from citekit.cite_modules.LLM import LLM from citekit.cite_modules.augment_model import ( Retriever, CitationSimplyfier, Verifier, Ranker, ) from citekit.pipeline.pipeline import Pipeline, PIPELINE_OUTPUT, PIPELINE_DOC_CACHE from citekit.prompt.prompt import Prompt, ALCEDocPrompt, DocPrompt, NewALCEVanillaPrompt from citekit.Dataset.Dataset import PromptDataset from citekit.evaluator.evaluator import ( DefaultEvaluator, compute_autoais, test_compute_autoais, ) from citekit.utils.utils import ( sentence, one_paragraph, each_make_as, each_make_as, make_as, remove_citations, compute_str_em, ) import json import argparse def segment(i, text): return [make_as("docs")(doc) for doc in text.split("\n") if doc] if __name__ == "__main__": # SETTING ARGS parser = argparse.ArgumentParser() parser.add_argument( "--save_path", type=str, default="res.json", help="Path to the config file" ) parser.add_argument( "--model", type=str, default="gpt-3.5-turbo", help="model name or path" ) parser.add_argument("--shots", type=int, default=2, help="number of shots") parser.add_argument("--ndoc", type=int, default=5, help="number of docs") parser.add_argument("--pr", action="store_true", help="use cite PR") parser.add_argument("--rouge", action="store_true", help="use rouge") parser.add_argument("--temp", type=float, default=0.5, help="temperature") parser.add_argument("--qa", action="store_true", help="eval qa") parser.add_argument("--mauve", action="store_true", help="eval mauve") parser.add_argument("--length", type=bool, default=True, help="eval length") parser.add_argument("--claims", action="store_true", help="eval claims") parser.add_argument("--qampari", type=str, default=False, help="eval qampari") parser.add_argument( "--dataset", type=str, default="data/asqa_eval_gtr_top100.json", help="dataset" ) parser.add_argument( "--demo", type=str, default="prompts/asqa_default.json", help="demo" ) parser.add_argument("--doctype", type=str, default="text", help="demo") parser.add_argument("--data_num", type=int, default=1000, help="num of data") parser.add_argument( "--mode", type=str, default="text", help="mode-granularity: text, extraction or summary", ) parser.add_argument("--k", type=float, default=1.5, help="coefficient of em") parser.add_argument("--topk", type=int, default=2, help="topk") args = parser.parse_args() def score(data): pr = compute_autoais(data) p = pr["citation_prec"] r = pr["citation_rec"] em = compute_str_em(data) return p + r + args.k * em return 1 # DATA LOADING file_path = args.dataset demo_path = args.demo with open(file_path, "r", encoding="utf-8") as file: dataset = json.load(file) with open(demo_path, "r", encoding="utf-8") as file: demo = json.load(file) data_num = min(args.data_num, len(dataset)) llm_instruction = demo["one_sentence_instruction"] query_inst = demo["query_instruction"] shots = "\n\n".join( NewALCEVanillaPrompt().load_data( [demo["demos"][1], demo["demos"][3]], "question", answer=lambda data: remove_citations( sentence("first")(data["answer"])["first"] ), INST=lambda _: llm_instruction, docs=lambda data: "".join( ALCEDocPrompt().default_load_data(data["docs"][1:2]) ), ) ) documents = [ DocPrompt().load_data( list(enumerate(data["docs"])), Title=lambda data: data[1]["title"], Passage=lambda data: data[1][args.mode], ) for data in dataset ] dataset = PromptDataset( dataset, "question", "answer", "answers", "qa_pairs", "claims", docs=lambda data: ALCEDocPrompt().default_load_data(data["docs"][: args.ndoc]), )[:data_num] prompt = Prompt( template="\nAnswer:", components={ "INST": "{INST}\n\n", "shots": "{shots}\n", "question": "Question:{question}\n\n", "ans": "Prefix:{ans}\n\n", "docs": "{docs}\n", }, ) queryprompt = Prompt( template="Please generate one query to help find relevent documents. If previous queries are provided, you shoud focus on an alternative perspective or subtopic different from the provided ones, enhancing diversity in retrieved documents. your query is:\n", components={ "question": "Given the original question: {question}\n", "ans": "The context is: {ans}\n", "prev": "\nPrevious queries:\n{prev}\n\n", "INST": "{INST}\n\n", }, ) retriever_prompt = Prompt(template="", components={"query": "{query}"}) query_generator = LLM( model=args.model, prompt_maker=queryprompt, self_prompt={"INST": query_inst} ) retriever_prompt = Prompt(template="", components={"query": "{query}"}) eval = DefaultEvaluator(args) ranker = Ranker(max_turn=2, iterative=True) # ranker.set_eval('length', output = 'answer') # ranker.new_eval('score', score , output = 'answer', docs = 'doc_cache', qa_pairs = 'qa_pairs') ranker.new_eval("score", score, output="answer", docs="doc_cache") # PIPELINE CONSTRUCTING llm = LLM( model=args.model, prompt_maker=prompt, self_prompt={"INST": llm_instruction, "shots": shots}, max_turn=10, auto_cite=True, share_model_with=query_generator, parallel=True, ) pipeline = Pipeline( save_path=args.save_path, llm=llm, module=[ranker, query_generator], head_prompt_maker=prompt, evaluator=eval, dataset=dataset, ) retriever = Retriever( prompt_maker=retriever_prompt, pipeline=pipeline, retrieve_by="bm25", topk=args.topk, documents=documents, ) query_generator.set_target(retriever, post_processing=make_as("query")) query_generator.add_to_head("prev", sub=False) retriever.set_target(llm, post_processing=segment) llm.set_target(ranker, post_processing=make_as("answer")) ranker.set_output(post_processing=lambda x: x["answer"], end=False) ranker.add_to_head( "ans", sub=True, process=lambda text: one_paragraph(text["answer"]) ) ranker.set_target(query_generator, post_processing=lambda x: {"ans": x["answer"]}) pipeline.set_initial_module(query_generator) pipeline.set_data_keys(["question"]) # graph = PipelineGraph(pipeline=pipeline) # html = graph.generate_html(results='results.json') # graph.visualize() # print(html) # with open('pipeline_.html','w') as file: # file.write(html) # RUN PIPELINE pipeline.run_on_dataset(datakeys=['question'],initial_module=query_generator)