Principle:Vibrantlabsai Ragas LLM Configuration

Field	Value
Sources	Paper: RAG Survey Papers (Gao et al., 2024; Es et al., 2023)
Domains	NLP, Evaluation, LLM Integration
Last Updated	2026-02-12 00:00 GMT

Overview

LLM_Configuration is the principle of abstracting LLM provider access behind a unified interface for evaluation metrics. Rather than coupling evaluation logic to a specific LLM vendor or SDK, this principle establishes a provider-agnostic configuration layer that allows metrics to request structured outputs from any supported language model through a consistent API.

Description

LLM-based evaluation metrics -- such as faithfulness, answer relevancy, and aspect criticism -- require invoking a language model to judge the quality of RAG outputs. Different organizations use different LLM providers (OpenAI, Anthropic, Google Gemini, Groq, Mistral, and others), each with its own client SDK, authentication mechanism, and parameter naming conventions.

The LLM Configuration principle addresses this heterogeneity by establishing:

• A unified LLM interface -- All LLM interactions flow through an abstract base class that defines generate() and agenerate() methods accepting a prompt string and a Pydantic response model. The LLM returns validated, structured output conforming to the response model schema.
• Provider abstraction -- The configuration layer translates provider-specific parameter names (e.g., max_tokens vs. max_output_tokens vs. max_completion_tokens), handles provider-specific constraints (e.g., reasoning models requiring temperature=1.0), and routes to the appropriate SDK patching (Instructor, LiteLLM).
• Client injection -- Rather than managing credentials internally, the configuration pattern accepts pre-initialized provider client objects, giving users full control over authentication, base URLs, and proxy settings.
• Adapter auto-detection -- The system automatically selects the best structured-output adapter (Instructor or LiteLLM) based on the provider and client type, while allowing explicit override.
• Caching support -- LLM configurations can optionally include a cache backend, enabling response caching that dramatically reduces cost and latency during iterative evaluation development.

Usage

Apply this principle when you need to:

• Configure which LLM provider and model will judge your RAG outputs during evaluation.
• Switch between providers without modifying metric code.
• Optimize evaluation cost by enabling response caching.
• Support both synchronous and asynchronous evaluation workflows from the same configuration.

Theoretical Basis

The abstraction of LLM access for evaluation draws on several design principles:

Separation of concerns: Evaluation metrics should focus on what to evaluate, not how to call an LLM. By separating the metric logic from the LLM client configuration, the same metric can be reused across providers without modification.

Structured output generation: LLM-as-a-judge evaluation requires the model to produce structured responses (scores, verdicts, classifications) rather than free-form text. The configuration layer integrates structured output libraries (such as Instructor) to guarantee that LLM responses conform to Pydantic schemas, eliminating parsing errors.

Provider-specific parameter mapping: Different LLM providers have divergent API contracts. For example, OpenAI reasoning models (o-series, GPT-5) require max_completion_tokens instead of max_tokens and enforce temperature=1.0. Google Gemini wraps parameters in a generation_config dictionary. The configuration principle centralizes this translation logic so that evaluation users specify intent (I want this model with these constraints) rather than provider-specific API details.

Async-first with sync fallback: Modern evaluation pipelines run metrics concurrently for performance. The configuration layer detects whether a client supports async operations and automatically handles the sync-to-async bridge, including Jupyter notebook compatibility via thread-based event loop management.

Related Pages

• Implementation:Vibrantlabsai_Ragas_Llm_Factory