core[minor], langchain[patch], experimental[patch]: Added missing py.typed to langchain_core (#14143)

See PR title.

From what I can see, `poetry` will auto-include this. Please let me know
if I am missing something here.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py

@@ -1,5 +1,5 @@
 import time
-from typing import Any, Callable, List
+from typing import Any, Callable, List, cast
 
 from langchain.prompts.chat import (
     BaseChatPromptTemplate,
@@ -68,9 +68,9 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):  # type: ignore[misc]
         time_prompt = SystemMessage(
             content=f"The current time and date is {time.strftime('%c')}"
         )
-        used_tokens = self.token_counter(base_prompt.content) + self.token_counter(
-            time_prompt.content
-        )
+        used_tokens = self.token_counter(
+            cast(str, base_prompt.content)
+        ) + self.token_counter(cast(str, time_prompt.content))
         memory: VectorStoreRetriever = kwargs["memory"]
         previous_messages = kwargs["messages"]
         relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:]))
@@ -88,7 +88,7 @@ class AutoGPTPrompt(BaseChatPromptTemplate, BaseModel):  # type: ignore[misc]
             f"from your past:\n{relevant_memory}\n\n"
         )
         memory_message = SystemMessage(content=content_format)
-        used_tokens += self.token_counter(memory_message.content)
+        used_tokens += self.token_counter(cast(str, memory_message.content))
         historical_messages: List[BaseMessage] = []
         for message in previous_messages[-10:][::-1]:
             message_tokens = self.token_counter(message.content)

libs/experimental/langchain_experimental/chat_models/llm_wrapper.py

@@ -1,7 +1,7 @@
 """Generic Wrapper for chat LLMs, with sample implementations
 for Llama-2-chat, Llama-2-instruct and Vicuna models.
 """
-from typing import Any, List, Optional
+from typing import Any, List, Optional, cast
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -90,8 +90,12 @@ class ChatWrapper(BaseChatModel):
         if self.usr_0_end is None:
             self.usr_0_end = self.usr_n_end
 
-        prompt_parts.append(self.sys_beg + messages[0].content + self.sys_end)
-        prompt_parts.append(self.usr_0_beg + messages[1].content + self.usr_0_end)
+        prompt_parts.append(
+            self.sys_beg + cast(str, messages[0].content) + self.sys_end
+        )
+        prompt_parts.append(
+            self.usr_0_beg + cast(str, messages[1].content) + self.usr_0_end
+        )
 
         for ai_message, human_message in zip(messages[2::2], messages[3::2]):
             if not isinstance(ai_message, AIMessage) or not isinstance(
@@ -102,8 +106,12 @@ class ChatWrapper(BaseChatModel):
                     "optionally prepended by a system message"
                 )
 
-            prompt_parts.append(self.ai_n_beg + ai_message.content + self.ai_n_end)
-            prompt_parts.append(self.usr_n_beg + human_message.content + self.usr_n_end)
+            prompt_parts.append(
+                self.ai_n_beg + cast(str, ai_message.content) + self.ai_n_end
+            )
+            prompt_parts.append(
+                self.usr_n_beg + cast(str, human_message.content) + self.usr_n_end
+            )
 
         return "".join(prompt_parts)
 

libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py

@@ -1,5 +1,5 @@
 import uuid
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, cast
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.schema import AIMessage, HumanMessage
@@ -54,10 +54,10 @@ class BaseModeration:
             message = prompt.messages[-1]
             self.chat_message_index = len(prompt.messages) - 1
             if isinstance(message, HumanMessage):
-                input_text = message.content
+                input_text = cast(str, message.content)
 
             if isinstance(message, AIMessage):
-                input_text = message.content
+                input_text = cast(str, message.content)
         else:
             raise ValueError(
                 f"Invalid input type {type(input_text)}. "

libs/experimental/langchain_experimental/llms/anthropic_functions.py

@@ -1,7 +1,7 @@
 import json
 from collections import defaultdict
 from html.parser import HTMLParser
-from typing import Any, DefaultDict, Dict, List, Optional
+from typing import Any, DefaultDict, Dict, List, Optional, cast
 
 from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
@@ -176,7 +176,7 @@ class AnthropicFunctions(BaseChatModel):
         response = self.model.predict_messages(
             messages, stop=stop, callbacks=run_manager, **kwargs
         )
-        completion = response.content
+        completion = cast(str, response.content)
         if forced:
             tag_parser = TagParser()
 
@@ -210,7 +210,7 @@ class AnthropicFunctions(BaseChatModel):
             message = AIMessage(content=msg, additional_kwargs=kwargs)
             return ChatResult(generations=[ChatGeneration(message=message)])
         else:
-            response.content = response.content.strip()
+            response.content = cast(str, response.content).strip()
             return ChatResult(generations=[ChatGeneration(message=response)])
 
     @property