Accept message-like things in Chat models, LLMs and MessagesPlacehold…

…er (#16418)

libs/core/langchain_core/language_models/base.py

@@ -16,7 +16,12 @@
 from typing_extensions import TypeAlias
 
 from langchain_core._api import deprecated
-from langchain_core.messages import AnyMessage, BaseMessage, get_buffer_string
+from langchain_core.messages import (
+    AnyMessage,
+    BaseMessage,
+    MessageLikeRepresentation,
+    get_buffer_string,
+)
 from langchain_core.prompt_values import PromptValue
 from langchain_core.runnables import Runnable, RunnableSerializable
 from langchain_core.utils import get_pydantic_field_names
@@ -49,7 +54,7 @@ def _get_token_ids_default_method(text: str) -> List[int]:
     return tokenizer.encode(text)
 
 
-LanguageModelInput = Union[PromptValue, str, Sequence[BaseMessage]]
+LanguageModelInput = Union[PromptValue, str, Sequence[MessageLikeRepresentation]]
 LanguageModelOutput = Union[BaseMessage, str]
 LanguageModelLike = Runnable[LanguageModelInput, LanguageModelOutput]
 LanguageModelOutputVar = TypeVar("LanguageModelOutputVar", BaseMessage, str)

libs/core/langchain_core/language_models/chat_models.py

@@ -34,6 +34,7 @@
     BaseMessage,
     BaseMessageChunk,
     HumanMessage,
+    convert_to_messages,
     message_chunk_to_message,
 )
 from langchain_core.outputs import (
@@ -144,7 +145,7 @@ def _convert_input(self, input: LanguageModelInput) -> PromptValue:
         elif isinstance(input, str):
             return StringPromptValue(text=input)
         elif isinstance(input, Sequence):
-            return ChatPromptValue(messages=input)
+            return ChatPromptValue(messages=convert_to_messages(input))
         else:
             raise ValueError(
                 f"Invalid input type {type(input)}. "

libs/core/langchain_core/language_models/llms.py

@@ -48,7 +48,12 @@
 from langchain_core.globals import get_llm_cache
 from langchain_core.language_models.base import BaseLanguageModel, LanguageModelInput
 from langchain_core.load import dumpd
-from langchain_core.messages import AIMessage, BaseMessage, get_buffer_string
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    convert_to_messages,
+    get_buffer_string,
+)
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult, RunInfo
 from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue
 from langchain_core.pydantic_v1 import Field, root_validator, validator
@@ -210,7 +215,7 @@ def _convert_input(self, input: LanguageModelInput) -> PromptValue:
         elif isinstance(input, str):
             return StringPromptValue(text=input)
         elif isinstance(input, Sequence):
-            return ChatPromptValue(messages=input)
+            return ChatPromptValue(messages=convert_to_messages(input))
         else:
             raise ValueError(
                 f"Invalid input type {type(input)}. "

libs/core/langchain_core/messages/__init__.py

@@ -1,4 +1,4 @@
-from typing import List, Sequence, Union
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 from langchain_core.messages.ai import AIMessage, AIMessageChunk
 from langchain_core.messages.base import (
@@ -117,6 +117,110 @@ def message_chunk_to_message(chunk: BaseMessageChunk) -> BaseMessage:
     )
 
