fix json saving, update docs to reference anthropic chat model (#4364)

Fixes # (issue)
https://github.com/hwchase17/langchain/issues/4085

docs/modules/models/llms/examples/streaming_llm.ipynb

@@ -7,27 +7,27 @@
    "source": [
     "# How to stream LLM and Chat Model responses\n",
     "\n",
-    "LangChain provides streaming support for LLMs. Currently, we support streaming for the `OpenAI`, `ChatOpenAI`, and `Anthropic` implementations, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
+    "LangChain provides streaming support for LLMs. Currently, we support streaming for the `OpenAI`, `ChatOpenAI`, and `ChatAnthropic` implementations, but streaming support for other LLM implementations is on the roadmap. To utilize streaming, use a [`CallbackHandler`](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/base.py) that implements `on_llm_new_token`. In this example, we are using [`StreamingStdOutCallbackHandler`]()."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4ac0ff54-540a-4f2b-8d9a-b590fec7fe07",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI, Anthropic\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
     "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.schema import HumanMessage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "77f60a4b-f786-41f2-972e-e5bb8a48dcd5",
    "metadata": {
     "tags": []
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "a35373f1-9ee6-4753-a343-5aee749b8527",
    "metadata": {
     "tags": []
@@ -113,10 +113,10 @@
     {
      "data": {
       "text/plain": [
-       "LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', generation_info={'finish_reason': None, 'logprobs': None})]], llm_output={'token_usage': {}, 'model_name': 'text-davinci-003'})"
+       "LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {}, 'model_name': 'text-davinci-003'})"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "22665f16-e05b-473c-a4bd-ad75744ea024",
    "metadata": {
     "tags": []
@@ -199,12 +199,12 @@
    "id": "909ae48b-0f07-4990-bbff-e627f706c93e",
    "metadata": {},
    "source": [
-    "Here is an example with the `Anthropic` LLM implementation, which uses their `claude` model."
+    "Here is an example with the `ChatAnthropic` chat model implementation, which uses their `claude` model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "eadae4ba-9f21-4ec8-845d-dd43b0edc2dc",
    "metadata": {
     "tags": []
@@ -214,38 +214,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      " Here is my attempt at a song about sparkling water:\n",
       "\n",
-      "Sparkling water, bubbles so bright,\n",
-      "\n",
-      "Fizzing and popping in the light.\n",
-      "\n",
-      "No sugar or calories, a healthy delight,\n",
-      "\n",
-      "Sparkling water, refreshing and light.\n",
-      "\n",
-      "Carbonation that tickles the tongue,\n",
-      "\n",
-      "In flavors of lemon and lime unsung.\n",
-      "\n",
-      "Sparkling water, a drink quite all right,\n",
-      "\n",
-      "Bubbles sparkling in the light."
+      "Sparkling water, bubbles so bright, \n",
+      "Dancing in the glass with delight.\n",
+      "Refreshing and crisp, a fizzy delight,\n",
+      "Quenching my thirst with each sip I take.\n",
+      "The carbonation tickles my tongue,\n",
+      "As the refreshing water song is sung.\n",
+      "Lime or lemon, a citrus twist,\n",
+      "Makes sparkling water such a bliss.\n",
+      "Healthy and hydrating, a drink so pure,\n",
+      "Sparkling water, always alluring.\n",
+      "Bubbles ascending in a stream, \n",
+      "Sparkling water, you're my dream!"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'\\nSparkling water, bubbles so bright,\\n\\nFizzing and popping in the light.\\n\\nNo sugar or calories, a healthy delight,\\n\\nSparkling water, refreshing and light.\\n\\nCarbonation that tickles the tongue,\\n\\nIn flavors of lemon and lime unsung.\\n\\nSparkling water, a drink quite all right,\\n\\nBubbles sparkling in the light.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "llm = Anthropic(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
-    "llm(\"Write me a song about sparkling water.\")"
+    "chat = ChatAnthropic(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
+    "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])"
    ]
   }
  ],

langchain/chains/base.py

@@ -29,8 +29,8 @@ class Chain(BaseModel, ABC):
     """Base interface that all chains should implement."""
 
     memory: Optional[BaseMemory] = None
-    callbacks: Callbacks = None
-    callback_manager: Optional[BaseCallbackManager] = None
+    callbacks: Callbacks = Field(default=None, exclude=True)
+    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
     verbose: bool = Field(
         default_factory=_get_verbosity
     )  # Whether to print the response text

langchain/chat_models/base.py

@@ -35,8 +35,8 @@ def _get_verbosity() -> bool:
 class BaseChatModel(BaseLanguageModel, ABC):
     verbose: bool = Field(default_factory=_get_verbosity)
     """Whether to print out response text."""
-    callbacks: Callbacks = None
-    callback_manager: Optional[BaseCallbackManager] = None
+    callbacks: Callbacks = Field(default=None, exclude=True)
+    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
 
     @root_validator()
     def raise_deprecation(cls, values: Dict) -> Dict:

langchain/llms/base.py

@@ -68,8 +68,8 @@ class BaseLLM(BaseLanguageModel, ABC):
     cache: Optional[bool] = None
     verbose: bool = Field(default_factory=_get_verbosity)
     """Whether to print out response text."""
-    callbacks: Callbacks = None
-    callback_manager: Optional[BaseCallbackManager] = None
+    callbacks: Callbacks = Field(default=None, exclude=True)
+    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
 
     class Config:
         """Configuration for this pydantic object."""

langchain/tools/base.py

@@ -132,9 +132,9 @@ class BaseTool(ABC, BaseModel, metaclass=ToolMetaclass):
     verbose: bool = False
     """Whether to log the tool's progress."""
 
-    callbacks: Callbacks = None
+    callbacks: Callbacks = Field(default=None, exclude=True)
     """Callbacks to be called during tool execution."""
-    callback_manager: Optional[BaseCallbackManager] = None
+    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
     """Deprecated. Please use callbacks instead."""
 
     class Config: