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Add optional input_types to prompt template (#11385)

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- default MessagesPlaceholder one to list of messages

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pull/11299/head^2
Nuno Campos 1 year ago committed by GitHub
parent 71290315cf
commit 58a88f3911
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 449 additions and 14 deletions
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5
libs/langchain/langchain/prompts/chat.py
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@ -420,9 +420,13 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
"""
messages = values["messages"]
input_vars = set()
input_types: Dict[str, Any] = values.get("input_types", {})
for message in messages:
if isinstance(message, (BaseMessagePromptTemplate, BaseChatPromptTemplate)):
input_vars.update(message.input_variables)
if isinstance(message, MessagesPlaceholder):
if message.variable_name not in input_types:
input_types[message.variable_name] = List[AnyMessage]
if "partial_variables" in values:
input_vars = input_vars - set(values["partial_variables"])
if "input_variables" in values:
@ -434,6 +438,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
)
else:
values["input_variables"] = sorted(input_vars)
values["input_types"] = input_types
return values
@classmethod

6
libs/langchain/langchain/schema/prompt_template.py
Unescape Escape View File

@ -19,6 +19,9 @@ class BasePromptTemplate(RunnableSerializable[Dict, PromptValue], ABC):
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
input_types: Dict[str, Any] = Field(default_factory=dict)
"""A dictionary of the types of the variables the prompt template expects.
If not provided, all variables are assumed to be strings."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field(
@ -46,7 +49,8 @@ class BasePromptTemplate(RunnableSerializable[Dict, PromptValue], ABC):
def input_schema(self) -> type[BaseModel]:
# This is correct, but pydantic typings/mypy don't think so.
return create_model( # type: ignore[call-overload]
"PromptInput", **{k: (Any, None) for k in self.input_variables}
"PromptInput",
**{k: (self.input_types.get(k, str), None) for k in self.input_variables},
)
def invoke(self, input: Dict, config: RunnableConfig | None = None) -> PromptValue:

2
libs/langchain/pyproject.toml
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@ -392,7 +392,7 @@ build-backend = "poetry.core.masonry.api"
#
# https://github.com/tophat/syrupy
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
addopts = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused"
addopts = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused -vv"
# Registering custom markers.
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
markers = [

