ReMe/doc/configuration_guide.md

Services Params Documentation

This document describes all available command-line parameters for ExperienceMaker Service. The application uses OmegaConf for configuration management, supporting both YAML files and command-line overrides.

Basic Usage

experiencemaker [parameter1=value1] [parameter2=value2] ...

Configuration Loading Priority

1. Default values from AppConfig dataclass
2. Pre-defined YAML configuration file (default: demo_config.yaml)
3. Custom YAML file (if config_path is specified)
4. Command-line overrides

Basic Configuration Parameters

Parameter	Type	Default Value	Description	Example
pre_defined_config	string	"demo_config"	Name of the pre-defined configuration file (without .yaml extension)	pre_defined_config=full_pipeline_config
config_path	string	""	Path to custom configuration YAML file	config_path=/path/to/config.yaml

HTTP Service Configuration

Parameter	Type	Default Value	Description	Example
http_service.host	string	"0.0.0.0"	Host address for the HTTP service	http_service.host=127.0.0.1
http_service.port	integer	8001	Port number for the HTTP service	http_service.port=8080
http_service.timeout_keep_alive	integer	600	Keep-alive timeout in seconds	http_service.timeout_keep_alive=600
http_service.limit_concurrency	integer	64	Maximum concurrent connections	http_service.limit_concurrency=128

Thread Pool Configuration

Parameter	Type	Default Value	Description	Example
thread_pool.max_workers	integer	10	Maximum number of worker threads	thread_pool.max_workers=20

API Pipeline Configuration

Parameter	Type	Default Value	Description	Example
api.retriever	string	""	Pipeline definition for retriever API	api.retriever="build_query_op->recall_vector_store_op"
api.summarizer	string	""	Pipeline definition for summarizer API	api.summarizer="simple_summary_op->update_vector_store_op"
api.vector_store	string	""	Pipeline definition for vector store API	api.vector_store="vector_store_action_op"

Operation Configuration

Operations are configured using the pattern op.{operation_name}.{parameter}. Each operation can have the following parameters:

Parameter	Type	Default Value	Description	Example
op.{name}.backend	string	""	Backend implementation class name	op.build_query_op.backend=build_query_op
op.{name}.prompt_file_path	string	""	Path to prompt template file	op.react_op.prompt_file_path=/path/to/prompt.yaml
op.{name}.prompt_dict	dict	{}	Direct prompt configuration dictionary	op.react_op.prompt_dict.system="You are an AI assistant"
op.{name}.llm	string	""	Reference to LLM configuration	op.react_op.llm=default
op.{name}.embedding_model	string	""	Reference to embedding model configuration	op.recall_op.embedding_model=default
op.{name}.vector_store	string	""	Reference to vector store configuration	op.recall_op.vector_store=default
op.{name}.params.{param}	any	{}	Operation-specific parameters	The parameter reference is in operations_documentation.md

LLM Configuration

Parameter	Type	Default Value	Description	Example
llm.{name}.backend	string	""	LLM backend implementation	llm.default.backend=openai_compatible
llm.{name}.model_name	string	""	Model name identifier	llm.default.model_name=qwen3-32b
llm.{name}.params.{param}	any	{}	LLM-specific parameters	llm.default.params.temperature=0.6

Embedding Model Configuration

Parameter	Type	Default Value	Description	Example
embedding_model.{name}.backend	string	""	Embedding model backend implementation	embedding_model.default.backend=openai_compatible
embedding_model.{name}.model_name	string	""	Embedding model name identifier	embedding_model.default.model_name=text-embedding-v4
embedding_model.{name}.params.{param}	any	{}	Model-specific parameters	embedding_model.default.params.dimensions=1024

Vector Store Configuration

Parameter	Type	Default Value	Description	Example
vector_store.{name}.backend	string	""	Vector store backend implementation	vector_store.default.backend=elasticsearch
vector_store.{name}.embedding_model	string	""	Reference to embedding model configuration	vector_store.default.embedding_model=default
vector_store.{name}.params.{param}	any	{}	Vector store-specific parameters	vector_store.default.params.store_dir=file_vector_store

Complete Example

Here's a complete example showing how to configure the entire system:

experiencemaker \
  http_service.port=8080 \
  thread_pool.max_workers=20 \
  llm.default.backend=openai_compatible \
  llm.default.model_name=qwen3-32b \
  llm.default.params.temperature=0.6 \
  embedding_model.default.backend=openai_compatible \
  embedding_model.default.model_name=text-embedding-v4 \
  embedding_model.default.params.dimensions=1024 \
  vector_store.default.backend=elasticsearch \
  vector_store.default.embedding_model=default \

Configuration File vs Command Line

You can also create a YAML configuration file and override specific parameters:

1. Create a custom configuration file (xxx/my_config.yaml)
2. Use it with command-line overrides:

experiencemaker config_path=xxx/my_config.yaml llm.default.model_name=qwen3-32b http_service.port=8080

Parameter Validation

• All parameters are validated according to their types
• Referenced configurations (like llm, embedding_model, vector_store) must exist
• Backend implementations must be registered in their respective registries
• Nested parameters use dot notation for access