mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
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_functions
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.schema.messages import AIMessage, HumanMessage
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from langchain.tools.render import format_tool_to_openai_function
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from langchain.tools.tavily_search import TavilySearchResults
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from langchain.utilities.tavily_search import TavilySearchAPIWrapper
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# Create the tool
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search = TavilySearchAPIWrapper()
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description = """"A search engine optimized for comprehensive, accurate, \
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and trusted results. Useful for when you need to answer questions \
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about current events or about recent information. \
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Input should be a search query. \
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If the user is asking about something that you don't know about, \
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you should probably use this tool to see if that can provide any information."""
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tavily_tool = TavilySearchResults(api_wrapper=search, description=description)
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tools = [tavily_tool]
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llm = ChatOpenAI(temperature=0)
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assistant_system_message = """You are a helpful assistant. \
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Use tools (only if necessary) to best answer the users questions."""
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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_functions(
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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(..., extra={"widget": {"type": "chat"}})
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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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agent_executor = agent_executor | (lambda x: x["output"])
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