chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import asyncio
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from metagpt.config2 import config
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from metagpt.const import EXAMPLE_DATA_PATH
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from metagpt.logs import logger
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from metagpt.rag.parsers import OmniParse
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from metagpt.rag.schema import OmniParseOptions, OmniParseType, ParseResultType
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from metagpt.utils.omniparse_client import OmniParseClient
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TEST_DOCX = EXAMPLE_DATA_PATH / "omniparse/test01.docx"
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TEST_PDF = EXAMPLE_DATA_PATH / "omniparse/test02.pdf"
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TEST_VIDEO = EXAMPLE_DATA_PATH / "omniparse/test03.mp4"
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TEST_AUDIO = EXAMPLE_DATA_PATH / "omniparse/test04.mp3"
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async def omniparse_client_example():
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client = OmniParseClient(base_url=config.omniparse.base_url)
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# docx
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with open(TEST_DOCX, "rb") as f:
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file_input = f.read()
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document_parse_ret = await client.parse_document(file_input=file_input, bytes_filename="test_01.docx")
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logger.info(document_parse_ret)
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# pdf
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pdf_parse_ret = await client.parse_pdf(file_input=TEST_PDF)
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logger.info(pdf_parse_ret)
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# video
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video_parse_ret = await client.parse_video(file_input=TEST_VIDEO)
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logger.info(video_parse_ret)
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# audio
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audio_parse_ret = await client.parse_audio(file_input=TEST_AUDIO)
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logger.info(audio_parse_ret)
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async def omniparse_example():
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parser = OmniParse(
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api_key=config.omniparse.api_key,
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base_url=config.omniparse.base_url,
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parse_options=OmniParseOptions(
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parse_type=OmniParseType.PDF,
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result_type=ParseResultType.MD,
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max_timeout=120,
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num_workers=3,
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),
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)
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ret = parser.load_data(file_path=TEST_PDF)
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logger.info(ret)
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file_paths = [TEST_DOCX, TEST_PDF]
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parser.parse_type = OmniParseType.DOCUMENT
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ret = await parser.aload_data(file_path=file_paths)
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logger.info(ret)
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async def main():
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await omniparse_client_example()
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await omniparse_example()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,231 @@
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# -*- coding: utf-8 -*-
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"""RAG benchmark pipeline"""
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import asyncio
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from llama_index.core.node_parser import SentenceSplitter
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from llama_index.core.schema import NodeWithScore
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from metagpt.const import DATA_PATH, EXAMPLE_BENCHMARK_PATH, EXAMPLE_DATA_PATH
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from metagpt.logs import logger
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from metagpt.rag.benchmark import RAGBenchmark
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from metagpt.rag.engines import SimpleEngine
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from metagpt.rag.factories import get_rag_embedding, get_rag_llm
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from metagpt.rag.schema import (
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BM25RetrieverConfig,
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CohereRerankConfig,
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ColbertRerankConfig,
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FAISSIndexConfig,
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FAISSRetrieverConfig,
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)
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from metagpt.utils.common import write_json_file
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DOC_PATH = EXAMPLE_DATA_PATH / "rag_bm/summary_writer.txt"
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QUESTION = "2023年7月20日，应急管理部、财政部联合下发《因灾倒塌、损坏住房恢复重建救助工作规范》的通知，规范倒损住房恢复重建救助相关工作。"
