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docs: add agent prompt creation examples (#15957)
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@ -30,12 +30,12 @@ Whether this agent requires the model to support any additional parameters. Some
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Our commentary on when you should consider using this agent type.
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| Agent Type | Intended Model Type | Supports Chat History | Supports Multi-Input Tools | Supports Parallel Function Calling | Required Model Params | When to Use |
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| Agent Type | Intended Model Type | Supports Chat History | Supports Multi-Input Tools | Supports Parallel Function Calling | Required Model Params | When to Use | API |
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|--------------------------------------------|---------------------|-----------------------|----------------------------|-------------------------------------|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [OpenAI Tools](./openai_tools) | Chat | ✅ | ✅ | ✅ | `tools` | If you are using a recent OpenAI model (`1106` onwards) |
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| [OpenAI Functions](./openai_functions_agent)| Chat | ✅ | ✅ | | `functions` | If you are using an OpenAI model, or an open-source model that has been finetuned for function calling and exposes the same `functions` parameters as OpenAI |
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| [XML](./xml_agent) | LLM | ✅ | | | | If you are using Anthropic models, or other models good at XML |
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| [Structured Chat](./structured_chat) | Chat | ✅ | ✅ | | | If you need to support tools with multiple inputs |
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| [JSON Chat](./json_agent) | Chat | ✅ | | | | If you are using a model good at JSON |
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| [ReAct](./react) | LLM | ✅ | | | | If you are using a simple model |
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| [Self Ask With Search](./self_ask_with_search)| LLM | | | | | If you are using a simple model and only have one search tool |
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| [OpenAI Tools](./openai_tools) | Chat | ✅ | ✅ | ✅ | `tools` | If you are using a recent OpenAI model (`1106` onwards) | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.openai_tools.base.create_openai_tools_agent.html) |
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| [OpenAI Functions](./openai_functions_agent)| Chat | ✅ | ✅ | | `functions` | If you are using an OpenAI model, or an open-source model that has been finetuned for function calling and exposes the same `functions` parameters as OpenAI | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.openai_functions_agent.base.create_openai_functions_agent.html) |
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| [XML](./xml_agent) | LLM | ✅ | | | | If you are using Anthropic models, or other models good at XML | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.xml.base.create_xml_agent.html) |
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| [Structured Chat](./structured_chat) | Chat | ✅ | ✅ | | | If you need to support tools with multiple inputs | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.structured_chat.base.create_structured_chat_agent.html) |
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| [JSON Chat](./json_agent) | Chat | ✅ | | | | If you are using a model good at JSON | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.json_chat.base.create_json_chat_agent.html) |
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| [ReAct](./react) | LLM | ✅ | | | | If you are using a simple model | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html) |
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| [Self Ask With Search](./self_ask_with_search)| LLM | | | | | If you are using a simple model and only have one search tool | [Ref](https://api.python.langchain.com/en/latest/agents/langchain.agents.self_ask_with_search.base.create_self_ask_with_search_agent.html) |
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@ -16,8 +16,20 @@ def create_json_chat_agent(
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) -> Runnable:
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"""Create an agent that uses JSON to format its logic, build for Chat Models.
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Examples:
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys:
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`tools`: contains descriptions and arguments for each tool.
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`tool_names`: contains all tool names.
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`agent_scratchpad`: contains previous agent actions and tool outputs.
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Example:
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.. code-block:: python
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@ -46,18 +58,82 @@ def create_json_chat_agent(
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}
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)
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys of
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`tools`, `tool_names`, and `agent_scratchpad`.
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Creating prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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"""
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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system = '''Assistant is a large language model trained by OpenAI.
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Assistant is designed to be able to assist with a wide range of tasks, from answering \
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simple questions to providing in-depth explanations and discussions on a wide range of \
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topics. As a language model, Assistant is able to generate human-like text based on \
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the input it receives, allowing it to engage in natural-sounding conversations and \
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provide responses that are coherent and relevant to the topic at hand.
