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LangChain Hello World

Table of Contents

Overview

LangChain is a powerful framework that aims to help developers build end-to-end applications using large language models (LLMs). It provides a set of tools, components, and interfaces that simplify the process of creating applications powered by LLMs and chat models.

Applications

  • LangChain can be used to build context-aware, reasoning applications with flexible abstractions and AI-first toolkit¹.
  • LangChain can also be used to create and deploy LLM apps with confidence using LangSmith, an all-in-one developer platform for every step of the application lifecycle².
  • LangChain can leverage new cognitive architectures and battle-tested orchestration to improve the performance and scalability of LLM apps¹².
  • LangChain can integrate with various domains and languages, and allow users to customize LLMs with their own data¹⁴.
  • LangChain can establish best practices and standards for LLM app development and evaluation²⁵.

Tools & Frameworks

  • LangChain offers a standard interface for chains, which are sequences of LLMs and chat models that perform different tasks on the input and output data¹⁴.
  • LangChain provides lots of integrations with other tools, such as Hugging Face, PyTorch, TensorFlow, and GPT-3¹⁴⁵.
  • LangChain supports end-to-end chains for common applications, such as chatbots, Q&A, summarization, copilots, workflow automation, document analysis, and custom search¹².
  • LangChain also provides a web-based playground where users can try out different models and use cases interactively¹².

Hello World!

Here is a code snippet that shows how to use the LangChain Python SDK to create a simple chain that generates a summary of a text document:

import langchain

# Initialize a chain object
chain = langchain.Chain()

# Add a LLM component that extracts the main points from the document
chain.add_component(
    name="extractor",
    model="gpt-3",
    prompt="Given the following document, extract the main points in bullet points:\n{input}\n\n-",
    max_tokens=100,
    stop="-"
)

# Add a LLM component that generates a summary from the main points
chain.add_component(
    name="summarizer",
    model="gpt-3",
    prompt="Given the following main points, generate a summary in one sentence:\n{input}\n\nSummary:",
    max_tokens=50,
    stop="."
)

# Run the chain on a sample document
document = "LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications."
output = chain.run(document)

# Print the output
print(output)

References