Merge branch 'old_retrival' into 'main'

Old retrival

See merge request kantz/tutor_react!2

math-tutor/README.md

@@ -39,7 +39,7 @@ Ingest markdown docs (expects `markdown/sections`, `markdown/subsections`, `mark
 ```powershell
 cd math-tutor/backend
 .\.venv\Scripts\Activate.ps1
-python .\scripts\retrieval_cli.py ingest --base markdown
+python .\scripts\retrieval_cli.py ingest --base markdown --clear
 ```
 
 Query via CLI:
@@ -61,3 +61,7 @@ Retrieval settings:
   - `neighbor_expand`
 - Backend defaults are in `math-tutor/backend/app/api/retrieval.py` (`QueryRequest`).
 - Core retrieval logic is in `math-tutor/backend/app/services/vector_store.py` (`retrieve`).
+
+## Testing
+python -m test.hint_test ...
+python -m test.vector_store_test --query "Was ist eine Teilmenge?" --k 8 --expand

math-tutor/backend/.env-Example

+MATHPIX_APP_ID=""
+MATHPIX_APP_KEY=""
+
+OPENAI_BASE_URL=""
+OPENAI_API_KEY=""
+OPENAI_CHAT_MODEL=""
+OPENAI_CHAT_TEMPERATURE=""
+OPENAI_EMBED_MODEL=""
+
+POSTGRES_URL=""
+
+OLLAMA_URL=""
+OLLAMA_MODEL= ""
+OLLAMA_TEMPERATURE=""
+
+FRONTEND_URL=""
\ No newline at end of file

math-tutor/backend/app/LLM_services/decision_LLM.py

@@ -13,16 +13,16 @@ CLASSIFIER_PROMPT = (
 
 def needs_more_context(history: str, context_sheet: str) -> dict:
     prompt = (
-        "Historie:\n"
-        + history
-        + "\n\nKontextblatt:\n"
+        "Kontextblatt:\n"
         + context_sheet
+        + "\n\nHistorie:\n"
+        + history
         + "\n\nAntwortformat: JSON."
     )
     result = llm_client.chat(
         messages=[
-            {"role": "system", "content": CLASSIFIER_PROMPT},
             {"role": "user", "content": prompt},
+            {"role": "system", "content": CLASSIFIER_PROMPT},
         ],
         use_ollama=True,
     )
@@ -31,7 +31,7 @@ def needs_more_context(history: str, context_sheet: str) -> dict:
         payload = json.loads(content)
         return {
             "needs_more_context": bool(payload.get("needs_more_context")),
-            "reason": payload.get("reason", ""),
+            "reason": content, 
         }
     except json.JSONDecodeError:
-        return {"needs_more_context": True, "reason": "classifier_parse_error"}
+        return {"needs_more_context": True, "reason": content}

math-tutor/backend/app/LLM_services/hint_LLM.py

@@ -3,60 +3,35 @@ from app.deterministic_services import llm_client
 
 def generate_hint(
     task: str,
-    solution: str,
+    LLM_solution: str,
+    math_solution: str | None = None,
     history: str | None = None,
-    retrival: str | None = None,
+    sources: str | None = None,
 ) -> str:
-    prompt = (
+    system_prompt = (
         "Du bist ein didaktischer Tutor. "
-        "Schaue dir Aufgabe, die dazugehörige Lösung und die bisherige Historie an."
-        "Entscheide basierend darauf was der nächste Schritt ist den der Nutzer machen muss um zur Lösung zu kommne"
-        "Schreibe einen kurzen und klaren Tipp zum nächsten Schritt"
+        "Gebe dem Nutzer hilfreiche Tipps um mathematische Aufgaben zu lösen. "
+        "Antworte nur mit dem Tipp, ohne weitere Erklärungen."
+        "Gibe keine komplette Lösung der Aufgabe."
+        "Die Mathematische Lösung hat immer Vorrang vor der LLM Lösung."
+        "Halte dich kurz und prägnant."
+    )
+    prompt = (
         "Aufgabe:\n"
         + task
         + "\n\n"
-        "Loesung (vom Mathe-Tool):\n"
-        + solution
+        "Loesung (von einem LLM):\n"
+        + LLM_solution
         + "\n"
     )
+    if math_solution:
+        prompt += "Mathematische Loesung:\n" + math_solution + "\n"
     if history:
-        prompt = "\nHistorie:\n" + history + "\n" + prompt
-    if retrival:
-        prompt = "\nKontext:\n" + retrival + "\n" + prompt
-    prompt += "Halte dich kurz und klar. Gibt nicht die Lösung aus."
-
+        prompt += "\nHistorie:\n" + history + "\n"
+    if sources:
+        prompt = "\nKontext:\n" + sources + "\n" + prompt
+    prompt += "\nGebe einen hilfreichen Tipp zur Lösung der Aufgabe. Halte dich kurz und prägnant."
     result = llm_client.chat(
-        messages=[{"role": "user", "content": prompt}],
+        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}],
     )
     return llm_client.get_message_content(result)
\ No newline at end of file
-
-
-TOOL_SPEC = {
-    "type": "function",
-    "function": {
-        "name": "generate_hint",
-        "description": (
-            "Gibt einen didaktisch wertvollen naechsten Hinweis "
-            "auf Basis der Aufgabe und der berechneten Loesung."
-        ),
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "task": {"type": "string", "description": "Die gegebene Aufgabe"},
-                "solution": {
-                    "type": "string",
-                    "description": "Loesung aus dem Mathe-Tool",
-                },
-                "history": {
-                    "type": "string",
-                    "description": "Optionaler Verlauf, kann leer sein",
-                },
-                "retrival": {
-                    "type": "string",
-                    "description": "Optionales Kontextblatt mit Werkzeug- und Retrieval-Infos",
-                },
-            },
-            "required": ["task", "solution"],
-        },
-    },
-}