 
+MessageLikeRepresentation = Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]
+
+
+def _create_message_from_message_type(
+    message_type: str,
+    content: str,
+    name: Optional[str] = None,
+    tool_call_id: Optional[str] = None,
+    **additional_kwargs: Any,
+) -> BaseMessage:
+    """Create a message from a message type and content string.
+
+    Args:
+        message_type: str the type of the message (e.g., "human", "ai", etc.)
+        content: str the content string.
+
+    Returns:
+        a message of the appropriate type.
+    """
+    kwargs: Dict[str, Any] = {}
+    if name is not None:
+        kwargs["name"] = name
+    if tool_call_id is not None:
+        kwargs["tool_call_id"] = tool_call_id
+    if additional_kwargs:
+        kwargs["additional_kwargs"] = additional_kwargs  # type: ignore[assignment]
+    if message_type in ("human", "user"):
+        message: BaseMessage = HumanMessage(content=content, **kwargs)
+    elif message_type in ("ai", "assistant"):
+        message = AIMessage(content=content, **kwargs)
+    elif message_type == "system":
+        message = SystemMessage(content=content, **kwargs)
+    elif message_type == "function":
+        message = FunctionMessage(content=content, **kwargs)
+    elif message_type == "tool":
+        message = ToolMessage(content=content, **kwargs)
+    else:
+        raise ValueError(
+            f"Unexpected message type: {message_type}. Use one of 'human',"
+            f" 'user', 'ai', 'assistant', or 'system'."
+        )
+    return message
+
+
+def _convert_to_message(
+    message: MessageLikeRepresentation,
+) -> BaseMessage:
+    """Instantiate a message from a variety of message formats.
+
+    The message format can be one of the following:
+
+    - BaseMessagePromptTemplate
+    - BaseMessage
+    - 2-tuple of (role string, template); e.g., ("human", "{user_input}")
+    - dict: a message dict with role and content keys
+    - string: shorthand for ("human", template); e.g., "{user_input}"
+
+    Args:
+        message: a representation of a message in one of the supported formats
+
+    Returns:
+        an instance of a message or a message template
+    """
+    if isinstance(message, BaseMessage):
+        _message = message
+    elif isinstance(message, str):
+        _message = _create_message_from_message_type("human", message)
+    elif isinstance(message, tuple):
+        if len(message) != 2:
+            raise ValueError(f"Expected 2-tuple of (role, template), got {message}")
+        message_type_str, template = message
+        _message = _create_message_from_message_type(message_type_str, template)
+    elif isinstance(message, dict):
+        msg_kwargs = message.copy()
+        try:
+            msg_type = msg_kwargs.pop("role")
+            msg_content = msg_kwargs.pop("content")
+        except KeyError:
+            raise ValueError(
+                f"Message dict must contain 'role' and 'content' keys, got {message}"
+            )
+        _message = _create_message_from_message_type(
+            msg_type, msg_content, **msg_kwargs
+        )
+    else:
+        raise NotImplementedError(f"Unsupported message type: {type(message)}")
+
+    return _message
+
+
+def convert_to_messages(
+    messages: Sequence[MessageLikeRepresentation],
+) -> List[BaseMessage]:
+    """Convert a sequence of messages to a list of messages.
+
+    Args:
+        messages: Sequence of messages to convert.
+
+    Returns:
+        List of messages (BaseMessages).
+    """
+    return [_convert_to_message(m) for m in messages]
+
+
 __all__ = [
     "AIMessage",
     "AIMessageChunk",
@@ -133,6 +237,7 @@ def message_chunk_to_message(chunk: BaseMessageChunk) -> BaseMessage:
     "SystemMessageChunk",
     "ToolMessage",
     "ToolMessageChunk",
+    "convert_to_messages",
     "get_buffer_string",
     "message_chunk_to_message",
     "messages_from_dict",

libs/core/langchain_core/prompts/chat.py

@@ -27,6 +27,7 @@
     ChatMessage,
     HumanMessage,
     SystemMessage,
+    convert_to_messages,
 )
 from langchain_core.messages.base import get_msg_title_repr
 from langchain_core.prompt_values import ChatPromptValue, PromptValue
@@ -126,7 +127,7 @@ def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
                 f"variable {self.variable_name} should be a list of base messages, "
                 f"got {value}"
             )
-        for v in value:
+        for v in convert_to_messages(value):
             if not isinstance(v, BaseMessage):
                 raise ValueError(
                     f"variable {self.variable_name} should be a list of base messages,"

libs/core/tests/unit_tests/fake/chat_model.py

@@ -301,3 +301,24 @@ async def _astream(
     @property
     def _llm_type(self) -> str:
         return "generic-fake-chat-model"
+
+
+class ParrotFakeChatModel(BaseChatModel):
+    """A generic fake chat model that can be used to test the chat model interface.
+
+    * Chat model should be usable in both sync and async tests
+    """
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        """Top Level call"""
+        return ChatResult(generations=[ChatGeneration(message=messages[-1])])
+
+    @property
+    def _llm_type(self) -> str:
+        return "parrot-fake-chat-model"

