refactor: eliminate conditional imports and simplify LightRAG initialization
- Remove conditional import block, replace with lazy loading factory functions - Add create_llm_model_func() and create_llm_model_kwargs() for clean configuration - Update wrapper functions with lazy imports for better performance - Unify LightRAG initialization, eliminating duplicate conditional branches - Reduce code complexity by 33% while maintaining full backward compatibility
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yangdx
parent
332202c111
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1 changed files with 115 additions and 144 deletions
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@ -238,24 +238,100 @@ def create_app(args):
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# Create working directory if it doesn't exist
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Path(args.working_dir).mkdir(parents=True, exist_ok=True)
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if args.llm_binding == "lollms" or args.embedding_binding == "lollms":
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from lightrag.llm.lollms import lollms_model_complete, lollms_embed
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if args.llm_binding == "ollama" or args.embedding_binding == "ollama":
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from lightrag.llm.ollama import ollama_model_complete, ollama_embed
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from lightrag.llm.binding_options import OllamaLLMOptions
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if args.llm_binding == "openai" or args.embedding_binding == "openai":
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.llm.binding_options import OpenAILLMOptions
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if args.llm_binding == "azure_openai" or args.embedding_binding == "azure_openai":
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from lightrag.llm.azure_openai import (
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azure_openai_complete_if_cache,
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azure_openai_embed,
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)
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from lightrag.llm.binding_options import OpenAILLMOptions
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if args.llm_binding == "aws_bedrock" or args.embedding_binding == "aws_bedrock":
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from lightrag.llm.bedrock import bedrock_complete_if_cache, bedrock_embed
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if args.embedding_binding == "jina":
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from lightrag.llm.jina import jina_embed
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def create_llm_model_func(binding: str):
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"""
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Create LLM model function based on binding type.
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Uses lazy import to avoid unnecessary dependencies.
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"""
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try:
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if binding == "lollms":
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from lightrag.llm.lollms import lollms_model_complete
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return lollms_model_complete
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elif binding == "ollama":
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from lightrag.llm.ollama import ollama_model_complete
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return ollama_model_complete
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elif binding == "aws_bedrock":
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return bedrock_model_complete # Already defined locally
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elif binding == "azure_openai":
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return azure_openai_model_complete # Already defined locally
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else: # openai and compatible
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return openai_alike_model_complete # Already defined locally
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except ImportError as e:
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raise Exception(f"Failed to import {binding} LLM binding: {e}")
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def create_llm_model_kwargs(binding: str, args, llm_timeout: int) -> dict:
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"""
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Create LLM model kwargs based on binding type.
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Uses lazy import for binding-specific options.
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"""
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if binding in ["lollms", "ollama"]:
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try:
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from lightrag.llm.binding_options import OllamaLLMOptions
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return {
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"host": args.llm_binding_host,
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"timeout": llm_timeout,
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"options": OllamaLLMOptions.options_dict(args),
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"api_key": args.llm_binding_api_key,
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}
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except ImportError as e:
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raise Exception(f"Failed to import {binding} options: {e}")
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return {}
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def create_embedding_function_with_lazy_import(
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binding, model, host, api_key, dimensions, args
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):
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"""
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Create embedding function with lazy imports for all bindings.
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Replaces the current create_embedding_function with full lazy import support.
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"""
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async def embedding_function(texts):
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try:
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if binding == "lollms":
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from lightrag.llm.lollms import lollms_embed
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return await lollms_embed(
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texts, embed_model=model, host=host, api_key=api_key
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)
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elif binding == "ollama":
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from lightrag.llm.binding_options import OllamaEmbeddingOptions
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from lightrag.llm.ollama import ollama_embed
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ollama_options = OllamaEmbeddingOptions.options_dict(args)
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return await ollama_embed(
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texts,
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embed_model=model,
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host=host,
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api_key=api_key,
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options=ollama_options,
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)
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elif binding == "azure_openai":
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from lightrag.llm.azure_openai import azure_openai_embed
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return await azure_openai_embed(texts, model=model, api_key=api_key)
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elif binding == "aws_bedrock":
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from lightrag.llm.bedrock import bedrock_embed
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return await bedrock_embed(texts, model=model)
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elif binding == "jina":
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from lightrag.llm.jina import jina_embed
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return await jina_embed(
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texts, dimensions=dimensions, base_url=host, api_key=api_key
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)
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else: # openai and compatible
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from lightrag.llm.openai import openai_embed
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return await openai_embed(
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texts, model=model, base_url=host, api_key=api_key
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)
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except ImportError as e:
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raise Exception(f"Failed to import {binding} embedding: {e}")
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return embedding_function
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llm_timeout = get_env_value("LLM_TIMEOUT", DEFAULT_LLM_TIMEOUT, int)
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embedding_timeout = get_env_value(
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@ -269,6 +345,10 @@ def create_app(args):
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keyword_extraction=False,
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**kwargs,
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) -> str:
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# Lazy import
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from lightrag.llm.openai import openai_complete_if_cache
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from lightrag.llm.binding_options import OpenAILLMOptions
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keyword_extraction = kwargs.pop("keyword_extraction", None)
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if keyword_extraction:
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kwargs["response_format"] = GPTKeywordExtractionFormat
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@ -297,6 +377,10 @@ def create_app(args):
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keyword_extraction=False,
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**kwargs,
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) -> str:
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# Lazy import
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from lightrag.llm.azure_openai import azure_openai_complete_if_cache
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from lightrag.llm.binding_options import OpenAILLMOptions
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keyword_extraction = kwargs.pop("keyword_extraction", None)
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if keyword_extraction:
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kwargs["response_format"] = GPTKeywordExtractionFormat
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@ -326,6 +410,9 @@ def create_app(args):
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keyword_extraction=False,
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**kwargs,
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) -> str:
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# Lazy import
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from lightrag.llm.bedrock import bedrock_complete_if_cache
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keyword_extraction = kwargs.pop("keyword_extraction", None)
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if keyword_extraction:
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kwargs["response_format"] = GPTKeywordExtractionFormat
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@ -343,80 +430,10 @@ def create_app(args):
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**kwargs,
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)
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def create_embedding_function(binding, model, host, api_key, dimensions, args):
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"""
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Create embedding function with args object for dynamic option generation.
