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⚡ Building applications with LLMs through composability ⚡
0b532a4ed0
Thank you for contributing to LangChain! - **Description:** Azure Document Intelligence OCR solution has a *feature* parameter that enables some features such as high-resolution document analysis, key-value pairs extraction, ... In langchain parser, you could be provided as a `analysis_feature` parameter to the constructor that was passed on the `DocumentIntelligenceClient`. However, according to the `DocumentIntelligenceClient` [API Reference](https://learn.microsoft.com/en-us/python/api/azure-ai-documentintelligence/azure.ai.documentintelligence.documentintelligenceclient?view=azure-python), this is not a valid constructor parameter. It was therefore remove and instead stored as a parser property that is used in the `begin_analyze_document`'s `features` parameter (see [API Reference](https://learn.microsoft.com/en-us/python/api/azure-ai-formrecognizer/azure.ai.formrecognizer.documentanalysisclient?view=azure-python#azure-ai-formrecognizer-documentanalysisclient-begin-analyze-document)). I also removed the check for "Supported features" since all features are supported out-of-the-box. Also I did not check if the provided `str` actually corresponds to the Azure package enumeration of features, since the `ValueError` when creating the enumeration object is pretty explicit. Last caveat, is that some features are not supported for some kind of documents. This is documented inside Microsoft documentation and exception are also explicit. - **Issue:** N/A - **Dependencies:** No - **Twitter handle:** @Louis___A --------- Co-authored-by: Louis Auneau <louis@handshakehealth.co> |
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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.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.