math-tutor/backend/app/LLM_services/solver_LLM.py

+from app.deterministic_services import llm_client
+
+
+SOLVER_PROMPT = (
+    "Du bist ein Mathe-Aufgaben Löser. Schreibe ein ausführliche genaue Lösung für die gestellt Fragen. "
+    "In der du alle Zwischenschritte aufführst und möglich Fehlerquellen auflistet."
+    "Deine Lösung soll korrekt sein und didaktisch gut formuliert."
+    "Benutzt die Begriffsbezeichungen aus dem Kontext"
+)
+
+
+def solve_question(question: str, context_sheet: str) -> str:
+    prompt = (
+        "Frage:\n"
+        + question
+        + "\nKontex:\n"
+        + context_sheet
+    )
+    result = llm_client.chat(
+        messages=[
+            {"role": "system", "content": SOLVER_PROMPT},
+            {"role": "user", "content": prompt},
+        ],
+    )
+    solution = llm_client.get_message_content(result)
+    return solution
\ No newline at end of file

math-tutor/backend/app/api/context.py

+from __future__ import annotations
+
+from typing import List, Optional
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, Field
+
+from app.deterministic_services import context_store
+from app.deterministic_services import Source
+
+
+router = APIRouter()
+
+
+class ChatMessage(BaseModel):
+    role: str = Field(..., pattern="^(user|assistant)$")
+    text: str = Field(..., min_length=1)
+
+
+class ContextRequest(BaseModel):
+    messages: List[ChatMessage]
+    draft: Optional[str] = None
+
+@router.post("/api/context/retrieval")
+def get_retrieval_context(request: ContextRequest) -> List[dict]:
+    if not request or not request.messages:
+        raise HTTPException(status_code=400, detail="messages required")
+
+    messages = [{"role": m.role, "content": m.text} for m in request.messages]
+    chat_id = context_store.get_chat_id(messages, draft=request.draft)
+    sheet = context_store.load_sheet(chat_id)
+    if not sheet:
+        raise HTTPException(status_code=404, detail="context sheet not found")
+
+    sources = context_store.get_retrieval(sheet)
+    if not sources:
+        return []
+    return [source.model_dump() for source in sources]

math-tutor/backend/app/api/retrieval.py

@@ -41,7 +41,7 @@ class QueryRequest(BaseModel):
     expand_links: bool = True
     section_index: Optional[int] = None
     subsection_index: Optional[int] = None
-    type_filter: Optional[List[str]] = None
+    source_type_filter: Optional[List[str]] = None
     neighbor_expand: int = 0
 
 
@@ -83,23 +83,11 @@ def query(request: QueryRequest) -> dict:
         k=request.k,
         section_index=request.section_index,
         subsection_index=request.subsection_index,
-        type_filter=request.type_filter,
+        source_type_filter=request.source_type_filter,
         expand_links=request.expand_links,
         neighbor_expand=request.neighbor_expand,
     )
-
-    def pack(items: list[vector_store.Retrieved]) -> list[dict]:
-        return [item.to_dict() for item in items]
-
-    return {
-        "query": result["query"],
-        "children": pack(result["children"]),
-        "children_direct": pack(result["children_direct"]),
-        "children_expanded": pack(result["children_expanded"]),
-        "subsections": pack(result["subsections"]),
-        "sections": pack(result["sections"]),
-        "neighbors": pack(result["neighbors"]),
-    }
+    return result
 
 
 @router.get("/api/retrieval/sections")

math-tutor/backend/app/config.py

@@ -12,6 +12,7 @@ class OllamaSettings:
     model: str
     timeout: float | None
     keepalive: str | None
+    temperature: float | None
 
 
 @dataclass(frozen=True)
@@ -27,6 +28,7 @@ class OpenAIChatSettings:
     api_key: str
     model: str
     timeout: float | None
+    temperature: float | None
 
 @dataclass(frozen=True)
 class MathpixSettings:
@@ -48,12 +50,19 @@ def get_mathpix_settings() -> MathpixSettings:
         app_key=os.getenv("MATHPIX_APP_KEY") 
     )
 
+def get_frontend_url() -> str:
+    url = os.getenv("FRONTEND_URL")
+    if not url:
+        raise ValueError("Missing FRONTEND_URL")
+    return url
+
 def get_ollama_settings() -> OllamaSettings:
     return OllamaSettings(
         base_url=os.getenv("OLLAMA_URL", "http://localhost:11434"),
         model=os.getenv("OLLAMA_MODEL", "qwen3"),
         timeout=_read_float(os.getenv("OLLAMA_TIMEOUT")),
         keepalive=os.getenv("OLLAMA_KEEPALIVE"),
+        temperature=_read_float(os.getenv("OLLAMA_TEMPERATURE")),
     )
 
