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