116 lines
3.2 KiB
Python
116 lines
3.2 KiB
Python
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import os
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import re
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import json
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import asyncio
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from lightrag import LightRAG, QueryParam
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from tqdm import tqdm
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from lightrag.llm import openai_complete_if_cache, openai_embedding
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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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_embedding(
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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 extract_queries(file_path):
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with open(file_path, "r") as f:
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data = f.read()
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data = data.replace("**", "")
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queries = re.findall(r"- Question \d+: (.+)", data)
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return queries
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async def process_query(query_text, rag_instance, query_param):
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try:
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result = await rag_instance.aquery(query_text, param=query_param)
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return {"query": query_text, "result": result}, None
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except Exception as e:
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return None, {"query": query_text, "error": str(e)}
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def always_get_an_event_loop() -> asyncio.AbstractEventLoop:
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try:
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loop = asyncio.get_event_loop()
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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return loop
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def run_queries_and_save_to_json(
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queries, rag_instance, query_param, output_file, error_file
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):
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loop = always_get_an_event_loop()
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with open(output_file, "a", encoding="utf-8") as result_file, open(
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error_file, "a", encoding="utf-8"
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) as err_file:
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result_file.write("[\n")
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first_entry = True
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for query_text in tqdm(queries, desc="Processing queries", unit="query"):
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result, error = loop.run_until_complete(
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process_query(query_text, rag_instance, query_param)
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)
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if result:
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if not first_entry:
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result_file.write(",\n")
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json.dump(result, result_file, ensure_ascii=False, indent=4)
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first_entry = False
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elif error:
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json.dump(error, err_file, ensure_ascii=False, indent=4)
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err_file.write("\n")
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result_file.write("\n]")
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if __name__ == "__main__":
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cls = "mix"
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mode = "hybrid"
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WORKING_DIR = f"../{cls}"
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rag = LightRAG(working_dir=WORKING_DIR)
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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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query_param = QueryParam(mode=mode)
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base_dir = "../datasets/questions"
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queries = extract_queries(f"{base_dir}/{cls}_questions.txt")
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run_queries_and_save_to_json(
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queries, rag, query_param, f"{base_dir}/result.json", f"{base_dir}/errors.json"
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)
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