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import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.llama_index_impl import (
llama_index_complete_if_cache,
llama_index_embed,
)
from lightrag.utils import EmbeddingFunc
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
import asyncio
import nest_asyncio
nest_asyncio.apply()
from lightrag.kg.shared_storage import initialize_pipeline_status
# Configure working directory
WORKING_DIR = "./index_default"
print(f"WORKING_DIR: {WORKING_DIR}")
# Model configuration
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4")
print(f"LLM_MODEL: {LLM_MODEL}")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
# OpenAI configuration
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "your-api-key-here")
if not os.path.exists(WORKING_DIR):
print(f"Creating working directory: {WORKING_DIR}")
os.mkdir(WORKING_DIR)
# Initialize LLM function
async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
try:
# Initialize OpenAI if not in kwargs
if "llm_instance" not in kwargs:
llm_instance = OpenAI(
model=LLM_MODEL,
api_key=OPENAI_API_KEY,
temperature=0.7,
)
kwargs["llm_instance"] = llm_instance
response = await llama_index_complete_if_cache(
kwargs["llm_instance"],
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
**kwargs,
)
return response
except Exception as e:
print(f"LLM request failed: {str(e)}")
raise
# Initialize embedding function
async def embedding_func(texts):
try:
embed_model = OpenAIEmbedding(
model=EMBEDDING_MODEL,
api_key=OPENAI_API_KEY,
)
return await llama_index_embed(texts, embed_model=embed_model)
except Exception as e:
print(f"Embedding failed: {str(e)}")
raise
# Get embedding dimension
async def get_embedding_dim():
test_text = ["This is a test sentence."]
embedding = await embedding_func(test_text)
embedding_dim = embedding.shape[1]
print(f"embedding_dim={embedding_dim}")
return embedding_dim
async def initialize_rag():
embedding_dimension = await get_embedding_dim()
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Insert example text
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Test different query modes
print("\nNaive Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
print("\nLocal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
print("\nGlobal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="global")
)
)
print("\nHybrid Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
)
)
if __name__ == "__main__":
main()
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