|
import os |
|
import sys |
|
|
|
from lightrag import LightRAG, QueryParam |
|
from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete |
|
from transformers import AutoModel,AutoTokenizer |
|
|
|
WORKING_DIR = "./dickens" |
|
|
|
if not os.path.exists(WORKING_DIR): |
|
os.mkdir(WORKING_DIR) |
|
|
|
rag = LightRAG( |
|
working_dir=WORKING_DIR, |
|
llm_model_func=gpt_4o_mini_complete |
|
|
|
) |
|
|
|
|
|
with open("./book.txt") as f: |
|
rag.insert(f.read()) |
|
|
|
|
|
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))) |
|
|
|
|
|
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))) |
|
|
|
|
|
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))) |
|
|
|
|
|
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))) |
|
|