diff --git a/docs/docs/modules/agents/agent_types/index.mdx b/docs/docs/modules/agents/agent_types/index.mdx index 550f9bd84ca..bcb543ccb03 100644 --- a/docs/docs/modules/agents/agent_types/index.mdx +++ b/docs/docs/modules/agents/agent_types/index.mdx @@ -30,12 +30,12 @@ Whether this agent requires the model to support any additional parameters. Some Our commentary on when you should consider using this agent type. -| Agent Type | Intended Model Type | Supports Chat History | Supports Multi-Input Tools | Supports Parallel Function Calling | Required Model Params | When to Use | +| Agent Type | Intended Model Type | Supports Chat History | Supports Multi-Input Tools | Supports Parallel Function Calling | Required Model Params | When to Use | API | |--------------------------------------------|---------------------|-----------------------|----------------------------|-------------------------------------|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [OpenAI Tools](./openai_tools) | Chat | ✅ | ✅ | ✅ | `tools` | If you are using a recent OpenAI model (`1106` onwards) | -| [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 | -| [XML](./xml_agent) | LLM | ✅ | | | | If you are using Anthropic models, or other models good at XML | -| [Structured Chat](./structured_chat) | Chat | ✅ | ✅ | | | If you need to support tools with multiple inputs | -| [JSON Chat](./json_agent) | Chat | ✅ | | | | If you are using a model good at JSON | -| [ReAct](./react) | LLM | ✅ | | | | If you are using a simple model | -| [Self Ask With Search](./self_ask_with_search)| LLM | | | | | If you are using a simple model and only have one search tool | +| [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) | +| [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) | +| [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) | +| [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) | +| [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) | +| [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) | +| [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) | \ No newline at end of file diff --git a/libs/langchain/langchain/agents/json_chat/base.py b/libs/langchain/langchain/agents/json_chat/base.py index 954dd375f25..3e34568a972 100644 --- a/libs/langchain/langchain/agents/json_chat/base.py +++ b/libs/langchain/langchain/agents/json_chat/base.py @@ -16,8 +16,20 @@ def create_json_chat_agent( ) -> Runnable: """Create an agent that uses JSON to format its logic, build for Chat Models. - 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 and arguments for each tool. + `tool_names`: contains all tool names. + `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 @@ -46,18 +58,82 @@ def create_json_chat_agent( } ) - 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`, `tool_names`, 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 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 ) diff --git a/libs/langchain/langchain/agents/openai_functions_agent/base.py b/libs/langchain/langchain/agents/openai_functions_agent/base.py index f388f5cae26..1d486dc6856 100644 --- a/libs/langchain/langchain/agents/openai_functions_agent/base.py +++ b/libs/langchain/langchain/agents/openai_functions_agent/base.py @@ -235,7 +235,20 @@ def create_openai_functions_agent( ) -> Runnable: """Create an agent that uses OpenAI function calling. - Examples: + Args: + llm: LLM to use as the agent. Should work with OpenAI function calling, + so either be an OpenAI model that supports that or a wrapper of + a different model that adds in equivalent support. + tools: Tools this agent has access to. + prompt: The prompt to use, must have input key `agent_scratchpad`, which will + contain agent action and tool output messages. + + 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: Creating an agent with no memory @@ -266,18 +279,20 @@ def create_openai_functions_agent( } ) - Args: - llm: LLM to use as the agent. Should work with OpenAI function calling, - so either be an OpenAI model that supports that or a wrapper of - a different model that adds in equivalent support. - tools: Tools this agent has access to. - prompt: The prompt to use, must have an input key of `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 ChatPromptTemplate, MessagesPlaceholder + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant"), + MessagesPlaceholder("chat_history", optional=True), + ("human", "{input}"), + MessagesPlaceholder("agent_scratchpad"), + ] + ) """ if "agent_scratchpad" not in prompt.input_variables: raise ValueError( diff --git a/libs/langchain/langchain/agents/openai_tools/base.py b/libs/langchain/langchain/agents/openai_tools/base.py index c1206ea4efc..4395fb32bbd 100644 --- a/libs/langchain/langchain/agents/openai_tools/base.py +++ b/libs/langchain/langchain/agents/openai_tools/base.py @@ -17,7 +17,18 @@ def create_openai_tools_agent( ) -> Runnable: """Create an agent that uses OpenAI tools. - Examples: + Args: + llm: LLM to use as the agent. + tools: Tools this agent has access to. + prompt: The prompt to use, must have input key `agent_scratchpad`, which will + contain agent action and tool output messages. + + 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 @@ -46,15 +57,20 @@ def create_openai_tools_agent( } ) - 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 `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 ChatPromptTemplate, MessagesPlaceholder + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant"), + MessagesPlaceholder("chat_history", optional=True), + ("human", "{input}"), + MessagesPlaceholder("agent_scratchpad"), + ] + ) """ missing_vars = {"agent_scratchpad"}.difference(prompt.input_variables) if missing_vars: diff --git a/libs/langchain/langchain/agents/react/agent.py b/libs/langchain/langchain/agents/react/agent.py index fe27a42b6a5..23277c36540 100644 --- a/libs/langchain/langchain/agents/react/agent.py +++ b/libs/langchain/langchain/agents/react/agent.py @@ -17,6 +17,20 @@ def create_react_agent( ) -> Runnable: """Create an agent that uses ReAct prompting. + 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 and arguments for each