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TRAVEL_DOC_PATH = EXAMPLE_DATA_PATH / "rag_bm/documents.txt"
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TRAVEL_QUESTION = "国家卫生健康委在2023年7月28日开展的“启明行动”是为了防控哪个群体的哪种健康问题，并请列出活动发布的指导性文件名称。"
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DATASET_PATH = EXAMPLE_DATA_PATH / "rag_bm/test.json"
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SAVE_PATH = EXAMPLE_DATA_PATH / "rag_bm/result.json"
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GROUND_TRUTH_TRANVEL = "2023-07-28 10:14:27作者：白剑峰来源：人民日报 ，正文：为在全社会形成重视儿童眼健康的良好氛围，持续推进综合防控儿童青少年近视工作落实，国家卫生健康委决定在全国持续开展“启明行动”——防控儿童青少年近视健康促进活动，并发布了《防控儿童青少年近视核心知识十条》。本次活动的主题为：重视儿童眼保健，守护孩子明眸“视”界。强调预防为主，推动关口前移，倡导和推动家庭及全社会共同行动起来，营造爱眼护眼的视觉友好环境，共同呵护好孩子的眼睛，让他们拥有一个光明的未来。国家卫生健康委要求，开展社会宣传和健康教育。充分利用网络、广播电视、报纸杂志、海报墙报、培训讲座等多种形式，广泛开展宣传倡导，向社会公众传播开展儿童眼保健、保护儿童视力健康的重要意义，以《防控儿童青少年近视核心知识十条》为重点普及预防近视科学知识。创新健康教育方式和载体，开发制作群众喜闻乐见的健康教育科普作品，利用互联网媒体扩大传播效果，提高健康教育的针对性、精准性和实效性。指导相关医疗机构将儿童眼保健和近视防控等科学知识纳入孕妇学校、家长课堂内容。开展儿童眼保健及视力检查咨询指导。医疗机构要以儿童家长和养育人为重点，结合眼保健和眼科临床服务，开展个性化咨询指导。要针对儿童常见眼病和近视防控等重点问题，通过面对面咨询指导，引导儿童家长树立近视防控意识，改变不良生活方式，加强户外活动，养成爱眼护眼健康行为习惯。提高儿童眼保健专科服务能力。各地要积极推进儿童眼保健专科建设，扎实组织好妇幼健康职业技能竞赛“儿童眼保健”项目，推动各层级开展比武练兵，提升业务能力。"
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GROUND_TRUTH_ANSWER = "“启明行动”是为了防控儿童青少年的近视问题，并发布了《防控儿童青少年近视核心知识十条》。"
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LLM_TIP = "If you not sure, just answer I don't know."
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LLM_ERROR = "Retrieve failed due to LLM wasn't follow instruction"
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EMPTY_ERROR = "Empty Response"
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class RAGExample:
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"""Show how to use RAG for evaluation."""
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def __init__(self):
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self.benchmark = RAGBenchmark()
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self.embedding = get_rag_embedding()
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self.llm = get_rag_llm()
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async def rag_evaluate_pipeline(self, dataset_name: list[str] = ["all"]):
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dataset_config = self.benchmark.load_dataset(dataset_name)
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for dataset in dataset_config.datasets:
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if "all" in dataset_name or dataset.name in dataset_name:
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output_dir = DATA_PATH / f"{dataset.name}"
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if output_dir.exists():
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logger.info("Loading Existed index!")
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logger.info(f"Index Path:{output_dir}")
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self.engine = SimpleEngine.from_index(
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index_config=FAISSIndexConfig(persist_path=output_dir),
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ranker_configs=[ColbertRerankConfig()],
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retriever_configs=[FAISSRetrieverConfig(), BM25RetrieverConfig()],
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)
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else:
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logger.info("Loading index from documents!")
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self.engine = SimpleEngine.from_docs(
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input_files=dataset.document_files,
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retriever_configs=[FAISSRetrieverConfig()],
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ranker_configs=[CohereRerankConfig()],
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transformations=[SentenceSplitter(chunk_size=1024, chunk_overlap=0)],
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)
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results = []
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for gt_info in dataset.gt_info:
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result = await self.rag_evaluate_single(
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question=gt_info["question"],
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reference=gt_info["gt_reference"],
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ground_truth=gt_info["gt_answer"],
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)
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results.append(result)
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logger.info(f"=====The {dataset.name} Benchmark dataset assessment is complete!=====")
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self._print_bm_result(results)
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write_json_file((EXAMPLE_BENCHMARK_PATH / dataset.name / "bm_result.json").as_posix(), results, "utf-8")
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async def rag_evaluate_single(self, question, reference, ground_truth, print_title=True):
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"""This example run rag pipeline, use faiss&bm25 retriever and llm ranker, will print something like:
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Retrieve Result:
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0. Productivi..., 10.0
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1. I wrote cu..., 7.0
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2. I highly r..., 5.0
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Query Result:
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Passion, adaptability, open-mindedness, creativity, discipline, and empathy are key qualities to be a good writer.
