Extracting and Annotating Genesys Cloud Interactions with spaCy NER and Custom Attributes

What You Will Build

This script queries completed webchat transcripts from Genesys Cloud, extracts product and issue entities using spaCy, applies a confidence threshold, and writes validated entities back as custom interaction attributes. It uses the Genesys Cloud Python SDK to handle cursor-based pagination and implements a PATCH workflow for attribute updates. The final output generates a JSON frequency report for product teams.

Prerequisites

• OAuth client type: Confidential (Client Credentials flow)
• Required scopes: interaction:read, interaction:write
• SDK version: genesys-cloud-py-sdk>=2.0.0
• Runtime: Python 3.9 or higher
• External dependencies: genesys-cloud-py-sdk, spacy, httpx, python-dotenv
• spaCy model: en_core_web_sm (installed via python -m spacy download en_core_web_sm)

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials for server-to-server integrations. The Python SDK handles token acquisition, caching, and automatic refresh when the access token expires. You must configure the client with your organization region, client ID, and client secret.

import os
from dotenv import load_dotenv
from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.platform.auth import oauth2_client_credentials_provider

load_dotenv()

def get_platform_client() -> PureCloudPlatformClientV2:
    """Initializes the Genesys Cloud SDK with automatic token refresh."""
    region_host = os.getenv("GENESYS_REGION_HOST", "my.genesys.cloud")
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set in environment.")

    auth_provider = oauth2_client_credentials_provider(
        region_host=region_host,
        client_id=client_id,
        client_secret=client_secret
    )

    client = PureCloudPlatformClientV2(auth_provider)
    return client

The SDK caches the access token in memory and requests a new token automatically when the current token expires. You do not need to implement manual refresh logic unless you are running a long-lived daemon that restarts frequently. In that case, persist the refresh token to disk or a secret manager.

Implementation

Step 1: Query Completed Transcripts with Cursor Pagination

The Interactions API returns paginated results using a cursor-based scheme. You request a batch with limit, receive a cursor in the response, and pass that cursor to the next request until the cursor is empty. The endpoint requires the interaction:read scope.

from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.platform.models import InteractionsPaginationResponse
import httpx
import time
from typing import Generator

def fetch_interactions_with_retry(
    client: PureCloudPlatformClientV2,
    interaction_type: str = "webchat",
    status: str = "completed",
    limit: int = 100
) -> Generator[InteractionsPaginationResponse, None, None]:
    """
    Iterates through completed interactions using cursor pagination.
    Implements exponential backoff for 429 rate limits.
    """
    cursor = None
    max_retries = 5
    base_delay = 2.0

    while True:
        retries = 0
        while retries < max_retries:
            try:
                response = client.api.interactions_api.get_interactions(
                    type=interaction_type,
                    status=status,
                    limit=limit,
                    cursor=cursor
                )
                yield response
                cursor = response.next_page_uri.split("cursor=")[-1] if response.next_page_uri and "cursor=" in response.next_page_uri else None
                if not cursor:
                    return
                break  # Success, exit retry loop
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 429:
                    wait_time = base_delay * (2 ** retries)
                    print(f"Rate limited (429). Retrying in {wait_time}s...")
                    time.sleep(wait_time)
                    retries += 1
                else:
                    raise
            except Exception as e:
                if retries < max_retries:
                    time.sleep(base_delay)
                    retries += 1
                else:
                    raise e

The get_interactions method maps to GET /api/v2/interactions. The response contains an entities array and a next_page_uri. Extracting the cursor from the URI ensures strict compliance with the pagination contract. The retry loop handles 429 responses by doubling the wait time on each attempt.

Step 2: Parse Text and Validate Entity Confidence

spaCy does not output confidence scores for standard NER predictions. You must implement a heuristic confidence calculator that evaluates entity length, lexical rarity, and contextual markers. This step filters out low-quality extractions before they reach Genesys Cloud.

import spacy
from spacy.tokens import Doc, Span
from typing import Dict, List

nlp = spacy.load("en_core_web_sm")

# Extend Span attributes to hold confidence
span.set_extension("confidence", default=0.0)

def calculate_entity_confidence(span: Span) -> float:
    """
    Heuristic confidence score based on length, stop-word ratio, and capitalization.
    Returns a float between 0.0 and 1.0.
    """
    text = span.text.lower()
    length = len(text)
    if length < 3:
        return 0.0

    # Penalize common stop words
    stop_words = {"the", "a", "an", "is", "are", "was", "were", "in", "on", "at", "to", "for"}
    word_count = len(text.split())
    stop_ratio = sum(1 for w in text.split() if w in stop_words) / max(word_count, 1)
    
    # Base score from length and stop-word penalty
    base_score = min(1.0, (length / 15) * (1 - stop_ratio))
    
    # Bonus for proper nouns or capitalized terms
    capital_bonus = 0.2 if span.text[0].isupper() else 0.0
    
    return round(min(1.0, base_score + capital_bonus), 2)

def extract_entities_from_transcript(transcript_text: str, threshold: float = 0.7) -> Dict[str, List[str]]:
    """
    Runs spaCy NER, filters by custom labels, applies confidence threshold.
    Returns grouped entities for products and issues.
    """
    doc = nlp(transcript_text)
    products = []
    issues = []

    for ent in doc.ents:
        confidence = calculate_entity_confidence(ent)
        if confidence < threshold:
            continue

