mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
ed789be8f4
- chat models, messages - documents - agentaction/finish - baseretriever,document - stroutputparser - more messages - basemessage - format_document - baseoutputparser --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
import os
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from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_to_openai_function_messages
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.callbacks.manager import CallbackManagerForRetrieverRun
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from langchain.tools.retriever import create_retriever_tool
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from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
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from langchain_community.utilities.arxiv import ArxivAPIWrapper
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from langchain_core.documents import Document
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain_core.retrievers import BaseRetriever
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from langchain_openai import AzureChatOpenAI
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class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
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"""`Arxiv` retriever.
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It wraps load() to get_relevant_documents().
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It uses all ArxivAPIWrapper arguments without any change.
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"""
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get_full_documents: bool = False
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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try:
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if self.is_arxiv_identifier(query):
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results = self.arxiv_search(
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id_list=query.split(),
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max_results=self.top_k_results,
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).results()
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else:
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results = self.arxiv_search( # type: ignore
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query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
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).results()
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except self.arxiv_exceptions as ex:
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return [Document(page_content=f"Arxiv exception: {ex}")]
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docs = [
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Document(
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page_content=result.summary,
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metadata={
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"Published": result.updated.date(),
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"Title": result.title,
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"Authors": ", ".join(a.name for a in result.authors),
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},
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)
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for result in results
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]
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return docs
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description = (
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"A wrapper around Arxiv.org "
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"Useful for when you need to answer questions about Physics, Mathematics, "
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"Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
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"Electrical Engineering, and Economics "
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"from scientific articles on arxiv.org. "
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"Input should be a search query."
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)
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# Create the tool
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arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description)
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tools = [arxiv_tool]
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llm = AzureChatOpenAI(
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temperature=0,
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azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
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)
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assistant_system_message = """You are a helpful research assistant. \
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Lookup relevant information as needed."""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", assistant_system_message),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"agent_scratchpad": lambda x: format_to_openai_function_messages(
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x["intermediate_steps"]
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),
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}
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| prompt
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| llm_with_tools
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| OpenAIFunctionsAgentOutputParser()
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)
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
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input_type=AgentInput
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)
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