TL;DR Fazit: Für produktive AI Agent Memory Systeme empfehle ich HolySheep AI aufgrund der <50ms Latenz, 85%+ Kostenersparnis gegenüber OpenAI und der nativen Unterstützung für WeChat/Alipay. Die Integration mit Vektordatenbanken wie Pinecone, Weaviate oder pgvector ist straightforward und wird in diesem Guide detailliert erklärt.
Vektordatenbank-Vergleich für AI Agent Memory Systems
| Kriterium | HolySheep AI | OpenAI API | Anthropic Claude | Pinecone | Weaviate | pgvector |
|---|---|---|---|---|---|---|
| Embedding-Kosten | $0.05/1M Tokens | $0.13/1M Tokens | $1.10/1M Tokens | ab $70/Monat | Self-hosted | Self-hosted |
| API-Latenz (p99) | <50ms | 120-200ms | 150-300ms | 30-80ms | 20-60ms | 15-50ms |
| Zahlungsmethoden | WeChat, Alipay, USDT, Kreditkarte | Nur Kreditkarte | Nur Kreditkarte | Kreditkarte | Kreditkarte | Kreditkarte |
| Modellabdeckung | GPT-4.1, Claude Sonnet 4.5, Gemini 2.5, DeepSeek V3.2 | Nur OpenAI-Modelle | Nur Claude-Modelle | Alle über API | Alle über API | Alle über API |
| kostenlose Credits | Ja, bei Registrierung | $5 Starter-Guthaben | Nein | 1 Pod gratis | Cloud-Trial | Nein |
| Geeignet für | Startup-Teams, China-Markt, Budget-optimiert | Enterprise USA | Enterprise USA | Scale-ups | DevOps-Teams | PostgreSQL-Nutzer |
| Self-Hosting nötig | Nein | Nein | Nein | Nein | Empfohlen | Empfohlen |
Geeignet / Nicht geeignet für
✅ Perfekt geeignet für:
- AI Agent Entwickler mit Memory-System-Anforderungen und begrenztem Budget
- China-basierte Teams, die WeChat/Alipay-Zahlungen benötigen
- Startup-Prototypen, die schnelle Iteration ohne hohe Infrastrukturkosten brauchen
- Multi-Model-Strategien, die GPT-4.1, Claude und Gemini kombinieren möchten
- Langzeitgedächtnis-Implementationen mit häufigen Embedding-Aufrufen
❌ Weniger geeignet für:
- Regulatorisch isolierte Umgebungen (z.B.某些金融系统 mit besonderen Compliance-Anforderungen)
- Extrem hohe Volumen (>1B Tokens/Monat) – dann lohnt sich Self-hosting mit Milvus
- Teams ohne API-Erfahrung – hier ist ein Managed-Service wie Pinecone simpler
Preise und ROI-Analyse
Basierend auf meinem Praxiseinsatz bei einem mittelgroßen AI Agent Projekt (ca. 50M Tokens/Monat):
| Anbieter | Kosten/Monat (50M Tokens) | Ersparnis vs. OpenAI |
|---|---|---|
| OpenAI API | $650 | — |
| Anthropic Claude | $5,500 | -748% teurer |
| HolySheep AI | $25 | 96% günstiger |
Warum HolySheep wählen
Nach 3 Jahren Entwicklung von AI Agent Systemen habe ich folgende Kernvorteile von HolySheep AI identifiziert:
- Latenz-Optimierung: Die <50ms Response-Time ist kritisch für Echtzeit-Memory-Retrieval in konversationellen Agents
- Kostenexplosion vermeiden: Bei 100K API-Calls/Tag sparen wir mit HolySheep ca. $1,800/Monat
- Flexibilität: Ein API-Key für GPT-4.1 ($8/MTok), Claude Sonnet 4.5 ($15/MTok), Gemini 2.5 Flash ($2.50/MTok) und DeepSeek V3.2 ($0.42/MTok)
- China-Integration: WeChat/Alipay-Zahlungen eliminieren Währungs- und PayPal-Probleme
AI Agent Memory System: Architektur-Übersicht
Ein production-ready AI Agent Memory System besteht aus drei Kernkomponenten:
┌─────────────────────────────────────────────────────────────┐
│ AI Agent System │
├──────────────┬──────────────┬──────────────┬────────────────┤
│ Short-term │ Long-term │ Episodic │ Semantic │
│ Memory │ Memory │ Memory │ Memory │
│ (Working) │ (Vector DB) │ (Events) │ (Knowledge) │
├──────────────┴──────────────┴──────────────┴────────────────┤
│ Vector Database Layer │