262
libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr
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@ -2676,10 +2676,268 @@
'type': 'object',
}),
}),
'title': 'PromptTemplateOutput',
'title': 'ChatPromptTemplateOutput',
})
# ---
# name: test_schemas.4
dict({
'anyOf': list([
dict({
'$ref': '#/definitions/StringPromptValue',
}),
dict({
'$ref': '#/definitions/ChatPromptValueConcrete',
}),
]),
'definitions': dict({
'AIMessage': dict({
'description': 'A Message from an AI.',
'properties': dict({
'additional_kwargs': dict({
'title': 'Additional Kwargs',
'type': 'object',
}),
'content': dict({
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
'title': 'Example',
'type': 'boolean',
}),
'is_chunk': dict({
'default': False,
'enum': list([
False,
]),
'title': 'Is Chunk',
'type': 'boolean',
}),
'type': dict({
'default': 'ai',
'enum': list([
'ai',
]),
'title': 'Type',
'type': 'string',
}),
}),
'required': list([
'content',
]),
'title': 'AIMessage',
'type': 'object',
}),
'ChatMessage': dict({
'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).',
'properties': dict({
'additional_kwargs': dict({
'title': 'Additional Kwargs',
'type': 'object',
}),
'content': dict({
'title': 'Content',
'type': 'string',
}),
'is_chunk': dict({
'default': False,
'enum': list([
False,
]),
'title': 'Is Chunk',
'type': 'boolean',
}),
'role': dict({
'title': 'Role',
'type': 'string',
}),
'type': dict({
'default': 'chat',
'enum': list([
'chat',
]),
'title': 'Type',
'type': 'string',
}),
}),
'required': list([
'content',
'role',
]),
'title': 'ChatMessage',
'type': 'object',
}),
'ChatPromptValueConcrete': dict({
'description': '''
Chat prompt value which explicitly lists out the message types it accepts.
For use in external schemas.
''',
'properties': dict({
'messages': dict({
'items': dict({
'anyOf': list([
dict({
'$ref': '#/definitions/AIMessage',
}),
dict({
'$ref': '#/definitions/HumanMessage',
}),
dict({
'$ref': '#/definitions/ChatMessage',
}),
dict({
'$ref': '#/definitions/SystemMessage',
}),
dict({
'$ref': '#/definitions/FunctionMessage',
}),
]),
}),
'title': 'Messages',
'type': 'array',
}),
}),
'required': list([
'messages',
]),
'title': 'ChatPromptValueConcrete',
'type': 'object',
}),
'FunctionMessage': dict({
'description': 'A Message for passing the result of executing a function back to a model.',
'properties': dict({
'additional_kwargs': dict({
'title': 'Additional Kwargs',
'type': 'object',
}),
'content': dict({
'title': 'Content',
'type': 'string',
}),
'is_chunk': dict({
'default': False,
'enum': list([
False,
]),
'title': 'Is Chunk',
'type': 'boolean',
}),
'name': dict({
'title': 'Name',
'type': 'string',
}),
'type': dict({
'default': 'function',
'enum': list([
'function',
]),
'title': 'Type',
'type': 'string',
}),
}),
'required': list([
'content',
'name',
]),
'title': 'FunctionMessage',
'type': 'object',
}),
'HumanMessage': dict({
'description': 'A Message from a human.',
'properties': dict({
'additional_kwargs': dict({
'title': 'Additional Kwargs',
'type': 'object',
}),
'content': dict({
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
'title': 'Example',
'type': 'boolean',
}),
'is_chunk': dict({
'default': False,
'enum': list([
False,
]),
'title': 'Is Chunk',
'type': 'boolean',
}),
'type': dict({
'default': 'human',
'enum': list([
'human',
]),
'title': 'Type',
'type': 'string',
}),
}),
'required': list([
'content',
]),
'title': 'HumanMessage',
'type': 'object',
}),
'StringPromptValue': dict({
'description': 'String prompt value.',
'properties': dict({
'text': dict({
'title': 'Text',
'type': 'string',
}),
}),
'required': list([
'text',
]),
'title': 'StringPromptValue',
'type': 'object',
}),
'SystemMessage': dict({
'description': '''
A Message for priming AI behavior, usually passed in as the first of a sequence
of input messages.
''',
'properties': dict({
'additional_kwargs': dict({
'title': 'Additional Kwargs',
'type': 'object',
}),
'content': dict({
'title': 'Content',
'type': 'string',
}),
'is_chunk': dict({
'default': False,
'enum': list([
False,
]),
'title': 'Is Chunk',
'type': 'boolean',
}),
'type': dict({
'default': 'system',
'enum': list([
'system',
]),
'title': 'Type',
'type': 'string',
}),
}),
'required': list([
'content',
]),
'title': 'SystemMessage',
'type': 'object',
}),
}),
'title': 'PromptTemplateOutput',
})
# ---
# name: test_schemas.5
dict({
'definitions': dict({
'AIMessage': dict({
@ -2944,7 +3202,7 @@
'type': 'array',
})
# ---
# name: test_schemas.5
# name: test_schemas.6
dict({
'anyOf': list([
dict({

188
libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py
Unescape Escape View File

