File size: 1,641 Bytes
e03ffeb 0553d6a e03ffeb 2ababad e03ffeb 2ababad e03ffeb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
# WorkingDir
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
print(f"WorkingDir: {WORKING_DIR}")
# mongo
os.environ["MONGO_URI"] = "mongodb://root:root@localhost:27017/"
os.environ["MONGO_DATABASE"] = "LightRAG"
# neo4j
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "neo4j"
# milvus
os.environ["MILVUS_URI"] = "http://localhost:19530"
os.environ["MILVUS_USER"] = "root"
os.environ["MILVUS_PASSWORD"] = "root"
os.environ["MILVUS_DB_NAME"] = "lightrag"
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="qwen2.5:14b",
llm_model_max_async=4,
llm_model_max_token_size=32768,
llm_model_kwargs={"host": "http://127.0.0.1:11434", "options": {"num_ctx": 32768}},
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: ollama_embed(
texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
),
),
kv_storage="MongoKVStorage",
graph_storage="Neo4JStorage",
vector_storage="MilvusVectorDBStorge",
)
file = "./book.txt"
with open(file, "r") as f:
rag.insert(f.read())
print(
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
)
|