Интегрирован wesp в сайт

WESP_REL/app/services/local_llm_client.py

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+"""HTTP-клиент к локальному LLM с OpenAI-совместимым API (например llama-server из llama.cpp)."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+import requests
+
+
+class LocalLlmError(Exception):
+    """Локальный LLM-сервер недоступен или вернул ошибку."""
+
+
+def _auth_headers(api_key: str) -> Dict[str, str]:
+    k = (api_key or "").strip()
+    if not k:
+        return {}
+    return {"Authorization": f"Bearer {k}"}
+
+
+def openai_list_models(
+    *, base_url: str, timeout_sec: float, api_key: str = ""
+) -> Dict[str, Any]:
+    """GET /v1/models — как у OpenAI; llama-server поддерживает."""
+    url = f"{base_url.rstrip('/')}/v1/models"
+    try:
+        r = requests.get(url, timeout=timeout_sec, headers=_auth_headers(api_key))
+    except requests.RequestException as e:
+        raise LocalLlmError(f"LLM недоступен: {e}") from e
+    if r.status_code != 200:
+        raise LocalLlmError(f"LLM GET /v1/models: HTTP {r.status_code}")
+    try:
+        return r.json()
+    except ValueError as e:
+        raise LocalLlmError("LLM /v1/models: не JSON") from e
+
+
+def model_in_openai_payload(payload: Dict[str, Any], want: str) -> bool:
+    """Проверяет, есть ли в ответе /v1/models модель с id, совпадающим с want (без учёта регистра)."""
+    want_l = (want or "").strip().lower()
+    if not want_l:
+        return False
+    data = payload.get("data")
+    if not isinstance(data, list):
+        return False
+    for item in data:
+        if not isinstance(item, dict):
+            continue
+        mid = str(item.get("id") or "").strip().lower()
+        if not mid:
+            continue
+        if mid == want_l or mid.endswith("/" + want_l) or mid.endswith("\\" + want_l):
+            return True
+        # имя файла без пути
+        if mid.rsplit("/", 1)[-1].rsplit("\\", 1)[-1] == want_l:
+            return True
+    return False
+
+
+def _normalize_message_content(content: Any) -> Optional[str]:
+    if content is None:
+        return None
+    if isinstance(content, str):
+        return content.strip() or None
+    if isinstance(content, list):
+        parts: List[str] = []
+        for c in content:
+            if isinstance(c, dict) and c.get("type") == "text":
+                parts.append(str(c.get("text") or ""))
+            elif isinstance(c, str):
+                parts.append(c)
+        joined = "".join(parts).strip()
+        return joined or None
+    return str(content).strip() or None
+
+
+def _parse_assistant_message(data: Dict[str, Any]) -> Dict[str, Any]:
+    choices = data.get("choices")
+    if not isinstance(choices, list) or not choices:
+        raise LocalLlmError("LLM /v1/chat/completions: нет choices")
+    first = choices[0]
+    if not isinstance(first, dict):
+        raise LocalLlmError("LLM /v1/chat/completions: choices[0] не объект")
+    msg = first.get("message")
+    if not isinstance(msg, dict):
+        raise LocalLlmError("LLM /v1/chat/completions: нет message")
+    content = _normalize_message_content(msg.get("content"))
+    tool_calls = msg.get("tool_calls")
+    if tool_calls is not None and not isinstance(tool_calls, list):
+        tool_calls = None
+    return {
+        "content": content,
+        "tool_calls": tool_calls,
+    }
+
+
+def openai_chat_completion_message(
+    *,
+    base_url: str,
+    model: str,
+    messages: List[Dict[str, Any]],
+    timeout_sec: float,
+    max_tokens: int = 512,
+    api_key: str = "",
+    tools: Optional[List[Dict[str, Any]]] = None,
+    tool_choice: Optional[str] = None,
+) -> Dict[str, Any]:
+    """POST /v1/chat/completions — возвращает content и опционально tool_calls."""
+    url = f"{base_url.rstrip('/')}/v1/chat/completions"
+    body: Dict[str, Any] = {
+        "model": model,
+        "messages": messages,
+        "max_tokens": max(16, min(int(max_tokens), 4096)),
+    }
+    if tools:
+        body["tools"] = tools
+        body["tool_choice"] = (tool_choice or "auto") if tool_choice else "auto"
+    try:
+        r = requests.post(url, json=body, timeout=timeout_sec, headers=_auth_headers(api_key))
+    except requests.RequestException as e:
+        raise LocalLlmError(f"LLM недоступен: {e}") from e
+    if r.status_code != 200:
+        raise LocalLlmError(f"LLM /v1/chat/completions: HTTP {r.status_code} {r.text[:500]}")
+    try:
+        data = r.json()
+    except ValueError as e:
+        raise LocalLlmError("LLM /v1/chat/completions: не JSON") from e
+    return _parse_assistant_message(data)
+
+
+def openai_chat_completion(
+    *,
+    base_url: str,
+    model: str,
+    messages: List[Dict[str, Any]],
+    timeout_sec: float,
+    max_tokens: int = 512,
+    api_key: str = "",
+) -> str:
+    """POST /v1/chat/completions — ответ ассистента из choices[0].message.content (без tool_calls)."""
+    msg = openai_chat_completion_message(
+        base_url=base_url,
+        model=model,
+        messages=messages,
+        timeout_sec=timeout_sec,
+        max_tokens=max_tokens,
+        api_key=api_key,
+        tools=None,
+        tool_choice=None,
+    )
+    content = msg.get("content")
+    if isinstance(content, str) and content.strip():
+        return content.strip()
+    if msg.get("tool_calls"):
+        raise LocalLlmError(
+            "LLM вернул вызовы инструментов без финального текста; включите режим инструментов в чате."
+        )
+    raise LocalLlmError("LLM /v1/chat/completions: нет choices[0].message.content")