langchain/templates/xml-agent/xml_agent/agent.py
Erick Friis ed789be8f4
docs, templates: update schema imports to core (#17885)
- chat models, messages
- documents
- agentaction/finish
- baseretriever,document
- stroutputparser
- more messages
- basemessage
- format_document
- baseoutputparser

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-02-22 15:58:44 -08:00

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.tools import DuckDuckGoSearchRun
from langchain.tools.render import render_text_description
from langchain_community.chat_models import ChatAnthropic
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.pydantic_v1 import BaseModel, Field
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"])