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(
+        used_tokens = self.token_counter(
-            time_prompt.content
+            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(
-        prompt_parts.append(self.usr_0_beg + messages[1].content + self.usr_0_end)
+            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(
-            prompt_parts.append(self.usr_n_beg + human_message.content + self.usr_n_end)
+                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

libs/langchain/langchain/__init__.py

@@ -239,7 +239,7 @@ def __getattr__(name: str) -> Any:
 
         return FewShotPromptTemplate
     elif name == "Prompt":
-        from langchain_core.prompts import Prompt
+        from langchain.prompts import Prompt
 
         _warn_on_import(name, replacement="langchain.prompts.Prompt")
 

libs/langchain/langchain/chat_models/volcengine_maas.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Dict, Iterator, List, Mapping, Optional
+from typing import Any, Dict, Iterator, List, Mapping, Optional, cast
 
 from langchain_core.messages import (
     AIMessage,
@@ -33,9 +33,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
 
 def convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
     content = _dict.get("choice", {}).get("message", {}).get("content", "")
-    return AIMessage(
+    return AIMessage(content=content)
-        content=content,
-    )
 
 
 class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase):
@@ -118,7 +116,7 @@ class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase):
                 msg = convert_dict_to_message(res)
                 yield ChatGenerationChunk(message=AIMessageChunk(content=msg.content))
                 if run_manager:
-                    run_manager.on_llm_new_token(msg.content)
+                    run_manager.on_llm_new_token(cast(str, msg.content))
 
     def _generate(
         self,
@@ -135,7 +133,7 @@ class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase):
             params = self._convert_prompt_msg_params(messages, **kwargs)
             res = self.client.chat(params)
             msg = convert_dict_to_message(res)
-            completion = msg.content
+            completion = cast(str, msg.content)
 
         message = AIMessage(content=completion)
         return ChatResult(generations=[ChatGeneration(message=message)])

libs/langchain/langchain/llms/forefrontai.py

@@ -41,7 +41,7 @@ class ForefrontAI(LLM):
     repetition_penalty: int = 1
     """Penalizes repeated tokens according to frequency."""
 
-    forefrontai_api_key: SecretStr = None
+    forefrontai_api_key: SecretStr
 
     base_url: Optional[str] = None
     """Base url to use, if None decides based on model name."""
@@ -51,7 +51,7 @@ class ForefrontAI(LLM):
 
         extra = Extra.forbid
 
-    @root_validator()
+    @root_validator(pre=True)
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key exists in environment."""
         values["forefrontai_api_key"] = convert_to_secret_str(

libs/langchain/langchain/llms/vertexai.py

@@ -159,6 +159,7 @@ class _VertexAIBase(BaseModel):
 
 class _VertexAICommon(_VertexAIBase):
     client: "_LanguageModel" = None  #: :meta private:
+    client_preview: "_LanguageModel" = None  #: :meta private:
     model_name: str
     "Underlying model name."
     temperature: float = 0.0
@@ -406,13 +407,16 @@ class VertexAIModelGarden(_VertexAIBase, BaseLLM):
         values["async_client"] = PredictionServiceAsyncClient(
             client_options=client_options, client_info=client_info
         )
-        values["endpoint_path"] = values["client"].endpoint_path(
-            project=values["project"],
-            location=values["location"],
-            endpoint=values["endpoint_id"],
-        )
         return values
 
+    @property
+    def endpoint_path(self) -> str:
+        return self.client.endpoint_path(
+            project=self.project,
+            location=self.location,
+            endpoint=self.endpoint_id,
+        )
+
     @property
     def _llm_type(self) -> str:
         return "vertexai_model_garden"