cherry-pick 5dec4dea
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Raphaël MANSUY
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commit
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1 changed files with 166 additions and 36 deletions
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@ -618,33 +618,108 @@ def create_app(args):
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def create_optimized_embedding_function(
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config_cache: LLMConfigCache, binding, model, host, api_key, args
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):
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) -> EmbeddingFunc:
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"""
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Create optimized embedding function with pre-processed configuration for applicable bindings.
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Uses lazy imports for all bindings and avoids repeated configuration parsing.
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Create optimized embedding function and return an EmbeddingFunc instance
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with proper max_token_size inheritance from provider defaults.
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This function:
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1. Imports the provider embedding function
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2. Extracts max_token_size and embedding_dim from provider if it's an EmbeddingFunc
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3. Creates an optimized wrapper that calls the underlying function directly (avoiding double-wrapping)
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4. Returns a properly configured EmbeddingFunc instance
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"""
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# Step 1: Import provider function and extract default attributes
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provider_func = None
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provider_max_token_size = None
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provider_embedding_dim = None
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try:
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if binding == "openai":
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from lightrag.llm.openai import openai_embed
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provider_func = openai_embed
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elif binding == "ollama":
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from lightrag.llm.ollama import ollama_embed
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provider_func = ollama_embed
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elif binding == "gemini":
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from lightrag.llm.gemini import gemini_embed
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provider_func = gemini_embed
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elif binding == "jina":
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from lightrag.llm.jina import jina_embed
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provider_func = jina_embed
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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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provider_func = azure_openai_embed
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elif binding == "aws_bedrock":
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from lightrag.llm.bedrock import bedrock_embed
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provider_func = bedrock_embed
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elif binding == "lollms":
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from lightrag.llm.lollms import lollms_embed
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provider_func = lollms_embed
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# Extract attributes if provider is an EmbeddingFunc
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if provider_func and isinstance(provider_func, EmbeddingFunc):
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provider_max_token_size = provider_func.max_token_size
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provider_embedding_dim = provider_func.embedding_dim
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logger.debug(
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f"Extracted from {binding} provider: "
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f"max_token_size={provider_max_token_size}, "
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f"embedding_dim={provider_embedding_dim}"
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)
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except ImportError as e:
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logger.warning(f"Could not import provider function for {binding}: {e}")
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# Step 2: Apply priority (user config > provider default)
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# For max_token_size: explicit env var > provider default > None
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final_max_token_size = args.embedding_token_limit or provider_max_token_size
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# For embedding_dim: user config (always has value) takes priority
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# Only use provider default if user config is explicitly None (which shouldn't happen)
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final_embedding_dim = (
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args.embedding_dim if args.embedding_dim else provider_embedding_dim
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)
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# Step 3: Create optimized embedding function (calls underlying function directly)
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async def optimized_embedding_function(texts, embedding_dim=None):
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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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# Get real function, skip EmbeddingFunc wrapper if present
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actual_func = (
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lollms_embed.func
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if isinstance(lollms_embed, EmbeddingFunc)
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else lollms_embed
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)
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return await actual_func(
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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.ollama import ollama_embed
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# Use pre-processed configuration if available, otherwise fallback to dynamic parsing
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# Get real function, skip EmbeddingFunc wrapper if present
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actual_func = (
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ollama_embed.func
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if isinstance(ollama_embed, EmbeddingFunc)
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else ollama_embed
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)
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# Use pre-processed configuration if available
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if config_cache.ollama_embedding_options is not None:
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ollama_options = config_cache.ollama_embedding_options
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else:
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# Fallback for cases where config cache wasn't initialized properly
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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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return await actual_func(
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texts,
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embed_model=model,
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host=host,
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@ -654,30 +729,53 @@ def create_app(args):
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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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actual_func = (
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azure_openai_embed.func
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if isinstance(azure_openai_embed, EmbeddingFunc)
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else azure_openai_embed
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)
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return await actual_func(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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actual_func = (
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bedrock_embed.func
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if isinstance(bedrock_embed, EmbeddingFunc)
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else bedrock_embed
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)
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return await actual_func(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, embedding_dim=embedding_dim, base_url=host, api_key=api_key
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actual_func = (
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jina_embed.func
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if isinstance(jina_embed, EmbeddingFunc)
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else jina_embed
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)
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return await actual_func(
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texts,
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embedding_dim=embedding_dim,
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base_url=host,
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api_key=api_key,
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)
