mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-07-03 23:19:22 +00:00
100 lines
2.9 KiB
Python
100 lines
2.9 KiB
Python
import os
|
|
import logging
|
|
import numpy as np
|
|
|
|
from dotenv import load_dotenv
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
from openai import AzureOpenAI
|
|
from lightrag import LightRAG, QueryParam
|
|
from lightrag.utils import EmbeddingFunc
|
|
|
|
# Configure Logging
|
|
logging.basicConfig(level=logging.INFO)
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
|
|
AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
|
|
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
|
|
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
|
|
|
|
|
|
async def llm_model_func(
|
|
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
|
|
) -> str:
|
|
# Create a client for AzureOpenAI
|
|
client = AzureOpenAI(
|
|
api_key=AZURE_OPENAI_API_KEY,
|
|
api_version=AZURE_OPENAI_API_VERSION,
|
|
azure_endpoint=AZURE_OPENAI_ENDPOINT,
|
|
)
|
|
|
|
# Build the messages list for the conversation
|
|
messages = []
|
|
if system_prompt:
|
|
messages.append({"role": "system", "content": system_prompt})
|
|
if history_messages:
|
|
messages.extend(history_messages)
|
|
messages.append({"role": "user", "content": prompt})
|
|
|
|
# Call the LLM
|
|
chat_completion = client.chat.completions.create(
|
|
model=AZURE_OPENAI_DEPLOYMENT,
|
|
messages=messages,
|
|
temperature=kwargs.get("temperature", 0),
|
|
top_p=kwargs.get("top_p", 1),
|
|
n=kwargs.get("n", 1),
|
|
)
|
|
|
|
return chat_completion.choices[0].message.content
|
|
|
|
|
|
async def embedding_func(texts: list[str]) -> np.ndarray:
|
|
model = SentenceTransformer("all-MiniLM-L6-v2")
|
|
embeddings = model.encode(texts, convert_to_numpy=True)
|
|
return embeddings
|
|
|
|
|
|
def main():
|
|
WORKING_DIR = "./dickens"
|
|
|
|
# Initialize LightRAG with the LLM model function and embedding function
|
|
rag = LightRAG(
|
|
working_dir=WORKING_DIR,
|
|
llm_model_func=llm_model_func,
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=384,
|
|
max_token_size=8192,
|
|
func=embedding_func,
|
|
),
|
|
vector_storage="FaissVectorDBStorage",
|
|
vector_db_storage_cls_kwargs={
|
|
"cosine_better_than_threshold": 0.3 # Your desired threshold
|
|
},
|
|
)
|
|
|
|
# Insert the custom chunks into LightRAG
|
|
book1 = open("./book_1.txt", encoding="utf-8")
|
|
book2 = open("./book_2.txt", encoding="utf-8")
|
|
|
|
rag.insert([book1.read(), book2.read()])
|
|
|
|
query_text = "What are the main themes?"
|
|
|
|
print("Result (Naive):")
|
|
print(rag.query(query_text, param=QueryParam(mode="naive")))
|
|
|
|
print("\nResult (Local):")
|
|
print(rag.query(query_text, param=QueryParam(mode="local")))
|
|
|
|
print("\nResult (Global):")
|
|
print(rag.query(query_text, param=QueryParam(mode="global")))
|
|
|
|
print("\nResult (Hybrid):")
|
|
print(rag.query(query_text, param=QueryParam(mode="hybrid")))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|