LightRAG/examples/lightrag_api_llamaindex_direct_demo_simplified.py

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.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
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-4")
print(f"LLM_MODEL: {LLM_MODEL}")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-small")
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):
    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

# 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")))