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elif binding == "gemini":
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from lightrag.llm.gemini import gemini_embed
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# Use pre-processed configuration if available, otherwise fallback to dynamic parsing
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actual_func = (
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gemini_embed.func
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if isinstance(gemini_embed, EmbeddingFunc)
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else gemini_embed
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)
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# Use pre-processed configuration if available
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if config_cache.gemini_embedding_options is not None:
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gemini_options = config_cache.gemini_embedding_options
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else:
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# Fallback for cases where config cache wasn't initialized properly
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from lightrag.llm.binding_options import GeminiEmbeddingOptions
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gemini_options = GeminiEmbeddingOptions.options_dict(args)
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return await gemini_embed(
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return await actual_func(
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texts,
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model=model,
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base_url=host,
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@ -688,13 +786,36 @@ def create_app(args):
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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, embedding_dim=embedding_dim
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actual_func = (
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openai_embed.func
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if isinstance(openai_embed, EmbeddingFunc)
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else openai_embed
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)
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return await actual_func(
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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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embedding_dim=embedding_dim,
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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 optimized_embedding_function
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# Step 4: Wrap in EmbeddingFunc and return
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embedding_func_instance = EmbeddingFunc(
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embedding_dim=final_embedding_dim,
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func=optimized_embedding_function,
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max_token_size=final_max_token_size,
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send_dimensions=False, # Will be set later based on binding requirements
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)
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# Log final embedding configuration
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logger.info(
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f"Embedding config: binding={binding} model={model} "
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f"embedding_dim={final_embedding_dim} max_token_size={final_max_token_size}"
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)
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return embedding_func_instance
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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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@ -728,25 +849,24 @@ def create_app(args):
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**kwargs,
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)
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# Create embedding function with optimized configuration
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# Create embedding function with optimized configuration and max_token_size inheritance
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import inspect
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# Create the optimized embedding function
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optimized_embedding_func = create_optimized_embedding_function(
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# Create the EmbeddingFunc instance (now returns complete EmbeddingFunc with max_token_size)
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embedding_func = create_optimized_embedding_function(
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config_cache=config_cache,
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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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api_key=args.embedding_binding_api_key,
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args=args, # Pass args object for fallback option generation
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args=args,
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)
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# Get embedding_send_dim from centralized configuration
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embedding_send_dim = args.embedding_send_dim
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# Check if the function signature has embedding_dim parameter
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# Note: Since optimized_embedding_func is an async function, inspect its signature
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sig = inspect.signature(optimized_embedding_func)
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# Check if the underlying function signature has embedding_dim parameter
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sig = inspect.signature(embedding_func.func)
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has_embedding_dim_param = "embedding_dim" in sig.parameters
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# Determine send_dimensions value based on binding type
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@ -764,23 +884,27 @@ def create_app(args):
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else:
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dimension_control = "by not hasparam"
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# Set send_dimensions on the EmbeddingFunc instance
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embedding_func.send_dimensions = send_dimensions
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logger.info(
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f"Send embedding dimension: {send_dimensions} {dimension_control} "
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f"(dimensions={args.embedding_dim}, has_param={has_embedding_dim_param}, "
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f"(dimensions={embedding_func.embedding_dim}, has_param={has_embedding_dim_param}, "
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f"binding={args.embedding_binding})"
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)
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# Create EmbeddingFunc with send_dimensions attribute
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embedding_func = EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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func=optimized_embedding_func,
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send_dimensions=send_dimensions,
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)
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# Set max_token_size if EMBEDDING_TOKEN_LIMIT is provided
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if args.embedding_token_limit is not None:
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embedding_func.max_token_size = args.embedding_token_limit
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logger.info(f"Set embedding max_token_size to {args.embedding_token_limit}")
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# Log max_token_size source
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if embedding_func.max_token_size:
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source = (
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"env variable"
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if args.embedding_token_limit
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else f"{args.embedding_binding} provider default"
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)
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logger.info(
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f"Embedding max_token_size: {embedding_func.max_token_size} (from {source})"
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)
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else:
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logger.info("Embedding max_token_size: not set (90% token warning disabled)")
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# Configure rerank function based on args.rerank_bindingparameter
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rerank_model_func = None
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@ -1212,6 +1336,12 @@ def check_and_install_dependencies():
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def main():
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# Explicitly initialize configuration for clarity
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# (The proxy will auto-initialize anyway, but this makes intent clear)
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from .config import initialize_config
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initialize_config()
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# Check if running under Gunicorn
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if "GUNICORN_CMD_ARGS" in os.environ:
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# If started with Gunicorn, return directly as Gunicorn will call get_application
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