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import sys
import os
from pathlib import Path
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm import openai_complete_if_cache, openai_embedding
from lightrag.utils import EmbeddingFunc
import numpy as np
from lightrag.kg.oracle_impl import OracleDB
print(os.getcwd())
script_directory = Path(__file__).resolve().parent.parent
sys.path.append(os.path.abspath(script_directory))
WORKING_DIR = "./dickens"
# We use OpenAI compatible API to call LLM on Oracle Cloud
# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway
BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/"
APIKEY = "ocigenerativeai"
CHATMODEL = "cohere.command-r-plus"
EMBEDMODEL = "cohere.embed-multilingual-v3.0"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], **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 openai_embedding(
texts,
model=EMBEDMODEL,
api_key=APIKEY,
base_url=BASE_URL,
)
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 Oracle DB connection
# The `config` parameter is the connection configuration of Oracle DB
# More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html
# We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
# Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
oracle_db = OracleDB(
config={
"user": "username",
"password": "xxxxxxxxx",
"dsn": "xxxxxxx_medium",
"config_dir": "dir/path/to/oracle/config",
"wallet_location": "dir/path/to/oracle/wallet",
"wallet_password": "xxxxxxxxx",
"workspace": "company", # specify which docs you want to store and query
}
)
# Check if Oracle DB tables exist, if not, tables will be created
await oracle_db.check_tables()
# Initialize LightRAG
# We use Oracle DB as the KV/vector/graph storage
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,
),
graph_storage="OracleGraphStorage",
kv_storage="OracleKVStorage",
vector_storage="OracleVectorDBStorage",
)
# Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
rag.graph_storage_cls.db = oracle_db
rag.key_string_value_json_storage_cls.db = oracle_db
rag.vector_db_storage_cls.db = oracle_db
# 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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