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ebf998acb6
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from typing import List, Tuple
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from langchain.schema.messages import HumanMessage, AIMessage
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import AgentExecutor
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from langchain.utilities.tavily_search import TavilySearchAPIWrapper
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from langchain.tools.tavily_search import TavilySearchResults
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.tools.render import format_tool_to_openai_function
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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.pydantic_v1 import BaseModel
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# Fake Tool
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search = TavilySearchAPIWrapper()
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tavily_tool = TavilySearchResults(api_wrapper=search)
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tools = [tavily_tool]
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llm = ChatOpenAI(temperature=0)
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prompt = ChatPromptTemplate.from_messages([
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("system", "You are very powerful assistant, but bad at calculating lengths of words."),
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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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llm_with_tools = llm.bind(
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functions=[format_tool_to_openai_function(t) for t in tools]
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)
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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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"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(x['intermediate_steps']),
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} | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser()
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]]
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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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