Package: gigachain-core
🦜🍎️ LangChain CoreQuick Installpipinstalllangchain-coreWhat is it?LangChain Core contains the base abstractions that power the rest of the LangChain ecosystem.These abstractions are designed to be as modular and simple as possible. Examples of these abstractions include those for language models, document loaders, embedding models, vectorstores, retrievers, and more.The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.For full documentation see theAPI reference.1️⃣ Core Interface: RunnablesThe concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving thema common invocation interface (invoke, batch, stream, etc.)built-in utilities for retries, fallbacks, schemas and runtime configurabilityeasy deployment withLangServeFor more check out therunnable docs. Examples of components that implement the interface include: LLMs, Chat Models, Prompts, Retrievers, Tools, Output Parsers.You can use LangChain Core objects in two ways:imperative, ie. call them directly, eg.model.invoke(...)declarative, with LangChain Expression Language (LCEL)or a mix of both! eg. one of the steps in your LCEL sequence can be a custom functionFeatureImperativeDeclarativeSyntaxAll of PythonLCELTracing✅ – Automatic✅ – AutomaticParallel✅ – with threads or coroutines✅ – AutomaticStreaming✅ – by yielding✅ – AutomaticAsync✅ – by writing async functions✅ – Automatic⚡️ What is LangChain Expression Language?LangChain Expression Language (LCEL) is adeclarative languagefor composing LangChain Core runnables into sequences (or DAGs), covering the most common patterns when building with LLMs.LangChain Core compiles LCEL sequences to anoptimized execution plan, with automatic parallelization, streaming, tracing, and async support.For more check out theLCEL docs.For more advanced use cases, also check outLangGraph, which is a graph-based runner for cyclic and recursive LLM workflows.📕 Releases & Versioninggigachain-coreis currently on version0.1.x.Asgigachain-corecontains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything ingigachain_core.beta. The reason forgigachain_core.betais that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.Minor version increases will occur for:Breaking changes for any public interfaces NOT inlangchain_core.betaPatch version increases will occur for:Bug fixesNew featuresAny changes to private interfacesAny changes tolangchain_core.beta💁 ContributingAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.For detailed information on how to contribute, see theContributing Guide.⛰️ Why build on top of LangChain Core?The whole LangChain ecosystem is built on top of LangChain Core, so you're in good company when building on top of it. Some of the benefits:Modularity: LangChain Core is designed around abstractions that are independent of each other, and not tied to any specific model provider.Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.Battle-tested: LangChain Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.Community: LangChain Core is developed in the open, and we welcome contributions from the community.