Messages and roles

messages = [
    {"role": "system", "content": "You are a concise ML tutor. Use plain language and at most 3 sentences."},
    {"role": "user", "content": "What is temperature in LLMs?"},
    {"role": "assistant", "content": "Temperature controls how random the model's word choices are. Low values make answers focused and repeatable; high values make them more varied."},
    {"role": "user", "content": "What value should I use for data extraction?"},
]

def fake_llm(messages: list[dict]) -> str:
    """Stand-in for a real model: shows how much context it was given."""
    last = messages[-1]["content"]
    return f"(seen {len(messages)} messages) You said: {last!r}"


class Chat:
    def __init__(self, system: str):
        self.messages = [{"role": "system", "content": system}]

    def send(self, text: str) -> str:
        self.messages.append({"role": "user", "content": text})
        reply = fake_llm(self.messages)              # the WHOLE history is sent
        self.messages.append({"role": "assistant", "content": reply})
        return reply


chat = Chat("You are a helpful tutor.")
print(chat.send("What is a token?"))
print(chat.send("And a context window?"))
print(chat.send("Summarise both."))
print("history length:", len(chat.messages))

SYSTEM_PROMPT = """You are the support assistant for Acme Cloud.

Audience: small-business owners with little technical background.
Scope: billing, accounts, and backups only. For other topics, reply:
"I can only help with Acme Cloud billing, accounts, and backups."

Rules:
- Answer only from the documentation between <docs> tags.
- If the docs do not contain the answer, say so and offer to open a ticket.
- Never ask for passwords or full card numbers.

Format: at most 4 short sentences; use numbered steps for procedures."""


def build_messages(docs: str, question: str) -> list[dict]:
    return [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": f"<docs>\n{docs}\n</docs>\n\nQuestion: {question}"},
    ]


msgs = build_messages("To reset a password, open Settings > Security.", "How do I reset my password?")
print(msgs[1]["content"])

messages = [
    {"role": "system", "content": "Classify the sentiment of product reviews as positive, negative, or mixed. Reply with one word."},
    {"role": "user", "content": "Battery lasts all day, love it."},
    {"role": "assistant", "content": "positive"},
    {"role": "user", "content": "Great screen but it overheats constantly."},
    {"role": "assistant", "content": "mixed"},
    {"role": "user", "content": "Stopped working after a week."},
    {"role": "assistant", "content": "negative"},
    {"role": "user", "content": "Fast delivery, but the case cracked on day two."},   # real input
]
print(len(messages), "messages,", sum(m["role"] == "assistant" for m in messages), "examples")

[
    {"role": "user", "content": "What's the weather in Pune?"},
    {"role": "assistant", "content": None, "tool_calls": [
        {"id": "call_1", "type": "function",
         "function": {"name": "get_weather", "arguments": "{\"city\": \"Pune\"}"}}
    ]},
    {"role": "tool", "tool_call_id": "call_1", "content": "{\"temp_c\": 27, \"sky\": \"clear\"}"},
    # -> next request: the model answers "It's 27°C and clear in Pune."
]

import json
from pathlib import Path

history = [
    {"role": "system", "content": "You are a helpful tutor."},
    {"role": "user", "content": "What is RAG?"},
    {"role": "assistant", "content": "Retrieval-augmented generation."},
]

path = Path("thread-42.json")
path.write_text(json.dumps(history, ensure_ascii=False, indent=2), encoding="utf-8")

restored = json.loads(path.read_text(encoding="utf-8"))
restored.append({"role": "user", "content": "Give an example."})
print(len(restored), restored[-2]["role"], "->", restored[-1]["role"])