Source code for langchain.agents.json_chat.base

from typing import List, Sequence, Union

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool

from langchain.agents.format_scratchpad import format_log_to_messages
from langchain.agents.json_chat.prompt import TEMPLATE_TOOL_RESPONSE
from langchain.agents.output_parsers import JSONAgentOutputParser
from langchain.tools.render import ToolsRenderer, render_text_description


[docs]def create_json_chat_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate, stop_sequence: Union[bool, List[str]] = True, tools_renderer: ToolsRenderer = render_text_description, template_tool_response: str = TEMPLATE_TOOL_RESPONSE, ) -> Runnable: """Create an agent that uses JSON to format its logic, build for Chat Models. Args: llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. stop_sequence: bool or list of str. If True, adds a stop token of "Observation:" to avoid hallucinates. If False, does not add a stop token. If a list of str, uses the provided list as the stop tokens. Default is True. You may to set this to False if the LLM you are using does not support stop sequences. tools_renderer: This controls how the tools are converted into a string and then passed into the LLM. Default is `render_text_description`. template_tool_response: Template prompt that uses the tool response (observation) to make the LLM generate the next action to take. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish. Example: .. code-block:: python from langchain import hub from langchain_community.chat_models import ChatOpenAI from langchain.agents import AgentExecutor, create_json_chat_agent prompt = hub.pull("hwchase17/react-chat-json") model = ChatOpenAI() tools = ... agent = create_json_chat_agent(model, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor.invoke({"input": "hi"}) # Using with chat history from langchain_core.messages import AIMessage, HumanMessage agent_executor.invoke( { "input": "what's my name?", "chat_history": [ HumanMessage(content="hi! my name is bob"), AIMessage(content="Hello Bob! How can I assist you today?"), ], } ) Prompt: The prompt must have input keys: * `tools`: contains descriptions and arguments for each tool. * `tool_names`: contains all tool names. * `agent_scratchpad`: must be a MessagesPlaceholder. Contains previous agent actions and tool outputs as messages. Here's an example: .. code-block:: python from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. Assistant is designed to be able to assist with a wide range of tasks, from answering \ simple questions to providing in-depth explanations and discussions on a wide range of \ topics. As a language model, Assistant is able to generate human-like text based on \ the input it receives, allowing it to engage in natural-sounding conversations and \ provide responses that are coherent and relevant to the topic at hand. Assistant is constantly learning and improving, and its capabilities are constantly \ evolving. It is able to process and understand large amounts of text, and can use this \ knowledge to provide accurate and informative responses to a wide range of questions. \ Additionally, Assistant is able to generate its own text based on the input it \ receives, allowing it to engage in discussions and provide explanations and \ descriptions on a wide range of topics. Overall, Assistant is a powerful system that can help with a wide range of tasks \ and provide valuable insights and information on a wide range of topics. Whether \ you need help with a specific question or just want to have a conversation about \ a particular topic, Assistant is here to assist.''' human = '''TOOLS ------ Assistant can ask the user to use tools to look up information that may be helpful in \ answering the users original question. The tools the human can use are: {tools} RESPONSE FORMAT INSTRUCTIONS ---------------------------- When responding to me, please output a response in one of two formats: **Option 1:** Use this if you want the human to use a tool. Markdown code snippet formatted in the following schema: ```json {{ "action": string, \ The action to take. Must be one of {tool_names} "action_input": string \ The input to the action }} ``` **Option #2:** Use this if you want to respond directly to the human. Markdown code snippet formatted \ in the following schema: ```json {{ "action": "Final Answer", "action_input": string \ You should put what you want to return to use here }} ``` USER'S INPUT -------------------- Here is the user's input (remember to respond with a markdown code snippet of a json \ blob with a single action, and NOTHING else): {input}''' prompt = ChatPromptTemplate.from_messages( [ ("system", system), MessagesPlaceholder("chat_history", optional=True), ("human", human), MessagesPlaceholder("agent_scratchpad"), ] ) """ # noqa: E501 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}") if "{observation}" not in template_tool_response: raise ValueError( "Template tool response missing required variable 'observation'" ) prompt = prompt.partial( tools=tools_renderer(list(tools)), tool_names=", ".join([t.name for t in tools]), ) if stop_sequence: stop = ["\nObservation"] if stop_sequence is True else stop_sequence llm_to_use = llm.bind(stop=stop) else: llm_to_use = llm agent = ( RunnablePassthrough.assign( agent_scratchpad=lambda x: format_log_to_messages( x["intermediate_steps"], template_tool_response=template_tool_response ) ) | prompt | llm_to_use | JSONAgentOutputParser() ) return agent