add chat tool calling

libs/ai-endpoints/docs/chat/nvidia_ai_endpoints.ipynb

@@ -548,6 +548,75 @@
    "source": [
     "conversation.invoke(\"Tell me about yourself.\")[\"response\"]"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3cbbba0",
+   "metadata": {},
+   "source": [
+    "## Tool calling\n",
+    "\n",
+    "Starting in v0.2, `ChatNVIDIA` supports [bind_tools](https://api.python.langchain.com/en/latest/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.bind_tools).\n",
+    "\n",
+    "`ChatNVIDIA` provides integration with the variety of models on [build.nvidia.com](https://build.nvidia.com) as well as local NIMs. Not all these models are trained for tool calling. Be sure to select a model that does have tool calling for your experimention and applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f7b535e",
+   "metadata": {},
+   "source": [
+    "You can get a list of models that are known to support tool calling with,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e36c8911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_tools]\n",
+    "tool_models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b01d75a7",
+   "metadata": {},
+   "source": [
+    "With a tool capable model,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd54f174",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.pydantic_v1 import Field\n",
+    "from langchain_core.tools import tool\n",
+    "\n",
+    "@tool\n",
+    "def get_current_weather(\n",
+    "    location: str = Field(..., description=\"The location to get the weather for.\")\n",
+    "):\n",
+    "    \"\"\"Get the current weather for a location.\"\"\"\n",
+    "    ...\n",
+    "\n",
+    "llm = ChatNVIDIA(model=tool_models[0].id).bind_tools(tools=[get_current_weather])\n",
+    "response = llm.invoke(\"What is the weather in Boston?\")\n",
+    "response.tool_calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e08df68c",
+   "metadata": {},
+   "source": [
+    "See [How to use chat models to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/) for additional examples."
+   ]
   }
  ],
  "metadata": {

libs/ai-endpoints/langchain_nvidia_ai_endpoints/_common.py

@@ -368,13 +368,17 @@ def _aggregate_msgs(self, msg_list: Sequence[dict]) -> Tuple[dict, bool]:
         content_buffer: Dict[str, Any] = dict()
         content_holder: Dict[Any, Any] = dict()
         usage_holder: Dict[Any, Any] = dict()  ####
+        finish_reason_holder: Optional[str] = None
         is_stopped = False
         for msg in msg_list:
             usage_holder = msg.get("usage", {})  ####
             if "choices" in msg:
                 ## Tease out ['choices'][0]...['delta'/'message']
                 msg = msg.get("choices", [{}])[0]
-                is_stopped = msg.get("finish_reason", "") == "stop"
+                # todo: this meeds to be fixed, the fact we only
+                #       use the first choice breaks the interface
+                finish_reason_holder = msg.get("finish_reason", None)
+                is_stopped = finish_reason_holder == "stop"
                 msg = msg.get("delta", msg.get("message", msg.get("text", "")))
                 if not isinstance(msg, dict):
                     msg = {"content": msg}
@@ -392,6 +396,8 @@ def _aggregate_msgs(self, msg_list: Sequence[dict]) -> Tuple[dict, bool]:
         content_holder = {**content_holder, **content_buffer}
         if usage_holder:
             content_holder.update(token_usage=usage_holder)  ####
+        if finish_reason_holder:
+            content_holder.update(finish_reason=finish_reason_holder)
         return content_holder, is_stopped
 
     ####################################################################################

libs/ai-endpoints/langchain_nvidia_ai_endpoints/_statics.py

@@ -1,3 +1,4 @@
+import os
 import warnings
 from typing import Literal, Optional
 
@@ -13,6 +14,7 @@ class Model(BaseModel):
     client: client name, e.g. ChatNVIDIA, NVIDIAEmbeddings, NVIDIARerank
     endpoint: custom endpoint for the model
     aliases: list of aliases for the model
+    supports_tools: whether the model supports tool calling
 
     All aliases are deprecated and will trigger a warning when used.
     """
@@ -25,6 +27,7 @@ class Model(BaseModel):
     client: Optional[Literal["ChatNVIDIA", "NVIDIAEmbeddings", "NVIDIARerank"]] = None
     endpoint: Optional[str] = None
     aliases: Optional[list] = None
+    supports_tools: Optional[bool] = False
     base_model: Optional[str] = None
 
     def __hash__(self) -> int:
@@ -286,16 +289,19 @@ def validate_client(cls, client: str, values: dict) -> str:
         id="meta/llama-3.1-8b-instruct",
         model_type="chat",
         client="ChatNVIDIA",
+        supports_tools=True,
     ),
     "meta/llama-3.1-70b-instruct": Model(
         id="meta/llama-3.1-70b-instruct",
         model_type="chat",
         client="ChatNVIDIA",
+        supports_tools=True,
     ),
     "meta/llama-3.1-405b-instruct": Model(
         id="meta/llama-3.1-405b-instruct",
         model_type="chat",
         client="ChatNVIDIA",
+        supports_tools=True,
     ),
 }
 
@@ -438,6 +444,18 @@ def validate_client(cls, client: str, values: dict) -> str:
 #     ),
 # }
 
+
+OPENAI_MODEL_TABLE = {
+    "gpt-3.5-turbo": Model(
+        id="gpt-3.5-turbo",
+        model_type="chat",
+        client="ChatNVIDIA",
+        endpoint="https://api.openai.com/v1/chat/completions",
+        supports_tools=True,
+    ),
+}
+
+
 MODEL_TABLE = {
     **CHAT_MODEL_TABLE,
     **QA_MODEL_TABLE,
@@ -446,6 +464,9 @@ def validate_client(cls, client: str, values: dict) -> str:
     **RANKING_MODEL_TABLE,
 }
 
+if "_INCLUDE_OPENAI" in os.environ:
+    MODEL_TABLE.update(OPENAI_MODEL_TABLE)
+
 
 def register_model(model: Model) -> None:
     """