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89 lines
2.2 KiB
89 lines
2.2 KiB
import os
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import json
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import time
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import asyncio
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import numpy as np
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from lightrag import LightRAG
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from lightrag.utils import EmbeddingFunc
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.kg.shared_storage import initialize_pipeline_status
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## For Upstage API
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# please check if embedding_dim=4096 in lightrag.py and llm.py in lightrag direcotry
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"solar-mini",
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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=os.getenv("UPSTAGE_API_KEY"),
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base_url="https://api.upstage.ai/v1/solar",
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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 openai_embed(
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texts,
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model="solar-embedding-1-large-query",
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api_key=os.getenv("UPSTAGE_API_KEY"),
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base_url="https://api.upstage.ai/v1/solar",
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)
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## /For Upstage API
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def insert_text(rag, file_path):
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with open(file_path, mode="r") as f:
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unique_contexts = json.load(f)
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retries = 0
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max_retries = 3
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while retries < max_retries:
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try:
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rag.insert(unique_contexts)
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break
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except Exception as e:
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retries += 1
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print(f"Insertion failed, retrying ({retries}/{max_retries}), error: {e}")
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time.sleep(10)
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if retries == max_retries:
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print("Insertion failed after exceeding the maximum number of retries")
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cls = "mix"
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WORKING_DIR = f"../{cls}"
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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 initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=4096, max_token_size=8192, func=embedding_func
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),
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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insert_text(rag, f"../datasets/unique_contexts/{cls}_unique_contexts.json")
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if __name__ == "__main__":
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main()
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