[ErnieBot Researcher]add FactCheckerAgent (#295)

* add erniebot_researcher_example

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

* add fact_check_agent

erniebot-agent/applications/erniebot_researcher/README.md

@@ -30,6 +30,7 @@ Agents利用ernie-4.0和ernie-longtext来完成研究任务， ernie-4.0主要
 + 多个报告Agent并行生成，并保持一定的多样性。
 + 使用思维链技术对多个报告进行质量评估和排序，克服伪随机性，并选择最优的报告。
 + 使用反思机制对报告进行修订和完善。
++ 新增检索增强和chain of verification对事实进行校验
 + 使用润色机制提升报告的整体可读性，融合更多的细节描述。
 
 **注意**

erniebot-agent/applications/erniebot_researcher/fact_check_agent.py

@@ -0,0 +1,244 @@
+import logging
+import re
+from typing import Any, List, Optional
+
+from tools.utils import JsonUtil, ReportCallbackHandler
+
+from erniebot_agent.chat_models.erniebot import BaseERNIEBot
+from erniebot_agent.memory import HumanMessage, Message, SystemMessage
+from erniebot_agent.prompt import PromptTemplate
+
+logger = logging.getLogger(__name__)
+PLAN_VERIFICATIONS_PROMPT = """
+为了验证给出的内容中数字性表述的准确性，您需要创建一系列验证问题，
+用于测试原始基线响应中的事实主张。例如，如果长格式响应的一部分包含
+“墨西哥-美国战争是 1846 年至 1848 年美国和墨西哥之间的武装冲突”，
+那么一种可能的验证问题可以是：“墨西哥-美国战争何时开始以及结束？”
+给定内容：{{base_context}}
+您需要按照列表输出，输出段落中的事实和相应的验证问题。
+[{"fact": <段落中的事实>, "question": <验证问题，通过结合查询和事实生成>},
+{"fact": <段落中的事实>, "question": <验证问题，通过结合查询和事实生成>}, ...]
+"""
+ANWSER_PROMPT = """
+根据外部知识回答问题。如果给出的外部知识不能解答提出的问题，
+请直接输出"无法回答"，无需提供答案。给定问题:\n{{question}}\n
+外部知识:{{content}}\n回答:
+"""
+CHECK_CLAIM_PROMPT = """
+根据给定的问题和回答，判断事实中数字描述是否正确。如果认为事实中的数字描述不正确，
+请根据问题和回答提供修正。您的输出应为 JSON 格式：
+{"is_correct": <事实是否正确>, "modify": <对不正确的事实进行修正>}
+给定问题: {{question}}\n回答: {{answer}}\n事实: {{claim}}
+"""
+
+FINAL_RESPONSE_PROMPT = """
+根据提供的背景知识，对原始内容进行改写。确保改写后的内容中的数字与背景知识中的数字一致。
+您必须修正原始内容中的数字。原始内容：{{origin_content}}\n背景知识：{{context}}
+改写内容：
+"""
+
+
+class FactCheckerAgent(JsonUtil):
+    DEFAULT_SYSTEM_MESSAGE = "你是一个事实检查助手，你的任务就是检查文本中的事实描述是否正确"
+
+    def __init__(
+        self,
+        name: str,
+        llm: BaseERNIEBot,
+        retriever_db: Any,
+        system_message: Optional[SystemMessage] = None,
+        callbacks=None,
+    ):
+        """
+        Initialize a fact_checker agent.
+
+        args:
+            name: The name of the agent.
+            llm: An LLM for the agent to use.
+            retriever_db: A database for the agent to use.
+            system_message: A message to be displayed when the agent starts.
+            callbacks: A callback handler for the agent.
+        """
+        self.name = name
+        self.llm = llm
+        self.retriever_db = retriever_db
+        self.prompt_plan_verifications = PromptTemplate(
+            PLAN_VERIFICATIONS_PROMPT, input_variables=["base_context"]
+        )
+        self.prompt_anwser = PromptTemplate(ANWSER_PROMPT, input_variables=["question", "content"])
+        self.prompt_check_claim = PromptTemplate(
+            CHECK_CLAIM_PROMPT, input_variables=["question", "answer", "claim"]
+        )
+        self.prompt_final_response = PromptTemplate(
+            FINAL_RESPONSE_PROMPT, input_variables=["origin_content", "context"]
+        )
+        self.system_message = (
+            system_message.content if system_message is not None else self.DEFAULT_SYSTEM_MESSAGE
+        )
+        if callbacks is None:
+            self._callback_manager = ReportCallbackHandler()
+        else:
+            self._callback_manager = callbacks
+
+    async def run(self, report: str):
+        """
+        The main logic of running the agent.
+
+        Args:
+            report: Entered report text.
+        Returns:
+            The results of the agent's operation.
+        """
+        await self._callback_manager.on_run_start(
+            agent=self, agent_name=self.name, prompt=self.system_message
+        )
+        agent_resp = await self._run(report=report)
+        await self._callback_manager.on_run_end(agent=self, response=agent_resp)
+        return agent_resp
+
+    async def generate_anwser(self, question: str, context: str):
+        """
+        Generate answers to questions based on background knowledge
+
+        Args:
+            question: Indicates a question to be answered.
+            context: Represents background knowledge relevant to the problem.
+
+        Returns:
+            Generated answers to questions.
+        """
+        messages: List[Message] = [
+            HumanMessage(content=self.prompt_anwser.format(question=question, content=context))
+        ]
+        responese = await self.llm.chat(messages)
+        result = responese.content
+        return result
+
+    async def check_claim(self, question: str, answer: str, claim: str):
+        """
+        Use a large language model to conduct a conversation, verify a fact,
+        and suggest corrections if the fact is incorrect
+
+        Args:
+            question: represents a fact-checking question
+            answer: represents a fact-checking answer
+            claim: indicates a fact that need to be verified
