🦜🔗 LangChain Overview
LangChain is a powerful framework for developing applications powered by language models. It provides a standard interface for chains, agents, and retrieval strategies, making it easier to build complex LLM-powered applications.
Composability
Build complex apps by chaining simple components
Flexibility
Support for multiple LLMs and vector stores
Production Ready
Built-in monitoring, caching, and error handling
Core Concepts
Chains
Sequential composition of LLM calls and processing steps
LangChain chains allow you to connect multiple components in sequence. Each chain takes inputs, processes them through one or more steps, and produces outputs that can feed into the next chain.
Agents
Autonomous decision-makers that can use tools and plan actions
Agents use LLMs to determine which tools to use and in what order. They can reason about problems, break them down into steps, and execute those steps using available tools.
Tools
Functions that agents can call to interact with external systems
Tools extend agent capabilities beyond text generation. Common tools include web search, calculators, databases, APIs, and custom functions.
Memory
State management for conversations and context retention
Memory systems store conversation history, summaries, and entity information. This allows for coherent multi-turn conversations and long-term context retention.
Retrievers
Components that fetch relevant information from vector stores
Retrievers query vector databases to find relevant documents based on semantic similarity. They form the backbone of RAG (Retrieval-Augmented Generation) systems.
Callbacks
Hooks for monitoring, streaming, and debugging LLM operations
Callbacks provide visibility into LangChain operations. Use them for logging, streaming responses, tracking token usage, and debugging chain execution.
Implementation Examples
Simple LLM Chain
Basic chain connecting prompt template with LLM
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
# Create a prompt template
template = """
You are a helpful assistant that translates {input_language} to {output_language}.
Text: {text}
Translation:
"""
prompt = PromptTemplate(
input_variables=["input_language", "output_language", "text"],
template=template
)
# Create LLM chain
llm = OpenAI(temperature=0.7)
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain
result = chain.run({
"input_language": "English",
"output_language": "Spanish",
"text": "Hello, how are you today?"
})
print(result) # "Hola, ¿cómo estás hoy?"Architecture Patterns
Sequential Chain Pattern
Chain multiple LLM calls where each output feeds into the next input
- • Input → Preprocessing → LLM → Postprocessing → Output
- • Useful for multi-step reasoning and data transformation
- • Easy to debug and monitor each step
Agent Executor Pattern
Autonomous agents that decide which tools to use
- • Agent reasons about the task and available tools
- • Executes tools in sequence or parallel as needed
- • Self-corrects based on tool outputs
RAG Pipeline Pattern
Retrieve relevant context before generating responses
- • Query → Retrieve Documents → Augment Prompt → Generate
- • Grounds responses in factual information
- • Reduces hallucinations and improves accuracy
Production Best Practices
✅ Do's
- • Implement proper error handling and retries
- • Use callbacks for monitoring and debugging
- • Cache LLM responses to reduce costs
- • Validate and sanitize all inputs
- • Set appropriate temperature and token limits
- • Use async operations for better performance
- • Version your prompts and chains
- • Implement fallback strategies
❌ Don'ts
- • Don't hardcode API keys in code
- • Don't ignore rate limits
- • Don't trust LLM outputs without validation
- • Don't use high temperature for factual tasks
- • Don't skip input/output validation
- • Don't create overly complex chains
- • Don't ignore token costs
- • Don't forget to handle edge cases