 
@@ -83,6 +92,7 @@ def get_openai_chat_settings() -> OpenAIChatSettings | None:
         api_key=api_key,
         model=model,
         timeout=_read_float(os.getenv("OPENAI_CHAT_TIMEOUT")),
+        temperature=_read_float(os.getenv("OPENAI_CHAT_TEMPERATURE")),
     )
 
 

math-tutor/backend/app/deterministic_services/__init__.py

 # Package marker for services
+from app.deterministic_services.vector_store import Source, SourceID
+__all__ = ["Source", "SourceID"]
\ No newline at end of file

math-tutor/backend/app/deterministic_services/context_store.py

@@ -7,6 +7,8 @@ from datetime import datetime
 from threading import Lock
 from typing import Any
 
+from app.deterministic_services import Source
+
 
 _CACHE: dict[str, dict[str, Any]] = {}
 _LOCK = Lock()
@@ -17,10 +19,6 @@ def _utc_now() -> str:
     return datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ")
 
 
-def is_new_chat(messages: list[dict]) -> bool:
-    return not any(m.get("role") == "assistant" for m in messages)
-
-
 def get_chat_id(messages: list[dict], draft: str | None = None) -> str:
     if draft:
         return f"draft_{draft}"
@@ -49,9 +47,8 @@ def init_sheet(chat_id: str, messages: list[dict]) -> dict[str, Any]:
         "history": messages[:],
         "retrieval_contexts": [],
         "math_solutions": [],
-        "tool_outputs": [],
+        "LLM_solutions":[],
         "decisions": [],
-        "sources": [],
         "initialized": False,
     }
 
@@ -79,13 +76,23 @@ def update_history(sheet: dict[str, Any], messages: list[dict]) -> None:
 def add_retrieval_context(
     sheet: dict[str, Any],
     query: str,
-    context: str,
-    sources: list[str],
+    sources: list[Source],
 ) -> None:
-    sheet["retrieval_contexts"].append(
-        {"query": query, "context": context, "sources": sources}
-    )
-    sheet["sources"] = list(dict.fromkeys(sheet["sources"] + sources))
+    retrieval_entry = {"query": query, "sources": [source.model_dump() for source in sources]}
+    sheet["retrieval_contexts"].append(retrieval_entry)
+    sheet["updated_at"] = _utc_now()
+
+
+def update_retrieval_context(
+    sheet: dict[str, Any],
+    query: str,
+    sources: list[Source],
+) -> None:
+    retrievals = sheet.get("retrieval_contexts", [])
+    if retrievals:
+        latest_retrieval = retrievals[-1]
+        latest_retrieval["sources"] = [source.model_dump() for source in sources]
+        latest_retrieval["query"] = query
         sheet["updated_at"] = _utc_now()
 
 
@@ -106,6 +113,17 @@ def add_math_solution(
     )
     sheet["updated_at"] = _utc_now()
 
+def add_LLM_solution(
+    sheet: dict[str, Any],
+    solution: str,
+) -> None:
+    sheet["LLM_solutions"].append(
+        {
+            "solution": solution,
+        }
+    )
+    sheet["updated_at"] = _utc_now()
+
 
 def add_decision(sheet: dict[str, Any], decision: dict[str, Any]) -> None:
     entry = {"timestamp": _utc_now(), **decision}
@@ -119,6 +137,19 @@ def latest_math_solution(sheet: dict[str, Any]) -> str:
         return ""
     return sheet["math_solutions"][-1].get("solution", "")
 
+def last_LLM_solution(sheet: dict[str, Any]) -> str:
+    if not sheet["LLM_solutions"]:
+        return ""
+    return sheet["LLM_solutions"][-1].get("solution", "")
+
+def get_task(sheet: dict[str, Any]) -> str:
+    history = sheet.get("history", [])
+    if history:
+        return history[0].get("content", "")
+    return ""
+
+def get_history(sheet: dict[str, Any]) -> str:
+    return format_history(sheet.get("history", []))
 
 def first_math_solution(sheet: dict[str, Any]) -> str:
     if not sheet["math_solutions"]:
@@ -135,7 +166,16 @@ def format_sheet(sheet: dict[str, Any]) -> str:
     if retrievals:
         blocks = []
         for item in retrievals:
-            blocks.append(f"QUERY: {item.get('query', '')}\n{item.get('context', '')}")
+            query = item.get('query', '')
+            sources = item.get('sources', [])
+            
+            # Erstelle eine formatierte Liste der Quellen mit ihren Scores
+            source_blocks = []
+            for source in sources:
+                source_blocks.append(Source.model_validate(source).to_string())
+            
+            source_info = "\n".join(source_blocks) if source_blocks else ""
+            blocks.append(f"QUERY: {query}\nSOURCES:\n{source_info}")
         parts.append("RETRIEVAL_CONTEXT:\n" + "\n\n".join(blocks))
     else:
         parts.append("RETRIEVAL_CONTEXT:\n(leer)")
@@ -156,19 +196,16 @@ def format_sheet(sheet: dict[str, Any]) -> str:
     else:
         parts.append("MATH_SOLUTIONS:\n(leer)")
 
-    if sheet.get("tool_outputs"):
-        parts.append("TOOL_OUTPUTS:\n" + json.dumps(sheet["tool_outputs"], ensure_ascii=True))
-    else:
-        parts.append("TOOL_OUTPUTS:\n(leer)")
 
     return "\n\n".join(parts)
 