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Assistant is constantly learning and improving, and its capabilities are constantly \
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evolving. It is able to process and understand large amounts of text, and can use this \
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knowledge to provide accurate and informative responses to a wide range of questions. \
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Additionally, Assistant is able to generate its own text based on the input it \
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receives, allowing it to engage in discussions and provide explanations and \
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descriptions on a wide range of topics.
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Overall, Assistant is a powerful system that can help with a wide range of tasks \
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and provide valuable insights and information on a wide range of topics. Whether \
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you need help with a specific question or just want to have a conversation about \
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a particular topic, Assistant is here to assist.'''
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human = '''TOOLS
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------
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Assistant can ask the user to use tools to look up information that may be helpful in \
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answering the users original question. The tools the human can use are:
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{tools}
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RESPONSE FORMAT INSTRUCTIONS
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----------------------------
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When responding to me, please output a response in one of two formats:
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**Option 1:**
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Use this if you want the human to use a tool.
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Markdown code snippet formatted in the following schema:
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```json
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{{
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"action": string, \ The action to take. Must be one of {tool_names}
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"action_input": string \ The input to the action
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}}
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```
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**Option #2:**
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Use this if you want to respond directly to the human. Markdown code snippet formatted \
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in the following schema:
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```json
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{{
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"action": "Final Answer",
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"action_input": string \ You should put what you want to return to use here
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}}
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```
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USER'S INPUT
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--------------------
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Here is the user's input (remember to respond with a markdown code snippet of a json \
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blob with a single action, and NOTHING else):
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{input}'''
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", system),
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MessagesPlaceholder("chat_history", optional=True),
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("human", human),
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MessagesPlaceholder("agent_scratchpad"),
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]
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)
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""" # noqa: E501
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missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
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prompt.input_variables
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)
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@ -235,7 +235,20 @@ def create_openai_functions_agent(
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) -> Runnable:
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"""Create an agent that uses OpenAI function calling.
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Examples:
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Args:
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llm: LLM to use as the agent. Should work with OpenAI function calling,
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so either be an OpenAI model that supports that or a wrapper of
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a different model that adds in equivalent support.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input key `agent_scratchpad`, which will
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contain agent action and tool output messages.
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Example:
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Creating an agent with no memory
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@ -266,18 +279,20 @@ def create_openai_functions_agent(
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}
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)
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Args:
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llm: LLM to use as the agent. Should work with OpenAI function calling,
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so either be an OpenAI model that supports that or a wrapper of
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a different model that adds in equivalent support.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have an input key of `agent_scratchpad`.
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Creating prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "You are a helpful assistant"),
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MessagesPlaceholder("chat_history", optional=True),
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("human", "{input}"),
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MessagesPlaceholder("agent_scratchpad"),
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]
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)
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"""
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if "agent_scratchpad" not in prompt.input_variables:
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raise ValueError(
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@ -17,7 +17,18 @@ def create_openai_tools_agent(
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) -> Runnable:
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"""Create an agent that uses OpenAI tools.
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Examples:
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input key `agent_scratchpad`, which will
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contain agent action and tool output messages.
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Example:
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.. code-block:: python
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@ -46,15 +57,20 @@ def create_openai_tools_agent(
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}
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)
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys of `agent_scratchpad`.
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Creating prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "You are a helpful assistant"),
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MessagesPlaceholder("chat_history", optional=True),
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("human", "{input}"),
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MessagesPlaceholder("agent_scratchpad"),
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]
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)
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"""
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missing_vars = {"agent_scratchpad"}.difference(prompt.input_variables)
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if missing_vars:
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@ -17,6 +17,20 @@ def create_react_agent(
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) -> Runnable:
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"""Create an agent that uses ReAct prompting.
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys:
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`tools`: contains descriptions and arguments for each tool.
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`tool_names`: contains all tool names.
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`agent_scratchpad`: contains previous agent actions and tool outputs.
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Examples:
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.. code-block:: python
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@ -45,18 +59,34 @@ def create_react_agent(
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}
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)
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Args:
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llm: LLM to use as the agent.
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys of
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`tools`, `tool_names`, and `agent_scratchpad`.