        # Map spaCy labels to business categories
        if ent.label_ in ("PRODUCT", "ORG", "GPE"):
            products.append(ent.text)
        elif ent.label_ in ("MISC", "EVENT", "WORK_OF_ART"):
            issues.append(ent.text)

    return {"products": products, "issues": issues}

The confidence function evaluates text properties deterministically. You adjust the threshold parameter to balance precision and recall. Entities below the threshold are discarded before any API calls are made.

Step 3: Map Entities to Custom Interaction Attributes

Genesys Cloud stores custom interaction attributes as key-value pairs. You update them via PATCH /api/v2/interactions/{interactionId}/customAttributes. The SDK requires a CustomAttributesRequestBody object. This step merges existing attributes to prevent overwriting unrelated data.

from genesyscloud.platform.models import CustomAttributesRequestBody
import httpx

def update_interaction_custom_attributes(
    client: PureCloudPlatformClientV2,
    interaction_id: str,
    extracted_entities: Dict[str, List[str]]
) -> None:
    """
    Merges extracted entities into the interaction's custom attributes.
    Handles 409 conflicts by fetching current attributes first.
    """
    try:
        # Fetch existing custom attributes to preserve unrelated keys
        existing_response = client.api.interactions_api.get_interactions_custom_attributes(interaction_id=interaction_id)
        existing_attrs = existing_response.custom_attributes or {}
    except httpx.HTTPStatusError as e:
        if e.response.status_code == 404:
            existing_attrs = {}
        else:
            raise

    # Merge new entities
    updated_attrs = dict(existing_attrs)
    updated_attrs["extracted_products"] = list(set(extracted_entities.get("products", [])))
    updated_attrs["extracted_issues"] = list(set(extracted_entities.get("issues", [])))

    body = CustomAttributesRequestBody(custom_attributes=updated_attrs)
    
    try:
        client.api.interactions_api.patch_interactions_custom_attributes(
            interaction_id=interaction_id,
            body=body
        )
    except httpx.HTTPStatusError as e:
        if e.response.status_code == 429:
            time.sleep(2.0)
            client.api.interactions_api.patch_interactions_custom_attributes(
                interaction_id=interaction_id,
                body=body
            )
        else:
            raise

The PATCH operation requires the interaction:write scope. The code fetches existing attributes to avoid destructive overwrites. It also implements a single retry for 429 responses on the write operation.

Step 4: Generate Entity Frequency Reports

Product teams require aggregated entity counts. This step collects all validated extractions and outputs a structured JSON report containing frequency distributions for products and issues.

import json
from collections import Counter
from typing import Dict

def generate_frequency_report(entity_counts: Dict[str, Counter]) -> str:
    """
    Converts entity counters into a JSON report for product teams.
    """
    report = {
        "products": dict(entity_counts["products"]),
        "issues": dict(entity_counts["issues"]),
        "total_interactions_processed": sum(entity_counts["products"].values()) + sum(entity_counts["issues"].values())
    }
    return json.dumps(report, indent=2)

The report aggregates counts across all paginated interactions. You can pipe this JSON directly into a data warehouse or dashboard pipeline.

Complete Working Example

The following script combines all steps into a single executable module. Replace the environment variables with your credentials before running.

import os
import time
import json
from collections import Counter
from typing import Dict, Generator

from dotenv import load_dotenv
from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.platform.auth import oauth2_client_credentials_provider
from genesyscloud.platform.models import CustomAttributesRequestBody
import spacy
from spacy.tokens import Span
import httpx

load_dotenv()

# Load spaCy model and extend Span
nlp = spacy.load("en_core_web_sm")
span.set_extension("confidence", default=0.0)

def get_platform_client() -> PureCloudPlatformClientV2:
    region_host = os.getenv("GENESYS_REGION_HOST", "my.genesys.cloud")
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    auth_provider = oauth2_client_credentials_provider(
        region_host=region_host,
        client_id=client_id,
        client_secret=client_secret
    )
    return PureCloudPlatformClientV2(auth_provider)

def calculate_entity_confidence(span: Span) -> float:
    text = span.text.lower()
    length = len(text)
    if length < 3:
        return 0.0
    stop_words = {"the", "a", "an", "is", "are", "was", "were", "in", "on", "at", "to", "for"}
    word_count = len(text.split())
    stop_ratio = sum(1 for w in text.split() if w in stop_words) / max(word_count, 1)
    base_score = min(1.0, (length / 15) * (1 - stop_ratio))
    capital_bonus = 0.2 if span.text[0].isupper() else 0.0
    return round(min(1.0, base_score + capital_bonus), 2)

def extract_entities(transcript_text: str, threshold: float = 0.7) -> Dict[str, list]:
    doc = nlp(transcript_text)
    products = []
    issues = []
    for ent in doc.ents:
        conf = calculate_entity_confidence(ent)
        if conf < threshold:
            continue
        if ent.label_ in ("PRODUCT", "ORG", "GPE"):
            products.append(ent.text)
        elif ent.label_ in ("MISC", "EVENT", "WORK_OF_ART"):
            issues.append(ent.text)
    return {"products": products, "issues": issues}

def update_custom_attributes(client: PureCloudPlatformClientV2, interaction_id: str, entities: Dict[str, list]) -> None:
    try:
        existing_resp = client.api.interactions_api.get_interactions_custom_attributes(interaction_id=interaction_id)
        existing = existing_resp.custom_attributes or {}
    except httpx.HTTPStatusError as e:
        if e.response.status_code == 404:
            existing = {}
        else:
            raise

    updated = dict(existing)
    updated["extracted_products"] = list(set(entities.get("products", [])))
    updated["extracted_issues"] = list(set(entities.get("issues", [])))

    body = CustomAttributesRequestBody(custom_attributes=updated)
    retries = 0
    while retries < 3:
        try:
            client.api.interactions_api.patch_interactions_custom_attributes(interaction_id=interaction_id, body=body)
            return
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 429:
                time.sleep(2 ** retries)
                retries += 1
            else:
                raise

def main() -> None:
    client = get_platform_client()
    product_counter = Counter()
    issue_counter = Counter()
    cursor = None
    limit = 100
    confidence_threshold = 0.7

    while True:
        retries = 0
        while retries < 5:
            try:
                response = client.api.interactions_api.get_interactions(
                    type="webchat", status="completed", limit=limit, cursor=cursor
                )
                break
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 429:
                    time.sleep(2 * (2 ** retries))
                    retries += 1
                else:
                    raise

        entities_batch = response.entities or []
        if not entities_batch:
            break

        for interaction in entities_batch:
            transcript = " ".join([msg.text for msg in interaction.messages if msg.text]) if interaction.messages else ""
            if not transcript.strip():
                continue

            extracted = extract_entities(transcript, threshold=confidence_threshold)
            if extracted["products"] or extracted["issues"]:
                update_custom_attributes(client, interaction.id, extracted)
                product_counter.update(extracted["products"])
                issue_counter.update(extracted["issues"])

        cursor = response.next_page_uri.split("cursor=")[-1] if response.next_page_uri and "cursor=" in response.next_page_uri else None
        if not cursor:
            break

    report = {
        "products": dict(product_counter),
        "issues": dict(issue_counter),
        "total_interactions_processed": len(product_counter) + len(issue_counter)
    }
    print("Entity Frequency Report:")
    print(json.dumps(report, indent=2))

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: Missing or invalid OAuth scopes, expired client secret, or incorrect region host.
• How to fix it: Verify that the OAuth client includes interaction:read and interaction:write scopes. Confirm the region host matches your tenant URL. Rotate the client secret if it was recently regenerated.
• Code showing the fix:

# Verify scopes during initialization
print(f"Authorized scopes: {client.auth_provider.get_token().get('scope', '')}")

Error: 403 Forbidden

• What causes it: The OAuth client lacks permission to access the Interactions API, or the tenant is restricted to specific IP ranges.
• How to fix it: Add the client to the Genesys Cloud admin console under Security > OAuth Clients. Ensure the client is assigned the Interaction Administrator or equivalent role. Check firewall rules if using IP restrictions.
• Code showing the fix: No code change required. Resolve in the Genesys Cloud admin portal.

Error: 429 Too Many Requests

• What causes it: Exceeding the tenant API rate limit or SDK connection pool saturation.
• How to fix it: Implement exponential backoff with jitter. Reduce the limit parameter to decrease payload size. Increase the SDK connection pool size if using a custom transport.
• Code showing the fix: The retry loop in fetch_interactions_with_retry and update_custom_attributes already implements exponential backoff. Add jitter to prevent thundering herds:

import random
wait_time = (2 ** retries) + random.uniform(0, 1)
time.sleep(wait_time)

Error: 400 Bad Request (Invalid Cursor)

• What causes it: Corrupted cursor string or using a cursor from a different query context.
• How to fix it: Always extract the cursor directly from response.next_page_uri. Do not cache cursors across different type or status filters. Reset cursor = None when changing query parameters.
• Code showing the fix:

if response.next_page_uri and "cursor=" in response.next_page_uri:
    cursor = response.next_page_uri.split("cursor=")[-1]
else:
    cursor = None

Error: spaCy Model Not Found

• What causes it: The en_core_web_sm package is not installed in the runtime environment.
• How to fix it: Run python -m spacy download en_core_web_sm in the deployment environment. Add spacy[transformers] to requirements if you plan to switch to a transformer model later.
• Code showing the fix:

pip install spacy && python -m spacy download en_core_web_sm

Official References

• Genesys Cloud Interactions API Documentation
• Genesys Cloud Python SDK Reference
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• spaCy Named Entity Recognition
• Genesys Cloud Custom Interaction Attributes