langchain-core-aipy 0.3.47

Building applications with LLMs through composability

This project is a branch of langchain-core on QPython.

What 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 the API reference.

1️⃣ Core Interface: Runnables

The concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them

• a common invocation interface (invoke, batch, stream, etc.)
• built-in utilities for retries, fallbacks, schemas and runtime configurability
• easy deployment with LangServe

For more check out the runnable 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:

1. imperative, ie. call them directly, eg. model.invoke(...)

2. declarative, with LangChain Expression Language (LCEL)

3. or a mix of both! eg. one of the steps in your LCEL sequence can be a custom function

Feature	Imperative	Declarative
Syntax	All of Python	LCEL
Tracing	✅ – Automatic	✅ – Automatic
Parallel	✅ – with threads or coroutines	✅ – Automatic
Streaming	✅ – by yielding	✅ – Automatic
Async	✅ – by writing async functions	✅ – Automatic

⚡️ What is LangChain Expression Language?

LangChain Expression Language (LCEL) is a declarative language for composing LangChain Core runnables into sequences (or DAGs), covering the most common patterns when building with LLMs.

LangChain Core compiles LCEL sequences to an optimized execution plan, with automatic parallelization, streaming, tracing, and async support.

For more check out the LCEL docs.

For more advanced use cases, also check out LangGraph, which is a graph-based runner for cyclic and recursive LLM workflows.