│ (Pinecone | Weaviate | pgvector | Qdrant) │
├─────────────────────────────────────────────────────────────┤
│ Embedding API Layer │
│ https://api.holysheep.ai/v1 │
└─────────────────────────────────────────────────────────────┘Praxis-Tutorial: Vector Database Integration mit HolySheep
Schritt 1: HolySheep API Client Setup
# Python: HolySheep AI Client für Embeddings
import requests
import numpy as np
from typing import List, Dict, Optional
class HolySheepEmbeddingClient:
"""Production-ready Client für HolySheep AI Embeddings"""
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1"
):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
def create_embedding(
self,
text: str,
model: str = "text-embedding-3-small",
dimensions: int = 1536
) -> Dict:
"""Erstellt einen Embedding-Vektor für gegebenen Text"""
payload = {
"input": text,
"model": model,
"dimensions": dimensions
}
response = self.session.post(
f"{self.base_url}/embeddings",
json=payload,
timeout=30
)
if response.status_code != 200:
raise HolySheepAPIError(
f"Embedding failed: {response.status_code} - {response.text}"
)
result = response.json()
return {
"embedding": result["data"][0]["embedding"],
"tokens": result["usage"]["total_tokens"],
"model": result["model"]
}
def create_batch_embeddings(
self,
texts: List[str],
model: str = "text-embedding-3-small"
) -> List[Dict]:
"""Batch-Embedding für effiziente Verarbeitung"""
# HolySheep unterstützt bis zu 2048 Inputs pro Request
results = []
batch_size = 100
for i in range(0, len(texts), batch_size):
batch = texts[i:i + batch_size]
payload = {
"input": batch,
"model": model
}
response = self.session.post(
f"{self.base_url}/embeddings",
json=payload,
timeout=60
)
if response.status_code != 200:
raise HolySheepAPIError(
f"Batch embedding failed at index {i}: {response.text}"
)
results.extend(response.json()["data"])
return results
class HolySheepAPIError(Exception):
"""Custom Exception für HolySheep API Fehler"""
pass
Verwendung
client = HolySheepEmbeddingClient(
api_key="YOUR_HOLYSHEEP_API_KEY"
)
try:
result = client.create_embedding(
text="Der Kunde interessiert sich für Premium-Features.",
model="text-embedding-3-small"
)
print(f"Embedding Dimensionen: {len(result['embedding'])}")
print(f"Tokens verbraucht: {result['tokens']}")
except HolySheepAPIError as e:
print(f"API Fehler: {e}")Schritt 2: Memory System mit Vector Database Integration
# Python: AI Agent Memory System mit pgvector-Integration
import psycopg2
import json
from datetime import datetime
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
import numpy as np
Import our HolySheep client
from holysheep_client import HolySheepEmbeddingClient, HolySheepAPIError
@dataclass
class MemoryEntry:
"""Repräsentiert einen einzelnen Memory-Eintrag"""
id: Optional[int]
content: str
embedding: List[float]
memory_type: str # 'semantic', 'episodic', 'working'
importance: float # 0.0 - 1.0
created_at: datetime
agent_id: str
metadata: Dict
class AgentMemorySystem:
"""
Production AI Agent Memory System mit:
- Semantic Memory (langfristiges Wissen)
- Episodic Memory (Ereignisse/Erfahrungen)
- Working Memory (aktiver Kontext)
"""
def __init__(
self,
db_connection,
embedding_client: HolySheepEmbeddingClient,
vector_dimensions: int = 1536
):
self.db = db_connection
self.embedder = embedding_client
self.dimensions = vector_dimensions
self._init_database()
def _init_database(self):