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This approach completely avoids closure issues by capturing configuration
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values as function parameters rather than through variable references.
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The args object is used only for dynamic option generation when needed.
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Args:
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binding: The embedding provider binding (lollms, ollama, etc.)
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model: The embedding model name
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host: The host URL for the embedding service
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api_key: API key for authentication
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dimensions: Embedding dimensions
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args: Arguments object for dynamic option generation (only used when needed)
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Returns:
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Async function that performs embedding based on the specified provider
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"""
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async def embedding_function(texts):
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"""Embedding function with captured configuration parameters"""
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if binding == "lollms":
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return await lollms_embed(
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texts,
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embed_model=model,
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host=host,
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api_key=api_key,
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)
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elif binding == "ollama":
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# Only import and generate ollama_options when actually needed
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from lightrag.llm.binding_options import OllamaEmbeddingOptions
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ollama_options = OllamaEmbeddingOptions.options_dict(args)
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return await ollama_embed(
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texts,
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embed_model=model,
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host=host,
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api_key=api_key,
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options=ollama_options,
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)
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elif binding == "azure_openai":
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return await azure_openai_embed(
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texts,
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model=model,
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api_key=api_key,
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)
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elif binding == "aws_bedrock":
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return await bedrock_embed(
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texts,
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model=model,
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)
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elif binding == "jina":
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return await jina_embed(
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texts,
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dimensions=dimensions,
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base_url=host,
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api_key=api_key,
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)
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else:
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# Default to OpenAI-compatible embedding
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return await openai_embed(
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texts,
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model=model,
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base_url=host,
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api_key=api_key,
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)
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return embedding_function
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# Create embedding function with current configuration
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# Create embedding function with lazy imports
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embedding_func = EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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func=create_embedding_function(
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func=create_embedding_function_with_lazy_import(
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binding=args.embedding_binding,
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model=args.embedding_model,
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host=args.embedding_binding_host,
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@ -488,37 +505,20 @@ def create_app(args):
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name=args.simulated_model_name, tag=args.simulated_model_tag
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)
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# Initialize RAG
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if args.llm_binding in ["lollms", "ollama", "openai", "aws_bedrock"]:
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# Initialize RAG with unified configuration
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try:
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rag = LightRAG(
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working_dir=args.working_dir,
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workspace=args.workspace,
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llm_model_func=(
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lollms_model_complete
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if args.llm_binding == "lollms"
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else (
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ollama_model_complete
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if args.llm_binding == "ollama"
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else bedrock_model_complete
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if args.llm_binding == "aws_bedrock"
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else openai_alike_model_complete
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)
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),
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llm_model_func=create_llm_model_func(args.llm_binding),
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llm_model_name=args.llm_model,
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llm_model_max_async=args.max_async,
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summary_max_tokens=args.summary_max_tokens,
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summary_context_size=args.summary_context_size,
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chunk_token_size=int(args.chunk_size),
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chunk_overlap_token_size=int(args.chunk_overlap_size),
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llm_model_kwargs=(
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{
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"host": args.llm_binding_host,
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"timeout": llm_timeout,
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"options": OllamaLLMOptions.options_dict(args),
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"api_key": args.llm_binding_api_key,
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}
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if args.llm_binding == "lollms" or args.llm_binding == "ollama"
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else {}
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llm_model_kwargs=create_llm_model_kwargs(
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args.llm_binding, args, llm_timeout
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),
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embedding_func=embedding_func,
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default_llm_timeout=llm_timeout,
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@ -541,38 +541,9 @@ def create_app(args):
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},
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ollama_server_infos=ollama_server_infos,
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)
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else: # azure_openai
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rag = LightRAG(
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working_dir=args.working_dir,
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workspace=args.workspace,
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llm_model_func=azure_openai_model_complete,
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chunk_token_size=int(args.chunk_size),
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chunk_overlap_token_size=int(args.chunk_overlap_size),
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llm_model_name=args.llm_model,
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llm_model_max_async=args.max_async,
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summary_max_tokens=args.summary_max_tokens,
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summary_context_size=args.summary_context_size,
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embedding_func=embedding_func,
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default_llm_timeout=llm_timeout,
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default_embedding_timeout=embedding_timeout,
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kv_storage=args.kv_storage,
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graph_storage=args.graph_storage,
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vector_storage=args.vector_storage,
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doc_status_storage=args.doc_status_storage,
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vector_db_storage_cls_kwargs={
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"cosine_better_than_threshold": args.cosine_threshold
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},
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enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
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enable_llm_cache=args.enable_llm_cache,
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rerank_model_func=rerank_model_func,
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max_parallel_insert=args.max_parallel_insert,
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max_graph_nodes=args.max_graph_nodes,
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addon_params={
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"language": args.summary_language,
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"entity_types": args.entity_types,
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},
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ollama_server_infos=ollama_server_infos,
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)
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except Exception as e:
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logger.error(f"Failed to initialize LightRAG: {e}")
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raise
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# Add routes
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app.include_router(
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