methods/self_RAG_attr.py

from citekit.cite_modules.LLM import LLM
from citekit.cite_modules.augment_model import (
    Retriever,
    CitationSimplyfier,
    Verifier,
    Ranker,
    AttributingModule,
)
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="<shots><INST><question><ans><docs><span>\nAnswer:",
        components={
            "INST": "{INST}\n\n",
            "shots": "{shots}\n",
            "question": "Question:{question}\n\n",
            "ans": "Prefix:{ans}\n\n",
            "docs": "{docs}\n",
            "span": "The highlighted spans are: \n{span}\n\n",
        },
    )
    queryprompt = Prompt(
        template="<INST><question><prev><ans>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="<query>", components={"query": "{query}"})

    query_generator = LLM(
        model=args.model, prompt_maker=queryprompt, self_prompt={"INST": query_inst}
    )
    retriever_prompt = Prompt(template="<query>", components={"query": "{query}"})
    eval = DefaultEvaluator(args)
    ranker = Ranker(max_turn=3, 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=30,
        auto_cite=True,
        auto_cite_from = 'span',
        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"])

    attributer = AttributingModule(model=args.model)
    attributer.connect_to(pipeline)
    retriever.set_target(attributer, post_processing=make_as("docs"))
    retriever.add_to_head("docs", sub=True)
    attributer.set_target(llm)
    # 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)