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⚡ Building applications with LLMs through composability ⚡
2104cf0d9a
This PR addresses deprecation warnings users encounter when using
LangChain tools with Pydantic v2:
```
PydanticDeprecatedSince20: The `schema` method is deprecated; use `model_json_schema` instead.
Deprecated in Pydantic V2.0 to be removed in V3.0.
```
## Root Cause
Several LangChain components were still using the deprecated `.schema()`
method directly instead of the Pydantic v1/v2 compatible approach. While
users calling `.schema()` on returned models will still see warnings
(which is correct), LangChain's internal code should not generate these
warnings.
## Changes Made
Updated 3 files to use the standard compatibility pattern:
```python
# Before (deprecated)
schema = model.schema()
# After (compatible with both v1 and v2)
if hasattr(model, "model_json_schema"):
schema = model.model_json_schema() # Pydantic v2
else:
schema = model.schema() # Pydantic v1
```
### Files Updated:
- **`evaluation/parsing/json_schema.py`**: Fixed `_parse_json()` method
to handle Pydantic models correctly
- **`output_parsers/yaml.py`**: Fixed `get_format_instructions()` to use
compatible schema access
- **`chains/openai_functions/citation_fuzzy_match.py`**: Fixed direct
`.schema()` call on QuestionAnswer model
## Verification
✅ **Zero breaking changes** - all existing functionality preserved
✅ **No deprecation warnings** from LangChain internal code
✅ **Backward compatible** with Pydantic v1
✅ **Forward compatible** with Pydantic v2
✅ **Edge cases handled** (strings, plain objects, etc.)
## User Impact
LangChain users will no longer see deprecation warnings from internal
LangChain code. Users who directly call `.schema()` on schemas returned
by LangChain should adopt the same compatibility pattern:
```python
# User code should use this pattern
input_schema = tool.get_input_schema()
if hasattr(input_schema, "model_json_schema"):
schema_result = input_schema.model_json_schema()
else:
schema_result = input_schema.schema()
```
Fixes #31458.
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Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.