-def get_retrival(sheet: dict[str, Any]) -> str:
+def get_retrieval(sheet: dict[str, Any]) -> list[Source]:
     retrievals = sheet.get("retrieval_contexts", [])
     if not retrievals:
-        return ""
-    latest = retrievals[-1]
-    return latest.get("context", "")
+        return []
+    latest_retrieval = retrievals[-1]
+    sources= latest_retrieval.get("sources", [])
+    return [Source.model_validate(source) for source in sources]
 
 def save_sheet(sheet: dict[str, Any]) -> None:
     os.makedirs(_LOG_DIR, exist_ok=True)

math-tutor/backend/app/deterministic_services/llm_client.py

@@ -21,10 +21,10 @@ def _chat_openai(messages: list[dict]) -> dict:
 
     timeout = settings.timeout or 60.0
     client = OpenAI(api_key=settings.api_key, base_url=settings.base_url, timeout=timeout)
-    response = client.chat.completions.create(
-        messages=messages,
-        model=settings.model,
-    )
+    kwargs = {"messages": messages, "model": settings.model}
+    if settings.temperature is not None:
+        kwargs["temperature"] = settings.temperature
+    response = client.chat.completions.create(**kwargs)
     message = response.choices[0].message if response.choices else {}
     return {"raw": response, "message": message}
 
@@ -47,6 +47,8 @@ def chat(
         kwargs["tools"] = tools
     if settings.keepalive:
         kwargs["keep_alive"] = settings.keepalive
+    if settings.temperature is not None:
+        kwargs["options"] = {"temperature": settings.temperature}
 
     response = client.chat(**_filter_kwargs(client.chat, kwargs))
     return {"raw": response, "message": _extract_message(response)}

math-tutor/backend/app/deterministic_services/orchestrator.py

 from app.deterministic_services import context_store, retrieval_service, tool_logging
 from app.tools import math_tool
-from app.LLM_services import hint_LLM, decision_LLM, math_intent_LLM
+from app.LLM_services import hint_LLM, decision_LLM, math_intent_LLM, solver_LLM
+from typing import List
 
 
+def _is_new_chat(messages: list[dict]) -> bool:
+    return not any(m.get("role") == "assistant" for m in messages)
+
 def _append_tool_log(tool_log: list[dict], name: str, args: dict, response: object) -> None:
     tool_log.append({"name": name, "arguments": args, "response": response})
 
 
 def _bootstrap_context(sheet: dict, query_text: str, tool_log: list[dict]) -> None:
-    context, sources = retrieval_service.retrieve_context(query_text=query_text)
-    context_store.add_retrieval_context(sheet, query_text, context, sources)
-    _append_tool_log(tool_log, "retrieve_context", {"query": query_text}, {"context": context, "sources": sources})
+    # Erstelle einen neuen Retrieval-Block oder aktualisiere den bestehenden
+    sources = retrieval_service.retrieve_context(query_text=query_text)
+    
+    retrievals = sheet.get("retrieval_contexts", [])
+    if retrievals:
+        context_store.update_retrieval_context(sheet, query_text, sources)
+        _append_tool_log(tool_log, "update_retrieve_context", {"query": query_text}, {"sources": [source.to_string() for source in sources]})
+    else:
+        context_store.add_retrieval_context(sheet, query_text, sources)
+        _append_tool_log(tool_log, "retrieve_context", {"query": query_text}, {"sources": [source.to_string() for source in sources]})
     
     math_request = math_intent_LLM.extract_math_request(query_text)
     if math_request:
@@ -24,19 +35,32 @@ def _bootstrap_context(sheet: dict, query_text: str, tool_log: list[dict]) -> No
         )
         _append_tool_log(tool_log, "sympy_solve", math_request, solution)
 
+def _extract_user_messages(messages: list[dict]) -> list[str]:
+    """
+    Extrahiert alle Inhalte von Messages mit der Rolle 'user' und gibt sie als Liste von Strings zurück.
+    Der letzte user-Content wird als letztes Element in der Liste enthalten sein.
+    """
+    user_contents = []
+    for message in messages:
+        if message.get("role") == "user":
+            content = message.get("content", "")
+            if content:  # Nur hinzufügen, wenn Inhalt vorhanden ist
+                user_contents.append(content)
+    return user_contents
+
 
 def run_chat(messages: list[dict], draft: str | None = None) -> dict:
     # Input-Fehlerbehandlung
     if not messages:
         raise ValueError("messages required")
 
-    last_user = next((m for m in reversed(messages) if m.get("role") == "user"), None)
+    last_user = _extract_user_messages(messages)[-1] if _extract_user_messages(messages) else None
     if not last_user:
         raise ValueError("last user message required")
 
