[ERNIEBot Researcher] update langchain (#305)

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

* update langchain

erniebot-agent/applications/erniebot_researcher/README.md

@@ -64,10 +64,42 @@ pip install -r requirements.txt
 wget https://paddlenlp.bj.bcebos.com/pipelines/fonts/SimSun.ttf
 ```
 
-> 第四步：运行
+> 第四步：创建索引
+
+下载实例数据
+
+```
+wget https://paddlenlp.bj.bcebos.com/pipelines/erniebot_researcher_example.tar.gz
+tar xvf erniebot_researcher_example.tar.gz
+```
 
 首先需要在[AI Studio星河社区](https://aistudio.baidu.com/index)注册并登录账号，然后在AI Studio的[访问令牌页面](https://aistudio.baidu.com/index/accessToken)获取`Access Token`，最后设置环境变量:
 
+```
+export EB_AGENT_ACCESS_TOKEN=<aistudio-access-token>
+export AISTUDIO_ACCESS_TOKEN=<aistudio-access-token>
+```
+
+如果用户有url链接，你可以传入存储url链接的txt。
+在txt中，每一行存储文件的路径和对应的url链接，例如:
+'https://zhuanlan.zhihu.com/p/659457816 erniebot_researcher_example/Ai_Agent的起源.md'
+
+如果用户不传入url文件，则默认文件的路径为其url链接
+
+用户可以自己传入文件摘要的存储路径。其中摘要需要用json文件存储。其中json文件内存储的是多个字典，每个字典有3组键值对，"page_content"存储文件的摘要，"url"是文件的url链接，"name"是文章的名字。例如:
+[{"page_content":"文章摘要","url":"https://zhuanlan.zhihu.com/p/659457816","name":Ai_Agent的起源},...]
+```
+python ./tools/preprocessing.py \
+--index_name_full_text <the index name of your full text> \
+--index_name_abstract <the index name of your abstract text> \
+--path_full_text <the folder path of your full text> \
+--url_path <the path of your url text> \
+--path_abstract <the json path of your abstract text>
+```
+
+> 第五步：运行
+
+
 ```
 export EB_AGENT_ACCESS_TOKEN=<aistudio-access-token>
 export AISTUDIO_ACCESS_TOKEN=<aistudio-access-token>
@@ -78,23 +110,23 @@ Base版本示例运行：
 
 ```
 python sample_report_example.py --num_research_agent 2 \
-                                --index_name_full_text <your full text> \
-                                --index_name_abstract <your abstract text>
+                                --index_name_full_text <the index name of your full text> \
+                                --index_name_abstract <the index name of your abstract text>
 ```
 
 Base版本WebUI运行：
 
 ```
 python ui.py --num_research_agent 2 \
-             --index_name_full_text <your full text> \
-             --index_name_abstract <your abstract text>
+             --index_name_full_text <the index name of your full text> \
+             --index_name_abstract <the index name of your abstract text>
 ```
 
 高阶版本多智能体自动调度示例脚本运行：
 
 ```
-python sample_group_agent.py --index_name_full_text <your full text> \
-                             --index_name_abstract <your abstract text>
+python sample_group_agent.py --index_name_full_text <the index name of your full text> \
+                             --index_name_abstract <the index name of your abstract text>
 ```
 
 ## Reference

erniebot-agent/applications/erniebot_researcher/fact_check_agent.py

@@ -9,6 +9,7 @@
 from erniebot_agent.prompt import PromptTemplate
 
 logger = logging.getLogger(__name__)
+MAX_RETRY = 10
 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
@@ -154,8 +155,8 @@ async def verifications(self, facts_problems: List[dict]):
         for item in facts_problems:
             question = item["question"]
             claim = item["fact"]
-            context = self.retriever_db.search(question)
-            context = [i["content"] for i in context]
+            context = await self.retriever_db(question)
+            context = [i["content"] for i in context["documents"]]
             item["evidence"] = context
             anwser = await self.generate_anwser(question, context)
             item["anwser"] = anwser
@@ -222,11 +223,24 @@ async def report_fact(self, report: str):
                     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)
+                    retry_count = 0
+                    while True:
+                        try:
+                            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)
+                            break
+                        except Exception as e:
+                            retry_count += 1
+                            logger.error(e)
+                            if retry_count > MAX_RETRY:
+                                raise Exception(
+                                    f"Failed to edit research for {report} after {MAX_RETRY} times."
+                                )
+                            continue
+
                 else:
                     text.append(item)
         return "\n\n".join(text)

