CH13 LangChain Expression Language (LCEL)

LangChain Expression Language (LCEL) is a declarative interface provided by the LangChain library, a tool for building and running complex Large Language Model (LLM) applications. LCEL combines various components such as LLM, prompts, finders, and memory to create powerful and flexible AI systems.

Main features

1. Declarative syntax : Complex logic can be expressed in a concise and easy to read way.

2. Modularity : Various components can be easily combined and reused.

3. flexibility : You can build various types of LLM applications.

4. scalability : Easily integrate custom components.

5. optimization : Automatically perform optimization when running.

Basic components

LCEL provides the following basic components:

1. Runnable : Basic class of all LCEL components.

2. Chain : Run multiple Runnables sequentially.

3. RunnableMap : Run multiple Runnables in parallel.

4. RunnableSequence : Defines the sequence of Runnable.

5. RunnableLambda : Wrap custom functions to Runnable.

Example of use

A simple example using LCEL:

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from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser

prompt = ChatPromptTemplate.from_template("Explain the following topic: {topic}")
model = ChatOpenAI()
output_parser = StrOutputParser()

chain = prompt | model | output_parser

result = chain.invoke({"topic": "LangChain"})
print(result)

In this example, we are making a simple chain by connecting the prompts, models, and output parsers into the pipeline.

Advanced features

1. Parallel processing : RunnableMap You can use it to run multiple tasks simultaneously.

2. Conditional execution : RunnableBranch You can use to run different paths depending on the conditions.

3. Retry and fallback : You can automatically retry on failure or run an alternate path.

4. streaming : Efficiently process large data.

pros and cons

Advantages

1. Code readability : Complex logic can be clearly and concisely expressed.

2. maintainability : Easy maintenance due to the modular structure.

3. performance : Automatic optimization enables efficient execution.

4. scalability : Easily add and integrate new components.

Disadvantages

1. Learning curve : You may need time to get used to the new paradigm.

2. Debugging : Debugging can be difficult for complex chains.

3. Performance overhead : For very simple tasks, overhead may occur.

Utilization case

LCEL can be used to build various LLM applications, such as:

• Interactive AI system

• Document summary and analysis tools

• Quality response system

• Data extraction and conversion pipeline

• Multilingual translation service

LangChain Expression Language is a powerful and flexible tool for developing LLM applications. Declarative syntax and modularity enable you to efficiently build complex AI systems, and are becoming a key element of the LangChain ecosystem. Through continuous development, LCEL is expected to be a more important tool for AI developers.