dd1413f3eb
Add comprehensive test suites for prompt evaluation: - test_prompt_accuracy.py: 365 lines testing prompt extraction accuracy - test_prompt_quality_deep.py: 672 lines for deep quality analysis - Refactor prompt.py to consolidate optimized variants (removed prompt_optimized.py) - Apply ruff formatting and type hints across 30 files - Update pyrightconfig.json for static type checking - Modernize reproduce scripts and examples with improved type annotations - Sync uv.lock dependencies
142 lines
4.1 KiB
Python
142 lines
4.1 KiB
Python
import asyncio
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import os
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import nest_asyncio
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from llama_index.embeddings.litellm import LiteLLMEmbedding
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from llama_index.llms.litellm import LiteLLM
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.llama_index_impl import (
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llama_index_complete_if_cache,
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llama_index_embed,
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)
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from lightrag.utils import EmbeddingFunc
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nest_asyncio.apply()
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# Configure working directory
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WORKING_DIR = './index_default'
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print(f'WORKING_DIR: {WORKING_DIR}')
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# Model configuration
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LLM_MODEL = os.environ.get('LLM_MODEL', 'gemma-3-4b')
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print(f'LLM_MODEL: {LLM_MODEL}')
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EMBEDDING_MODEL = os.environ.get('EMBEDDING_MODEL', 'arctic-embed')
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print(f'EMBEDDING_MODEL: {EMBEDDING_MODEL}')
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get('EMBEDDING_MAX_TOKEN_SIZE', 8192))
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print(f'EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}')
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# LiteLLM configuration
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LITELLM_URL = os.environ.get('LITELLM_URL', 'http://localhost:4000')
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print(f'LITELLM_URL: {LITELLM_URL}')
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LITELLM_KEY = os.environ.get('LITELLM_KEY', '')
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if not LITELLM_KEY:
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raise ValueError('LITELLM_KEY environment variable must be set')
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# Initialize LLM function
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async def llm_model_func(prompt, system_prompt=None, history_messages=None, **kwargs):
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if history_messages is None:
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history_messages = []
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try:
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# Initialize LiteLLM if not in kwargs
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if 'llm_instance' not in kwargs:
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llm_instance = LiteLLM(
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model=f'openai/{LLM_MODEL}', # Format: "provider/model_name"
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api_base=LITELLM_URL,
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api_key=LITELLM_KEY,
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temperature=0.7,
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)
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kwargs['llm_instance'] = llm_instance
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chat_kwargs = {}
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chat_kwargs['litellm_params'] = {
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'metadata': {
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'opik': {
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'project_name': 'lightrag_llamaindex_litellm_opik_demo',
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'tags': ['lightrag', 'litellm'],
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}
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}
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}
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response = await llama_index_complete_if_cache(
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kwargs['llm_instance'],
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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chat_kwargs=chat_kwargs,
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)
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return response
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except Exception as e:
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print(f'LLM request failed: {e!s}')
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raise
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# Initialize embedding function
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async def embedding_func(texts):
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try:
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embed_model = LiteLLMEmbedding(
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model_name=f'openai/{EMBEDDING_MODEL}',
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api_base=LITELLM_URL,
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api_key=LITELLM_KEY,
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)
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return await llama_index_embed(texts, embed_model=embed_model)
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except Exception as e:
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print(f'Embedding failed: {e!s}')
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raise
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# Get embedding dimension
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async def get_embedding_dim():
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test_text = ['This is a test sentence.']
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embedding = await embedding_func(test_text)
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embedding_dim = embedding.shape[1]
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print(f'embedding_dim={embedding_dim}')
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return embedding_dim
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async def initialize_rag():
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embedding_dimension = await get_embedding_dim()
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
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func=embedding_func,
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),
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)
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await rag.initialize_storages() # Auto-initializes pipeline_status
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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# Insert example text
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with open('./book.txt', encoding='utf-8') as f:
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rag.insert(f.read())
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# Test different query modes
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print('\nNaive Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='naive')))
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print('\nLocal Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='local')))
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print('\nGlobal Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='global')))
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print('\nHybrid Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='hybrid')))
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if __name__ == '__main__':
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main()
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