     # Context Store Mangement
     chat_id = context_store.get_chat_id(messages, draft=draft)
-    new_chat = context_store.is_new_chat(messages)
+    new_chat = _is_new_chat(messages)
     sheet = context_store.load_sheet(chat_id)
     if new_chat or not sheet:
         sheet = context_store.init_sheet(chat_id, messages)
@@ -45,24 +69,32 @@ def run_chat(messages: list[dict], draft: str | None = None) -> dict:
     tool_log: list[dict] = []
 
     if new_chat or not sheet.get("initialized"):
-        _bootstrap_context(sheet, last_user.get("content", ""), tool_log)
+        _bootstrap_context(sheet, last_user, tool_log)
         sheet["initialized"] = True
 
     history_text = context_store.format_history(messages)
     sheet_text = context_store.format_sheet(sheet)
+    if new_chat:
+        llm_solution = solver_LLM.solve_question(last_user, sheet_text)
+        _append_tool_log(tool_log, "LLM_Solution", {"question": last_user, "sheet": sheet_text}, llm_solution)
+        context_store.add_LLM_solution(sheet, llm_solution)
+    else:
         decision = decision_LLM.needs_more_context(history_text, sheet_text)
+        _append_tool_log(tool_log, "decision", {"sheet": sheet_text}, decision)
         context_store.add_decision(sheet, decision)
 
         if decision.get("needs_more_context"):
-        _bootstrap_context(sheet, last_user.get("content", ""), tool_log)
-        sheet_text = context_store.format_sheet(sheet)
+            full_query = ("\n").join(_extract_user_messages(messages))
+            _bootstrap_context(sheet, full_query, tool_log)
 
     # Hinweis und Ausgaben generierung
+
     hint_args = {
-        "task": last_user.get("content", ""),
-        "solution": context_store.first_math_solution(sheet),
-        "history": history_text,
-        "retrival": context_store.get_retrival(sheet),
+        "task": context_store.get_task(sheet),
+        "LLM_solution": context_store.last_LLM_solution(sheet),
+        "math_solution": context_store.first_math_solution(sheet),
+        "history": context_store.format_history(messages),
+        "sources": "\n".join([source.to_string() for source in context_store.get_retrieval(sheet)]),
     }
     reply = hint_LLM.generate_hint(**hint_args)
     _append_tool_log(tool_log, "generate_hint", hint_args, reply)

math-tutor/backend/app/deterministic_services/retrieval_service.py

 from __future__ import annotations
 
-from typing import List
+from collections import defaultdict
+from typing import Dict, List
 
+from app.deterministic_services import Source
 from app import config
 from app.deterministic_services.embeddings import OpenAILikeEmbeddings
 from app.deterministic_services import vector_store
 
-CONTEXT_LIMITS = {
-    "direct": 4,
-    "indirect": 6,
-    "subsection": 2,
-    "section": 1,
-}
-
-
 def _get_embedder() -> OpenAILikeEmbeddings:
     settings = config.get_embedding_settings()
     return OpenAILikeEmbeddings(
@@ -24,68 +18,14 @@ def _get_embedder() -> OpenAILikeEmbeddings:
     )
 
 
-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 _append_group(
-    label: str,
-    items: List[vector_store.Retrieved],
-    limit: int,
-    blocks: List[str],
-    sources: List[str],
-) -> None:
-    if not items:
-        return
-    for doc in items[:limit]:
-        ref = _format_ref(doc)
-        blocks.append(f"{label} {ref}\n{doc.markdown}")
-        sources.append(ref)
-
-
-def build_context(result: dict) -> tuple[str, List[str]]:
-    blocks: List[str] = []
-    sources: List[str] = []
-
-    _append_group("DIRECT", result.get("children_direct", []), CONTEXT_LIMITS["direct"], blocks, sources)
-    _append_group("INDIRECT", result.get("children_expanded", []), CONTEXT_LIMITS["indirect"], blocks, sources)
-    _append_group("SUBSECTION", result.get("subsections", []), CONTEXT_LIMITS["subsection"], blocks, sources)
-    _append_group("SECTION", result.get("sections", []), CONTEXT_LIMITS["section"], blocks, sources)
-
-    if not blocks:
-        return "KONTEXT: (leer)", sources
-    return "KONTEXT:\n" + "\n\n".join(blocks), sources
-
-
-def retrieve_context(query_text: str, pg_url: str | None = None) -> tuple[str, List[str]]:
+def retrieve_context(query_text: str, pg_url: str | None = None) -> List[Source]:
     url = pg_url or config.get_postgres_url()
     embedder = _get_embedder()
-    retrieval = vector_store.retrieve(
+    sources = vector_store.retrieve(
         pg_url=url,
         embedder=embedder,
         query=query_text,
         k=8,
         expand_links=True,
     )
-    return build_context(retrieval)
+    return sources

math-tutor/backend/app/deterministic_services/tool_logging.py

@@ -29,8 +29,17 @@ def write_tool_log(entries: list[dict], created_at: str | None = None, chat_id:
     path = os.path.join("logs", f"tool_calls_{suffix}_{filename_ts}.json")
     payload = {
         "timestamp": timestamp,
-        "entries": entries,
+        "entries": list(entries),
     }
+    if os.path.exists(path):
+        try:
+            with open(path, "r", encoding="utf-8") as f:
+                existing = json.load(f)
+            if isinstance(existing, dict) and isinstance(existing.get("entries"), list):
+                payload["entries"] = existing["entries"] + payload["entries"]
+                payload["timestamp"] = existing.get("timestamp", payload["timestamp"])
+        except (OSError, json.JSONDecodeError):
+            pass
     with open(path, "w", encoding="utf-8") as f:
         json.dump(payload, f, ensure_ascii=True, indent=2)
     return path

math-tutor/backend/app/deterministic_services/vector_store.py

@@ -10,6 +10,7 @@ from psycopg.rows import dict_row
 from pgvector import Vector
 from pgvector.psycopg import register_vector
 import yaml
+from pydantic import BaseModel
 