+
+        Returns:
+            A dictionary containing verification results,
+        including whether the facts are correct and suggestions for correction.
+        """
+        messages: List[Message] = [
+            HumanMessage(
+                content=self.prompt_check_claim.format(question=question, answer=answer, claim=claim)
+            )
+        ]
+        responese = await self.llm.chat(messages)
+        result = responese.content
+        result = self.parse_json(result)
+        return result
+
+    async def verifications(self, facts_problems: List[dict]):
+        """
+        Answer questions using external knowledge and then use the answers to the questions to verify
+        relevant facts. As it processes each question and fact pair, it obtains the context
+        relevant to the question, generates an answer to the question, checks whether the fact
+        is correct, and records the verification results.
+
+        Args:
+            facts_problems: A list of dictionaries containing questions and related facts.
+
+        Returns:
+            Updated dictionary list of verified questions and facts.
+        """
+        for item in facts_problems:
+            question = item["question"]
+            claim = item["fact"]
+            context = self.retriever_db.search(question)
+            context = [i["content"] for i in context]
+            item["evidence"] = context
+            anwser = await self.generate_anwser(question, context)
+            item["anwser"] = anwser
+            result = await self.check_claim(question, anwser, claim)
+            item["is_correct"] = result["is_correct"]
+            if result["is_correct"] is False:
+                item["modify"] = result["modify"]
+            else:
+                item["modify"] = claim
+        self._callback_manager._agent_info(msg=item["modify"], subject="事实验证的结果", state="End")
+        return facts_problems
+
+    async def generate_final_response(self, content: str, verifications: List[dict]):
+        """
+        If the original factual questions pass fact verification,
+        the original content will be returned directly.
+        Otherwise, the original content will be corrected based
+        on the results of factual verification.
+
+        Args:
+            content: Original text content.
+            verifications: List of dictionaries containing fact verification results.
+
+        Returns:
+            The final generated reply content.
+        """
+        if all([item["is_correct"] for item in verifications]):
+            return content
+        else:
+            context = "".join([item["modify"] for item in verifications])
+            messages: List[Message] = [
+                HumanMessage(
+                    content=self.prompt_final_response.format(origin_content=content, context=context)
+                )
+            ]
+            resulte = await self.llm.chat(messages)
+            result = resulte.content
+            return result
+
+    async def report_fact(self, report: str):
+        """
+        Filter out sentences containing numbers in text.
+        Extract facts from the filtered sentences.
+        Extract validation questions and verify each extracted fact.
+        Example:
+        "Mexican-American War
+        was the armed conflict between the United States and Mexico from 1846 to 1848," then one possibility
+        A validation question to check these dates could be When did the Mexican-American War begin and end?
+
+        Args:
+            report: The original text content.
+
+        Returns:
+            The final generated reply content.
+        """
+        report_list = report.split("\n\n")
+        text = []
+        for item in report_list:
+            if item.strip()[0] == "#":
+                text.append(item)
+            else:
+                contains_numbers = re.findall(r"\b\d+\b", item)
+                if contains_numbers:
+                    messages: List[Message] = [
+                        HumanMessage(content=self.prompt_plan_verifications.format(base_context=item))
+                    ]
+                    responese = await self.llm.chat(messages)
+                    result: List[dict] = self.parse_json(responese.content)
+                    fact_check_result: List[dict] = await self.verifications(result)
+                    new_item: str = await self.generate_final_response(item, fact_check_result)
+                    text.append(new_item)
+                else:
+                    text.append(item)
+        return "\n\n".join(text)
+
+    async def _run(self, report: str):
+        """
+        The main logic of running the agent.
+
+        Args:
+            report: Entered report text.
+        Returns:
+            Processed report text.
+        """
+        report = await self.report_fact(report)
+        return report