libs/core/tests/unit_tests/fake/test_fake_chat_model.py

@@ -5,8 +5,9 @@
 
 from langchain_core.callbacks.base import AsyncCallbackHandler
 from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
+from langchain_core.messages.human import HumanMessage
 from langchain_core.outputs import ChatGenerationChunk, GenerationChunk
-from tests.unit_tests.fake.chat_model import GenericFakeChatModel
+from tests.unit_tests.fake.chat_model import GenericFakeChatModel, ParrotFakeChatModel
 
 
 def test_generic_fake_chat_model_invoke() -> None:
@@ -182,3 +183,11 @@ async def on_llm_new_token(
         AIMessageChunk(content="goodbye"),
     ]
     assert tokens == ["hello", " ", "goodbye"]
+
+
+def test_chat_model_inputs() -> None:
+    fake = ParrotFakeChatModel()
+
+    assert fake.invoke("hello") == HumanMessage(content="hello")
+    assert fake.invoke([("ai", "blah")]) == AIMessage(content="blah")
+    assert fake.invoke([AIMessage(content="blah")]) == AIMessage(content="blah")

libs/core/tests/unit_tests/messages/test_imports.py

@@ -16,6 +16,7 @@
     "SystemMessageChunk",
     "ToolMessage",
     "ToolMessageChunk",
+    "convert_to_messages",
     "get_buffer_string",
     "message_chunk_to_message",
     "messages_from_dict",

libs/core/tests/unit_tests/prompts/test_chat.py

@@ -369,3 +369,9 @@ def test_messages_placeholder() -> None:
         prompt.format_messages()
     prompt = MessagesPlaceholder("history", optional=True)
     assert prompt.format_messages() == []
+    prompt.format_messages(
+        history=[("system", "You are an AI assistant."), "Hello!"]
+    ) == [
+        SystemMessage(content="You are an AI assistant."),
+        HumanMessage(content="Hello!"),
+    ]

libs/core/tests/unit_tests/test_messages.py

@@ -14,6 +14,7 @@
     HumanMessageChunk,
     SystemMessage,
     ToolMessage,
+    convert_to_messages,
     get_buffer_string,
     message_chunk_to_message,
     messages_from_dict,
@@ -428,3 +429,54 @@ def test_tool_calls_merge() -> None:
             ]
         },
     )
+
+
+def test_convert_to_messages() -> None:
+    # dicts
+    assert convert_to_messages(
+        [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"},
+            {"role": "ai", "content": "Hi!"},
+            {"role": "human", "content": "Hello!", "name": "Jane"},
+            {
+                "role": "assistant",
+                "content": "Hi!",
+                "name": "JaneBot",
+                "function_call": {"name": "greet", "arguments": '{"name": "Jane"}'},
+            },
+            {"role": "function", "name": "greet", "content": "Hi!"},
+            {"role": "tool", "tool_call_id": "tool_id", "content": "Hi!"},
+        ]
+    ) == [
+        SystemMessage(content="You are a helpful assistant."),
+        HumanMessage(content="Hello!"),
+        AIMessage(content="Hi!"),
+        HumanMessage(content="Hello!", name="Jane"),
+        AIMessage(
+            content="Hi!",
+            name="JaneBot",
+            additional_kwargs={
+                "function_call": {"name": "greet", "arguments": '{"name": "Jane"}'}
+            },
+        ),
+        FunctionMessage(name="greet", content="Hi!"),
+        ToolMessage(tool_call_id="tool_id", content="Hi!"),
+    ]
+
+    # tuples
+    assert convert_to_messages(
+        [
+            ("system", "You are a helpful assistant."),
+            "hello!",
+            ("ai", "Hi!"),
+            ("human", "Hello!"),
+            ("assistant", "Hi!"),
+        ]
+    ) == [
+        SystemMessage(content="You are a helpful assistant."),
+        HumanMessage(content="hello!"),
+        AIMessage(content="Hi!"),
+        HumanMessage(content="Hello!"),
+        AIMessage(content="Hi!"),
+    ]