Initial commit

2026-04-29 08:17:35 +00:00
commit ef55253cbd
49 changed files with 3073 additions and 0 deletions
--- a/app/routers/documents.py
+++ b/app/routers/documents.py
@@ -0,0 +1,113 @@
+import json
+import httpx
+import os
+import logging
+from fastapi import APIRouter, Depends, HTTPException
+from app.auth import verify_api_key
+from app.database import get_db
+from app.models import UpsertRequest, QueryRequest
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+LITELLM_URL     = os.getenv("LITELLM_PROXY_URL", "http://litellm:4000")
+EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL",    "text-embedding-ada-002")
+
+
+async def _embed(text: str, token: str) -> list[float]:
+    async with httpx.AsyncClient() as client:
+        resp = await client.post(
+            f"{LITELLM_URL}/embeddings",
+            headers={
+                "Authorization": f"Bearer {token}",
+                "Content-Type":  "application/json"
+            },
+            json={
+                "model": EMBEDDING_MODEL,
+                "input": text
+            },
+            timeout=30.0
+        )
+
+    if resp.status_code != 200:
+        logger.error(f"Embedding Fehler: {resp.status_code} - {resp.text}")
+        raise HTTPException(
+            502,
+            f"Embedding fehlgeschlagen: {resp.status_code} - {resp.text}"
+        )
+
+    return resp.json()["data"][0]["embedding"]
+
+
+async def _check_access(db, store_id: str, user_id: str):
+    row = await db.fetchrow(
+        "SELECT owner_user_id FROM vector_stores WHERE id=$1", store_id
+    )
+    if not row:
+        raise HTTPException(404, "Store nicht gefunden")
+    if row["owner_user_id"] != user_id:
+        shared = await db.fetchval(
+            "SELECT 1 FROM store_permissions WHERE store_id=$1 AND user_id=$2",
+            store_id, user_id
+        )
+        if not shared:
+            raise HTTPException(403, "Kein Zugriff")
+
+
+@router.post("/upsert")
+async def upsert(
+    body: UpsertRequest,
+    user: dict = Depends(verify_api_key),
+    db=Depends(get_db)
+):
+    await _check_access(db, str(body.store_id), user["user_id"])
+
+    ids = []
+    for i, text in enumerate(body.texts):
+        embedding = await _embed(text, user["token"])
+        meta      = body.metadata[i] if i < len(body.metadata) else {}
+
+        doc_id = await db.fetchval(
+            """INSERT INTO documents (store_id, content, metadata, embedding)
+               VALUES ($1, $2, $3, $4::vector) RETURNING id""",
+            str(body.store_id),
+            text,
+            json.dumps(meta),
+            str(embedding)
+        )
+        ids.append(str(doc_id))
+
+    return {"inserted": len(ids), "ids": ids}
+
+
+@router.post("/query")
+async def query(
+    body: QueryRequest,
+    user: dict = Depends(verify_api_key),
+    db=Depends(get_db)
+):
+    await _check_access(db, str(body.store_id), user["user_id"])
+
+    q_emb = await _embed(body.query, user["token"])
+
+    rows = await db.fetch(
+        """SELECT id, content, metadata,
+                  1 - (embedding <=> $1::vector) AS similarity
+           FROM documents
+           WHERE store_id = $2
+           ORDER BY embedding <=> $1::vector
+           LIMIT $3""",
+        str(q_emb),
+        str(body.store_id),
+        body.top_k
+    )
+
+    return {"results": [
+        {
+            "id":         str(r["id"]),
+            "content":    r["content"],
+            "metadata":   r["metadata"],
+            "similarity": float(r["similarity"])
+        }
+        for r in rows
+    ]}