erniebot-agent/applications/erniebot_researcher/research_team.py

@@ -2,6 +2,7 @@
 from typing import List, Optional
 
 from editor_actor_agent import EditorActorAgent
+from fact_check_agent import FactCheckerAgent
 from polish_agent import PolishAgent
 from ranking_agent import RankingAgent
 from research_agent import ResearchAgent
@@ -16,6 +17,7 @@ def __init__(
         ranker_actor: RankingAgent,
         editor_actor: EditorActorAgent,
         reviser_actor: ReviserActorAgent,
+        checker_actor: FactCheckerAgent,
         polish_actor: Optional[PolishAgent] = None,
         user_agent: Optional[UserProxyAgent] = None,
         use_reflection: bool = False,
@@ -25,6 +27,7 @@ def __init__(
         self.revise_actor_instance = reviser_actor
         self.ranker_actor_instance = ranker_actor
         self.polish_actor_instance = polish_actor
+        self.checker_actor_instance = checker_actor
         self.user_agent = user_agent
         self.polish_actor = polish_actor
         self.use_reflection = use_reflection
@@ -77,9 +80,9 @@ async def run(self, query, iterations=3):
                 immedia_report = list_reports[0]
 
             revised_report = immedia_report
-
+        checked_report = await self.checker_actor_instance.run(report=revised_report["report"])
         revised_report, path = await self.polish_actor_instance.run(
-            report=revised_report["report"],
+            report=checked_report,
             summarize=revised_report["paragraphs"],
         )
         return revised_report, path

erniebot-agent/applications/erniebot_researcher/sample_report_example.py

@@ -5,6 +5,7 @@
 import time
 
 from editor_actor_agent import EditorActorAgent
+from fact_check_agent import FactCheckerAgent
 from langchain.embeddings.openai import OpenAIEmbeddings
 from polish_agent import PolishAgent
 from ranking_agent import RankingAgent
@@ -160,6 +161,7 @@ def get_agents(retriever_sets, tool_sets, llm, llm_long):
         dir_path=target_path,
         report_type=args.report_type,
     )
+    checker_actor = FactCheckerAgent(name="fact_check", llm=llm, retriever_db=retriever_sets["full_text"])
     ranker_actor = RankingAgent(
         llm=llm,
         llm_long=llm_long,
@@ -171,6 +173,7 @@ def get_agents(retriever_sets, tool_sets, llm, llm_long):
         "editor_actor": editor_actor,
         "reviser_actor": reviser_actor,
         "ranker_actor": ranker_actor,
+        "checker_actor": checker_actor,
         "polish_actor": polish_actor,
     }
 

erniebot-agent/applications/erniebot_researcher/ui.py

@@ -5,6 +5,7 @@
 
 import gradio as gr
 from editor_actor_agent import EditorActorAgent
+from fact_check_agent import FactCheckerAgent
 from langchain.embeddings.openai import OpenAIEmbeddings
 from polish_agent import PolishAgent
 from ranking_agent import RankingAgent
@@ -168,6 +169,12 @@ def get_agents(retriever_sets, tool_sets, llm, llm_long, dir_path, target_path):
         llm_long=llm_long,
         callbacks=ReportCallbackHandler(logger=logger),
     )
+    checker_actor = FactCheckerAgent(
+        name="fact_check",
+        llm=llm,
+        retriever_db=retriever_sets["full_text"],
+        callbacks=ReportCallbackHandler(logger=logger),
+    )
     polish_actor = PolishAgent(
         name="polish",
         llm=llm,
@@ -184,6 +191,7 @@ def get_agents(retriever_sets, tool_sets, llm, llm_long, dir_path, target_path):
         "editor_actor": editor_actor,
         "reviser_actor": reviser_actor,
         "ranker_actor": ranker_actor,
+        "checker_actor": checker_actor,
         "polish_actor": polish_actor,
     }