger fallbacks when needed

Multiple data sources can be integrated including Notion, Google Drive, Slack, and custom databases

The framework provides tracing and monitoring support for production deployments