create_prompt() — langchain Function Reference

Architecture documentation for the create_prompt() function in base.py from the langchain codebase.

Function python AgentOrchestration ClassicChains called by 1

Entity Profile

AgentOrchestration→ ClassicChains→ create_prompt() — langchain Function Reference

Dependency Diagram

graph TD
  2fef2a4a_7604_ff34_36d8_f37a8a5d4c24["create_prompt()"]
  5a1abcb7_8fb7_225b_061e_00eff67795db["StructuredChatAgent"]
  2fef2a4a_7604_ff34_36d8_f37a8a5d4c24 -->|defined in| 5a1abcb7_8fb7_225b_061e_00eff67795db
  8bb74a99_0c88_ed39_398b_2f6f455b302d["from_llm_and_tools()"]
  8bb74a99_0c88_ed39_398b_2f6f455b302d -->|calls| 2fef2a4a_7604_ff34_36d8_f37a8a5d4c24
  style 2fef2a4a_7604_ff34_36d8_f37a8a5d4c24 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/agents/structured_chat/base.py lines 93–119

    def create_prompt(
        cls,
        tools: Sequence[BaseTool],
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: list[str] | None = None,
        memory_prompts: list[BasePromptTemplate] | None = None,
    ) -> BasePromptTemplate:
        tool_strings = []
        for tool in tools:
            args_schema = re.sub("}", "}}", re.sub("{", "{{", str(tool.args)))
            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
        formatted_tools = "\n".join(tool_strings)
        tool_names = ", ".join([tool.name for tool in tools])
        format_instructions = format_instructions.format(tool_names=tool_names)
        template = f"{prefix}\n\n{formatted_tools}\n\n{format_instructions}\n\n{suffix}"
        if input_variables is None:
            input_variables = ["input", "agent_scratchpad"]
        _memory_prompts = memory_prompts or []
        messages = [
            SystemMessagePromptTemplate.from_template(template),
            *_memory_prompts,
            HumanMessagePromptTemplate.from_template(human_message_template),
        ]
        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]