[langchain] agents code changes (#15278)

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libs/langchain/langchain/agents/__init__.py

@@ -56,20 +56,32 @@
 from langchain.agents.conversational.base import ConversationalAgent
 from langchain.agents.conversational_chat.base import ConversationalChatAgent
 from langchain.agents.initialize import initialize_agent
+from langchain.agents.json_chat.base import create_json_chat_agent
 from langchain.agents.load_tools import (
     get_all_tool_names,
     load_huggingface_tool,
     load_tools,
 )
 from langchain.agents.loading import load_agent
 from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
-from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+from langchain.agents.openai_functions_agent.base import (
+    OpenAIFunctionsAgent,
+    create_openai_functions_agent,
+)
 from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent
+from langchain.agents.openai_tools.base import create_openai_tools_agent
+from langchain.agents.react.agent import create_react_agent
 from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
-from langchain.agents.self_ask_with_search.base import SelfAskWithSearchChain
-from langchain.agents.structured_chat.base import StructuredChatAgent
+from langchain.agents.self_ask_with_search.base import (
+    SelfAskWithSearchChain,
+    create_self_ask_with_search_agent,
+)
+from langchain.agents.structured_chat.base import (
+    StructuredChatAgent,
+    create_structured_chat_agent,
+)
 from langchain.agents.tools import Tool, tool
-from langchain.agents.xml.base import XMLAgent
+from langchain.agents.xml.base import XMLAgent, create_xml_agent
 
 DEPRECATED_CODE = [
     "create_csv_agent",
@@ -133,4 +145,11 @@ def __getattr__(name: str) -> Any:
     "load_tools",
     "tool",
     "XMLAgent",
+    "create_openai_functions_agent",
+    "create_xml_agent",
+    "create_react_agent",
+    "create_openai_tools_agent",
+    "create_self_ask_with_search_agent",
+    "create_json_chat_agent",
+    "create_structured_chat_agent",
 ]

libs/langchain/langchain/agents/json_chat/base.py

@@ -0,0 +1,83 @@
+from typing import Sequence
+
+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 render_text_description
+
+
+def create_json_chat_agent(
+    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
+) -> Runnable:
+    """Create an agent that uses JSON to format its logic, build for Chat Models.
+
+    Examples:
+
+
+        .. code-block:: python
+
+            from langchain import hub
+            from langchain.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?"),
+                    ],
+                }
+            )
+
+    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`.
+
+    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.
+
+    """
+    missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
+        prompt.input_variables
+    )
+    if missing_vars:
+        raise ValueError(f"Prompt missing required variables: {missing_vars}")
+
+    prompt = prompt.partial(
+        tools=render_text_description(list(tools)),
+        tool_names=", ".join([t.name for t in tools]),
+    )
+    llm_with_stop = llm.bind(stop=["\nObservation"])
+
+    agent = (
+        RunnablePassthrough.assign(
+            agent_scratchpad=lambda x: format_log_to_messages(
+                x["intermediate_steps"], template_tool_response=TEMPLATE_TOOL_RESPONSE
+            )
+        )
+        | prompt
+        | llm_with_stop
+        | JSONAgentOutputParser()
+    )
+    return agent

libs/langchain/langchain/agents/json_chat/prompt.py

@@ -0,0 +1,9 @@
+# flake8: noqa
+TEMPLATE_TOOL_RESPONSE = """TOOL RESPONSE: 
+---------------------
+{observation}
+
+USER'S INPUT
+--------------------
+
+Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. Do NOT respond with anything except a JSON snippet no matter what!"""

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -15,6 +15,7 @@
     MessagesPlaceholder,
 )
 from langchain_core.pydantic_v1 import root_validator
+from langchain_core.runnables import Runnable, RunnablePassthrough
 from langchain_core.tools import BaseTool
 