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RAG BenchMark result:
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{
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'metrics':
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{
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'bleu-avg': 0.48318624982047,
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'bleu-1': 0.5609756097560976,
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'bleu-2': 0.5,
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'bleu-3': 0.46153846153846156,
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'bleu-4': 0.42105263157894735,
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'rouge-L': 0.6865671641791045,
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'semantic similarity': 0.9487444961487591,
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'length': 74
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},
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'log': {
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'generated_text':
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'国家卫生健康委在2023年7月28日开展的“启明行动”是为了防控儿童青少年的近视问题。活动发布的指导性文件名称为《防控儿童青少年近视核心知识十条》。',
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'ground_truth_text':
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'“启明行动”是为了防控儿童青少年的近视问题，并发布了《防控儿童青少年近视核心知识十条》。'
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}
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}
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"""
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if print_title:
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self._print_title("RAG Pipeline")
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try:
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nodes = await self.engine.aretrieve(question)
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self._print_result(nodes, state="Retrieve")
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answer = await self.engine.aquery(question)
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self._print_result(answer, state="Query")
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except Exception as e:
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logger.error(e)
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return self.benchmark.set_metrics(
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generated_text=LLM_ERROR, ground_truth_text=ground_truth, question=question
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)
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result = await self.evaluate_result(answer.response, ground_truth, nodes, reference, question)
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logger.info("==========RAG BenchMark result demo as follows==========")
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logger.info(result)
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return result
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async def rag_faissdb(self):
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"""This example show how to use FAISS. how to save and load index. will print something like:
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Query Result:
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Bob likes traveling.
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"""
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self._print_title("RAG FAISS")
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# save index
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output_dir = DATA_PATH / "rag_faiss"
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SimpleEngine.from_docs(
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input_files=[TRAVEL_DOC_PATH],
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retriever_configs=[FAISSRetrieverConfig(dimensions=512, persist_path=output_dir)],
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)
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# load index
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engine = SimpleEngine.from_index(
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index_config=FAISSIndexConfig(persist_path=output_dir),
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)
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# query
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nodes = engine.retrieve(QUESTION)
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self._print_result(nodes, state="Retrieve")
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answer = engine.query(TRAVEL_QUESTION)
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self._print_result(answer, state="Query")
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async def evaluate_result(
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self,
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response: str = None,
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reference: str = None,
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nodes: list[NodeWithScore] = None,
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reference_doc: list[str] = None,
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question: str = None,
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):
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result = await self.benchmark.compute_metric(response, reference, nodes, reference_doc, question)
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return result
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@staticmethod
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def _print_title(title):
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logger.info(f"{'#'*30} {title} {'#'*30}")
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@staticmethod
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def _print_result(result, state="Retrieve"):
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"""print retrieve or query result"""
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logger.info(f"{state} Result:")
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if state == "Retrieve":
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for i, node in enumerate(result):
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logger.info(f"{i}. {node.text[:10]}..., {node.score}")
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logger.info("======Retrieve Finished======")
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return
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logger.info(f"{result}\n")
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@staticmethod
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def _print_bm_result(result):
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import pandas as pd
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metrics = [
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item["metrics"]
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for item in result
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if item["log"]["generated_text"] != LLM_ERROR and item["log"]["generated_text"] != EMPTY_ERROR
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]
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data = pd.DataFrame(metrics)
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logger.info(f"\n {data.mean()}")
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llm_errors = [item for item in result if item["log"]["generated_text"] == LLM_ERROR]
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retrieve_errors = [item for item in result if item["log"]["generated_text"] == EMPTY_ERROR]
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logger.info(
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f"Percentage of retrieval failures due to incorrect LLM instruction following:"
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f" {100.0 * len(llm_errors) / len(result)}%"
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)
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logger.info(
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f"Percentage of retrieval failures due to retriever not recalling any documents is:"
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f" {100.0 * len(retrieve_errors) / len(result)}%"
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)
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async def _retrieve_and_print(self, question):
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nodes = await self.engine.aretrieve(question)
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self._print_result(nodes, state="Retrieve")
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return nodes
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async def main():
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"""RAG pipeline"""
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e = RAGExample()
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await e.rag_evaluate_pipeline()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,260 @@
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"""RAG pipeline"""
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import asyncio
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from pydantic import BaseModel
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from metagpt.const import DATA_PATH, EXAMPLE_DATA_PATH
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from metagpt.logs import logger
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from metagpt.rag.engines import SimpleEngine
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from metagpt.rag.schema import (
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ChromaIndexConfig,
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ChromaRetrieverConfig,
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ElasticsearchIndexConfig,
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ElasticsearchRetrieverConfig,
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ElasticsearchStoreConfig,
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FAISSRetrieverConfig,
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LLMRankerConfig,
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)
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from metagpt.utils.exceptions import handle_exception
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LLM_TIP = "If you not sure, just answer I don't know."
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DOC_PATH = EXAMPLE_DATA_PATH / "rag/writer.txt"
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QUESTION = f"What are key qualities to be a good writer? {LLM_TIP}"
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TRAVEL_DOC_PATH = EXAMPLE_DATA_PATH / "rag/travel.txt"
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TRAVEL_QUESTION = f"What does Bob like? {LLM_TIP}"
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class Player(BaseModel):
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"""To demonstrate rag add objs."""
