initial: novel-app snapshot

services/llm.py

@@ -0,0 +1,96 @@
+"""LLM client: streaming and non-streaming chat completions, SSE framing.
+
+Pure functions — no Flask imports. Uses ``requests`` for HTTP against an
+OpenAI-compatible endpoint (``config.LLM_URL``). The ``_sse`` helper is
+shared by deconstruct / chapter_writer so every SSE stream uses the same
+wire format.
+"""
+
+import json
+import logging
+import time
+
+import requests as http_requests
+
+import config
+
+logger = logging.getLogger(__name__)
+
+_KEEPALIVE_INTERVAL = 15  # seconds
+
+
+def _sse(event, payload):
+    """Return a single SSE frame: ``event: <event>\\ndata: <json>\\n\\n``."""
+    return f"event: {event}\ndata: {json.dumps(payload, ensure_ascii=False)}\n\n"
+
+
+def stream_chat(messages, max_tokens=4096, model=None, timeout=None):
+    """Generator yielding raw token strings from a streaming chat completion.
+
+    POSTs to ``{config.LLM_URL}/chat/completions`` with ``stream=True``.
+    Parses ``data:`` lines, handles ``[DONE]`` terminator, extracts
+    ``choices[0].delta.content``.  Raises ``RuntimeError`` on HTTP failure.
+    """
+    url = f"{config.LLM_URL}/chat/completions"
+    body = {
+        "model": model or config.LLM_MODEL,
+        "messages": messages,
+        "stream": True,
+        "max_tokens": max_tokens,
+    }
+    resp = http_requests.post(url, json=body, stream=True, timeout=timeout or config.LLM_TIMEOUT)
+    resp.raise_for_status()
+
+    for line in resp.iter_lines():
+        if not line:
+            continue
+        line = line.decode("utf-8") if isinstance(line, bytes) else line
+        if not line.startswith("data: "):
+            continue
+        chunk_data = line[6:]
+        if chunk_data.strip() == "[DONE]":
+            break
+        try:
+            chunk = json.loads(chunk_data)
+        except json.JSONDecodeError:
+            continue
+        delta = chunk.get("choices", [{}])[0].get("delta", {})
+        token_text = delta.get("content", "")
+        if token_text:
+            yield token_text
+
+
+def complete_chat(messages, max_tokens=2048, model=None, timeout=None):
+    """Non-streaming call returning the assistant content string."""
+    url = f"{config.LLM_URL}/chat/completions"
+    body = {
+        "model": model or config.LLM_MODEL,
+        "messages": messages,
+        "stream": False,
+        "max_tokens": max_tokens,
+    }
+    resp = http_requests.post(url, json=body, timeout=timeout or config.LLM_TIMEOUT)
+    resp.raise_for_status()
+    return resp.json().get("choices", [{}])[0].get("message", {}).get("content", "")
+
+
+def make_token_stream(messages, on_token, max_tokens=4096):
+    """Generator wrapping :func:`stream_chat` with SSE framing + keepalive.
+
+    Yields ``_sse('token', {'text': t})`` for each token.  Calls
+    ``on_token(t)`` per token so callers can accumulate the full text.
+    Emits ``: keepalive\\n\\n`` comments if >15 s silence between chunks.
+    On exception yields ``_sse('error', {'error': ...})`` and stops.
+    """
+    last_emit = time.time()
+    try:
+        for token in stream_chat(messages, max_tokens=max_tokens):
+            now = time.time()
+            if now - last_emit > _KEEPALIVE_INTERVAL:
+                yield ": keepalive\n\n"
+            yield _sse("token", {"text": token})
+            on_token(token)
+            last_emit = time.time()
+    except Exception as exc:
+        logger.exception("make_token_stream: LLM error")
+        yield _sse("error", {"error": str(exc)})