langchain

LangChain Framework

progressive_disclosure: entry_point: summary: "LLM application framework with chains, agents, RAG, and memory" when_to_use: - "When building LLM-powered applications" - "When implementing RAG (Retrieval Augmented Generation)" - "When creating AI agents with tools" - "When chaining multiple LLM calls" quick_start: - "pip install langchain langchain-anthropic" - "Set up LLM (ChatAnthropic or ChatOpenAI)" - "Create chain with prompts and LLM" - "Invoke chain with input" token_estimate: entry: 85 full: 5200

Core Concepts

LangChain Expression Language (LCEL)

Modern composable syntax for building chains with | operator.

Basic Chain:

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Components
llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
output_parser = StrOutputParser()

# Compose with LCEL
chain = prompt | llm | output_parser

# Invoke
result = chain.invoke({"topic": "programming"})

Why LCEL:

• Type safety and auto-completion
• Streaming support built-in
• Async by default
• Observability with LangSmith
• Easier debugging

Chain Components

Prompts:

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

# Simple template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])

# With message history
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder(variable_name="history"),
    ("user", "{input}")
])

# Few-shot examples
from langchain_core.prompts import FewShotChatMessagePromptTemplate

examples = [
    {"input": "2+2", "output": "4"},
    {"input": "3*5", "output": "15"}
]

example_prompt = ChatPromptTemplate.from_messages([
    ("human", "{input}"),
    ("ai", "{output}")
])

few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=example_prompt,
    examples=examples
)