 
 @dataclass
@@ -129,8 +130,7 @@ CREATE TABLE IF NOT EXISTS docs (
   section_title TEXT NULL,
   subsection_title TEXT NULL,
   title TEXT NULL,
-  type TEXT NULL,
-  box_hint TEXT NULL,
+  source_type TEXT NULL,
 
   path TEXT NOT NULL,
   markdown TEXT NOT NULL,
@@ -141,7 +141,7 @@ CREATE TABLE IF NOT EXISTS docs (
 CREATE INDEX IF NOT EXISTS docs_embedding_cos_idx
   ON docs USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
 
-CREATE INDEX IF NOT EXISTS docs_type_idx ON docs(type);
+CREATE INDEX IF NOT EXISTS docs_source_type_idx ON docs(source_type);
 CREATE INDEX IF NOT EXISTS docs_doc_type_idx ON docs(doc_type);
 CREATE INDEX IF NOT EXISTS docs_section_idx ON docs(section_index);
 CREATE INDEX IF NOT EXISTS docs_subsection_idx ON docs(section_index, subsection_index);
@@ -151,12 +151,12 @@ UPSERT_SQL = """
 INSERT INTO docs (
   uid, doc_type,
   section_index, subsection_index, child_index,
-  section_title, subsection_title, title, type, box_hint,
+  section_title, subsection_title, title, source_type,
   path, markdown, embedding
 ) VALUES (
   %(uid)s, %(doc_type)s,
   %(section_index)s, %(subsection_index)s, %(child_index)s,
-  %(section_title)s, %(subsection_title)s, %(title)s, %(type)s, %(box_hint)s,
+  %(section_title)s, %(subsection_title)s, %(title)s, %(source_type)s,
   %(path)s, %(markdown)s, %(embedding)s
 )
 ON CONFLICT (uid) DO UPDATE SET
@@ -167,8 +167,7 @@ ON CONFLICT (uid) DO UPDATE SET
   section_title = EXCLUDED.section_title,
   subsection_title = EXCLUDED.subsection_title,
   title = EXCLUDED.title,
-  type = EXCLUDED.type,
-  box_hint = EXCLUDED.box_hint,
+  source_type = EXCLUDED.source_type,
   path = EXCLUDED.path,
   markdown = EXCLUDED.markdown,
   embedding = EXCLUDED.embedding
@@ -211,8 +210,7 @@ def upsert_docs(pg_url: str, docs: List[DocRecord], embeddings: List[List[float]
                 "section_title": m.get("section_title"),
                 "subsection_title": m.get("subsection_title"),
                 "title": m.get("title"),
-                "type": m.get("type"),
-                "box_hint": m.get("box_hint"),
+                "source_type": m.get("source_type"),
                 "path": doc.path,
                 "markdown": doc.markdown,
                 "embedding": Vector(emb),
@@ -227,6 +225,13 @@ def upsert_docs(pg_url: str, docs: List[DocRecord], embeddings: List[List[float]
     return len(rows)
 
 
+def clear_docs(pg_url: str) -> None:
+    with psycopg.connect(pg_url) as conn:
+        with conn.cursor() as cur:
+            cur.execute("TRUNCATE TABLE docs;")
+        conn.commit()
+
+
 @dataclass
 class Retrieved:
     uid: str
@@ -244,6 +249,57 @@ class Retrieved:
             "markdown": self.markdown,
         }
 
+class Source(BaseModel):
+    source_id: SourceID
+    retrieved_as: str
+    source_type: str
+    score: float
+    markdown: str
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "source_id": self.source_id.to_dict(),
+            "retrieved_as": self.retrieved_as,
+            "source_type": self.source_type,
+            "score": self.score,
+            "markdown": self.markdown,
+        }
+
+    def to_string(self) -> str:
+        return (
+            f"Source(source_id={self.source_id.to_string()},\n"
+            f" retrieved_as={self.retrieved_as},\n"
+            f" source_type={self.source_type},\n"
+            f" score={self.score},\n"
+            f" markdown_length={len(self.markdown)})"
+        )
+
+class SourceID(BaseModel):
+    chapter_title: Optional[str] = None
+    section_title: Optional[str] = None
+    subsection_title: Optional[str] = None
+    title: str
+    doc_type: str
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "chapter_title": self.chapter_title,
+            "section_title": self.section_title,
+            "subsection_title": self.subsection_title,
+            "title": self.title,
+            "doc_type": self.doc_type,
+        }
+
+    def to_string(self) -> str:
+        string_rep = self.title
+        if self.subsection_title:
+            string_rep = f"{self.subsection_title}|{string_rep}"
+        if self.section_title:
+            string_rep = f"{self.section_title}|{string_rep}"
+        if self.chapter_title:
+            string_rep = f"{self.chapter_title}|{string_rep}"
+        return f"[{string_rep}|{self.doc_type}]"
+
 
 def _row_to_retrieved(row: Dict[str, Any]) -> Retrieved:
     meta = {
@@ -255,8 +311,7 @@ def _row_to_retrieved(row: Dict[str, Any]) -> Retrieved:
         "section_title": row["section_title"],
         "subsection_title": row["subsection_title"],
         "title": row["title"],
-        "type": row["type"],
-        "box_hint": row["box_hint"],
+        "source_type": row["source_type"],
         "path": row["path"],
     }
     return Retrieved(
@@ -268,6 +323,26 @@ def _row_to_retrieved(row: Dict[str, Any]) -> Retrieved:
     )
 
