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from __future__ import annotations
from typing import List, Optional, Sequence, Union
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.tools.render import ToolsRenderer, render_text_description
from langchain.agents import AgentOutputParser
from langchain.agents.output_parsers import ReActSingleInputOutputParser
def create_react_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: BasePromptTemplate,
output_parser: Optional[AgentOutputParser] = None,
tools_renderer: ToolsRenderer = render_text_description,
*,
stop_sequence: Union[bool, List[str]] = True,
) -> Runnable:
missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
prompt.input_variables + list(prompt.partial_variables)
)
if missing_vars:
raise ValueError(f"Prompt missing required variables: {missing_vars}")
prompt = prompt.partial(
tools=tools_renderer(list(tools)),
tool_names=", ".join([t.name for t in tools]),
)
if stop_sequence:
stop = (
["\nObservation", "\nFinal", "Answer:"]
if stop_sequence is True
else stop_sequence
)
llm_with_stop = llm.bind(stop=stop)
else:
llm_with_stop = llm
output_parser = output_parser or ReActSingleInputOutputParser()
agent = (
{
"agent_scratchpad": RunnablePassthrough(),
}
| prompt
| llm_with_stop
# | output_parser
)
return agent
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