 from langchain.agents import BaseSingleActionAgent
@@ -226,3 +227,73 @@ def from_llm_and_tools(
             callback_manager=callback_manager,
             **kwargs,
         )
+
+
+def create_openai_functions_agent(
+    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
+) -> Runnable:
+    """Create an agent that uses OpenAI function calling.
+
+    Examples:
+
+        Creating an agent with no memory
+
+        .. code-block:: python
+
+            from langchain.chat_models import ChatOpenAI
+            from langchain.agents import AgentExecutor, create_openai_functions_agent
+            from langchain import hub
+
+            prompt = hub.pull("hwchase17/openai-functions-agent")
+            model = ChatOpenAI()
+            tools = ...
+
+            agent = create_openai_functions_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?"),
+                    ],
+                }
+            )
+
+    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`.
+
+    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.
+
+    """
+    if "agent_scratchpad" not in prompt.input_variables:
+        raise ValueError(
+            "Prompt must have input variable `agent_scratchpad`, but wasn't found. "
+            f"Found {prompt.input_variables} instead."
+        )
+    llm_with_tools = llm.bind(
+        functions=[format_tool_to_openai_function(t) for t in tools]
+    )
+    agent = (
+        RunnablePassthrough.assign(
+            agent_scratchpad=lambda x: format_to_openai_function_messages(
+                x["intermediate_steps"]
+            )
+        )
+        | prompt
+        | llm_with_tools
+        | OpenAIFunctionsAgentOutputParser()
+    )
+    return agent

libs/langchain/langchain/agents/openai_tools/base.py

@@ -0,0 +1,79 @@
+from typing import Sequence
+
+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.openai_tools import (
+    format_to_openai_tool_messages,
+)
+from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
+from langchain.tools.render import format_tool_to_openai_tool
+
+
+def create_openai_tools_agent(
+    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
+) -> Runnable:
+    """Create an agent that uses OpenAI tools.
+
+    Examples:
+
+
+        .. code-block:: python
+
+            from langchain import hub
+            from langchain.chat_models import ChatOpenAI
+            from langchain.agents import AgentExecutor, create_openai_tools_agent
+
+            prompt = hub.pull("hwchase17/openai-tools-agent")
+            model = ChatOpenAI()
+            tools = ...
+
+            agent = create_openai_tools_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?"),
+                    ],
+                }
+            )
+
+    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`.
+
+    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.
+
+    """
+    missing_vars = {"agent_scratchpad"}.difference(prompt.input_variables)
+    if missing_vars:
+        raise ValueError(f"Prompt missing required variables: {missing_vars}")
+
+    llm_with_tools = llm.bind(
+        tools=[format_tool_to_openai_tool(tool) for tool in tools]
+    )
+
+    agent = (
+        RunnablePassthrough.assign(
+            agent_scratchpad=lambda x: format_to_openai_tool_messages(
+                x["intermediate_steps"]
+            )
+        )
+        | prompt
+        | llm_with_tools
+        | OpenAIToolsAgentOutputParser()
+    )
+    return agent

libs/langchain/langchain/agents/react/agent.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from typing import Sequence
+
+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.agents.format_scratchpad import format_log_to_str
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain.tools.render import render_text_description
+
+
+def create_react_agent(
+    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate
+) -> Runnable:
+    """Create an agent that uses ReAct prompting.
+
+    Examples:
+
+        .. code-block:: python
+
+            from langchain import hub
+            from langchain.llms import OpenAI
+            from langchain.agents import AgentExecutor, create_react_agent
+
+            prompt = hub.pull("hwchase17/react")
+            model = OpenAI()
+            tools = ...
+
+            agent = create_react_agent(model, tools, prompt)
+            agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+            agent_executor.invoke({"input": "hi"})
+
+            # Use with chat history
+            from langchain_core.messages import AIMessage, HumanMessage
+            agent_executor.invoke(
+                {
+                    "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!",
+                }
+            )
+
+    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`.
+
+    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.
+
+    """
+    missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
+        prompt.input_variables
+    )
+    if missing_vars:
+        raise ValueError(f"Prompt missing required variables: {missing_vars}")
+
+    prompt = prompt.partial(
+        tools=render_text_description(list(tools)),
+        tool_names=", ".join([t.name for t in tools]),
+    )
+    llm_with_stop = llm.bind(stop=["\nObservation"])
+    agent = (
+        RunnablePassthrough.assign(
+            agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
+        )
+        | prompt
+        | llm_with_stop
+        | ReActSingleInputOutputParser()
+    )
+    return agent