 
+def _retrivla_to_sources(retrievd : Dict[str, List[Retrieved]]) -> List[Source]:
+    sources = []
+    for name, retrieved_grouep in retrievd.items():
+        for retrival in retrieved_grouep:
+            sources.append(Source(
+                source_id=SourceID(
+                    chapter_title = "none",
+                    section_title=retrival.metadata.get("section_title"),
+                    subsection_title=retrival.metadata.get("subsection_title"),
+                    title=retrival.metadata.get("title"),
+                    doc_type=retrival.doc_type
+                ),
+                retrieved_as=name,
+                source_type=retrival.metadata.get("source_type") or "unknown",
+                score=retrival.score,
+                markdown=retrival.markdown
+        ))
+    return sources
+
+
 def retrieve(
     pg_url: str,
     embedder: EmbeddingLike,
@@ -275,10 +350,10 @@ def retrieve(
     k: int = 4,
     section_index: Optional[int] = None,
     subsection_index: Optional[int] = None,
-    type_filter: Optional[List[str]] = None,
+    source_type_filter: Optional[List[str]] = None,
     expand_links: bool = True,
     neighbor_expand: int = 0,
-) -> Dict[str, Any]:
+) -> List[Source]:
     qvec = Vector(embed_query(embedder, query))
 
     where = ["doc_type = 'child'"]
@@ -292,9 +367,9 @@ def retrieve(
         where.append("subsection_index = %(subsection_index)s")
         params["subsection_index"] = subsection_index
 
-    if type_filter:
-        where.append("type = ANY(%(type_filter)s)")
-        params["type_filter"] = type_filter
+    if source_type_filter:
+        where.append("source_type = ANY(%(source_type_filter)s)")
+        params["source_type_filter"] = source_type_filter
 
     where_sql = " AND ".join(where)
 
@@ -302,7 +377,7 @@ def retrieve(
     SELECT
       uid, doc_type,
       section_index, subsection_index, child_index,
-      section_title, subsection_title, title, type, box_hint,
+      section_title, subsection_title, title, source_type,
       path, markdown,
       1 - (embedding <=> %(qvec)s) AS score
     FROM docs
@@ -344,7 +419,7 @@ def retrieve(
                         SELECT
                           d.uid, d.doc_type,
                           d.section_index, d.subsection_index, d.child_index,
-                          d.section_title, d.subsection_title, d.title, d.type, d.box_hint,
+                          d.section_title, d.subsection_title, d.title, d.source_type,
                           d.path, d.markdown,
                           1.0 AS score
                         FROM docs d
@@ -361,7 +436,7 @@ def retrieve(
                         SELECT
                           uid, doc_type,
                           section_index, subsection_index, child_index,
-                          section_title, subsection_title, title, type, box_hint,
+                          section_title, subsection_title, title, source_type,
                           path, markdown,
                           1.0 AS score
                         FROM docs
@@ -377,7 +452,7 @@ def retrieve(
                         SELECT
                           d.uid, d.doc_type,
                           d.section_index, d.subsection_index, d.child_index,
-                          d.section_title, d.subsection_title, d.title, d.type, d.box_hint,
+                          d.section_title, d.subsection_title, d.title, d.source_type,
                           d.path, d.markdown,
                           1 - (d.embedding <=> %(qvec)s) AS score
                         FROM docs d
@@ -420,9 +495,9 @@ def retrieve(
                                 SELECT
                                   d.uid, d.doc_type,
                                   d.section_index, d.subsection_index, d.child_index,
-                                  d.section_title, d.subsection_title, d.title, d.type, d.box_hint,
+                                  d.section_title, d.subsection_title, d.title, d.source_type,
                                   d.path, d.markdown,
-                                  0.9 AS score
+                                  0 AS score
                                 FROM docs d
                                 JOIN unnest(%(sec_arr)s::int[], %(sub_arr)s::int[], %(child_arr)s::int[]) AS u(sec, sub, child)
                                 ON d.section_index = u.sec AND d.subsection_index = u.sub AND d.child_index = u.child
@@ -434,16 +509,15 @@ def retrieve(
 
     child_uids = {child.uid for child in children}
     neighbors = [neighbor for neighbor in neighbors if neighbor.uid not in child_uids]
-
-    return {
-        "children": children,
+    retrivla_dict = {
         "children_direct": children_direct,
         "children_expanded": children_expanded,
         "subsections": subsections,
         "sections": sections_docs,
         "neighbors": neighbors,
-        "query": query,
     }
+    sources = _retrivla_to_sources(retrivla_dict)
+    return sources
 
 
 def list_sections(pg_url: str) -> List[Dict[str, Any]]:

math-tutor/backend/app/main.py

@@ -2,7 +2,9 @@ from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from app.api import canvas, chat, health, retrieval
+from app.api import canvas, chat, health, retrieval, context
+from app.config import get_frontend_url
+
 
 @asynccontextmanager
 async def lifespan(_app: FastAPI):
@@ -14,7 +16,7 @@ app = FastAPI(title="Math Tutor API", version="0.1.0", lifespan=lifespan)
 
