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import os
from lightrag import LightRAG, QueryParam
from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete
from pprint import pprint
#########
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
# import nest_asyncio
# nest_asyncio.apply()
#########
WORKING_DIR = "./dickensTestEmbedcall"
# G = nx.read_graphml('./dickensTestEmbedcall/graph_chunk_entity_relation.graphml')
# nx.write_gexf(G, "graph_chunk_entity_relation.gefx")
import networkx as nx
from networkx_query import search_nodes, search_edges
G = nx.read_graphml('./dickensTestEmbedcall/graph_chunk_entity_relation.graphml')
query = {} # Empty query matches all nodes
result = search_nodes(G, query)
# Extract node IDs from the result
node_ids = sorted([node for node in result])
print("All node IDs in the graph:")
pprint(node_ids)
raise Exception
# raise Exception
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=gpt_4o_mini_complete # Use gpt_4o_mini_complete LLM model
# llm_model_func=gpt_4o_complete # Optionally, use a stronger model
)
with open("./book.txt") as f:
rag.insert(f.read())
# Perform naive search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
# Perform local search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))
# Perform global search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))
# Perform hybrid search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))) |