langchain[patch]: add tools renderer for various non-openai agents

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

@@ -8,14 +8,15 @@
 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
+from langchain.tools.render import ToolsRenderer, render_text_description
 
 
 def create_json_chat_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: ChatPromptTemplate,
     stop_sequence: bool = True,
+    tools_renderer: ToolsRenderer = render_text_description,
 ) -> Runnable:
     """Create an agent that uses JSON to format its logic, build for Chat Models.
 
@@ -26,6 +27,9 @@ def create_json_chat_agent(
         stop_sequence: Adds a stop token of "Observation:" to avoid hallucinates. 
             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`.
+
     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
@@ -150,7 +154,7 @@ def create_json_chat_agent(
         raise ValueError(f"Prompt missing required variables: {missing_vars}")
 
     prompt = prompt.partial(
-        tools=render_text_description(list(tools)),
+        tools=tools_renderer(list(tools)),
         tool_names=", ".join([t.name for t in tools]),
     )
     if stop_sequence:

libs/langchain/langchain/agents/mrkl/base.py

@@ -17,6 +17,7 @@
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
 from langchain.chains import LLMChain
+from langchain.tools.render import render_text_description
 
 
 class ChainConfig(NamedTuple):
@@ -79,7 +80,7 @@ def create_prompt(
         Returns:
             A PromptTemplate with the template assembled from the pieces here.
         """
-        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
+        tool_strings = render_text_description(list(tools))
         tool_names = ", ".join([tool.name for tool in tools])
         format_instructions = format_instructions.format(tool_names=tool_names)
         template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])

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

@@ -10,14 +10,15 @@
 from langchain.agents import AgentOutputParser
 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
+from langchain.tools.render import ToolsRenderer, render_text_description
 
 
 def create_react_agent(
     llm: BaseLanguageModel,
     tools: Sequence[BaseTool],
     prompt: BasePromptTemplate,
     output_parser: Optional[AgentOutputParser] = None,
+    tools_renderer: ToolsRenderer = render_text_description,
 ) -> Runnable:
     """Create an agent that uses ReAct prompting.
 
@@ -26,6 +27,8 @@ def create_react_agent(
         tools: Tools this agent has access to.
         prompt: The prompt to use. See Prompt section below for more.
         output_parser: AgentOutputParser for parse the LLM output.
+        tools_renderer: This controls how the tools are converted into a string and
+            then passed into the LLM. Default is `render_text_description`.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -102,7 +105,7 @@ def create_react_agent(
         raise ValueError(f"Prompt missing required variables: {missing_vars}")
 
     prompt = prompt.partial(
-        tools=render_text_description(list(tools)),
+        tools=tools_renderer(list(tools)),
         tool_names=", ".join([t.name for t in tools]),
     )
     llm_with_stop = llm.bind(stop=["\nObservation"])

libs/langchain/langchain/agents/structured_chat/base.py

@@ -23,7 +23,7 @@
 )
 from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.chains.llm import LLMChain
-from langchain.tools.render import render_text_description_and_args
+from langchain.tools.render import ToolsRenderer, render_text_description_and_args
 
 HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
 
@@ -151,14 +151,19 @@ def _agent_type(self) -> str:
 
 
 def create_structured_chat_agent(
-    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: ChatPromptTemplate,
+    tools_renderer: ToolsRenderer = render_text_description_and_args,
 ) -> Runnable:
     """Create an agent aimed at supporting tools with multiple inputs.
 
     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.
+        tools_renderer: This controls how the tools are converted into a string and
+            then passed into the LLM. Default is `render_text_description`.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -265,7 +270,7 @@ def create_structured_chat_agent(
         raise ValueError(f"Prompt missing required variables: {missing_vars}")
 
     prompt = prompt.partial(
-        tools=render_text_description_and_args(list(tools)),
+        tools=tools_renderer(list(tools)),
         tool_names=", ".join([t.name for t in tools]),
     )
     llm_with_stop = llm.bind(stop=["Observation"])

libs/langchain/langchain/agents/xml/base.py

@@ -14,7 +14,7 @@
 from langchain.agents.output_parsers import XMLAgentOutputParser
 from langchain.agents.xml.prompt import agent_instructions
 from langchain.chains.llm import LLMChain
-from langchain.tools.render import render_text_description
+from langchain.tools.render import ToolsRenderer, render_text_description
 
 
 @deprecated("0.1.0", alternative="create_xml_agent", removal="0.2.0")
@@ -108,7 +108,10 @@ async def aplan(
 
 
 def create_xml_agent(
-    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: BasePromptTemplate,
+    tools_renderer: ToolsRenderer = render_text_description,
 ) -> Runnable:
     """Create an agent that uses XML to format its logic.
 
@@ -118,6 +121,8 @@ def create_xml_agent(
         prompt: The prompt to use, must have input keys
             `tools`: contains descriptions for each tool.
             `agent_scratchpad`: contains previous agent actions and tool outputs.
+        tools_renderer: This controls how the tools are converted into a string and
+            then passed into the LLM. Default is `render_text_description`.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -194,7 +199,7 @@ def create_xml_agent(
         raise ValueError(f"Prompt missing required variables: {missing_vars}")
 
     prompt = prompt.partial(
-        tools=render_text_description(list(tools)),
+        tools=tools_renderer(list(tools)),
     )
     llm_with_stop = llm.bind(stop=["</tool_input>"])
 

libs/langchain/langchain/tools/render.py

@@ -4,7 +4,7 @@
 you may want Tools to be rendered in a different way.
 This module contains various ways to render tools.
 """
-from typing import List
+from typing import Callable, List
 
 # For backwards compatibility
 from langchain_core.tools import BaseTool
@@ -14,13 +14,17 @@
 )
 
 __all__ = [
+    "ToolsRenderer",
     "render_text_description",
     "render_text_description_and_args",
     "format_tool_to_openai_tool",
     "format_tool_to_openai_function",
 ]
 
 
+ToolsRenderer = Callable[[List[BaseTool]], str]
+
+
 def render_text_description(tools: List[BaseTool]) -> str:
     """Render the tool name and description in plain text.