From b3c7ed49132e05756ff4d6e5e98fa34af976e81f Mon Sep 17 00:00:00 2001
From: Sebastian Cherny <sebascherny@gmail.com>
Date: Sun, 8 Sep 2024 20:38:43 +0200
Subject: [PATCH] Adding bind_tools in ChatOctoAI (#26168)

The object extends from
langchain_community.chat_models.openai.ChatOpenAI which doesn't have
`bind_tools` defined. I tried extending from
`langchain_openai.ChatOpenAI` in
https://github.com/langchain-ai/langchain/pull/25975 but that PR got
closed because this is not correct.
So adding our own `bind_tools` (which for now copying from ChatOpenAI is
good enough) will solve the tool calling issue we are having now.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
---
 .../langchain_community/chat_models/octoai.py | 66 ++++++++++++++++++-
 1 file changed, 65 insertions(+), 1 deletion(-)

diff --git a/libs/community/langchain_community/chat_models/octoai.py b/libs/community/langchain_community/chat_models/octoai.py
index f79f9d192c0..6d9a2588022 100644
--- a/libs/community/langchain_community/chat_models/octoai.py
+++ b/libs/community/langchain_community/chat_models/octoai.py
@@ -1,9 +1,23 @@
 """OctoAI Endpoints chat wrapper. Relies heavily on ChatOpenAI."""
 
-from typing import Dict
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Literal,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+)
 
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import BaseMessage
 from langchain_core.pydantic_v1 import Field, SecretStr
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
+from langchain_core.utils.function_calling import convert_to_openai_tool
 
 from langchain_community.chat_models.openai import ChatOpenAI
 from langchain_community.utils.openai import is_openai_v1
@@ -92,3 +106,53 @@ class ChatOctoAI(ChatOpenAI):
             )
 
         return values
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
+        *,
+        tool_choice: Optional[
+            Union[dict, str, Literal["auto", "none", "required", "any"], bool]
+        ] = None,
+        strict: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Imitating bind_tool method from langchain_openai.ChatOpenAI"""
+
+        formatted_tools = [
+            convert_to_openai_tool(tool, strict=strict) for tool in tools
+        ]
+        if tool_choice:
+            if isinstance(tool_choice, str):
+                # tool_choice is a tool/function name
+                if tool_choice not in ("auto", "none", "any", "required"):
+                    tool_choice = {
+                        "type": "function",
+                        "function": {"name": tool_choice},
+                    }
+                # 'any' is not natively supported by OpenAI API.
+                # We support 'any' since other models use this instead of 'required'.
+                if tool_choice == "any":
+                    tool_choice = "required"
+            elif isinstance(tool_choice, bool):
+                tool_choice = "required"
+            elif isinstance(tool_choice, dict):
+                tool_names = [
+                    formatted_tool["function"]["name"]
+                    for formatted_tool in formatted_tools
+                ]
+                if not any(
+                    tool_name == tool_choice["function"]["name"]
+                    for tool_name in tool_names
+                ):
+                    raise ValueError(
+                        f"Tool choice {tool_choice} was specified, but the only "
+                        f"provided tools were {tool_names}."
+                    )
+            else:
+                raise ValueError(
+                    f"Unrecognized tool_choice type. Expected str, bool or dict. "
+                    f"Received: {tool_choice}"
+                )
+            kwargs["tool_choice"] = tool_choice
+        return super().bind(tools=formatted_tools, **kwargs)