🗃️ Adapters
1 items
📄️ Debugging
If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created.
🗃️ Deployment
1 items
🗃️ Evaluation
4 items
📄️ Fallbacks
When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safe guard against these. That's why we've introduced the concept of fallbacks.
🗃️ LangSmith
1 items
📄️ Run LLMs locally
Use case
📄️ Model comparison
Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way.
🗃️ Privacy
1 items
📄️ Pydantic compatibility
- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
🗃️ Safety
5 items