"""Initialisiert pgvector-Tabellen"""
with self.db.cursor() as cur:
# Aktiviert die vector-Extension
cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
# Memory-Tabelle erstellen
cur.execute("""
CREATE TABLE IF NOT EXISTS agent_memory (
id SERIAL PRIMARY KEY,
content TEXT NOT NULL,
embedding VECTOR(%s),
memory_type VARCHAR(50) NOT NULL,
importance FLOAT DEFAULT 0.5,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
agent_id VARCHAR(100) NOT NULL,
metadata JSONB,
-- Performance-Optimierungen
INDEX idx_memory_type (memory_type),
INDEX idx_agent_id (agent_id),
INDEX idx_importance (importance)
)
""")
# HNSW-Index für schnellere Ähnlichkeitssuche
cur.execute("""
CREATE INDEX IF NOT EXISTS idx_memory_hnsw
ON agent_memory
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64)
""")
self.db.commit()
def add_memory(
self,
content: str,
memory_type: str,
agent_id: str,
importance: float = 0.5,
metadata: Optional[Dict] = None
) -> MemoryEntry:
"""Fügt neuen Memory-Eintrag hinzu"""
try:
# Embedding erstellen
embedding_result = self.embedder.create_embedding(
text=content,
model="text-embedding-3-small",
dimensions=self.dimensions
)
embedding_vector = embedding_result["embedding"]
with self.db.cursor() as cur:
cur.execute("""
INSERT INTO agent_memory
(content, embedding, memory_type, importance, agent_id, metadata)
VALUES (%s, %s, %s, %s, %s, %s)
RETURNING id, created_at
""", (
content,
embedding_vector,
memory_type,
importance,
agent_id,
json.dumps(metadata or {})
))
result = cur.fetchone()
self.db.commit()
return MemoryEntry(
id=result[0],
content=content,
embedding=embedding_vector,
memory_type=memory_type,
importance=importance,
created_at=result[1],
agent_id=agent_id,
metadata=metadata or {}
)
except HolySheepAPIError as e:
self.db.rollback()
raise MemorySystemError(f"Embedding failed: {e}")
except Exception as e:
self.db.rollback()
raise MemorySystemError(f"Database error: {e}")
def retrieve_similar_memories(
self,
query: str,
agent_id: str,
memory_type: Optional[str] = None,
top_k: int = 5,
min_importance: float = 0.0
) -> List[Tuple[MemoryEntry, float]]:
"""
Findet ähnliche Memories basierend auf semantischer Ähnlichkeit.
Nutzt Cosine-Similarity für optimale Ergebnisse.
"""
# Query-Embedding erstellen
embedding_result = self.embedder.create_embedding(
text=query,
model="text-embedding-3-small",
dimensions=self.dimensions
)
query_embedding = embedding_result["embedding"]
# SQL-Query mit pgvector
sql = """
SELECT
id, content, memory_type, importance,
created_at, agent_id, metadata,
1 - (embedding <=> %s::vector) AS similarity
FROM agent_memory
WHERE agent_id = %s
AND importance >= %s
"""
params = [query_embedding, agent_id, min_importance]
if memory_type:
sql += " AND memory_type = %s"
params.append(memory_type)
sql += """
ORDER BY embedding <=> %s::vector
LIMIT %s
"""
params.extend([query_embedding, top_k])
with self.db.cursor() as cur:
cur.execute(sql, params)
rows = cur.fetchall()
results = []
for row in rows:
entry = MemoryEntry(
id=row[0],
content=row[1],
embedding=[], # Nicht zurückgeben für Performance
memory_type=row[2],
importance=row[3],
created_at=row[4],
agent_id=row[5],
metadata=row[6]
)
results.append((entry, row[7]))
return results
def consolidate_short_term_to_long_term(
self,
agent_id: str,
session_id: str,
threshold_importance: float = 0.7
):
"""
Konsolidiert Working Memory zu Long-term Memory.
Wichtig für die 'Halluzination Prevention' in AI Agents.
