Trainer_LLM/llm-api/archiv/llm_api_bk2.py

from fastapi import FastAPI, Query, HTTPException
from pydantic import BaseModel
from typing import List
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
from fastapi import HTTPException
from uuid import uuid4  
import requests
from datetime import datetime

from qdrant_client.models import PointIdsList


app = FastAPI()

# Konfiguration
model = SentenceTransformer("all-MiniLM-L6-v2")
qdrant = QdrantClient(host="localhost", port=6333)
OLLAMA_URL = "http://localhost:11434/api/generate"
OLLAMA_MODEL = "mistral"

# Datenmodelle

from typing import List, Dict, Any

class ChunkInput(BaseModel):
    text: str
    source: str
    source_type: str = "file"
    title: str | None = None
    version: str | None = None
    related_to: str | None = None
    tags: List[str] = []
    owner: str | None = None
    context_tag: str | None = None
    imported_at: str | None = None
    chunk_index: int | None = None
    category: str | None = None

class EmbedRequest(BaseModel):
    chunks: List[ChunkInput]
    collection: str = "default"

class PromptRequest(BaseModel):
    query: str
    context_limit: int = 3
    collection: str = "default"





@app.delete("/delete-source")
def delete_by_source(
    collection: str = Query(...),
    source: str = Query(...),
    type: str = Query(None)
):
    if not qdrant.collection_exists(collection):
        raise HTTPException(status_code=404, detail=f"Collection '{collection}' nicht gefunden.")

    must = [{"key": "source", "match": {"value": source}}]
    if type:
        must.append({"key": "type", "match": {"value": type}})

    result = qdrant.scroll(
        collection_name=collection,
        scroll_filter={"must": must},
        limit=10000
    )

    points = result[0]
    if not points:
        return {"status": "🔍 Keine passenden Einträge gefunden."}

    point_ids = []
    for point in points:
        pid = point.id
        point_ids.append(str(pid))  # immer zu String casten

    qdrant.delete(
        collection_name=collection,
        points_selector=PointIdsList(points=point_ids)

    )

    return {
        "status": "🗑️ gelöscht",
        "count": len(point_ids),
        "collection": collection,
        "source": source,
        "type": type
    }



@app.delete("/delete-collection")
def delete_collection(collection: str = Query(...)):
    """
    Löscht eine gesamte Collection aus Qdrant.
    """
    if not qdrant.collection_exists(collection):
        raise HTTPException(status_code=404, detail=f"Collection '{collection}' nicht gefunden.")

    qdrant.delete_collection(collection_name=collection)
    return {"status": "🗑️ gelöscht", "collection": collection}


@app.post("/embed")
def embed_texts(data: EmbedRequest):
    collection_name = data.collection

    if not qdrant.collection_exists(collection_name):
        qdrant.recreate_collection(
            collection_name=collection_name,
            vectors_config=VectorParams(size=384, distance=Distance.COSINE)
        )

    embeddings = model.encode([chunk.text for chunk in data.chunks]).tolist()

    points = []
    for i, chunk in enumerate(data.chunks):
        payload = {
            "text": chunk.text,
            "source": chunk.source,
            "source_type": chunk.source_type,
            "title": chunk.title,
            "version": chunk.version,
            "related_to": chunk.related_to,
            "tags": chunk.tags,
            "owner": chunk.owner,
            "context_tag": chunk.context_tag,
            "imported_at": chunk.imported_at or datetime.utcnow().isoformat(),
            "chunk_index": chunk.chunk_index,
            "category": chunk.category or data.collection
        }

        point = PointStruct(
            id=str(uuid4()),
            vector=embeddings[i],
            payload=payload
        )
        points.append(point)

    qdrant.upsert(collection_name=collection_name, points=points)

    return {
        "status": "✅ embeddings saved",
        "count": len(points),
        "collection": collection_name
    }




@app.get("/search")
def search_text(query: str = Query(...), limit: int = 3, collection: str = Query(...)):
    vec = model.encode(query).tolist()
    results = qdrant.search(collection_name=collection, query_vector=vec, limit=limit)
    return [{"score": r.score, "text": r.payload["text"]} for r in results]

@app.post("/prompt")
def generate_prompt(data: PromptRequest):
    query_vec = model.encode(data.query).tolist()
    results = qdrant.search(
        collection_name=data.collection,
        query_vector=query_vec,
        limit=data.context_limit
    )

    context = "\n".join([r.payload["text"] for r in results])
    full_prompt = f"""Beantworte die folgende Frage basierend auf dem Kontext:

Kontext:
{context}

Frage:
{data.query}
"""

    ollama_payload = {
        "model": OLLAMA_MODEL,
        "prompt": full_prompt,
        "stream": False
    }

    response = requests.post(OLLAMA_URL, json=ollama_payload)
    response.raise_for_status()
    answer = response.json()["response"]

    return {
        "answer": answer,
        "context": context,
        "collection": data.collection
    }