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import asyncio
import os
import numpy as np
from lightrag import LightRAG, QueryParam
from lightrag.kg.tidb_impl import TiDB
from lightrag.llm import siliconcloud_embedding, openai_complete_if_cache
from lightrag.utils import EmbeddingFunc
WORKING_DIR = "./dickens"
# We use SiliconCloud API to call LLM on Oracle Cloud
# More docs here https://docs.siliconflow.cn/introduction
BASE_URL = "https://api.siliconflow.cn/v1/"
APIKEY = ""
CHATMODEL = ""
EMBEDMODEL = ""
TIDB_HOST = ""
TIDB_PORT = ""
TIDB_USER = ""
TIDB_PASSWORD = ""
TIDB_DATABASE = "lightrag"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
return await openai_complete_if_cache(
CHATMODEL,
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
api_key=APIKEY,
base_url=BASE_URL,
**kwargs,
)
async def embedding_func(texts: list[str]) -> np.ndarray:
return await siliconcloud_embedding(
texts,
# model=EMBEDMODEL,
api_key=APIKEY,
)
async def get_embedding_dim():
test_text = ["This is a test sentence."]
embedding = await embedding_func(test_text)
embedding_dim = embedding.shape[1]
return embedding_dim
async def main():
try:
# Detect embedding dimension
embedding_dimension = await get_embedding_dim()
print(f"Detected embedding dimension: {embedding_dimension}")
# Create TiDB DB connection
tidb = TiDB(
config={
"host": TIDB_HOST,
"port": TIDB_PORT,
"user": TIDB_USER,
"password": TIDB_PASSWORD,
"database": TIDB_DATABASE,
"workspace": "company", # specify which docs you want to store and query
}
)
# Check if TiDB DB tables exist, if not, tables will be created
await tidb.check_tables()
# Initialize LightRAG
# We use TiDB DB as the KV/vector
# You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
rag = LightRAG(
enable_llm_cache=False,
working_dir=WORKING_DIR,
chunk_token_size=512,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=512,
func=embedding_func,
),
kv_storage="TiDBKVStorage",
vector_storage="TiDBVectorDBStorage",
graph_storage="TiDBGraphStorage",
)
if rag.llm_response_cache:
rag.llm_response_cache.db = tidb
rag.full_docs.db = tidb
rag.text_chunks.db = tidb
rag.entities_vdb.db = tidb
rag.relationships_vdb.db = tidb
rag.chunks_vdb.db = tidb
rag.chunk_entity_relation_graph.db = tidb
# Extract and Insert into LightRAG storage
with open("./dickens/demo.txt", "r", encoding="utf-8") as f:
await rag.ainsert(f.read())
# Perform search in different modes
modes = ["naive", "local", "global", "hybrid"]
for mode in modes:
print("=" * 20, mode, "=" * 20)
print(
await rag.aquery(
"What are the top themes in this story?",
param=QueryParam(mode=mode),
)
)
print("-" * 100, "\n")
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
asyncio.run(main())
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