tool. + `tool_names`: contains all tool names. + `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. + Examples: .. code-block:: python @@ -45,18 +59,34 @@ def create_react_agent( } ) - 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`, `tool_names`, 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 = '''Answer the following questions as best you can. You have access to the following tools: + + {tools} + + Use the following format: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take, should be one of [{tool_names}] + Action Input: the input to the action + Observation: the result of the action + ... (this Thought/Action/Action Input/Observation can repeat N times) + Thought: I now know the final answer + Final Answer: the final answer to the original input question + + Begin! + + Question: {input} + Thought:{agent_scratchpad}''' + + prompt = PromptTemplate.from_template(template) + """ # noqa: E501 missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference( prompt.input_variables ) diff --git a/libs/langchain/langchain/agents/self_ask_with_search/base.py b/libs/langchain/langchain/agents/self_ask_with_search/base.py index 32c482eeff2..9df14cb4335 100644 --- a/libs/langchain/langchain/agents/self_ask_with_search/base.py +++ b/libs/langchain/langchain/agents/self_ask_with_search/base.py @@ -91,8 +91,19 @@ def create_self_ask_with_search_agent( ) -> Runnable: """Create an agent that uses self-ask with search prompting. - Examples: + Args: + llm: LLM to use as the agent. + tools: List of tools. Should just be of length 1, with that tool having + name `Intermediate Answer` + prompt: The prompt to use, must have input key `agent_scratchpad` which will + contain 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. + + Examples: .. code-block:: python @@ -111,18 +122,53 @@ def create_self_ask_with_search_agent( agent_executor.invoke({"input": "hi"}) - Args: - llm: LLM to use as the agent. - tools: List of tools. Should just be of length 1, with that tool having - name `Intermediate Answer` - prompt: The prompt to use, must have input keys of `agent_scratchpad`. + Create 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 = '''Question: Who lived longer, Muhammad Ali or Alan Turing? + Are follow up questions needed here: Yes. + Follow up: How old was Muhammad Ali when he died? + Intermediate answer: Muhammad Ali was 74 years old when he died. + Follow up: How old was Alan Turing when he died? + Intermediate answer: Alan Turing was 41 years old when he died. + So the final answer is: Muhammad Ali + + Question: When was the founder of craigslist born? + Are follow up questions needed here: Yes. + Follow up: Who was the founder of craigslist? + Intermediate answer: Craigslist was founded by Craig Newmark. + Follow up: When was Craig Newmark born? + Intermediate answer: Craig Newmark was born on December 6, 1952. + So the final answer is: December 6, 1952 + + Question: Who was the maternal grandfather of George Washington? + Are follow up questions needed here: Yes. + Follow up: Who was the mother of George Washington? + Intermediate answer: The mother of George Washington was Mary Ball Washington. + Follow up: Who was the father of Mary Ball Washington? + Intermediate answer: The father of Mary Ball Washington was Joseph Ball. + So the final answer is: Joseph Ball + + Question: Are both the directors of Jaws and Casino Royale from the same country? + Are follow up questions needed here: Yes. + Follow up: Who is the director of Jaws? + Intermediate answer: The director of Jaws is Steven Spielberg. + Follow up: Where is Steven Spielberg from? + Intermediate answer: The United States. + Follow up: Who is the director of Casino Royale? + Intermediate answer: The director of Casino Royale is Martin Campbell. + Follow up: Where is Martin Campbell from? + Intermediate answer: New Zealand. + So the final answer is: No + + Question: {input} + Are followup questions needed here:{agent_scratchpad}''' + + prompt = PromptTemplate.from_template(template) + """ # noqa: E501 missing_vars = {"agent_scratchpad"}.difference(prompt.input_variables) if missing_vars: raise ValueError(f"Prompt missing required variables: {missing_vars}") diff --git a/libs/langchain/langchain/agents/structured_chat/base.py b/libs/langchain/langchain/agents/structured_chat/base.py index 68bb381897d..53e11b33c01 100644 --- a/libs/langchain/langchain/agents/structured_chat/base.py +++ b/libs/langchain/langchain/agents/structured_chat/base.py @@ -155,8 +155,20 @@ def create_structured_chat_agent( ) -> Runnable: """Create an agent aimed at supporting tools with multiple inputs. - 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 and arguments for each tool. + `tool_names`: contains all tool names. + `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. + + Examples: .. code-block:: python @@ -185,18 +197,63 @@ def create_structured_chat_agent( } ) - 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`, `tool_names`, 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 ChatPromptTemplate, MessagesPlaceholder + + system = '''Respond to the human as helpfully and accurately as possible. You have access to the following tools: + + {tools} + + Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). + + Valid "action" values: "Final Answer" or {tool_names} + + Provide only ONE action per $JSON_BLOB, as shown: + + ``` + {{ + "action": $TOOL_NAME, + "action_input": $INPUT + }} + ``` + + Follow this format: + + 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 ) diff --git a/libs/langchain/langchain/agents/xml/base.py b/libs/langchain/langchain/agents/xml/base.py index abdacf48803..bd678979f74 100644 --- a/libs/langchain/langchain/agents/xml/base.py +++ b/libs/langchain/langchain/agents/xml/base.py @@ -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 and tags. You will then get back a response in the form + 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: + + searchweather in SF + 64 degrees + + When you are done, respond with a final answer between . For example: + + The weather in SF is 64 degrees + + 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}")