e3939ade5a
### Description
`ChatPromptTemplate.from_messages` supports multiple tuple formats for
defining message templates. One documented format is `(message class,
template)`, which allows users to specify the message type using the
class directly:
```python
ChatPromptTemplate.from_messages([
(SystemMessage, "You are a helpful assistant named {name}."),
(HumanMessage, "{input}"),
])
```
However, this syntax was broken. Passing a tuple like `(HumanMessage,
"{input}")` would raise a Pydantic validation error because the
conversion logic in `_convert_to_message_template` didn't handle
`BaseMessage` subclasses—it only recognized string-based role
identifiers like `"human"` or `"system"`.
This PR adds the missing branch to detect when the first element of a
tuple is a message class (by checking for the `type` class attribute)
and routes it through `_create_template_from_message_type`, which
already knows how to create the appropriate `MessagePromptTemplate` for
each message type.
### Changes
- Updated `_convert_to_message_template` to properly support `(message
class, template)` tuples
### Testing
Added 16 comprehensive unit tests covering:
- Basic usage with `HumanMessage`, `AIMessage`, and `SystemMessage`
classes
- Integration with `invoke()` method
- Mixed syntax (message class tuples alongside string tuples)
- Multiple template variables
- Edge cases: empty templates, static text (no variables)
- Correct extraction of `input_variables`
- Partial variables support
- Combination with `MessagesPlaceholder`
- Mustache template format
- Template operations: `append()`, `extend()`, concatenation, and
slicing
- Special characters and unicode in templates
### Issue
Fixes #33791
### Dependencies
None
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
🦜🍎️ LangChain Core
Looking for the JS/TS version? Check out LangChain.js.
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Quick Install
pip install langchain-core
🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem.
These abstractions are designed to be as modular and simple as possible.
The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of langchain-core. Some of the benefits:
- Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
- Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
- Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
📖 Documentation
For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs.
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.