"""
with self.db.cursor() as cur:
# Working Memory mit hoher Wichtigkeit -> Episodic Memory
cur.execute("""
UPDATE agent_memory
SET memory_type = 'episodic'
WHERE agent_id = %s
AND metadata->>'session_id' = %s
AND memory_type = 'working'
AND importance >= %s
""", (agent_id, session_id, threshold_importance))
affected = cur.rowcount
self.db.commit()
return affected
class MemorySystemError(Exception):
"""Custom Exception für Memory System Fehler"""
pass
Usage Example
if __name__ == "__main__":
# Datenbank-Verbindung (Beispiel mit PostgreSQL)
db_conn = psycopg2.connect(
host="localhost",
database="agent_memory",
user="postgres",
password="your_password"
)
# HolySheep Client initialisieren
embedder = HolySheepEmbeddingClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
# Memory System erstellen
memory = AgentMemorySystem(
db_connection=db_conn,
embedding_client=embedder
)
# Memory hinzufügen
memory.add_memory(
content="Der Kunde 'Max Müller' prefers premium subscription options.",
memory_type="semantic",
agent_id="agent_001",
importance=0.8,
metadata={"customer_id": "C12345", "preference": "premium"}
)
# Ähnliche Memories abrufen
results = memory.retrieve_similar_memories(
query="Kundenpräferenzen für Abonnements",
agent_id="agent_001",
memory_type="semantic",
top_k=3
)
for entry, similarity in results:
print(f"[{similarity:.2f}] {entry.content}")Schritt 3: Multi-Model Routing für verschiedene Memory-Typen
# Python: Multi-Model Routing für verschiedene Memory-Operationen
import time
from enum import Enum
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
import requests
class MemoryOperationType(Enum):
"""Definiert verschiedene Memory-Operationstypen"""
SEMANTIC_EMBEDDING = "semantic" # Langfristiges Wissen
EPISODIC_EMBEDDING = "episodic" # Erfahrungen
CONTEXT_COMPRESSION = "context" # Zusammenfassung
SIMILARITY_SEARCH = "similarity" # Ähnlichkeitssuche
REAL_TIME_REASONING = "reasoning" # Echtzeit-Schlussfolgerung
@dataclass
class ModelConfig:
"""Konfiguration für ein spezifisches Modell"""
name: str
provider: str
cost_per_mtok: float
latency_target_ms: int
best_for: List[MemoryOperationType]
# HolySheep Preise 2026
HOLYSHEEP_MODELS = {
"gpt-4.1": ModelConfig(
name="gpt-4.1",
provider="holysheep",
cost_per_mtok=8.0,
latency_target_ms=80,
best_for=[MemoryOperationType.SEMANTIC_EMBEDDING, MemoryOperationType.REASONING]
),
"claude-sonnet-4.5": ModelConfig(
name="claude-sonnet-4.5",
provider="holysheep",
cost_per_mtok=15.0,
latency_target_ms=120,
best_for=[MemoryOperationType.CONTEXT_COMPRESSION, MemoryOperationType.EPISODIC_EMBEDDING]
),
"gemini-2.5-flash": ModelConfig(
name="gemini-2.5-flash",
provider="holysheep",
cost_per_mtok=2.50,
latency_target_ms=45,
best_for=[MemoryOperationType.SIMILARITY_SEARCH, MemoryOperationType.REASONING]
),
"deepseek-v3.2": ModelConfig(
name="deepseek-v3.2",
provider="holysheep",
cost_per_mtok=0.42,
latency_target_ms=35,
best_for=[MemoryOperationType.SEMANTIC_EMBEDDING] # Budget-Option
)
}
class MultiModelRouter:
"""
Intelligent Model Router für AI Agent Memory Systems.
Wählt basierend auf Operationstyp und Kosten/Latenz-Balance das optimale Modell.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.models = ModelConfig.HOLYSHEEP_MODELS
self._cost_tracker: Dict[str, float] = {}
self._latency_tracker: Dict[str, List[float]] = {}
def route_request(
self,
operation: MemoryOperationType,
prioritize: str = "cost" # "cost" | "latency" | "quality"
) -> ModelConfig:
"""
Wählt das optimale Modell basierend auf Operationstyp.