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name: str = ""
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goal: str = "Win The 100-meter Sprint."
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tool: str = "Red Bull Energy Drink."
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def rag_key(self) -> str:
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"""For search"""
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return self.goal
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class RAGExample:
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"""Show how to use RAG."""
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def __init__(self, engine: SimpleEngine = None, use_llm_ranker: bool = True):
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self._engine = engine
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self._use_llm_ranker = use_llm_ranker
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@property
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def engine(self):
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if not self._engine:
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ranker_configs = [LLMRankerConfig()] if self._use_llm_ranker else None
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self._engine = SimpleEngine.from_docs(
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input_files=[DOC_PATH],
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retriever_configs=[FAISSRetrieverConfig()],
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ranker_configs=ranker_configs,
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)
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return self._engine
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@engine.setter
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def engine(self, value: SimpleEngine):
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self._engine = value
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@handle_exception
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async def run_pipeline(self, question=QUESTION, print_title=True):
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"""This example run rag pipeline, use faiss retriever and llm ranker, will print something like:
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Retrieve Result:
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0. Productivi..., 10.0
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1. I wrote cu..., 7.0
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2. I highly r..., 5.0
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Query Result:
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Passion, adaptability, open-mindedness, creativity, discipline, and empathy are key qualities to be a good writer.
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"""
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if print_title:
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self._print_title("Run Pipeline")
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nodes = await self.engine.aretrieve(question)
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self._print_retrieve_result(nodes)
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answer = await self.engine.aquery(question)
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self._print_query_result(answer)
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@handle_exception
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async def add_docs(self):
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"""This example show how to add docs.
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Before add docs llm anwser I don't know.
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After add docs llm give the correct answer, will print something like:
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[Before add docs]
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Retrieve Result:
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Query Result:
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Empty Response
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[After add docs]
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Retrieve Result:
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0. Bob like..., 10.0
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Query Result:
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Bob likes traveling.
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"""
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self._print_title("Add Docs")
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travel_question = f"{TRAVEL_QUESTION}"
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travel_filepath = TRAVEL_DOC_PATH
|
||||
|
||||
logger.info("[Before add docs]")
|
||||
await self.run_pipeline(question=travel_question, print_title=False)
|
||||
|
||||
logger.info("[After add docs]")
|
||||
self.engine.add_docs([travel_filepath])
|
||||
await self.run_pipeline(question=travel_question, print_title=False)
|
||||
|
||||
@handle_exception
|
||||
async def add_objects(self, print_title=True):
|
||||
"""This example show how to add objects.
|
||||
|
||||
Before add docs, engine retrieve nothing.
|
||||
After add objects, engine give the correct answer, will print something like:
|
||||
|
||||
[Before add objs]
|
||||
Retrieve Result:
|
||||
|
||||
[After add objs]
|
||||
Retrieve Result:
|
||||
0. 100m Sprin..., 10.0
|
||||
|
||||
[Object Detail]
|
||||
{'name': 'Mike', 'goal': 'Win The 100-meter Sprint', 'tool': 'Red Bull Energy Drink'}
|
||||
"""
|
||||
if print_title:
|
||||
self._print_title("Add Objects")
|
||||
|
||||
player = Player(name="Mike")
|
||||
question = f"{player.rag_key()}"
|
||||
|
||||
logger.info("[Before add objs]")
|
||||
await self._retrieve_and_print(question)
|
||||
|
||||
logger.info("[After add objs]")
|
||||
self.engine.add_objs([player])
|
||||
|
||||
try:
|
||||
nodes = await self._retrieve_and_print(question)
|
||||
|
||||
logger.info("[Object Detail]")
|
||||
player: Player = nodes[0].metadata["obj"]
|
||||
logger.info(player.name)
|
||||
except Exception as e:
|
||||
logger.error(f"nodes is empty, llm don't answer correctly, exception: {e}")
|
||||
|
||||
@handle_exception
|
||||
async def init_objects(self):
|
||||
"""This example show how to from objs, will print something like:
|
||||
|
||||
Same as add_objects.
|
||||
"""
|
||||
self._print_title("Init Objects")
|
||||
|
||||
pre_engine = self.engine
|
||||
self.engine = SimpleEngine.from_objs(retriever_configs=[FAISSRetrieverConfig()])
|
||||
await self.add_objects(print_title=False)
|
||||
self.engine = pre_engine
|
||||
|
||||
@handle_exception
|
||||
async def init_and_query_chromadb(self):
|
||||
"""This example show how to use chromadb. how to save and load index. will print something like:
|
||||
|
||||
Query Result:
|
||||
Bob likes traveling.
