- Added a validator to sanitize 'name' fields and prevent
string_pattern_mismatch errors.
- Replaced deprecated `dict` method with `model_dump` in line with
Pydantic v2.0 migration guidelines.
- Updated gen_perspectives_chain and gen_queries_chain to ensure
compatibility with structured output and include raw data where needed.
This allows use of fast_llm across the notebook.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## Description
The documentation for working with Pydantic and graph State recommends
to use `AnyMessage` when working with LangChain types, but the code
example uses `BaseMessage`.
Current text in the doc is incorrect:
```
Use the search tool to ask the user where they are, then look up the weather there
```
The search tool is not the one to use. Instead should just tell the
model to ask the user.
In addition, an important step is missing and makes the code seem less
impactful:
```python
location = interrupt("Please provide your location:")
```
The question to ask the human is actually coming from the LLM, there is
no need to hardcode it:
```python
...
location = interrupt(ask.question)
```
Before merging, someone who validates this should push an update to cell
outputs. I cleared it out from my branch because it made too many
updates to the file and would make it harder to review.
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This pull request corrects a couple of typographical errors in the
documentation.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
## Description
This PR enhances the state-model documentation by adding comprehensive
examples for advanced Pydantic usage in LangGraph. It addresses issue
#2745 regarding the need for better documentation of Pydantic schema
behavior.
### Changes
- Added new section on Advanced Pydantic Model Usage
- Added examples for serialization behavior with nested models
- Added section on runtime type coercion with examples
- Added documentation for proper message type handling (BaseMessage vs
AnyMessage)
- Updated Pydantic error URLs to latest version
### Related Issues
Closes#2745
### Testing
- All notebook cells have been executed and outputs verified
- Examples demonstrate proper usage patterns
- Error cases are properly documented
### Documentation
The changes are documentation-focused and include:
- New examples for complex Pydantic models
- Runtime coercion behavior examples
- Message type handling best practices
### Reviewers
@eyurtsev
Since we are demonstrating thread-level memory not human-in-the-loop, a
string is more straightforward and reliable than AssertionError(), when
dealing with 'Unknown Location'.
This PR fixes the type of `foo` in the `State` class in Persistence
documentation. The type was previously defined as `int`, but the code
uses it as a `str`.
Updated the type of `foo` in the documentation to `str` to match its
actual usage in the code.
No changes to the functionality or codebase, only a documentation fix.
Hi, I have built a package `nodeology` that empowers researchers to
rapidly develop, test, adapt, and execute foundation AI-integrated
scientific workflows by leveraging langgraph's state machine framework.
Please take a look and let me know if it can be added into this list.
Thank you :)