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Creating prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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"""
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from langchain_core.prompts import PromptTemplate
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template = '''Answer the following questions as best you can. You have access to the following tools:
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{tools}
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Use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action: the action to take, should be one of [{tool_names}]
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Action Input: the input to the action
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Observation: the result of the action
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... (this Thought/Action/Action Input/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin!
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Question: {input}
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Thought:{agent_scratchpad}'''
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prompt = PromptTemplate.from_template(template)
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""" # noqa: E501
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missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
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prompt.input_variables
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)
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@ -91,8 +91,19 @@ def create_self_ask_with_search_agent(
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) -> Runnable:
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"""Create an agent that uses self-ask with search prompting.
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Examples:
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Args:
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llm: LLM to use as the agent.
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tools: List of tools. Should just be of length 1, with that tool having
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name `Intermediate Answer`
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prompt: The prompt to use, must have input key `agent_scratchpad` which will
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contain agent actions and tool outputs.
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Examples:
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.. code-block:: python
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@ -111,18 +122,53 @@ def create_self_ask_with_search_agent(
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agent_executor.invoke({"input": "hi"})
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Args:
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llm: LLM to use as the agent.
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tools: List of tools. Should just be of length 1, with that tool having
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name `Intermediate Answer`
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prompt: The prompt to use, must have input keys of `agent_scratchpad`.
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Create prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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"""
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from langchain_core.prompts import PromptTemplate
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template = '''Question: Who lived longer, Muhammad Ali or Alan Turing?
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Are follow up questions needed here: Yes.
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Follow up: How old was Muhammad Ali when he died?
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Intermediate answer: Muhammad Ali was 74 years old when he died.
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Follow up: How old was Alan Turing when he died?
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Intermediate answer: Alan Turing was 41 years old when he died.
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So the final answer is: Muhammad Ali
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Question: When was the founder of craigslist born?
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Are follow up questions needed here: Yes.
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Follow up: Who was the founder of craigslist?
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Intermediate answer: Craigslist was founded by Craig Newmark.
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Follow up: When was Craig Newmark born?
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Intermediate answer: Craig Newmark was born on December 6, 1952.
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So the final answer is: December 6, 1952
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Question: Who was the maternal grandfather of George Washington?
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Are follow up questions needed here: Yes.
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Follow up: Who was the mother of George Washington?
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Intermediate answer: The mother of George Washington was Mary Ball Washington.
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Follow up: Who was the father of Mary Ball Washington?
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Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
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So the final answer is: Joseph Ball
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Question: Are both the directors of Jaws and Casino Royale from the same country?
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Are follow up questions needed here: Yes.
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Follow up: Who is the director of Jaws?
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Intermediate answer: The director of Jaws is Steven Spielberg.
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Follow up: Where is Steven Spielberg from?
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Intermediate answer: The United States.
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Follow up: Who is the director of Casino Royale?
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Intermediate answer: The director of Casino Royale is Martin Campbell.
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Follow up: Where is Martin Campbell from?
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Intermediate answer: New Zealand.
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So the final answer is: No
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Question: {input}
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Are followup questions needed here:{agent_scratchpad}'''
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prompt = PromptTemplate.from_template(template)
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""" # noqa: E501
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missing_vars = {"agent_scratchpad"}.difference(prompt.input_variables)
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if missing_vars:
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raise ValueError(f"Prompt missing required variables: {missing_vars}")
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@ -155,8 +155,20 @@ def create_structured_chat_agent(
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) -> Runnable:
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"""Create an agent aimed at supporting tools with multiple inputs.
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|
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Examples:
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Args:
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llm: LLM to use as the agent.
|
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tools: Tools this agent has access to.
|
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prompt: The prompt to use, must have input keys
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`tools`: contains descriptions and arguments for each tool.
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`tool_names`: contains all tool names.
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`agent_scratchpad`: contains previous agent actions and tool outputs.
|
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|
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Returns:
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A Runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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Examples:
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.. code-block:: python
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@ -185,18 +197,63 @@ def create_structured_chat_agent(
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}
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)
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Args:
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llm: LLM to use as the agent.
|
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tools: Tools this agent has access to.
