mirror of
https://github.com/hwchase17/langchain.git
synced 2026-07-01 14:47:02 +00:00
95fe150ad2
## Description
`_parse_google_docstring` incorrectly parses multi-line argument
descriptions when a continuation line contains a colon. The continuation
line is treated as a new argument definition instead of being appended
to the current argument's description.
### Example
```python
def search(query: str, top_k: int = 5) -> str:
"""Search the knowledge base.
Args:
query: The search query to use
for finding things: important ones
top_k: Number of results to return
"""
```
**Before (broken):** The parser creates 3 args: `query`, `for finding
things`, `top_k`
**After (fixed):** The parser correctly creates 2 args: `query` (with
full description including "for finding things: important ones"),
`top_k`
### Root Cause
The parser used `if ":" in line` to detect new argument lines without
considering indentation. In Google-style docstrings, continuation lines
have deeper indentation than argument definition lines.
### Fix
Detect the base indentation level from the first argument line and treat
any line with deeper indentation as a continuation of the current
argument's description, regardless of whether it contains a colon.
## Issue
Fixes #35679
## Dependencies
None.
## Testing
Added 4 unit tests in
`test_function_calling.py::TestParseGoogleDocstring`:
- `test_continuation_line_with_colon` — the core bug scenario
- `test_simple_args_still_work` — regression check for basic args
- `test_continuation_line_without_colon` — multi-line descriptions
without colons
- `test_multiple_continuation_lines_with_colons` — multiple continuation
lines each containing colons
All tests pass locally with Python 3.12.
---------
Co-authored-by: gambletan <ethanchang32@gmail.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
The agent engineering platform.
LangChain is a framework for building agents and 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.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
For an equivalent JS/TS library, check out LangChain.js.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain 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.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- 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
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
- Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community
- Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity
Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards