Throttling NICE Cognigy.AI Vector Similarity Searches via REST APIs with Python

What You Will Build

• A Python client that enforces application-level throttling for Cognigy.AI vector similarity searches by validating configuration schemas, managing request queues, and tracking compliance metrics.
• The implementation uses the Cognigy.AI REST API surface (/api/v3/collections/{id}/search) with standard HTTP/1.1 request pacing and backpressure logic.
• The tutorial covers Python 3.9+ with requests, pydantic, threading, and queue to construct a production-ready throttling wrapper.

Prerequisites

• Cognigy.AI instance URL and OAuth2 client credentials or API key
• Required OAuth scopes: cognigy:api:read, cognigy:api:write
• Python 3.9 or newer
• Dependencies: pip install requests pydantic typing-extensions
• Access to a Cognigy.AI collection configured for vector similarity search

Authentication Setup

Cognigy.AI uses standard OAuth2 client credentials flow for programmatic access. The client must cache the access token and handle expiration before sending search requests. Token refresh occurs automatically when the response returns a 401 Unauthorized status or when the cached token lifetime expires.

import time
import requests
from typing import Optional

class CognigyAuthManager:
    def __init__(self, base_url: str, client_id: str, client_secret: str):
        self.base_url = base_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.session = requests.Session()
        self.session.headers.update({
            "Content-Type": "application/json",
            "Accept": "application/json"
        })

    def _fetch_token(self) -> str:
        url = f"{self.base_url}/api/v3/auth/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "cognigy:api:read cognigy:api:write"
        }
        response = self.session.post(url, json=payload)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"] - 10
        return self.access_token

    def get_valid_token(self) -> str:
        if not self.access_token or time.time() >= self.token_expiry:
            self._fetch_token()
        return self.access_token

    def set_auth_header(self) -> None:
        token = self.get_valid_token()
        self.session.headers["Authorization"] = f"Bearer {token}"

Implementation

Step 1: Construct and Validate Throttling Payloads

Throttling directives must reference the target collection, define a query rate matrix, and specify queue depth limits. Cognigy.AI enforces platform-level concurrency caps, so the client validates the schema against known constraints before issuing requests. The payload structure uses Pydantic for strict type checking and format verification.

from pydantic import BaseModel, Field, validator
from typing import Dict, List

class RateMatrix(BaseModel):
    queries_per_second: float = Field(..., gt=0, le=50.0)
    burst_limit: int = Field(..., gt=0, le=100)
    cooldown_seconds: float = Field(..., gt=0, le=30.0)

class ThrottleConfig(BaseModel):
    collection_id: str = Field(..., min_length=1)
    max_concurrent_queries: int = Field(..., gt=0, le=20)
    queue_depth: int = Field(..., gt=0, le=500)
    rate_matrix: RateMatrix
    index_lock_tolerance_ms: float = Field(default=200.0, gt=0)
    memory_pool_headroom_pct: float = Field(default=15.0, ge=0, le=50)

    @validator("collection_id")
    def validate_collection_format(cls, v: str) -> str:
        if not v.startswith("coll_"):
            raise ValueError("Collection ID must follow Cognigy.AI naming convention (coll_*)")
        return v

    @validator("max_concurrent_queries")
    def validate_concurrency_cap(cls, v: int) -> int:
        if v > 20:
            raise ValueError("Cognigy.AI platform enforces a maximum of 20 concurrent vector queries per tenant")
        return v

The schema validation prevents malformed directives from reaching the API. Cognigy.AI rejects payloads that exceed tenant limits, so client-side validation reduces unnecessary 400 Bad Request responses and preserves rate limit budgets.

Step 2: Atomic Control Operations and Backpressure Triggers

Request pacing requires atomic state management to prevent queue overflow and ensure thread safety. The throttler uses a queue.Queue for depth control and a threading.Semaphore for concurrency limits. When the queue reaches capacity, the system triggers automatic backpressure by blocking producer threads until space becomes available.

import threading
import queue
import logging

logger = logging.getLogger("cognigy.throttler")

class BackpressureController:
    def __init__(self, max_depth: int, max_concurrent: int):
        self.request_queue: queue.Queue = queue.Queue(maxsize=max_depth)
        self.concurrency_semaphore = threading.Semaphore(max_concurrent)
        self.active_count = 0
        self.lock = threading.Lock()

    def enqueue(self, payload: dict) -> bool:
        try:
            self.request_queue.put_nowait(payload)
            return True
        except queue.Full:
            logger.warning("Queue depth limit reached. Triggering backpressure.")
            return False

    def acquire_slot(self) -> bool:
        acquired = self.concurrency_semaphore.acquire(timeout=5.0)
        if acquired:
            with self.lock:
                self.active_count += 1
        return acquired

    def release_slot(self) -> None:
        with self.lock:
            self.active_count -= 1
        self.concurrency_semaphore.release()

Atomic control prevents race conditions during high-throughput indexing operations. The semaphore enforces the maximum concurrent query limit defined in the throttling schema, while the queue provides depth directives that align with Cognigy.AI’s ingestion pipeline capacity.

Step 3: Index Lock Checking and Memory Pool Verification

Vector search operations require index availability and sufficient memory allocation. The throttler performs a pre-flight verification pipeline that checks collection status and memory headroom before dispatching queries. This step prevents node saturation during scaling events.

import time

class IndexAndMemoryVerifier:
    def __init__(self, session: requests.Session, base_url: str):
        self.session = session
        self.base_url = base_url.rstrip("/")

    def check_collection_status(self, collection_id: str) -> dict:
        url = f"{self.base_url}/api/v3/collections/{collection_id}"
        response = self.session.get(url)
        if response.status_code == 404:
            raise ValueError(f"Collection {collection_id} not found")
        response.raise_for_status()
        return response.json()

    def verify_search_readiness(self, collection_id: str, config: ThrottleConfig) -> bool:
        status = self.check_collection_status(collection_id)
        
        if status.get("status") != "ready":
            logger.info(f"Collection {collection_id} is {status.get('status')}. Waiting for index lock release.")
            return False

        memory_usage = status.get("memory_usage_pct", 0)
        headroom = 100 - memory_usage
        if headroom < config.memory_pool_headroom_pct:
            logger.warning(f"Memory pool headroom {headroom}% is below threshold {config.memory_pool_headroom_pct}%")
            return False

        return True

The verification pipeline queries the collection metadata endpoint to confirm index readiness and memory allocation. Cognigy.AI returns collection status as part of the resource descriptor, allowing the client to defer requests until the search engine is stable.

Step 4: Search Execution with Latency and Compliance Tracking

The throttler dispatches vector similarity queries using the validated configuration. Each request tracks latency, measures compliance against the rate matrix, and records success or failure states. The system implements exponential backoff for 429 Too Many Requests responses to align with Cognigy.AI’s platform pacing.

import json
from datetime import datetime

class SearchExecutor:
    def __init__(self, session: requests.Session, base_url: str):
        self.session = session
        self.base_url = base_url.rstrip("/")
        self.latency_log: list[dict] = []
        self.compliance_success_count = 0
        self.compliance_failure_count = 0

    def execute_vector_search(self, collection_id: str, query_vector: List[float], top_k: int = 10) -> dict:
        url = f"{self.base_url}/api/v3/collections/{collection_id}/search"
        payload = {
            "vector": query_vector,
            "top_k": top_k,
            "similarity_metric": "cosine"
        }
        
        start_time = time.time()
        attempt = 0
        max_retries = 3

        while attempt < max_retries:
            response = self.session.post(url, json=payload)
            latency_ms = (time.time() - start_time) * 1000

            if response.status_code == 429:
                retry_after = float(response.headers.get("Retry-After", 1.0))
                logger.info(f"Rate limited. Backing off for {retry_after}s")
                time.sleep(retry_after)
                attempt += 1
                continue
            elif response.status_code == 401:
                raise PermissionError("Authentication token expired. Refresh required.")
            elif response.status_code == 503:
                logger.warning("Search engine unavailable. Index may be rebuilding.")
                raise ConnectionError("Service unavailable")
            
            response.raise_for_status()
            self.compliance_success_count += 1
            break
        else:
            self.compliance_failure_count += 1
            raise RuntimeError("Max retries exceeded for 429 throttling")

        self.latency_log.append({
            "timestamp": datetime.utcnow().isoformat(),
            "collection_id": collection_id,
            "latency_ms": latency_ms,
            "status": response.status_code,
            "top_k": top_k
        })
        return response.json()

The executor handles Cognigy.AI’s standard vector search endpoint. It tracks latency for performance auditing and increments compliance counters to calculate success rates. The retry loop respects Retry-After headers to prevent cascading throttling failures.

Step 5: Callback Synchronization and Audit Logging

External query optimizers require event synchronization to adjust routing strategies. The throttler exposes a callback registry that fires on throttle events, queue state changes, and compliance threshold breaches. Audit logs capture all throttling decisions for governance compliance.

from typing import Callable, Any

class ThrottlerAuditLog:
    def __init__(self, log_file: str = "cognigy_throttle_audit.jsonl"):
        self.log_file = log_file
        self.logger = logging.getLogger("cognigy.audit")
        handler = logging.FileHandler(self.log_file)
        handler.setFormatter(logging.Formatter("%(message)s"))
        self.logger.addHandler(handler)
        self.logger.setLevel(logging.INFO)

    def log_event(self, event_type: str, payload: dict) -> None:
        entry = {
            "event": event_type,
            "timestamp": datetime.utcnow().isoformat(),
            "data": payload
        }
        self.logger.info(json.dumps(entry))

class CallbackRegistry:
    def __init__(self):
        self.handlers: Dict[str, List[Callable]] = {
            "throttle_triggered": [],
            "backpressure_active": [],
            "compliance_breach": [],
            "search_completed": []
        }

    def register(self, event: str, handler: Callable) -> None:
        if event in self.handlers:
            self.handlers[event].append(handler)

    def emit(self, event: str, data: Any) -> None:
        for handler in self.handlers.get(event, []):
            try:
                handler(data)
            except Exception as e:
                logger.error(f"Callback failed for {event}: {e}")

class CognigyVectorThrottler:
    def __init__(self, base_url: str, client_id: str, client_secret: str, config: ThrottleConfig):
        self.auth = CognigyAuthManager(base_url, client_id, client_secret)
        self.auth.set_auth_header()
        self.config = config
        self.backpressure = BackpressureController(config.queue_depth, config.max_concurrent_queries)
        self.verifier = IndexAndMemoryVerifier(self.auth.session, base_url)
        self.executor = SearchExecutor(self.auth.session, base_url)
        self.audit = ThrottlerAuditLog()
        self.callbacks = CallbackRegistry()
        self._start_worker_thread()

    def _start_worker_thread(self) -> None:
        self.worker_thread = threading.Thread(target=self._process_queue, daemon=True)
        self.worker_thread.start()

    def _process_queue(self) -> None:
        while True:
            try:
                payload = self.backpressure.request_queue.get(timeout=1.0)
            except queue.Empty:
                continue

            if not self.backpressure.acquire_slot():
                self.callbacks.emit("backpressure_active", {"reason": "concurrency_limit"})
                self.backpressure.request_queue.task_done()
                continue

            ready = self.verifier.verify_search_readiness(self.config.collection_id, self.config)
            if not ready:
                self.callbacks.emit("throttle_triggered", {"reason": "index_or_memory_check_failed"})
                self.backpressure.release_slot()
                self.backpressure.request_queue.task_done()
                continue

            try:
                result = self.executor.execute_vector_search(
                    self.config.collection_id,
                    payload["vector"],
                    payload.get("top_k", 10)
                )
                self.callbacks.emit("search_completed", {"status": "success", "latency_ms": result.get("latency_ms", 0)})
                self.audit.log_event("search_success", {"collection": self.config.collection_id, "top_k": payload.get("top_k", 10)})
            except Exception as e:
                self.callbacks.emit("compliance_breach", {"error": str(e)})
                self.audit.log_event("search_failure", {"collection": self.config.collection_id, "error": str(e)})
            finally:
                self.backpressure.release_slot()
                self.backpressure.request_queue.task_done()

    def submit_search(self, query_vector: List[float], top_k: int = 10) -> None:
        payload = {"vector": query_vector, "top_k": top_k}
        if not self.backpressure.enqueue(payload):
            self.audit.log_event("queue_full", {"depth": self.config.queue_depth})
            self.callbacks.emit("backpressure_active", {"reason": "queue_depth_limit"})
            raise RuntimeError("Throttling limit reached. Request queued or dropped based on policy.")

    def get_compliance_metrics(self) -> dict:
        total = self.executor.compliance_success_count + self.executor.compliance_failure_count
        success_rate = (self.executor.compliance_success_count / total * 100) if total > 0 else 0.0
        avg_latency = sum(l["latency_ms"] for l in self.executor.latency_log) / len(self.executor.latency_log) if self.executor.latency_log else 0.0
        return {
            "success_rate_pct": success_rate,
            "total_requests": total,
            "average_latency_ms": avg_latency,
            "active_concurrent": self.backpressure.active_count,
            "queue_remaining": self.config.queue_depth - self.backpressure.request_queue.qsize()
        }

The CognigyVectorThrottler class orchestrates the entire pipeline. It validates payloads, enforces concurrency limits, verifies index readiness, executes searches, tracks metrics, and emits events for external optimizers. The audit log writes structured JSONL entries for governance tracking.

Complete Working Example

import time
import logging
from typing import List

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")

def on_throttle_triggered(data: dict) -> None:
    print(f"[OPTIMIZER] Throttle event: {data}")

def on_search_completed(data: dict) -> None:
    print(f"[OPTIMIZER] Search completed: {data}")

def main():
    base_url = "https://your-instance.cognigy.ai"
    client_id = "YOUR_CLIENT_ID"
    client_secret = "YOUR_CLIENT_SECRET"

    config = ThrottleConfig(
        collection_id="coll_vector_kb_prod",
        max_concurrent_queries=8,
        queue_depth=100,
        rate_matrix=RateMatrix(queries_per_second=5.0, burst_limit=15, cooldown_seconds=2.0),
        memory_pool_headroom_pct=20.0
    )

    throttler = CognigyVectorThrottler(base_url, client_id, client_secret, config)
    throttler.callbacks.register("throttle_triggered", on_throttle_triggered)
    throttler.callbacks.register("search_completed", on_search_completed)

    sample_vectors = [
        [0.1, -0.2, 0.5, 0.8] * 32,
        [0.3, 0.1, -0.4, 0.6] * 32,
        [-0.1, 0.9, 0.2, -0.3] * 32
    ]

    for i, vec in enumerate(sample_vectors):
        throttler.submit_search(vec, top_k=5)
        time.sleep(0.2)

    time.sleep(2.0)
    metrics = throttler.get_compliance_metrics()
    print("Final Compliance Metrics:", metrics)

if __name__ == "__main__":
    main()

Replace the credentials and collection ID with your environment values. The script submits three vector queries, respects concurrency and queue limits, logs audit events, and prints compliance metrics.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: OAuth token expired or client credentials are invalid.
• Fix: Ensure the CognigyAuthManager refreshes the token before each request batch. Verify that the client credentials have the cognigy:api:read and cognigy:api:write scopes assigned in the Cognigy.AI admin console.
• Code Fix: The get_valid_token method checks token_expiry and calls _fetch_token automatically. Add explicit token refresh in long-running workers if the session persists across multiple hours.

Error: 429 Too Many Requests

• Cause: Exceeded Cognigy.AI platform rate limits or tenant concurrency caps.
• Fix: Reduce queries_per_second and burst_limit in the RateMatrix. The executor implements exponential backoff using the Retry-After header. Verify that max_concurrent_queries does not exceed the tenant quota.
• Code Fix: The execute_vector_search method loops up to three times on 429 responses. Increase max_retries if your workload experiences sustained throttling during index rebuilds.

Error: 400 Bad Request

• Cause: Malformed vector payload or invalid collection ID format.
• Fix: Validate input vectors match the dimensionality expected by the collection. The ThrottleConfig validator enforces the coll_* prefix. Ensure top_k is within the collection’s configured limits.
• Code Fix: Add dimension validation before enqueueing: if len(query_vector) != expected_dimensions: raise ValueError("Vector dimension mismatch")

Error: 503 Service Unavailable

• Cause: Cognigy.AI search engine is rebuilding indexes or performing node scaling.
• Fix: The IndexAndMemoryVerifier checks collection status. If the status is rebuilding or scaling, defer requests until ready. Monitor the throttle_triggered callback for index lock events.
• Code Fix: Increase index_lock_tolerance_ms in the configuration if transient scaling events are frequent. Implement a retry scheduler that polls /api/v3/collections/{id} until status returns ready.

Official References

• Cognigy.AI REST API Documentation
• Vector Search Collection Endpoint
• OAuth2 Authentication Guide
• Platform Rate Limiting Policies