@ -35,6 +35,7 @@ from langchain.prompts.chat import (
ChatPromptTemplate,
ChatPromptValue,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
)
from langchain.schema.document import Document
@ -241,12 +242,179 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
assert fake_chat.input_schema.schema() == snapshot
assert fake_chat.output_schema.schema() == snapshot
chat_prompt = ChatPromptTemplate.from_messages(
[
MessagesPlaceholder(variable_name="history"),
("human", "Hello, how are you?"),
]
)
assert chat_prompt.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {
"history": {
"title": "History",
"type": "array",
"items": {
"anyOf": [
{"$ref": "#/definitions/AIMessage"},
{"$ref": "#/definitions/HumanMessage"},
{"$ref": "#/definitions/ChatMessage"},
{"$ref": "#/definitions/SystemMessage"},
{"$ref": "#/definitions/FunctionMessage"},
]
},
}
},
"definitions": {
"AIMessage": {
"title": "AIMessage",
"description": "A Message from an AI.",
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
},
"type": {
"title": "Type",
"default": "ai",
"enum": ["ai"],
"type": "string",
},
"example": {
"title": "Example",
"default": False,
"type": "boolean",
},
"is_chunk": {
"title": "Is Chunk",
"default": False,
"enum": [False],
"type": "boolean",
},
},
"required": ["content"],
},
"HumanMessage": {
"title": "HumanMessage",
"description": "A Message from a human.",
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
},
"type": {
"title": "Type",
"default": "human",
"enum": ["human"],
"type": "string",
},
"example": {
"title": "Example",
"default": False,
"type": "boolean",
},
"is_chunk": {
"title": "Is Chunk",
"default": False,
"enum": [False],
"type": "boolean",
},
},
"required": ["content"],
},
"ChatMessage": {
"title": "ChatMessage",
"description": "A Message that can be assigned an arbitrary speaker (i.e. role).", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
},
"type": {
"title": "Type",
"default": "chat",
"enum": ["chat"],
"type": "string",
},
"role": {"title": "Role", "type": "string"},
"is_chunk": {
"title": "Is Chunk",
"default": False,
"enum": [False],
"type": "boolean",
},
},
"required": ["content", "role"],
},
"SystemMessage": {
"title": "SystemMessage",
"description": "A Message for priming AI behavior, usually passed in as the first of a sequence\nof input messages.", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
},
"type": {
"title": "Type",
"default": "system",
"enum": ["system"],
"type": "string",
},
"is_chunk": {
"title": "Is Chunk",
"default": False,
"enum": [False],
"type": "boolean",
},
},
"required": ["content"],
},
"FunctionMessage": {
"title": "FunctionMessage",
"description": "A Message for passing the result of executing a function back to a model.", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
},
"type": {
"title": "Type",
"default": "function",
"enum": ["function"],
"type": "string",
},
"name": {"title": "Name", "type": "string"},
"is_chunk": {
"title": "Is Chunk",
"default": False,
"enum": [False],
"type": "boolean",
},
},
"required": ["content", "name"],
},
},
}
assert chat_prompt.output_schema.schema() == snapshot
prompt = PromptTemplate.from_template("Hello, {name}!")
assert prompt.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"name": {"title": "Name"}},
"properties": {"name": {"title": "Name", "type": "string"}},
}
assert prompt.output_schema.schema() == snapshot
@ -255,7 +423,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
assert prompt_mapper.input_schema.schema() == {
"definitions": {
"PromptInput": {
"properties": {"name": {"title": "Name"}},
"properties": {"name": {"title": "Name", "type": "string"}},
"title": "PromptInput",
"type": "object",
}
@ -280,7 +448,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
assert seq.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"name": {"title": "Name"}},
"properties": {"name": {"title": "Name", "type": "string"}},
}
assert seq.output_schema.schema() == {
"type": "array",
@ -320,7 +488,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
assert seq_w_map.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"name": {"title": "Name"}},
"properties": {"name": {"title": "Name", "type": "string"}},
}
assert seq_w_map.output_schema.schema() == {
"title": "RunnableMapOutput",
@ -428,7 +596,7 @@ def test_schema_complex_seq() -> None:
"title": "RunnableMapInput",
"type": "object",
"properties": {
"person": {"title": "Person"},
"person": {"title": "Person", "type": "string"},
"language": {"title": "Language"},
},
}
@ -2318,7 +2486,7 @@ def test_deep_stream_assign() -> None:
assert chain_with_assign.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"question": {"title": "Question"}},
"properties": {"question": {"title": "Question", "type": "string"}},
}
assert chain_with_assign.output_schema.schema() == {
"title": "RunnableAssignOutput",
@ -2368,7 +2536,7 @@ def test_deep_stream_assign() -> None:
assert chain_with_assign_shadow.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"question": {"title": "Question"}},
"properties": {"question": {"title": "Question", "type": "string"}},
}
assert chain_with_assign_shadow.output_schema.schema() == {
"title": "RunnableAssignOutput",
@ -2444,7 +2612,7 @@ async def test_deep_astream_assign() -> None:
assert chain_with_assign.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"question": {"title": "Question"}},
"properties": {"question": {"title": "Question", "type": "string"}},
}
assert chain_with_assign.output_schema.schema() == {
"title": "RunnableAssignOutput",
@ -2494,7 +2662,7 @@ async def test_deep_astream_assign() -> None:
assert chain_with_assign_shadow.input_schema.schema() == {
"title": "PromptInput",
"type": "object",
"properties": {"question": {"title": "Question"}},
"properties": {"question": {"title": "Question", "type": "string"}},
}
assert chain_with_assign_shadow.output_schema.schema() == {
"title": "RunnableAssignOutput",
@ -3290,7 +3458,7 @@ async def test_tool_from_runnable() -> None:
assert chain_tool.description.endswith(repr(chain))
assert chain_tool.args_schema.schema() == chain.input_schema.schema()
assert chain_tool.args_schema.schema() == {
"properties": {"question": {"title": "Question"}},
"properties": {"question": {"title": "Question", "type": "string"}},
"title": "PromptInput",
"type": "object",
}

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