Fix lambda closure bug in embedding function configuration
• Replace lambda with proper async function • Capture config values at creation time • Avoid closure variable reference issues • Add factory function for embeddings • Remove test file for closure bug
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yangdx
parent
414d47d12a
commit
332202c111
2 changed files with 78 additions and 134 deletions
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@ -237,6 +237,7 @@ 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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@ -253,8 +254,6 @@ def create_app(args):
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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 == "ollama":
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from lightrag.llm.binding_options import OllamaEmbeddingOptions
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if args.embedding_binding == "jina":
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from lightrag.llm.jina import jina_embed
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@ -344,63 +343,86 @@ def create_app(args):
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**kwargs,
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)
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embedding_binding = args.embedding_binding
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embedding_model = args.embedding_model
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embedding_host = args.embedding_binding_host
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embedding_api_key = args.embedding_binding_api_key
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embedding_dim_val = args.embedding_dim
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ollama_options_val = OllamaEmbeddingOptions.options_dict(args)
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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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embedding_func = EmbeddingFunc(
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embedding_dim=args.embedding_dim,
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func=lambda texts: (
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lollms_embed(
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texts,
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embed_model=embedding_model,
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host=embedding_host,
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api_key=embedding_api_key,
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)
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if embedding_binding == "lollms"
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else (
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ollama_embed(
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texts,
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embed_model=embedding_model,
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host=embedding_host,
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api_key=embedding_api_key,
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options=ollama_options_val,
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)
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if embedding_binding == "ollama"
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else (
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azure_openai_embed(
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texts,
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model=embedding_model, # no host is used for openai,
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api_key=embedding_api_key,
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)
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if embedding_binding == "azure_openai"
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else (
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bedrock_embed(
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texts,
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model=embedding_model,
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)
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if embedding_binding == "aws_bedrock"
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else (
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jina_embed(
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texts,
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dimensions=embedding_dim_val,
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base_url=embedding_host,
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api_key=embedding_api_key,
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)
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if embedding_binding == "jina"
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else openai_embed(
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texts,
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model=embedding_model,
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base_url=embedding_host,
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api_key=embedding_api_key,
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)
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)
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)
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)
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)
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func=create_embedding_function(
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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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dimensions=args.embedding_dim,
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args=args, # Pass args object for dynamic option generation
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),
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)
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@ -1,78 +0,0 @@
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"""
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Tests the fix for the lambda closure bug in the API server's embedding function.
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Issue: https://github.com/HKUDS/LightRAG/issues/2023
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"""
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import pytest
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from unittest.mock import Mock, patch, AsyncMock
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import numpy as np
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# Functions to be patched
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from lightrag.llm.ollama import ollama_embed
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from lightrag.llm.openai import openai_embed
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@pytest.fixture
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def mock_args():
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"""Provides a mock of the server's arguments object."""
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args = Mock()
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args.embedding_binding = "ollama"
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args.embedding_model = "mxbai-embed-large:latest"
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args.embedding_binding_host = "http://localhost:11434"
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args.embedding_binding_api_key = None
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args.embedding_dim = 1024
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args.OllamaEmbeddingOptions.options_dict.return_value = {"num_ctx": 4096}
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return args
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@pytest.mark.asyncio
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@patch("lightrag.llm.openai.openai_embed", new_callable=AsyncMock)
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@patch("lightrag.llm.ollama.ollama_embed", new_callable=AsyncMock)
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async def test_embedding_func_captures_values_correctly(
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mock_ollama_embed, mock_openai_embed, mock_args
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):
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"""
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Verifies that the embedding function correctly captures configuration
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values at creation time and is not affected by later mutations of its source.
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"""
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# --- Setup Mocks ---
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mock_ollama_embed.return_value = np.array([[0.1, 0.2, 0.3]])
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mock_openai_embed.return_value = np.array([[0.4, 0.5, 0.6]])
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# --- SIMULATE THE FIX: Capture values before creating the function ---
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binding = mock_args.embedding_binding
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model = mock_args.embedding_model
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host = mock_args.embedding_binding_host
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api_key = mock_args.embedding_binding_api_key
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# CORRECTED: Use an async def instead of a lambda
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async def fixed_func(texts):
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if binding == "ollama":
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return await ollama_embed(
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texts, embed_model=model, host=host, api_key=api_key
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)
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else:
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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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# --- VERIFICATION ---
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# 1. First call: The function should use the initial "ollama" binding.
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await fixed_func(["hello world"])
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mock_ollama_embed.assert_awaited_once()
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mock_openai_embed.assert_not_called()
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# 2. CRITICAL STEP: Mutate the original args object AFTER the function is created.
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mock_args.embedding_binding = "openai"
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# 3. Reset mocks and call the function AGAIN.
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mock_ollama_embed.reset_mock()
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mock_openai_embed.reset_mock()
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await fixed_func(["see you again"])
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# 4. Final check: The function should STILL call ollama_embed.
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mock_ollama_embed.assert_awaited_once()
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mock_openai_embed.assert_not_called()
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