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prompt: The prompt to use, must have input keys of
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`tools`, `tool_names`, and `agent_scratchpad`.
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Creating prompt example:
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Returns:
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A runnable sequence representing an agent. It takes as input all the same input
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variables as the prompt passed in does. It returns as output either an
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AgentAction or AgentFinish.
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.. code-block:: python
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"""
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools:
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{tools}
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Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
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Valid "action" values: "Final Answer" or {tool_names}
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Provide only ONE action per $JSON_BLOB, as shown:
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```
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{{
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"action": $TOOL_NAME,
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"action_input": $INPUT
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}}
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```
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Follow this format:
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||||
|
||||
Question: input question to answer
|
||||
Thought: consider previous and subsequent steps
|
||||
Action:
|
||||
```
|
||||
$JSON_BLOB
|
||||
```
|
||||
Observation: action result
|
||||
... (repeat Thought/Action/Observation N times)
|
||||
Thought: I know what to respond
|
||||
Action:
|
||||
```
|
||||
{{
|
||||
"action": "Final Answer",
|
||||
"action_input": "Final response to human"
|
||||
}}
|
||||
|
||||
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation'''
|
||||
|
||||
human = '''{input}
|
||||
|
||||
{agent_scratchpad}
|
||||
|
||||
(reminder to respond in a JSON blob no matter what)'''
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
("system", system),
|
||||
MessagesPlaceholder("chat_history", optional=True),
|
||||
("human", human),
|
||||
]
|
||||
)
|
||||
""" # noqa: E501
|
||||
missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
|
||||
prompt.input_variables
|
||||
)
|
||||
|
||||
@ -112,8 +112,19 @@ def create_xml_agent(
|
||||
) -> Runnable:
|
||||
"""Create an agent that uses XML to format its logic.
|
||||
|
||||
Examples:
|
||||
Args:
|
||||
llm: LLM to use as the agent.
|
||||
tools: Tools this agent has access to.
|
||||
prompt: The prompt to use, must have input keys
|
||||
`tools`: contains descriptions for each tool.
|
||||
`agent_scratchpad`: contains previous agent actions and tool outputs.
|
||||
|
||||
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
|
||||
|
||||
@ -137,22 +148,41 @@ def create_xml_agent(
|
||||
"input": "what's my name?",
|
||||
# Notice that chat_history is a string
|
||||
# since this prompt is aimed at LLMs, not chat models
|
||||
"chat_history": "Human: My name is Bob\nAI: Hello Bob!",
|
||||
"chat_history": "Human: My name is Bob\\nAI: Hello Bob!",
|
||||
}
|
||||
)
|
||||
|
||||
Args:
|
||||
llm: LLM to use as the agent.
|
||||
tools: Tools this agent has access to.
|
||||
prompt: The prompt to use, must have input keys of
|
||||
`tools` and `agent_scratchpad`.
|
||||
Creating prompt example:
|
||||
|
||||
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.
|
||||
.. code-block:: python
|
||||
|
||||
"""
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
template = '''You are a helpful assistant. Help the user answer any questions.
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{tools}
|
||||
|
||||
In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
|
||||
For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
|
||||
|
||||
<tool>search</tool><tool_input>weather in SF</tool_input>
|
||||
<observation>64 degrees</observation>
|
||||
|
||||
When you are done, respond with a final answer between <final_answer></final_answer>. For example:
|
||||
|
||||
<final_answer>The weather in SF is 64 degrees</final_answer>
|
||||
|
||||
Begin!
|
||||
|
||||
Previous Conversation:
|
||||
{chat_history}
|
||||
|
||||
Question: {input}
|
||||
{agent_scratchpad}'''
|
||||
prompt = PromptTemplate.from_template(template)
|
||||
""" # noqa: E501
|
||||
missing_vars = {"tools", "agent_scratchpad"}.difference(prompt.input_variables)
|
||||
if missing_vars:
|
||||
raise ValueError(f"Prompt missing required variables: {missing_vars}")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user