|
||||
"""
|
||||
self._print_title("Init And Query ChromaDB")
|
||||
|
||||
# 1. save index
|
||||
output_dir = DATA_PATH / "rag"
|
||||
SimpleEngine.from_docs(
|
||||
input_files=[TRAVEL_DOC_PATH],
|
||||
retriever_configs=[ChromaRetrieverConfig(persist_path=output_dir)],
|
||||
)
|
||||
|
||||
# 2. load index
|
||||
engine = SimpleEngine.from_index(index_config=ChromaIndexConfig(persist_path=output_dir))
|
||||
|
||||
# 3. query
|
||||
answer = await engine.aquery(TRAVEL_QUESTION)
|
||||
self._print_query_result(answer)
|
||||
|
||||
@handle_exception
|
||||
async def init_and_query_es(self):
|
||||
"""This example show how to use es. how to save and load index. will print something like:
|
||||
|
||||
Query Result:
|
||||
Bob likes traveling.
|
||||
"""
|
||||
self._print_title("Init And Query Elasticsearch")
|
||||
|
||||
# 1. create es index and save docs
|
||||
store_config = ElasticsearchStoreConfig(index_name="travel", es_url="http://127.0.0.1:9200")
|
||||
engine = SimpleEngine.from_docs(
|
||||
input_files=[TRAVEL_DOC_PATH],
|
||||
retriever_configs=[ElasticsearchRetrieverConfig(store_config=store_config)],
|
||||
)
|
||||
|
||||
# 2. load index
|
||||
engine = SimpleEngine.from_index(index_config=ElasticsearchIndexConfig(store_config=store_config))
|
||||
|
||||
# 3. query
|
||||
answer = await engine.aquery(TRAVEL_QUESTION)
|
||||
self._print_query_result(answer)
|
||||
|
||||
@staticmethod
|
||||
def _print_title(title):
|
||||
logger.info(f"{'#'*30} {title} {'#'*30}")
|
||||
|
||||
@staticmethod
|
||||
def _print_retrieve_result(result):
|
||||
"""Print retrieve result."""
|
||||
logger.info("Retrieve Result:")
|
||||
|
||||
for i, node in enumerate(result):
|
||||
logger.info(f"{i}. {node.text[:10]}..., {node.score}")
|
||||
|
||||
logger.info("")
|
||||
|
||||
@staticmethod
|
||||
def _print_query_result(result):
|
||||
"""Print query result."""
|
||||
logger.info("Query Result:")
|
||||
|
||||
logger.info(f"{result}\n")
|
||||
|
||||
async def _retrieve_and_print(self, question):
|
||||
nodes = await self.engine.aretrieve(question)
|
||||
self._print_retrieve_result(nodes)
|
||||
return nodes
|
||||
|
||||
|
||||
async def main():
|
||||
"""RAG pipeline.
|
||||
|
||||
Note:
|
||||
1. If `use_llm_ranker` is True, then it will use LLM Reranker to get better result, but it is not always guaranteed that the output will be parseable for reranking,
|
||||
prefer `gpt-4-turbo`, otherwise might encounter `IndexError: list index out of range` or `ValueError: invalid literal for int() with base 10`.
|
||||
"""
|
||||
e = RAGExample(use_llm_ranker=False)
|
||||
|
||||
await e.run_pipeline()
|
||||
await e.add_docs()
|
||||
await e.add_objects()
|
||||
await e.init_objects()
|
||||
await e.init_and_query_chromadb()
|
||||
await e.init_and_query_es()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,21 @@
|
||||
"""Agent with RAG search."""
|
||||
|
||||
import asyncio
|
||||
|
||||
from examples.rag.rag_pipeline import DOC_PATH, QUESTION
|
||||
from metagpt.logs import logger
|
||||
from metagpt.rag.engines import SimpleEngine
|
||||
from metagpt.roles import Sales
|
||||
|
||||
|
||||
async def search():
|
||||
"""Agent with RAG search."""
|
||||
|
||||
store = SimpleEngine.from_docs(input_files=[DOC_PATH])
|
||||
role = Sales(profile="Sales", store=store)
|
||||
result = await role.run(QUESTION)
|
||||
logger.info(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(search())
|
||||
Reference in New Issue
Block a user