mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-11-04 03:39:35 +00:00
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
import os
|
||
from lightrag import LightRAG, QueryParam
|
||
from lightrag.llm.ollama import ollama_embed, openai_complete_if_cache
|
||
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}")
|
||
|
||
# redis
|
||
os.environ["REDIS_URI"] = "redis://localhost:6379"
|
||
|
||
# neo4j
|
||
BATCH_SIZE_NODES = 500
|
||
BATCH_SIZE_EDGES = 100
|
||
os.environ["NEO4J_URI"] = "bolt://117.50.173.35:7687"
|
||
os.environ["NEO4J_USERNAME"] = "neo4j"
|
||
os.environ["NEO4J_PASSWORD"] = "12345678"
|
||
|
||
# milvus
|
||
os.environ["MILVUS_URI"] = "http://117.50.173.35:19530"
|
||
os.environ["MILVUS_USER"] = "root"
|
||
os.environ["MILVUS_PASSWORD"] = "Milvus"
|
||
os.environ["MILVUS_DB_NAME"] = "lightrag"
|
||
|
||
|
||
async def llm_model_func(
|
||
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
|
||
) -> str:
|
||
return await openai_complete_if_cache(
|
||
"deepseek-chat",
|
||
prompt,
|
||
system_prompt=system_prompt,
|
||
history_messages=history_messages,
|
||
api_key="sk-91d0b59f25554251aa813ed756d79a6d",
|
||
base_url="https://api.deepseek.com",
|
||
**kwargs,
|
||
)
|
||
|
||
|
||
embedding_func = EmbeddingFunc(
|
||
embedding_dim=768,
|
||
max_token_size=512,
|
||
func=lambda texts: ollama_embed(
|
||
texts, embed_model="shaw/dmeta-embedding-zh", host="http://117.50.173.35:11434"
|
||
),
|
||
)
|
||
|
||
rag = LightRAG(
|
||
working_dir=WORKING_DIR,
|
||
llm_model_func=llm_model_func,
|
||
llm_model_max_token_size=32768,
|
||
embedding_func=embedding_func,
|
||
chunk_token_size=512,
|
||
chunk_overlap_token_size=256,
|
||
kv_storage="RedisKVStorage",
|
||
graph_storage="Neo4JStorage",
|
||
vector_storage="MilvusVectorDBStorage",
|
||
doc_status_storage="RedisKVStorage",
|
||
)
|
||
|
||
file = "../book.txt"
|
||
with open(file, "r", encoding="utf-8") as f:
|
||
rag.insert(f.read())
|
||
|
||
|
||
print(rag.query("谁会3D建模 ？", param=QueryParam(mode="mix")))
|