mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from typing import List, Tuple
|
|
|
|
from langchain.agents import AgentExecutor
|
|
from langchain.agents.format_scratchpad import format_xml
|
|
from langchain.chat_models import ChatAnthropic
|
|
from langchain.pydantic_v1 import BaseModel, Field
|
|
from langchain.schema import AIMessage, HumanMessage
|
|
from langchain.tools import DuckDuckGoSearchRun
|
|
from langchain.tools.render import render_text_description
|
|
|
|
from xml_agent.prompts import conversational_prompt, parse_output
|
|
|
|
|
|
def _format_chat_history(chat_history: List[Tuple[str, str]]):
|
|
buffer = []
|
|
for human, ai in chat_history:
|
|
buffer.append(HumanMessage(content=human))
|
|
buffer.append(AIMessage(content=ai))
|
|
return buffer
|
|
|
|
|
|
model = ChatAnthropic(model="claude-2")
|
|
|
|
tools = [DuckDuckGoSearchRun()]
|
|
|
|
prompt = conversational_prompt.partial(
|
|
tools=render_text_description(tools),
|
|
tool_names=", ".join([t.name for t in tools]),
|
|
)
|
|
llm_with_stop = model.bind(stop=["</tool_input>"])
|
|
|
|
agent = (
|
|
{
|
|
"question": lambda x: x["question"],
|
|
"agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),
|
|
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
|
|
}
|
|
| prompt
|
|
| llm_with_stop
|
|
| parse_output
|
|
)
|
|
|
|
|
|
class AgentInput(BaseModel):
|
|
question: str
|
|
chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
|
|
|
|
|
|
agent_executor = AgentExecutor(
|
|
agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
|
|
).with_types(input_type=AgentInput)
|
|
|
|
agent_executor = agent_executor | (lambda x: x["output"])
|