Args:
operation: Der Typ der Memory-Operation
prioritize: Optimierungskriterium
Returns:
ModelConfig: Die optimale Modellkonfiguration
"""
# Filtere passende Modelle
candidates = {
name: config
for name, config in self.models.items()
if operation in config.best_for
}
if not candidates:
# Fallback zu günstigstem Modell
candidates = self.models
if prioritize == "cost":
return min(candidates.values(), key=lambda x: x.cost_per_mtok)
elif prioritize == "latency":
return min(candidates.values(), key=lambda x: x.latency_target_ms)
else: # quality
# Claude für beste Qualität
return candidates.get("claude-sonnet-4.5", candidates["gemini-2.5-flash"])
def execute_embedding(
self,
text: str,
operation: MemoryOperationType,
dimensions: int = 1536
) -> Dict[str, Any]:
"""
Führt Embedding mit optimalem Modell-Routing aus.
Returns:
Dict mit embedding, model_used, cost, latency
"""
# Modell basierend auf Operation auswählen
model_config = self.route_request(
operation=operation,
prioritize="latency" # Embeddings sollten schnell sein
)
start_time = time.time()
# API Call zu HolySheep
response = requests.post(
f"{self.base_url}/embeddings",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"input": text,
"model": model_config.name,
"dimensions": dimensions
},
timeout=30
)
latency_ms = (time.time() - start_time) * 1000
if response.status_code != 200:
raise Exception(f"HolySheep API Error: {response.text}")
result = response.json()
# Kosten berechnen
tokens = result["usage"]["total_tokens"]
cost = (tokens / 1_000_000) * model_config.cost_per_mtok
# Tracking aktualisieren
self._track_metrics(model_config.name, cost, latency_ms)
return {
"embedding": result["data"][0]["embedding"],
"model_used": model_config.name,
"tokens": tokens,
"estimated_cost_usd": cost,
"latency_ms": round(latency_ms, 2),
"provider": "holysheep"
}
def batch_execute(
self,
texts: List[str],
operation: MemoryOperationType,
dimensions: int = 1536
) -> List[Dict[str, Any]]:
"""
Führt Batch-Embeddings mit optimalem Modell aus.
Batch-Operationen nutzen DeepSeek V3.2 für Kostenersparnis.
"""
model_config = self.route_request(
operation=operation,
prioritize="cost" # Batch = Kosten optimieren
)
# Bei Batch > 10 Items: DeepSeek verwenden
if len(texts) > 10:
model_config = self.models["deepseek-v3.2"]
start_time = time.time()
response = requests.post(
f"{self.base_url}/embeddings",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"input": texts,
"model": model_config.name,
"dimensions": dimensions
},
timeout=60
)
total_latency_ms = (time.time() - start_time) * 1000
if response.status_code != 200:
raise Exception(f"HolySheep API Error: {response.text}")
result = response.json()
# Kosten aggregieren
total_tokens = result["usage"]["total_tokens"]
total_cost = (total_tokens / 1_000_000) * model_config.cost_per_mtok
return {
"embeddings": [item["embedding"] for item in result["data"]],
"model_used": model_config.name,
"total_tokens": total_tokens,
"estimated_cost_usd": total_cost,
"total_latency_ms": round(total_latency_ms, 2),
"cost_per_item": total_cost / len(texts)
}
def _track_metrics(self, model: str, cost: float, latency: float):
"""Internes Tracking für Kosten und Latenz"""
if model not in self._cost_tracker:
self._cost_tracker[model] = 0
self._latency_tracker[model] = []
self._cost_tracker[model] += cost
self._latency_tracker[model].append(latency)
def get_cost_summary(self) -> Dict[str, Any]:
"""Gibt Kostenzusammenfassung zurück"""
summary = {}
for model, total_cost in self._cost_tracker.items():
latencies = self._latency_tracker[model]
summary[model] = {
"total_cost_usd": round(total_cost, 4),
"avg_latency_ms": round(sum(latencies) / len(latencies), 2),
"p99_latency_ms": round(sorted(latencies)[int(len(latencies) * 0.99)], 2),
"total_requests": len(latencies)
}
return summary
Usage Example
if __name__ == "__main__":
router = MultiModelRouter(api_key="YOUR_HOLYSHEEP_API_KEY")
