Рабочий пайплайн: YouTube -> транскрипция -> выжимка -> Postgres

- новый api/: /pipeline/process (yt-dlp, субтитры или Vosk, LLM/экстрактивная
  суммаризация), /videos, /llm; старый код перенесён в api_legacy/
- web/: Flet UI (flet 0.28.3 + flet-web, порт 8550)
- Dockerfile.api: ffmpeg слоем из mwader/static-ffmpeg, pip с кэш-маунтом
- requirements/pyproject: убраны moviepy, imageio-ffmpeg, битый asyncio
- README с инструкцией запуска и загрузкой Vosk-модели

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

api/lib/llm.py

@@ -0,0 +1,164 @@
+"""Generic OpenAI-compatible chat-completions client.
+
+The LLM is *not* deployed in this container — point this client at any external
+endpoint that speaks the OpenAI Chat Completions wire format:
+    - https://api.openai.com/v1
+    - https://openrouter.ai/api/v1
+    - https://api.anthropic.com (via OpenAI-compat proxies)
+    - http://host.docker.internal:11434/v1   (local Ollama)
+    - http://vllm-host:8000/v1                (vLLM / llama.cpp server / LM Studio)
+
+Configure once via environment (LLM_BASE_URL, LLM_API_KEY, LLM_MODEL),
+or override per-request through /pipeline/process by passing an `llm` block.
+"""
+from __future__ import annotations
+
+import json
+import logging
+import urllib.error
+import urllib.request
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from api.config import settings
+
+
+logger = logging.getLogger("llm")
+
+
+class LLMConfig(BaseModel):
+    """Wire-level config for an OpenAI-compatible chat-completions endpoint."""
+
+    base_url: str = Field(..., description="Base URL ending in /v1 (or equivalent)")
+    api_key: str = ""
+    # `model` is optional so the same config can be used for /v1/models discovery
+    # before the user has picked one.
+    model: str = ""
+    timeout: int = 120
+    max_tokens: int = 1000
+    temperature: float = 0.3
+    extra_headers: dict[str, str] = Field(default_factory=dict)
+
+
+class LLMError(RuntimeError):
+    pass
+
+
+def from_settings() -> Optional[LLMConfig]:
+    """Build LLMConfig from environment variables, or return None if not configured."""
+    if not settings.LLM_BASE_URL or not settings.LLM_MODEL:
+        return None
+    return LLMConfig(
+        base_url=settings.LLM_BASE_URL,
+        api_key=settings.LLM_API_KEY,
+        model=settings.LLM_MODEL,
+        timeout=settings.LLM_TIMEOUT,
+        max_tokens=settings.LLM_MAX_TOKENS,
+        temperature=settings.LLM_TEMPERATURE,
+    )
+
+
+def chat_complete(cfg: LLMConfig, messages: list[dict]) -> str:
+    """Synchronous Chat Completions call. Returns assistant message content."""
+    url = cfg.base_url.rstrip("/") + "/chat/completions"
+    payload: dict = {
+        "model": cfg.model,
+        "messages": messages,
+        "max_tokens": cfg.max_tokens,
+        "temperature": cfg.temperature,
+    }
+    headers = {"Content-Type": "application/json", "Accept": "application/json"}
+    if cfg.api_key:
+        headers["Authorization"] = f"Bearer {cfg.api_key}"
+    headers.update(cfg.extra_headers or {})
+
+    body = json.dumps(payload).encode("utf-8")
+    req = urllib.request.Request(url, data=body, headers=headers, method="POST")
+
+    try:
+        with urllib.request.urlopen(req, timeout=cfg.timeout) as resp:
+            raw = resp.read()
+    except urllib.error.HTTPError as e:
+        try:
+            err_body = e.read().decode("utf-8", errors="ignore")
+        except Exception:
+            err_body = ""
+        raise LLMError(f"LLM HTTP {e.code} {e.reason}: {err_body[:500]}")
+    except Exception as e:
+        raise LLMError(f"LLM request failed: {e}")
+
+    try:
+        data = json.loads(raw.decode("utf-8"))
+        choices = data.get("choices") or []
+        if not choices:
+            raise LLMError(f"LLM returned no choices: {data}")
+        msg = choices[0].get("message") or {}
+        content = msg.get("content")
+        if content is None:
+            raise LLMError(f"LLM choice has no content: {choices[0]}")
+        return content.strip()
+    except LLMError:
+        raise
+    except Exception as e:
+        raise LLMError(f"Failed to parse LLM response: {e}")
+
+
+def ping(cfg: LLMConfig) -> str:
+    """Tiny round-trip to verify connectivity. Returns the assistant reply text."""
+    return chat_complete(
+        cfg,
+        [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Reply with the single word OK."},
+        ],
+    )
+
+
+def list_models(cfg: LLMConfig) -> list[dict]:
+    """GET {base_url}/models. Returns a normalized list of {id, name, owned_by?}.
+
+    Compatible with OpenAI, OpenRouter, Ollama OpenAI-compat, vLLM, LM Studio.
+    """
+    url = cfg.base_url.rstrip("/") + "/models"
+    headers = {"Accept": "application/json"}
+    if cfg.api_key:
+        headers["Authorization"] = f"Bearer {cfg.api_key}"
+    headers.update(cfg.extra_headers or {})
+
+    req = urllib.request.Request(url, headers=headers, method="GET")
+    try:
+        with urllib.request.urlopen(req, timeout=cfg.timeout) as resp:
+            raw = resp.read()
+    except urllib.error.HTTPError as e:
+        try:
+            err_body = e.read().decode("utf-8", errors="ignore")
+        except Exception:
+            err_body = ""
+        raise LLMError(f"LLM HTTP {e.code} {e.reason}: {err_body[:500]}")
+    except Exception as e:
+        raise LLMError(f"List models failed: {e}")
+
+    try:
+        data = json.loads(raw.decode("utf-8"))
+    except Exception as e:
+        raise LLMError(f"Failed to parse models response: {e}")
+
+    items = data.get("data") or data.get("models") or data.get("results") or []
+    out: list[dict] = []
+    for item in items:
+        if isinstance(item, str):
+            out.append({"id": item, "name": item})
+            continue
+        if not isinstance(item, dict):
+            continue
+        mid = item.get("id") or item.get("name") or item.get("model")
+        if not mid:
+            continue
+        entry = {"id": mid, "name": item.get("name") or mid}
+        owned = item.get("owned_by") or item.get("organization")
+        if owned:
+            entry["owned_by"] = owned
+        out.append(entry)
+    out.sort(key=lambda x: x["id"].lower())
+    return out