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LangChain Architecture Fundamentals

LangChain provides a unified interface for composing Large Language Models with external data, tools, and memory modules.

Core Abstractions

LangChain standardizes model interactions across providers (OpenAI, Google Gemini, Anthropic, Ollama, HuggingFace) into modular primitives.

Key Modules​

1. Models​

Interfaces for LLMs and Chat Models.

  • ChatModels: Take a sequence of structured messages (SystemMessage, HumanMessage, AIMessage) and return a ChatResult.
  • LLMs: Take a plain text string and return a plain text completion string.
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o", temperature=0.2)
messages = [
    SystemMessage(content="You are an expert AI systems architect."),
    HumanMessage(content="Explain the difference between Bi-Encoders and Cross-Encoders.")
]

response = model.invoke(messages)
print(response.content)

2. Prompts​

Templates for structuring inputs to the model.

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert code reviewer specializing in {language}."),
    ("user", "Review the following code snippet:\n{code}")
])

3. Output Parsers​

Tools for structuring model responses (e.g., extracting JSON or Pydantic models from text).

from langchain_core.output_parsers import StrOutputParser

parser = StrOutputParser()

4. Retrieval and Memory​

Mechanisms for Retrieval-Augmented Generation (RAG) including Document Loaders, Text Splitters, Embeddings, and Vector Stores. Memory allows persisting state across interactions.

from langchain_core.tools import tool

@tool
def calculate_matrix_norm(vector: list[float]) -> float:
    """Calculates the Euclidean norm of a floating point vector."""
    return sum(x**2 for x in vector) ** 0.5

model_with_tools = model.bind_tools([calculate_matrix_norm])