Principle:Marker Inc Korea AutoRAG Configuration Loading
| Knowledge Sources | |
|---|---|
| Domains | Configuration Management, RAG Pipeline Optimization |
| Last Updated | 2026-02-12 00:00 GMT |
Overview
Configuration loading is the process of reading and validating YAML pipeline definitions that describe the full structure of a Retrieval-Augmented Generation (RAG) pipeline.
Description
In any automated RAG optimization system, the pipeline structure must be declaratively specified before evaluation can begin. Configuration loading addresses this need by parsing YAML files that define the complete pipeline topology, including node lines, individual nodes, module candidates, evaluation strategies, and metric selections.
The configuration file serves as the single source of truth for an optimization trial. It declares which retrieval, reranking, and generation modules to evaluate, what metrics to compute, and which strategy to use when selecting the best-performing module at each node. A well-formed configuration is a prerequisite for all downstream evaluation steps.
Beyond simple YAML parsing, configuration loading applies two critical transformations. First, environment variable substitution replaces ${VAR} patterns with their runtime values, enabling secrets such as API keys to remain outside version control. Second, tuple conversion transforms string representations of tuples into native Python tuple objects, ensuring that parameters requiring tuple types are correctly interpreted by the modules that consume them.
Usage
Configuration loading should be used at the very start of any AutoRAG workflow, whether running a full optimization trial, a validation-only check, or a deployment pipeline. It is the entry point that transforms a static YAML document into an in-memory dictionary that the rest of the system consumes.
Theoretical Basis
The configuration loading process follows a straightforward pipeline:
Step 1 -- File existence check: Verify that the specified YAML path points to an existing file. Raise a descriptive error immediately if the path is invalid, preventing downstream failures with obscure error messages.
Step 2 -- Safe YAML parsing: Use a safe loader to parse the YAML content into a Python dictionary. Safe loading prevents arbitrary code execution through YAML deserialization attacks.
Step 3 -- Tuple conversion: Recursively traverse the dictionary and convert any string values that match Python tuple syntax (e.g., "(1, 2, 3)") into actual tuple objects. This is necessary because YAML does not have a native tuple type.
Step 4 -- Environment variable substitution: Recursively traverse the dictionary and replace any ${VARIABLE_NAME} patterns with the corresponding value from the process environment. This enables externalized configuration of sensitive or environment-specific values.
The pseudocode for the full process is:
FUNCTION load_config(yaml_path):
IF NOT file_exists(yaml_path):
RAISE ValueError
raw_dict = yaml_safe_load(yaml_path)
processed_dict = convert_string_to_tuple(raw_dict)
final_dict = substitute_env_variables(processed_dict)
RETURN final_dict
The resulting dictionary has the following top-level structure:
| Key | Type | Description |
|---|---|---|
| node_lines | List[Dict] | Ordered list of node line definitions |
| vectordb | List[Dict] | Optional vector database configurations |
Each node_line contains a node_line_name and a list of nodes, where each node specifies its node_type, candidate modules, evaluation strategy, and target metrics.