Remove unnesessary CLI arguments, reduce CLI arguments complexity

• Move storage config from CLI
• Move LLM and embedding binding config from CLI
• Remove chunk config from CLI
This commit is contained in:
yangdx
2025-02-21 19:34:17 +08:00
parent 9ba12a4f31
commit a848884a7b

View File

@@ -122,47 +122,6 @@ def parse_args() -> argparse.Namespace:
description="LightRAG FastAPI Server with separate working and input directories" description="LightRAG FastAPI Server with separate working and input directories"
) )
parser.add_argument(
"--kv-storage",
default=get_env_value(
"LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE
),
help=f"KV storage implementation (default: {DefaultRAGStorageConfig.KV_STORAGE})",
)
parser.add_argument(
"--doc-status-storage",
default=get_env_value(
"LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE
),
help=f"Document status storage implementation (default: {DefaultRAGStorageConfig.DOC_STATUS_STORAGE})",
)
parser.add_argument(
"--graph-storage",
default=get_env_value(
"LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE
),
help=f"Graph storage implementation (default: {DefaultRAGStorageConfig.GRAPH_STORAGE})",
)
parser.add_argument(
"--vector-storage",
default=get_env_value(
"LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE
),
help=f"Vector storage implementation (default: {DefaultRAGStorageConfig.VECTOR_STORAGE})",
)
# Bindings configuration
parser.add_argument(
"--llm-binding",
default=get_env_value("LLM_BINDING", "ollama"),
help="LLM binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
)
parser.add_argument(
"--embedding-binding",
default=get_env_value("EMBEDDING_BINDING", "ollama"),
help="Embedding binding to be used. Supported: lollms, ollama, openai (default: from env or ollama)",
)
# Server configuration # Server configuration
parser.add_argument( parser.add_argument(
"--host", "--host",
@@ -188,65 +147,6 @@ def parse_args() -> argparse.Namespace:
help="Directory containing input documents (default: from env or ./inputs)", help="Directory containing input documents (default: from env or ./inputs)",
) )
# LLM Model configuration
parser.add_argument(
"--llm-binding-host",
default=get_env_value("LLM_BINDING_HOST", None),
help="LLM server host URL. If not provided, defaults based on llm-binding:\n"
+ "- ollama: http://localhost:11434\n"
+ "- lollms: http://localhost:9600\n"
+ "- openai: https://api.openai.com/v1",
)
default_llm_api_key = get_env_value("LLM_BINDING_API_KEY", None)
parser.add_argument(
"--llm-binding-api-key",
default=default_llm_api_key,
help="llm server API key (default: from env or empty string)",
)
parser.add_argument(
"--llm-model",
default=get_env_value("LLM_MODEL", "mistral-nemo:latest"),
help="LLM model name (default: from env or mistral-nemo:latest)",
)
# Embedding model configuration
parser.add_argument(
"--embedding-binding-host",
default=get_env_value("EMBEDDING_BINDING_HOST", None),
help="Embedding server host URL. If not provided, defaults based on embedding-binding:\n"
+ "- ollama: http://localhost:11434\n"
+ "- lollms: http://localhost:9600\n"
+ "- openai: https://api.openai.com/v1",
)
default_embedding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
parser.add_argument(
"--embedding-binding-api-key",
default=default_embedding_api_key,
help="embedding server API key (default: from env or empty string)",
)
parser.add_argument(
"--embedding-model",
default=get_env_value("EMBEDDING_MODEL", "bge-m3:latest"),
help="Embedding model name (default: from env or bge-m3:latest)",
)
parser.add_argument(
"--chunk_size",
default=get_env_value("CHUNK_SIZE", 1200),
help="chunk chunk size default 1200",
)
parser.add_argument(
"--chunk_overlap_size",
default=get_env_value("CHUNK_OVERLAP_SIZE", 100),
help="chunk overlap size default 100",
)
def timeout_type(value): def timeout_type(value):
if value is None or value == "None": if value is None or value == "None":
return None return None
@@ -272,18 +172,6 @@ def parse_args() -> argparse.Namespace:
default=get_env_value("MAX_TOKENS", 32768, int), default=get_env_value("MAX_TOKENS", 32768, int),
help="Maximum token size (default: from env or 32768)", help="Maximum token size (default: from env or 32768)",
) )
parser.add_argument(
"--embedding-dim",
type=int,
default=get_env_value("EMBEDDING_DIM", 1024, int),
help="Embedding dimensions (default: from env or 1024)",
)
parser.add_argument(
"--max-embed-tokens",
type=int,
default=get_env_value("MAX_EMBED_TOKENS", 8192, int),
help="Maximum embedding token size (default: from env or 8192)",
)
# Logging configuration # Logging configuration
parser.add_argument( parser.add_argument(
@@ -376,6 +264,42 @@ def parse_args() -> argparse.Namespace:
args.working_dir = os.path.abspath(args.working_dir) args.working_dir = os.path.abspath(args.working_dir)
args.input_dir = os.path.abspath(args.input_dir) args.input_dir = os.path.abspath(args.input_dir)
# Inject storage configuration from environment variables
args.kv_storage = get_env_value(
"LIGHTRAG_KV_STORAGE", DefaultRAGStorageConfig.KV_STORAGE
)
args.doc_status_storage = get_env_value(
"LIGHTRAG_DOC_STATUS_STORAGE", DefaultRAGStorageConfig.DOC_STATUS_STORAGE
)
args.graph_storage = get_env_value(
"LIGHTRAG_GRAPH_STORAGE", DefaultRAGStorageConfig.GRAPH_STORAGE
)
args.vector_storage = get_env_value(
"LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE
)
# Inject binding configuration
args.llm_binding = get_env_value("LLM_BINDING", "ollama")
args.embedding_binding = get_env_value("EMBEDDING_BINDING", "ollama")
args.llm_binding_host = get_env_value(
"LLM_BINDING_HOST", get_default_host(args.llm_binding)
)
args.embedding_binding_host = get_env_value(
"EMBEDDING_BINDING_HOST", get_default_host(args.embedding_binding)
)
args.llm_binding_api_key = get_env_value("LLM_BINDING_API_KEY", None)
args.embedding_binding_api_key = get_env_value("EMBEDDING_BINDING_API_KEY", "")
# Inject model configuration
args.llm_model = get_env_value("LLM_MODEL", "mistral-nemo:latest")
args.embedding_model = get_env_value("EMBEDDING_MODEL", "bge-m3:latest")
args.embedding_dim = get_env_value("EMBEDDING_DIM", 1024, int)
args.max_embed_tokens = get_env_value("MAX_EMBED_TOKENS", 8192, int)
# Inject chunk configuration
args.chunk_size = get_env_value("CHUNK_SIZE", 1200, int)
args.chunk_overlap_size = get_env_value("CHUNK_OVERLAP_SIZE", 100, int)
ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
return args return args