altlasten entfernt

math-tutor/backend/app/services/chat_engine.py

-from __future__ import annotations
-
-from typing import List
-
-import os
-import httpx
-from openai import OpenAI
-
-from app.services.embeddings import OpenAILikeEmbeddings
-from app.services import vector_store
-
-SYSTEM_PROMPT = """Du bist ein Mathe-Tutor. Antworte auf Deutsch, klar und korrekt.
-Nutze ausschließlich den bereitgestellten Kontext. Wenn nichts zur Frage im Kontext steht, antworte mit "Dazu steht nichts im Material" und nichts weiter.
-Gib wenn möglich eine kurze Struktur: (1) Idee, 
-(2) Definition, 
-(3) kurzer Begründungs-/Rechenweg, 
-(4) Mini-Beispiel.
-Zitiere Quellen inline mit den eckigen Klammern, die im Kontext vorangestellt sind, z.B. [s2/ss1/c3 | definition | ...].
-"""
-
-CONTEXT_LIMITS = {
-    "direct": 4,
-    "indirect": 6,
-    "subsection": 2,
-    "section": 1,
-}
-
-
-def _get_embedder() -> OpenAILikeEmbeddings:
-    base_url = os.getenv("OPENAI_BASE_URL")
-    api_key = os.getenv("OPENAI_API_KEY")
-    model = os.getenv("OPENAI_EMBED_MODEL", "text-embedding-3-large")
-    if not base_url or not api_key:
-        raise ValueError("Missing OPENAI_BASE_URL or OPENAI_API_KEY")
-    return OpenAILikeEmbeddings(base_url=base_url, api_key=api_key, model=model, target_dim=1024)
-
-
-def _format_ref(doc: vector_store.Retrieved) -> str:
-    meta = doc.metadata
-    sec = meta.get("section_index")
-    sub = meta.get("subsection_index")
-    child = meta.get("child_index")
-    ref = []
-    if sec is not None:
-        ref.append(f"s{sec}")
-    if sub is not None:
-        ref.append(f"ss{sub}")
-    if child is not None:
-        ref.append(f"c{child}")
-    ref_id = "/".join(ref) if ref else "unknown"
-
-    doc_type = meta.get("type") or doc.doc_type
-    title = (
-        meta.get("title")
-        or meta.get("subsection_title")
-        or meta.get("section_title")
-        or meta.get("path")
-        or "Untitled"
-    )
-    return f"[{ref_id} | {doc_type} | {title}]"
-
-
-def build_context(result: dict) -> str:
-    blocks: List[str] = []
-
-    def add_group(label: str, items: List[vector_store.Retrieved], limit: int) -> None:
-        if not items:
-            return
-        for doc in items[:limit]:
-            blocks.append(f"{label} {_format_ref(doc)}\n{doc.markdown}")
-
-    add_group("DIRECT", result.get("children_direct", []), CONTEXT_LIMITS["direct"])
-    add_group("INDIRECT", result.get("children_expanded", []), CONTEXT_LIMITS["indirect"])
-    add_group("SUBSECTION", result.get("subsections", []), CONTEXT_LIMITS["subsection"])
-    add_group("SECTION", result.get("sections", []), CONTEXT_LIMITS["section"])
-
-    if not blocks:
-        return "KONTEXT: (leer)"
-    return "KONTEXT:\n" + "\n\n".join(blocks)
-
-
-def build_messages(messages: list[dict], context: str) -> list[dict]:
-    return [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "system", "content": context},
-    ] + messages
-
-
-def retrieve_context(pg_url: str, query_text: str) -> str:
-    embedder = _get_embedder()
-    retrieval = vector_store.retrieve(
-        pg_url=pg_url,
-        embedder=embedder,
-        query=query_text,
-        k=8,
-        expand_links=True,
-    )
-    return build_context(retrieval)
-
-
-def call_chat_api(messages_payload: list[dict]) -> str:
-    base_url = os.getenv("OPENAI_BASE_URL")
-    api_key = os.getenv("OPENAI_API_KEY")
-    model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
-    if not base_url or not api_key:
-        raise ValueError("Missing OPENAI_BASE_URL or OPENAI_API_KEY")
-
-    url = f"{base_url.rstrip('/')}/chat/completions"
-    payload = {
-        "model": model,
-        "messages": messages_payload,
-    }
-    headers = {"Authorization": f"Bearer {api_key}"}
-    with httpx.Client(timeout=60.0) as client:
-        response = client.post(url, headers=headers, json=payload)
-        response.raise_for_status()
-    data = response.json()
-    return data["choices"][0]["message"]["content"]
-
-
-def stream_chat(messages_payload: list[dict]):
-    base_url = os.getenv("OPENAI_BASE_URL")
-    api_key = os.getenv("OPENAI_API_KEY")
-    model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
-    if not base_url or not api_key:
-        raise ValueError("Missing OPENAI_BASE_URL or OPENAI_API_KEY")
-
-    client = OpenAI(api_key=api_key, base_url=base_url)
-    return client.chat.completions.create(
-        model=model,
-        messages=messages_payload,
-        stream=True,
-    )

math-tutor/backend/app/services/orchestrator_engine.py

-from app.services import context_store, decision_tool, math_intent, retrieval_service, tool_logging
-from app.tools import hint_tool, math_tool
+from app.services import context_store, math_intent, retrieval_service, tool_logging
+from app.tools import hint_tool, math_tool, decision_tool
 
 
 def _append_tool_log(tool_log: list[dict], name: str, args: dict, response: object) -> None: