Interactive Code Playground — AI Tutorial Examples

Try the code examples from our AI tutorials directly in your browser. Each example is editable and runnable via linked Colab notebooks or local setup instructions.

Available Examples

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.chat_history import InMemoryChatMessageHistory

llm = ChatOpenAI(model="gpt-4o-mini", streaming=True)
history = InMemoryChatMessageHistory()
history.add_message(SystemMessage(content="You are a helpful assistant."))

def chat(text):
    history.add_message(HumanMessage(content=text))
    response = ""
    for chunk in llm.stream(history.messages):
        print(chunk.content or "", end="")
        response += chunk.content or ""
    history.add_message(AIMessage(content=response))
    return response

chat("Hello! What can you help me with?")

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Build vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(
    ["Document 1 content...", "Document 2 content..."],
    embeddings
)
retriever = vectorstore.as_retriever()

# RAG chain
prompt = ChatPromptTemplate.from_template(
    "Answer based on context:n{context}nnQuestion: {question}"
)
llm = ChatOpenAI(model="gpt-4o-mini")

def format_docs(docs):
    return "nn".join(d.page_content for d in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt | llm
)

result = rag_chain.invoke("What is the main topic?")
print(result.content)

from unsloth import FastLanguageModel
from trl import SFTTrainer
from transformers import TrainingArguments
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/Llama-3.2-1B-Instruct",
    max_seq_length=2048,
    load_in_4bit=True,
)

# Apply LoRA
model = FastLanguageModel.get_peft_model(
    model, r=16,
    target_modules=["q_proj","k_proj","v_proj","o_proj",
                    "gate_proj","up_proj","down_proj"],
)

# Train
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset["train"],
    dataset_text_field="text",
    max_seq_length=2048,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        num_train_epochs=3,
        learning_rate=2e-4,
        output_dir="outputs",
        optim="adamw_8bit",
    ),
)
trainer.train()

# Save
model.save_pretrained_merged("my_finetuned_model", 
                              tokenizer, save_method="merged_16bit")
print("Training complete! Model saved.")

from langchain.evaluation import load_evaluator
from langchain_openai import ChatOpenAI

evaluator = load_evaluator(
    "criteria",
    criteria="conciseness",
    llm=ChatOpenAI(model="gpt-4o-mini")
)

prediction = "The capital of France, which is a country in Western Europe, is Paris."
reference = "Paris"

result = evaluator.evaluate_strings(
    prediction=prediction,
    reference=reference,
)
print(f"Score: {result['score']}")
print(f"Reasoning: {result['reasoning']}")

Running Locally

To run these examples on your own machine:

git clone https://github.com/datagate-ch/ai-tutorials.git
cd ai-tutorials
pip install -r requirements.txt
# Copy .env.example and fill in your API keys
cp .env.example .env
# Run any example
python examples/01_basic_chatbot.py

Tips

• Most examples work with both OpenAI and local Ollama models — just change the model name
• Colab free tier provides a T4 GPU, sufficient for inference and small fine-tuning runs
• For larger fine-tuning, use a cloud GPU (Lambda, Modal, or Vast.ai)

Related tutorials: Build Your First AI Chatbot · Fine-Tune an LLM on Custom Data