initial commit

backend/app/services/llm_service.py

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+"""FluentGerman.ai — LLM service (provider-agnostic via LiteLLM)."""
+
+import logging
+from collections.abc import AsyncGenerator
+
+import litellm
+
+from app.config import get_settings
+
+logger = logging.getLogger("fluentgerman.llm")
+
+
+def _resolve_model(model: str, provider: str) -> str:
+    """Ensure Gemini models have the 'gemini/' prefix for Google AI Studio."""
+    if provider == "gemini" and not model.startswith("gemini/"):
+        resolved = f"gemini/{model}"
+        logger.info("Auto-prefixed model: %s → %s (Google AI Studio)", model, resolved)
+        return resolved
+    return model
+
+
+async def chat_stream(
+    messages: list[dict[str, str]],
+    model: str | None = None,
+) -> AsyncGenerator[str, None]:
+    """Stream chat completion tokens from the configured LLM provider."""
+    settings = get_settings()
+    model = _resolve_model(model or settings.llm_model, settings.llm_provider)
+
+    logger.info("LLM request: model=%s messages=%d", model, len(messages))
+
+    response = await litellm.acompletion(
+        model=model,
+        messages=messages,
+        api_key=settings.llm_api_key,
+        stream=True,
+    )
+
+    async for chunk in response:
+        delta = chunk.choices[0].delta
+        if delta.content:
+            yield delta.content
+
+
+async def summarize_instruction(raw_text: str) -> str:
+    """Ask the LLM to distill raw voice transcript into a structured instruction."""
+    settings = get_settings()
+    model = _resolve_model(settings.llm_model, settings.llm_provider)
+
+    meta_prompt = (
+        "You are an expert assistant for a language teacher. "
+        "The following text is a raw transcript of the teacher describing a learning instruction, "
+        "homework, or teaching method. Distill it into a clear, concise, structured instruction "
+        "that can be used as a system prompt for a language-learning LLM assistant. "
+        "Output ONLY the instruction text, no preamble.\n\n"
+        f"Transcript:\n{raw_text}"
+    )
+
+    response = await litellm.acompletion(
+        model=model,
+        messages=[{"role": "user", "content": meta_prompt}],
+        api_key=settings.llm_api_key,
+    )
+
+    return response.choices[0].message.content.strip()