core[patch], community[patch], openai[patch]: consolidate openai tool… (#16485)

… converters

One way to convert anything to an OAI function:
convert_to_openai_function
One way to convert anything to an OAI tool: convert_to_openai_tool
Corresponding bind functions on OAI models: bind_functions, bind_tools

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -5,13 +5,13 @@ from json import JSONDecodeError
 from time import sleep
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union
 
-from langchain_community.tools.convert_to_openai import format_tool_to_openai_tool
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.callbacks import CallbackManager
 from langchain_core.load import dumpd
 from langchain_core.pydantic_v1 import Field
 from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config
 from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_tool
 
 if TYPE_CHECKING:
     import openai
@@ -180,16 +180,10 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
             OpenAIAssistantRunnable configured to run using the created assistant.
         """
         client = client or _get_openai_client()
-        openai_tools: List = []
-        for tool in tools:
-            oai_tool = (
-                tool if isinstance(tool, dict) else format_tool_to_openai_tool(tool)
-            )
-            openai_tools.append(oai_tool)
         assistant = client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=openai_tools,
+            tools=[convert_to_openai_tool(tool) for tool in tools],
             model=model,
         )
         return cls(assistant_id=assistant.id, client=client, **kwargs)

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

@@ -1,7 +1,6 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
 from typing import Any, List, Optional, Sequence, Tuple, Type, Union
 
-from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
 from langchain_core._api import deprecated
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.callbacks import BaseCallbackManager, Callbacks
@@ -20,6 +19,7 @@ from langchain_core.prompts.chat import (
 from langchain_core.pydantic_v1 import root_validator
 from langchain_core.runnables import Runnable, RunnablePassthrough
 from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import convert_to_openai_function
 
 from langchain.agents import BaseSingleActionAgent
 from langchain.agents.format_scratchpad.openai_functions import (
@@ -71,7 +71,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
 
     @property
     def functions(self) -> List[dict]:
-        return [dict(format_tool_to_openai_function(t)) for t in self.tools]
+        return [dict(convert_to_openai_function(t)) for t in self.tools]
 
     def plan(
         self,
@@ -303,9 +303,7 @@ def create_openai_functions_agent(
             "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]
-    )
+    llm_with_tools = llm.bind(functions=[convert_to_openai_function(t) for t in tools])
     agent = (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_to_openai_function_messages(

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

@@ -1,10 +1,10 @@
 from typing import Sequence
 
-from langchain_community.tools.convert_to_openai import format_tool_to_openai_tool
 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_core.utils.function_calling import convert_to_openai_tool
 
 from langchain.agents.format_scratchpad.openai_tools import (
     format_to_openai_tool_messages,
@@ -82,9 +82,7 @@ def create_openai_tools_agent(
     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]
-    )
+    llm_with_tools = llm.bind(tools=[convert_to_openai_tool(tool) for tool in tools])
 
     agent = (
         RunnablePassthrough.assign(

libs/langchain/langchain/tools/convert_to_openai.py

@@ -1,4 +1,4 @@
-from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
+from langchain_core.utils.function_calling import format_tool_to_openai_function
 
 # For backwards compatibility
 __all__ = ["format_tool_to_openai_function"]

libs/langchain/langchain/tools/render.py

@@ -7,11 +7,11 @@ This module contains various ways to render tools.
 from typing import List
 
 # For backwards compatibility
-from langchain_community.tools.convert_to_openai import (
+from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import (
     format_tool_to_openai_function,
     format_tool_to_openai_tool,
 )
-from langchain_core.tools import BaseTool
 
 __all__ = [
     "render_text_description",

libs/langchain/tests/unit_tests/utils/test_openai_functions.py

@@ -15,13 +15,10 @@ def test_convert_pydantic_to_openai_function() -> None:
         "name": "Data",
         "description": "The data to return.",
         "parameters": {
-            "title": "Data",
-            "description": "The data to return.",
             "type": "object",
             "properties": {
-                "key": {"title": "Key", "description": "API key", "type": "string"},
+                "key": {"description": "API key", "type": "string"},
                 "days": {
-                    "title": "Days",
                     "description": "Number of days to forecast",
                     "default": 0,
                     "type": "integer",
@@ -50,22 +47,17 @@ def test_convert_pydantic_to_openai_function_nested() -> None:
         "name": "Model",
         "description": "The model to return.",
         "parameters": {
-            "title": "Model",
-            "description": "The model to return.",
             "type": "object",
             "properties": {
                 "data": {
-                    "title": "Data",
                     "description": "The data to return.",
                     "type": "object",
                     "properties": {
                         "key": {
-                            "title": "Key",
                             "description": "API key",
                             "type": "string",
                         },
                         "days": {
-                            "title": "Days",
                             "description": "Number of days to forecast",
                             "default": 0,
                             "type": "integer",