erniebot-agent/applications/erniebot_researcher/polish_agent.py

@@ -38,6 +38,8 @@ def __init__(
         citation_index_name: str,
         dir_path: str,
         report_type: str,
+        build_index_function: Any,
+        search_tool: Any,
         system_message: Optional[SystemMessage] = None,
         callbacks=None,
     ):
@@ -58,6 +60,8 @@ def __init__(
         self.prompt_template_polish = PromptTemplate(
             template=self.template_polish, input_variables=["content"]
         )
+        self.build_index_function = build_index_function
+        self.search_tool = search_tool
         if callbacks is None:
             self._callback_manager = ReportCallbackHandler()
         else:
@@ -143,13 +147,19 @@ async def _run(self, report, summarize=None):
         final_report = await self.polish_paragraph(report, abstract, key)
         await self._callback_manager.on_tool_start(self, tool=self.citation_tool, input_args=final_report)
         if summarize is not None:
-            citation_search = add_citation(summarize, self.citation_index_name, self.embeddings)
+            citation_search = add_citation(
+                summarize,
+                self.citation_index_name,
+                self.embeddings,
+                self.build_index_function,
+                self.search_tool,
+            )
             final_report, path = await self.citation_tool(
                 report=final_report,
                 agent_name=self.name,
                 report_type=self.report_type,
                 dir_path=self.dir_path,
-                citation_faiss_research=citation_search,
+                citation_research=citation_search,
             )
         path = write_md_to_pdf(self.report_type, self.dir_path, final_report)
         await self._callback_manager.on_tool_end(

erniebot-agent/applications/erniebot_researcher/requirements.txt

@@ -4,3 +4,6 @@ langchain
 scikit-learn
 markdown
 WeasyPrint==52.5
+openai>1.0
+langchain_openai
+zhon

erniebot-agent/applications/erniebot_researcher/research_agent.py

@@ -75,25 +75,21 @@ def __init__(
 
     async def run_search_summary(self, query: str):
         responses = []
-        url_dict = {}
-        results = self.retriever_fulltext_db.search(query, top_k=3)
+        results = await self.retriever_fulltext_db(query, top_k=3)
         length_limit = 0
         await self._callback_manager.on_tool_start(agent=self, tool=self.summarize_tool, input_args=query)
-        for doc in results:
+        for doc in results["documents"]:
             res = await self.summarize_tool(doc["content"], query)
             # Add reference to avoid hallucination
-            data = {"summary": res, "url": doc["url"], "name": doc["title"]}
+            data = {"summary": res, "url": doc["meta"]["url"], "name": doc["meta"]["name"]}
             length_limit += len(res)
             if length_limit < SUMMARIZE_MAX_LENGTH:
                 responses.append(data)
-                key = doc["title"]
-                value = doc["url"]
-                url_dict[key] = value
             else:
                 logger.warning(f"summary size exceed {SUMMARIZE_MAX_LENGTH}")
                 break
         await self._callback_manager.on_tool_end(self, tool=self.summarize_tool, response=responses)
-        return responses, url_dict
+        return responses
 
     async def run(self, query: str):
         """
@@ -117,8 +113,8 @@ async def run(self, query: str):
 
         if self.use_context_planning:
             sub_queries = []
-            res = self.retriever_abstract_db.search(query, top_k=3)
-            context = [item["content"] for item in res]
+            res = await self.retriever_abstract_db(query, top_k=3)
+            context = [item["content"] for item in res["documents"]]
             context_content = ""
             await self._callback_manager.on_tool_start(
                 agent=self, tool=self.task_planning_tool, input_args=query
@@ -157,7 +153,7 @@ async def run(self, query: str):
         # Run Sub-Queries
         paragraphs_item = []
         for sub_query in sub_queries:
-            research_result, url_dict = await self.run_search_summary(sub_query)
+            research_result = await self.run_search_summary(sub_query)
             paragraphs_item.extend(research_result)
 
         paragraphs = []

erniebot-agent/applications/erniebot_researcher/sample_group_agent.py

@@ -6,19 +6,19 @@
 
 from editor_actor_agent import EditorActorAgent
 from group_agent import GroupChat, GroupChatManager
-from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain_openai import AzureOpenAIEmbeddings
 from polish_agent import PolishAgent
 from ranking_agent import RankingAgent
 from research_agent import ResearchAgent
 from reviser_actor_agent import ReviserActorAgent
 from tools.intent_detection_tool import IntentDetectionTool
 from tools.outline_generation_tool import OutlineGenerationTool
+from tools.preprocessing import get_retriver_by_type
 from tools.ranking_tool import TextRankingTool
 from tools.report_writing_tool import ReportWritingTool
 from tools.semantic_citation_tool import SemanticCitationTool
 from tools.summarization_tool import TextSummarizationTool
 from tools.task_planning_tool import TaskPlanningTool
-from tools.utils import FaissSearch, build_index
 
 from erniebot_agent.chat_models import ERNIEBot
 from erniebot_agent.extensions.langchain.embeddings import ErnieEmbeddings
@@ -71,22 +71,29 @@
     default="openai_embedding",
     help="['openai_embedding','baizhong','ernie_embedding']",
 )
+parser.add_argument(
+    "--framework",
+    type=str,
+    default="langchain",
+    choices=["langchain", "llama_index"],
+    help="['langchain','llama_index']",
+)
 args = parser.parse_args()
 