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["http://localhost:5173"],
+    allow_origins=["http://localhost:5173", get_frontend_url()],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -23,4 +25,5 @@ app.add_middleware(
 app.include_router(chat.router)
 app.include_router(canvas.router)
 app.include_router(retrieval.router)
+app.include_router(context.router)
 app.include_router(health.router)

math-tutor/backend/scripts/retrieval_cli.py

@@ -43,6 +43,8 @@ def cli_ingest(args: argparse.Namespace) -> None:
     pg_url = args.pg or os.getenv("POSTGRES_URL")
     if not pg_url:
         raise ValueError("Missing POSTGRES_URL")
+    if args.clear:
+        vector_store.clear_docs(pg_url)
     upserted = vector_store.upsert_docs(pg_url, docs, embeddings)
     print(f"Ingested/upserted docs: {upserted}")
 
@@ -92,6 +94,7 @@ def main() -> int:
     ap_ing = sub.add_parser("ingest", help="Embed and upsert markdown docs")
     ap_ing.add_argument("--pg", default=None, help="Postgres URL (or set POSTGRES_URL)")
     ap_ing.add_argument("--base", default="markdown", help="Base folder with sections/subsections/childs")
+    ap_ing.add_argument("--clear", action="store_true", help="Clear docs table before ingest")
     ap_ing.set_defaults(func=cli_ingest)
 
     ap_q = sub.add_parser("query", help="Run retrieval")

math-tutor/backend/test/hint_test.py

+import argparse
+import json
+import os
+from typing import Any
+
+from app.LLM_services import hint_LLM
+from app.deterministic_services import context_store
+
+
+def _load_sheet_from_path(path: str) -> dict[str, Any]:
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _resolve_sheet(args: argparse.Namespace) -> dict[str, Any]:
+    if args.sheet:
+        return _load_sheet_from_path(args.sheet)
+    if args.chat_id:
+        sheet = context_store.load_sheet(args.chat_id)
+        if sheet is None:
+            raise FileNotFoundError(f"Kein Context-Sheet gefunden fuer chat_id={args.chat_id}")
+        return sheet
+    raise ValueError("Bitte --sheet oder --chat-id angeben.")
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Isolierter Hint-LLM Test mit Context Sheet.")
+    parser.add_argument("--sheet", help="Pfad zu einem Context Sheet JSON.")
+    parser.add_argument("--chat-id", help="Chat-ID zum Laden aus logs/context_sheets.")
+
+    args = parser.parse_args()
+
+    sheet = _resolve_sheet(args)
+
+    hint_args = {
+        "task": context_store.get_task(sheet),
+        "LLM_solution": context_store.last_LLM_solution(sheet),
+        "math_solution": context_store.first_math_solution(sheet),
+        "history": context_store.get_history(sheet),
+        "retrival": context_store.get_retrieval(sheet),
+    }
+    reply = hint_LLM.generate_hint(**hint_args)
+
+    print(reply)
+
+
+if __name__ == "__main__":
+    main()

math-tutor/backend/test/vector_store_test.py

+import argparse
+
+from app import config
+from app.deterministic_services.embeddings import OpenAILikeEmbeddings
+from app.deterministic_services import vector_store
+
+
+def _get_embedder() -> OpenAILikeEmbeddings:
+    settings = config.get_embedding_settings()
+    return OpenAILikeEmbeddings(
+        base_url=settings.base_url,
+        api_key=settings.api_key,
+        model=settings.model,
+        target_dim=settings.target_dim,
+    )
+
+
+def _normalize_sources(result: object) -> list[vector_store.Source]:
+    if isinstance(result, list):
+        return result
+    if isinstance(result, dict):
+        groups = {k: v for k, v in result.items() if isinstance(v, list)}
+        return vector_store._retrivla_to_sources(groups)
+    raise TypeError("Unexpected retrieval result type")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Isolierter Vector-Store Retrieval Test.")
+    parser.add_argument("--query", required=True, help="Query text")
+    parser.add_argument("--pg", default=None, help="Postgres URL (oder set POSTGRES_URL)")
+    parser.add_argument("--k", type=int, default=8)
+    parser.add_argument("--expand", action="store_true")
+    parser.add_argument("--section-index", type=int, default=None)
+    parser.add_argument("--subsection-index", type=int, default=None)
+    parser.add_argument("--source-type-filter", nargs="*", default=None)
+    parser.add_argument("--neighbor-expand", type=int, default=0)
+
+    args = parser.parse_args()
+
+    pg_url = args.pg or config.get_postgres_url()
+    embedder = _get_embedder()
+
+    result = vector_store.retrieve(
+        pg_url=pg_url,
+        embedder=embedder,
+        query=args.query,
+        k=args.k,
+        expand_links=args.expand,
+        section_index=args.section_index,
+        subsection_index=args.subsection_index,
+        source_type_filter=args.source_type_filter,
+        neighbor_expand=args.neighbor_expand,
+    )
+
+    sources = _normalize_sources(result)
+    if not sources:
+        print("Keine Quellen gefunden.")
+        return
+
+    print(f"Gefundene Quellen: {len(sources)}")
+    for source in sources:
+        print(source.to_string())
+
+
+if __name__ == "__main__":
+    main()