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import os
from lightrag import LightRAG, QueryParam
from lightrag.wrapper.llama_index_impl import (
llama_index_complete_if_cache,
llama_index_embed,
)
from lightrag.utils import EmbeddingFunc
from llama_index.llms.litellm import LiteLLM
from llama_index.embeddings.litellm import LiteLLMEmbedding
import asyncio
# Configure working directory
DEFAULT_RAG_DIR = "index_default"
WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
print(f"WORKING_DIR: {WORKING_DIR}")
# Model configuration
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o")
print(f"LLM_MODEL: {LLM_MODEL}")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "embedding-model")
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}")
# LiteLLM configuration
LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000")
print(f"LITELLM_URL: {LITELLM_URL}")
LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-1234")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
# Initialize LLM function
async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
try:
# Initialize LiteLLM if not in kwargs
if "llm_instance" not in kwargs:
llm_instance = LiteLLM(
model=f"openai/{LLM_MODEL}", # Format: "provider/model_name"
api_base=LITELLM_URL,
api_key=LITELLM_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 = LiteLLMEmbedding(
model_name=f"openai/{EMBEDDING_MODEL}",
api_base=LITELLM_URL,
api_key=LITELLM_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
# Initialize RAG instance
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=asyncio.run(get_embedding_dim()),
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
func=embedding_func,
),
)
# 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"))
)
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