langchain

LangChain is an open-source framework for developing applications powered by language models. It provides a set of tools and abstractions that simplify the process of building complex, context-aware AI applications. LangChain enables developers to create chatbots, question-answering systems, and other AI-driven applications with ease.

Pros

• Modular and flexible architecture, allowing for easy customization and integration
• Extensive documentation and active community support
• Supports multiple language models and integrations with various APIs and tools
• Provides high-level abstractions for common AI application patterns

Cons

• Steep learning curve for beginners due to the wide range of features and concepts
• Rapid development pace may lead to frequent breaking changes
• Some advanced features may require additional setup or external services
• Performance overhead for some abstractions compared to direct API calls

Code Examples

1. Creating a simple chain for question-answering:

from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["question"],
    template="Q: {question}\nA:",
)
chain = LLMChain(llm=llm, prompt=prompt)

response = chain.run("What is the capital of France?")
print(response)

2. Using a vector store for similarity search:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter

with open("document.txt") as f:
    raw_text = f.read()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(raw_text)

embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_texts(texts, embeddings)

query = "What is the main topic of this document?"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)

3. Creating an agent with tools:

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")

Getting Started

To get started with LangChain, follow these steps:

1. Install LangChain using pip:

   pip install langchain
   

2. Set up your API keys as environment variables:

   export OPENAI_API_KEY="your-api-key-here"
   

3. Create a simple chain:

   from langchain import PromptTemplate, LLMChain
   from langchain.llms import OpenAI
   
   llm = OpenAI(temperature=0.7)
   prompt = PromptTemplate(
       input_variables=["product"],
       template="What is a good name for a company that makes {product}?",
   )
   chain = LLMChain(llm=llm, prompt=prompt)
   
   print(chain.run("eco-friendly water bottles"))
   

This example sets up a basic LangChain application that generates company names based on a given product.