Function calling logging fixup (#8153)

Fix bad overwriting of "functions" arg in invocation params.
Cleanup precedence in the dict
Clean up some inappropriate types (mapping should be dict)


Example:
https://dev.smith.langchain.com/public/9a7a6817-1679-49d8-8775-c13916975aae/r


![image](https://github.com/langchain-ai/langchain/assets/13333726/94cd0775-b6ef-40c3-9e5a-3ab65e466ab9)

libs/langchain/langchain/chat_models/azure_openai.py

@@ -116,18 +116,18 @@ class AzureChatOpenAI(ChatOpenAI):
         }
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""
         return {**self._default_params}
 
     @property
     def _client_params(self) -> Dict[str, Any]:
         """Get the config params used for the openai client."""
-        openai_creds = {
+        return {
+            **super()._client_params,
             "api_type": self.openai_api_type,
             "api_version": self.openai_api_version,
         }
-        return {**super()._client_params, **openai_creds}
 
     @property
     def _llm_type(self) -> str:

libs/langchain/langchain/chat_models/base.py

@@ -3,7 +3,7 @@ import inspect
 import warnings
 from abc import ABC, abstractmethod
 from functools import partial
-from typing import Any, Dict, List, Mapping, Optional, Sequence
+from typing import Any, Dict, List, Optional, Sequence
 
 from pydantic import Field, root_validator
 
@@ -424,7 +424,7 @@ class BaseChatModel(BaseLanguageModel, ABC):
         return await self._call_async(messages, stop=_stop, **kwargs)
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""
         return {}
 

libs/langchain/langchain/chat_models/fake.py

@@ -1,5 +1,5 @@
 """Fake ChatModel for testing purposes."""
-from typing import Any, List, Mapping, Optional
+from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chat_models.base import SimpleChatModel
@@ -29,5 +29,5 @@ class FakeListChatModel(SimpleChatModel):
         return response
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {"responses": self.responses}

libs/langchain/langchain/chat_models/google_palm.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
 
 from pydantic import BaseModel, root_validator
 from tenacity import (
@@ -40,8 +40,6 @@ logger = logging.getLogger(__name__)
 class ChatGooglePalmError(Exception):
     """Error raised when there is an issue with the Google PaLM API."""
 
-    pass
-
 
 def _truncate_at_stop_tokens(
     text: str,
@@ -323,7 +321,7 @@ class ChatGooglePalm(BaseChatModel, BaseModel):
         return _response_to_result(response, stop)
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""
         return {
             "model_name": self.model_name,

libs/langchain/langchain/chat_models/human.py

@@ -2,7 +2,7 @@
 import asyncio
 from functools import partial
 from io import StringIO
-from typing import Any, Callable, List, Mapping, Optional
+from typing import Any, Callable, Dict, List, Mapping, Optional
 
 import yaml
 from pydantic import Field
@@ -76,7 +76,7 @@ class HumanInputChatModel(BaseChatModel):
     message_kwargs: Mapping[str, Any] = {}
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {
             "input_func": self.input_func.__name__,
             "message_func": self.message_func.__name__,

libs/langchain/langchain/chat_models/openai.py

@@ -355,7 +355,7 @@ class ChatOpenAI(BaseChatModel):
     def _create_message_dicts(
         self, messages: List[BaseMessage], stop: Optional[List[str]]
     ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
-        params = dict(self._client_params)
+        params = self._client_params
         if stop is not None:
             if "stop" in params:
                 raise ValueError("`stop` found in both the input and default params.")
@@ -419,12 +419,12 @@ class ChatOpenAI(BaseChatModel):
             return self._create_chat_result(response)
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""
         return {**{"model_name": self.model_name}, **self._default_params}
 
     @property
-    def _client_params(self) -> Mapping[str, Any]:
+    def _client_params(self) -> Dict[str, Any]:
         """Get the parameters used for the openai client."""
         openai_creds: Dict[str, Any] = {
             "api_key": self.openai_api_key,
@@ -436,17 +436,17 @@ class ChatOpenAI(BaseChatModel):
             import openai
 
             openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[assignment]  # noqa: E501
-        return {**openai_creds, **self._default_params}
+        return {**self._default_params, **openai_creds}
 
     def _get_invocation_params(
         self, stop: Optional[List[str]] = None, **kwargs: Any
     ) -> Dict[str, Any]:
-        """Get the parameters used to invoke the model FOR THE CALLBACKS."""
+        """Get the parameters used to invoke the model."""
         return {
-            **super()._get_invocation_params(stop=stop, **kwargs),
-            **self._default_params,
             "model": self.model_name,
-            "function": kwargs.get("functions"),
+            **super()._get_invocation_params(stop=stop),
+            **self._default_params,
+            **kwargs,
         }
 
     @property

libs/langchain/langchain/chat_models/promptlayer_openai.py

@@ -1,6 +1,6 @@
 """PromptLayer wrapper."""
 import datetime
-from typing import Any, List, Mapping, Optional
+from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -121,7 +121,7 @@ class PromptLayerChatOpenAI(ChatOpenAI):
         return "promptlayer-openai-chat"
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {
             **super()._identifying_params,
             "pl_tags": self.pl_tags,

libs/langchain/tests/unit_tests/agents/test_agent.py

@@ -1,6 +1,6 @@
 """Unit tests for agents."""
 
-from typing import Any, List, Mapping, Optional
+from typing import Any, Dict, List, Optional
 
 from langchain.agents import AgentExecutor, AgentType, initialize_agent
 from langchain.agents.tools import Tool
@@ -36,7 +36,7 @@ class FakeListLLM(LLM):
         return self._call(*args, **kwargs)
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {}
 
     @property

libs/langchain/tests/unit_tests/chains/test_natbot.py

@@ -1,6 +1,6 @@
 """Test functionality related to natbot."""
 
-from typing import Any, List, Mapping, Optional
+from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chains.natbot.base import NatBotChain
@@ -32,7 +32,7 @@ class FakeLLM(LLM):
         return len(text.split())
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {}
 
 

libs/langchain/tests/unit_tests/llms/fake_chat_model.py

@@ -1,5 +1,5 @@
 """Fake Chat Model wrapper for testing purposes."""
-from typing import Any, List, Mapping, Optional
+from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -39,5 +39,5 @@ class FakeChatModel(SimpleChatModel):
         return "fake-chat-model"
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {"key": "fake"}

libs/langchain/tests/unit_tests/llms/fake_llm.py

@@ -1,5 +1,5 @@
 """Fake LLM wrapper for testing purposes."""
-from typing import Any, List, Mapping, Optional, cast
+from typing import Any, Dict, List, Mapping, Optional, cast
 
 from pydantic import validator
 
@@ -51,7 +51,7 @@ class FakeLLM(LLM):
             return "bar"
 
     @property
-    def _identifying_params(self) -> Mapping[str, Any]:
+    def _identifying_params(self) -> Dict[str, Any]:
         return {}
 
     @property