|
|
@ -4,21 +4,66 @@ from typing import List, Tuple
|
|
|
|
from langchain.agents import AgentExecutor
|
|
|
|
from langchain.agents import AgentExecutor
|
|
|
|
from langchain.agents.format_scratchpad import format_to_openai_function_messages
|
|
|
|
from langchain.agents.format_scratchpad import format_to_openai_function_messages
|
|
|
|
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
|
|
|
|
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
|
|
|
|
|
|
|
|
from langchain.callbacks.manager import CallbackManagerForRetrieverRun
|
|
|
|
from langchain.chat_models import AzureChatOpenAI
|
|
|
|
from langchain.chat_models import AzureChatOpenAI
|
|
|
|
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
|
|
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
|
|
|
|
from langchain.pydantic_v1 import BaseModel, Field
|
|
|
|
from langchain.pydantic_v1 import BaseModel, Field
|
|
|
|
|
|
|
|
from langchain.schema import BaseRetriever, Document
|
|
|
|
from langchain.schema.messages import AIMessage, HumanMessage
|
|
|
|
from langchain.schema.messages import AIMessage, HumanMessage
|
|
|
|
from langchain.tools import ArxivQueryRun
|
|
|
|
|
|
|
|
from langchain.tools.render import format_tool_to_openai_function
|
|
|
|
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):
|
|
|
|
class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
|
|
|
|
query: str = Field(description="search query to look up")
|
|
|
|
"""`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
|
|
|
|
# Create the tool
|
|
|
|
arxiv_tool = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(), args_schema=ArxivInput)
|
|
|
|
arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description)
|
|
|
|
tools = [arxiv_tool]
|
|
|
|
tools = [arxiv_tool]
|
|
|
|
llm = AzureChatOpenAI(
|
|
|
|
llm = AzureChatOpenAI(
|
|
|
|
temperature=0,
|
|
|
|
temperature=0,
|
|
|
|