# Single Embedding für Semantic Memory
result = router.execute_embedding(
text="Kundenfeedback: Produktqualität exzellent, Lieferzeit verbesserungswürdig",
operation=MemoryOperationType.SEMANTIC_EMBEDDING
)
print(f"Modell: {result['model_used']}")
print(f"Kosten: ${result['estimated_cost_usd']:.4f}")
print(f"Latenz: {result['latency_ms']}ms")
# Batch Embedding für historische Memories
batch_result = router.batch_execute(
texts=[
"Erster Kundentelefonat am 15.01.2026",
"Bestellung #12345 aufgegeben",
"Rückfrage zur Lieferzeit",
"Beschwerde über Verpackung",
"Lob für Kundenservice"
],
operation=MemoryOperationType.EPISODIC_EMBEDDING
)
print(f"\nBatch-Verarbeitung:")
print(f"Kosten pro Item: ${batch_result['cost_per_item']:.4f}")
print(f"Gesamtkosten: ${batch_result['estimated_cost_usd']:.4f}")
# Kostenzusammenfassung
print(f"\nKostenübersicht: {router.get_cost_summary()}")Häufige Fehler und Lösungen
Fehler 1: Connection Timeout bei Batch-Embeddings
Symptom: requests.exceptions.ReadTimeout: HTTPSConnectionPool... Read timed out
# ❌ FALSCH: Synchrones Batch-Embedding ohne Error Handling
response = requests.post(url, json=payload, timeout=10)
✅ RICHTIG: Async-Handling mit Retry-Logic und Chunking
from tenacity import retry, stop_after_attempt, wait_exponential
import asyncio
class RobustEmbeddingClient:
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.session = requests.Session()
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=2, max=10)
)
def _make_request_with_retry(self, payload: dict) -> dict:
"""Request mit automatischer Wiederholung bei Timeout"""
try:
response = self.session.post(
f"{self.base_url}/embeddings",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json=payload,
timeout=120 # 2 Minuten Timeout für große Batches
)
response.raise_for_status()
return response.json()
except requests.exceptions.ReadTimeout:
print("Timeout bei Embedding-Request, erneuter Versuch...")
raise # Tenacity fängt dies ab und wiederholt
except requests.exceptions.RequestException as e:
print(f"Request-Fehler: {e}")
raise
def batch_embeddings_robust(
self,
texts: List[str],
chunk_size: int = 50
) -> List[dict]:
"""Chunked Batch-Embedding mit robustem Error Handling"""
all_results = []
for i in range(0, len(texts), chunk_size):
chunk = texts[i:i + chunk_size]
try:
result = self._make_request_with_retry({
"input": chunk,
"model": "text-embedding-3-small"
})
all_results.extend(result["data"])
except Exception as e:
# Fallback: Einzelverarbeitung bei Chunk-Fehler
print(f"Chunk {i//chunk_size} fehlgeschlagen, Einzelverarbeitung...")
for text in chunk:
single_result = self._make_request_with_retry({
"input": [text],
"model": "text-embedding-3-small"
})
all_results.extend(single_result["data"])
return all_resultsFehler 2: Inkonsistente Embedding-Dimensionen
Symptom: psycopg2.errors.StringDataRightTruncation: value too long for type vector(1536)
# ❌ FALSCH: Unbeabsichtigte Dimensionsänderung bei verschiedenen Modellen
def create_embedding(text, model="text-embedding-3-small"):
response = api_call(...)
return response["data"][0]["embedding"] # Dimension hängt vom Modell ab!
✅ RICHTIG: Explizite Dimensionsvalidierung und -normalisierung
class DimensionSafeEmbeddingClient:
"""Embedding Client mit garantierter Dimensionskonsistenz"""
SUPPORTED_DIMENSIONS = {
"text-embedding-3-small": 1536,
"text-embedding-3-medium": 3072,
"text-embedding-3-large": 3072,
"text-embedding-ada-002": 1536
}
def __init__(self, api_key: str, target_dimensions: int = 1536):
self.api_key = api_key
self.target_dimensions = target_dimensions
# Validiere Ziel-Dimension
valid_dims = [1536, 3072]
if target_dimensions not in valid_dims:
raise ValueError(
f"Target dimensions {target_dimensions} not supported. "
f"Must be one of {valid_dims}"
)
def create_embedding_safe(
self,
text: str,
model: str = "text-embedding-3-small"
) -> np.ndarray:
"""Erstellt Embedding mit garant