complete restructure

vibe_bot/llm_client.py

@@ -0,0 +1,126 @@
+"""Async OpenAI-compatible LLM, image, and embedding clients.
+
+Public API facade: chat completion and the tool registry live in the
+``vibe_bot.llm`` subpackage; the embedding HTTP plumbing stays in this
+module.
+
+``image_edit`` (``!retcon``) requests a fixed 768x768 output rather than
+matching the source image's aspect ratio. Matching it would require
+decoding the downloaded image (Pillow is not a dependency) and most
+OpenAI-compatible edit endpoints only accept a fixed set of sizes anyway;
+the square output bounds request cost and is universally honored.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+import requests
+
+from vibe_bot.llm.chat import (
+    ToolCallNotifier,
+    ToolExecutor,
+    chat_complete,
+    chat_completion_instruct,
+    chat_completion_with_history,
+    chat_completion_with_tools,
+    get_chat_client,
+)
+from vibe_bot.llm.images import (
+    get_image_edit_client,
+    get_image_gen_client,
+    image_edit,
+    image_generation,
+)
+from vibe_bot.llm.registry import ToolRegistry, get_tool_registry
+
+__all__ = [
+    "ToolCallNotifier",
+    "ToolExecutor",
+    "ToolRegistry",
+    "chat_complete",
+    "chat_completion_instruct",
+    "chat_completion_with_history",
+    "chat_completion_with_tools",
+    "embedding",
+    "get_chat_client",
+    "get_embedding_session",
+    "get_image_edit_client",
+    "get_image_gen_client",
+    "get_tool_registry",
+    "image_edit",
+    "image_generation",
+]
+
+logger = logging.getLogger(__name__)
+
+_embedding_session: requests.Session | None = None
+
+
+def get_embedding_session() -> requests.Session:
+    """Return the shared requests session for embedding HTTP calls."""
+    global _embedding_session
+    if _embedding_session is None:
+        _embedding_session = requests.Session()
+    return _embedding_session
+
+
+def embedding(
+    text: str,
+    *,
+    url: str,
+    api_key: str,
+    model: str,
+) -> list[float]:
+    """Generate an embedding vector for the given text (synchronous).
+
+    Uses a raw HTTP request (shared session) to avoid the SDK injecting
+    unsupported parameters like encoding_format.
+    """
+    endpoint = f"{url.rstrip('/')}/embeddings"
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+    }
+    payload = {"model": model, "input": [text]}
+
+    try:
+        resp = get_embedding_session().post(
+            endpoint, headers=headers, json=payload, timeout=30
+        )
+        resp.raise_for_status()
+        # A 2xx body can still be non-JSON (e.g. an HTML error page);
+        # resp.json() would raise JSONDecodeError (a ValueError).
+        data = resp.json()
+    except (requests.RequestException, ValueError):
+        return []
+
+    # Handle both OpenAI-style response ({"data": [...]}) and
+    # Ollama-style response ([{...}]) where the API returns a list directly
+    if isinstance(data, list):
+        first = data[0]
+        if not isinstance(first, dict):
+            return []
+        raw: Any = first.get("embedding")
+    elif isinstance(data, dict):
+        if not data.get("data"):
+            return []
+        raw = data["data"][0].get("embedding")
+    else:
+        return []
+
+    if raw is None:
+        return []
+
+    if isinstance(raw, str):
+        try:
+            raw = json.loads(raw)
+        except ValueError:
+            return []
+    if not isinstance(raw, list):
+        raw = list(raw)
+    if not raw:
+        return []
+    return list[float](raw)