news-mcp/news_mcp/llm.py

from __future__ import annotations

import json
from pathlib import Path
from typing import Any, Dict, Iterable, List

import httpx

from news_mcp.config import (
    NEWS_EXTRACT_PROVIDER,
    NEWS_EXTRACT_MODEL,
    NEWS_SUMMARY_PROVIDER,
    NEWS_SUMMARY_MODEL,
    PROMPTS_DIR,
)


SYSTEM_PROMPT = "You are a news signal extraction engine. Return STRICT JSON only."


class LLMError(RuntimeError):
    pass


def load_prompt(name: str) -> str:
    path = PROMPTS_DIR / name
    return path.read_text(encoding="utf-8")


def _render_prompt(template: str, **kwargs: Any) -> str:
    rendered = template
    for key, value in kwargs.items():
        rendered = rendered.replace("{" + key + "}", str(value))
    return rendered


def active_llm_config() -> dict[str, str]:
    return {
        "extract_provider": NEWS_EXTRACT_PROVIDER,
        "extract_model": NEWS_EXTRACT_MODEL,
        "summary_provider": NEWS_SUMMARY_PROVIDER,
        "summary_model": NEWS_SUMMARY_MODEL,
    }


async def _call_groq(model: str, messages: List[Dict[str, str]], response_json: bool = True) -> str:
    from news_mcp.config import GROQ_API_KEY

    if not GROQ_API_KEY:
        raise LLMError("GROQ_API_KEY is not configured")
    req = {"model": model, "messages": messages, "temperature": 0.2}
    if response_json:
        req["response_format"] = {"type": "json_object"}
    async with httpx.AsyncClient(timeout=45.0) as client:
        resp = await client.post(
            "https://api.groq.com/openai/v1/chat/completions",
            headers={"Authorization": f"Bearer {GROQ_API_KEY}"},
            json=req,
        )
        resp.raise_for_status()
        data = resp.json()
    return data["choices"][0]["message"]["content"]


async def _call_openai(model: str, messages: List[Dict[str, str]], response_json: bool = True) -> str:
    # OpenAI-compatible chat endpoint; uses NEWS_OPENAI_API_KEY.
    from news_mcp.config import OPENAI_API_KEY

    if not OPENAI_API_KEY:
        raise LLMError("OPENAI_API_KEY is not configured")
    req = {"model": model, "messages": messages}
    if response_json:
        req["response_format"] = {"type": "json_object"}
    async with httpx.AsyncClient(timeout=45.0) as client:
        resp = await client.post(
            "https://api.openai.com/v1/chat/completions",
            headers={"Authorization": f"Bearer {OPENAI_API_KEY}"},
            json=req,
        )
        resp.raise_for_status()
        data = resp.json()
    return data["choices"][0]["message"]["content"]


async def call_llm(provider: str, model: str, system_prompt: str, user_prompt: str) -> str:
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ]
    provider = provider.lower().strip()
    if provider == "groq":
        return await _call_groq(model, messages)
    if provider == "openai":
        return await _call_openai(model, messages)
    raise LLMError(f"Unsupported provider: {provider}")


def build_extraction_prompt(cluster: Dict[str, Any]) -> str:
    prompt = load_prompt("extract_entities.prompt")
    return _render_prompt(prompt, cluster_json=json.dumps(cluster, ensure_ascii=False))


async def call_extraction(cluster: Dict[str, Any]) -> Dict[str, Any]:
    user_prompt = build_extraction_prompt(cluster)
    content = await call_llm(NEWS_EXTRACT_PROVIDER, NEWS_EXTRACT_MODEL, SYSTEM_PROMPT, user_prompt)
    return json.loads(content)


async def call_summary(cluster: Dict[str, Any]) -> Dict[str, Any]:
    prompt = load_prompt("summarize_cluster.prompt")
    user_prompt = _render_prompt(prompt, cluster_json=json.dumps(cluster, ensure_ascii=False))
    content = await call_llm(NEWS_SUMMARY_PROVIDER, NEWS_SUMMARY_MODEL, "You are a summarization engine for news clusters. Return strict JSON only.", user_prompt)
    return json.loads(content)