arxiv retrieval agent improvement (#13329)

templates/retrieval-agent/retrieval_agent/chain.py

@@ -4,21 +4,66 @@ from typing import List, Tuple
 from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_to_openai_function_messages
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.chat_models import AzureChatOpenAI
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.pydantic_v1 import BaseModel, Field
+from langchain.schema import BaseRetriever, Document
 from langchain.schema.messages import AIMessage, HumanMessage
-from langchain.tools import ArxivQueryRun
 from langchain.tools.render import format_tool_to_openai_function
-from langchain.utilities import ArxivAPIWrapper
+from langchain.tools.retriever import create_retriever_tool
+from langchain.utilities.arxiv import ArxivAPIWrapper
 
 
-class ArxivInput(BaseModel):
-    query: str = Field(description="search query to look up")
+class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
+    """`Arxiv` retriever.
 
+    It wraps load() to get_relevant_documents().
+    It uses all ArxivAPIWrapper arguments without any change.
+    """
+
+    get_full_documents: bool = False
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        try:
+            if self.is_arxiv_identifier(query):
+                results = self.arxiv_search(
+                    id_list=query.split(),
+                    max_results=self.top_k_results,
+                ).results()
+            else:
+                results = self.arxiv_search(  # type: ignore
+                    query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
+                ).results()
+        except self.arxiv_exceptions as ex:
+            return [Document(page_content=f"Arxiv exception: {ex}")]
+        docs = [
+            Document(
+                page_content=result.summary,
+                metadata={
+                    "Published": result.updated.date(),
+                    "Title": result.title,
+                    "Authors": ", ".join(a.name for a in result.authors),
+                },
+            )
+            for result in results
+        ]
+        return docs
+
+
+description = (
+    "A wrapper around Arxiv.org "
+    "Useful for when you need to answer questions about Physics, Mathematics, "
+    "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
+    "Electrical Engineering, and Economics "
+    "from scientific articles on arxiv.org. "
+    "Input should be a search query."
+)
 
 # Create the tool
-arxiv_tool = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(), args_schema=ArxivInput)
+arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description)
 tools = [arxiv_tool]
 llm = AzureChatOpenAI(
     temperature=0,