 
-def get_retrievers():
+def get_retrievers(build_index_function, retrieval_tool):
     if args.embedding_type == "openai_embedding":
-        embeddings = OpenAIEmbeddings(deployment="text-embedding-ada")
-        paper_db = build_index(faiss_name=args.index_name_full_text, embeddings=embeddings)
-        abstract_db = build_index(faiss_name=args.index_name_abstract, embeddings=embeddings)
-        abstract_search = FaissSearch(abstract_db, embeddings=embeddings)
-        retriever_search = FaissSearch(paper_db, embeddings=embeddings)
+        embeddings = AzureOpenAIEmbeddings(azure_deployment="text-embedding-ada")
+        fulltext_db = build_index_function(index_name=args.index_name_full_text, embeddings=embeddings)
+        abstract_db = build_index_function(index_name=args.index_name_abstract, embeddings=embeddings)
+        abstract_search = retrieval_tool(abstract_db, embeddings=embeddings)
+        retriever_search = retrieval_tool(fulltext_db, embeddings=embeddings)
     elif args.embedding_type == "ernie_embedding":
         embeddings = ErnieEmbeddings(aistudio_access_token=access_token)
-        paper_db = build_index(faiss_name=args.index_name_full_text, embeddings=embeddings)
-        abstract_db = build_index(faiss_name=args.index_name_abstract, embeddings=embeddings)
-        abstract_search = FaissSearch(abstract_db, embeddings=embeddings)
-        retriever_search = FaissSearch(paper_db, embeddings=embeddings)
+        fulltext_db = build_index_function(index_name=args.index_name_full_text, embeddings=embeddings)
+        abstract_db = build_index_function(index_name=args.index_name_abstract, embeddings=embeddings)
+        abstract_search = retrieval_tool(abstract_db, embeddings=embeddings)
+        retriever_search = retrieval_tool(fulltext_db, embeddings=embeddings)
     elif args.embedding_type == "baizhong":
         embeddings = ErnieEmbeddings(aistudio_access_token=access_token)
         retriever_search = BaizhongSearch(
@@ -102,7 +109,9 @@ def get_retrievers():
     return {"full_text": retriever_search, "abstract": abstract_search, "embeddings": embeddings}
 
 
-def get_agents(retriever_sets, tool_sets, llm, llm_long, dir_path, target_path):
+def get_agents(
+    retriever_sets, tool_sets, llm, llm_long, dir_path, target_path, build_index_function, retrieval_tool
+):
     research_actor = ResearchAgent(
         name="generate_report",
         system_message=SystemMessage("你是一个报告生成助手。你可以根据用户的指定内容生成一份报告手稿"),
@@ -134,6 +143,8 @@ def get_agents(retriever_sets, tool_sets, llm, llm_long, dir_path, target_path):
         dir_path=target_path,
         report_type=args.report_type,
         citation_tool=tool_sets["semantic_citation"],
+        build_index_function=build_index_function,
+        search_tool=retrieval_tool,
     )
     return {
         "research_agents": research_actor,
@@ -171,10 +182,12 @@ def main(query):
     os.makedirs(target_path, exist_ok=True)
     llm_long = ERNIEBot(model="ernie-longtext")
     llm = ERNIEBot(model="ernie-4.0")
-
-    retriever_sets = get_retrievers()
+    build_index_function, retrieval_tool = get_retriver_by_type(args.framework)
+    retriever_sets = get_retrievers(build_index_function, retrieval_tool)
     tool_sets = get_tools(llm, llm_long)
-    agent_sets = get_agents(retriever_sets, tool_sets, llm, llm_long, dir_path, target_path)
+    agent_sets = get_agents(
+        retriever_sets, tool_sets, llm, llm_long, dir_path, target_path, build_index_function, retrieval_tool
+    )
     research_actor = agent_sets["research_agents"]
     report = asyncio.run(research_actor.run(query))
     report = {"report": report[0], "paragraphs": report[1]}