Injecting RAG Context into NICE CXone Agent Assist with a Python FastAPI Microservice

What You Will Build

• A FastAPI microservice that accepts natural language queries from agents, generates dense vector embeddings, and queries a ChromaDB index to retrieve semantically relevant knowledge base chunks.
• The service formats the retrieved context into a structured JSON payload and pushes it directly to the NICE CXone Agent Assist REST API to surface LLM-driven suggestions in the agent desktop.
• All implementation uses Python 3.10+, FastAPI, sentence-transformers, ChromaDB, and httpx for direct REST communication.

Prerequisites

• NICE CXone OAuth 2.0 Client Credentials grant with agentassists:write scope
• CXone REST API v2 (Assist module)
• Python 3.10 or higher
• External dependencies: fastapi, uvicorn, httpx, chromadb, sentence-transformers, pydantic, python-dotenv
• A pre-populated ChromaDB collection containing chunked knowledge base documents and their corresponding metadata

Authentication Setup

NICE CXone uses standard OAuth 2.0 Client Credentials flow. The microservice must acquire an access token before issuing any Assist API calls. Tokens expire after thirty minutes, so the service implements a TTL-based cache with automatic refresh logic.

The following class manages token lifecycle, serializes concurrent requests, and handles 401 responses by forcing a refresh.

import os
import time
import httpx
from typing import Optional

class CXoneAuthManager:
    def __init__(self, client_id: str, client_secret: str, base_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url.rstrip("/")
        self.token_url = f"{self.base_url}/oauth/token"
        self._access_token: Optional[str] = None
        self._token_expiry: float = 0.0
        self._http_client = httpx.AsyncClient(timeout=15.0)

    async def get_access_token(self) -> str:
        if self._access_token and time.time() < self._token_expiry:
            return self._access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "agentassists:write"
        }

        response = await self._http_client.post(self.token_url, data=payload)
        response.raise_for_status()

        token_data = response.json()
        self._access_token = token_data["access_token"]
        self._token_expiry = time.time() + (token_data["expires_in"] - 60)

        return self._access_token

    async def close(self):
        await self._http_client.aclose()

The scope parameter explicitly requests agentassists:write. CXone rejects requests with missing or insufficient scopes with a 403 Forbidden response. The TTL buffer subtracts sixty seconds from the official expiry to prevent edge-case expiration during payload transmission.

Implementation

Step 1: Initialize Embedding Model and ChromaDB Client

The microservice loads a lightweight transformer model for local embedding generation and connects to a persistent ChromaDB instance. Loading the model once at startup avoids cold-start latency on subsequent requests.

import chromadb
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI(title="CXone RAG Assist Service")

# Local embedding model (runs on CPU, ~80MB)
EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
CHROMA_DB = chromadb.PersistentClient(path="./chroma_db")
COLLECTION_NAME = "kb_chunks"

class AgentQuery(BaseModel):
    conversation_id: str
    agent_id: str
    query_text: str

@app.on_event("startup")
async def startup_event():
    global COLLECTION
    try:
        COLLECTION = CHROMA_DB.get_collection(COLLECTION_NAME)
    except Exception:
        raise HTTPException(status_code=500, detail="ChromaDB collection not found or corrupted")

The all-MiniLM-L6-v2 model produces 384-dimensional vectors. ChromaDB stores these vectors alongside raw text chunks and source metadata. The collection must exist before the service starts. Populate it using standard ChromaDB add() calls with ids, documents, embeddings, and metadatas arrays.

Step 2: Generate Embeddings and Execute Similarity Search

When an agent submits a query, the service normalizes the text, generates a dense vector, and queries ChromaDB for the top-k nearest neighbors. The similarity search uses cosine distance by default in ChromaDB.

import numpy as np

def retrieve_context(query_text: str, top_k: int = 3) -> list[dict]:
    query_embedding = EMBEDDING_MODEL.encode(query_text).tolist()
    
    results = COLLECTION.query(
        query_embeddings=[query_embedding],
        n_results=top_k,
        include=["documents", "metadatas", "distances"]
    )
    
    context_chunks = []
    if results["documents"] and results["documents"][0]:
        for doc, meta, dist in zip(
            results["documents"][0],
            results["metadatas"][0],
            results["distances"][0]
        ):
            context_chunks.append({
                "text": doc,
                "source": meta.get("source", "unknown"),
                "relevance_score": round(1.0 - dist, 4)
            })
            
    return context_chunks

The distances array returns cosine distance values between zero and two. A value near zero indicates high similarity. The service converts distance to a relevance score by subtracting from one. The top_k parameter controls how many chunks enter the context window. Increasing top_k beyond five typically degrades LLM instruction following due to context dilution.

Step 3: Format Assist Payload and Inject via CXone REST API

CXone Agent Assist accepts structured content arrays. The service assembles the retrieved chunks into a single formatted string, wraps it in the required Assist schema, and transmits it using exponential backoff for 429 rate limit responses.

import asyncio
import httpx

CXONE_BASE_URL = os.getenv("CXONE_BASE_URL", "https://platform.niceincontact.com")
CXONE_API_PATH = "/api/v2/assist/agentassists"

async def inject_assist_payload(
    auth_manager: CXoneAuthManager,
    conversation_id: str,
    agent_id: str,
    context_chunks: list[dict]
) -> dict:
    formatted_context = "\n\n".join(
        f"[{chunk['source']}] {chunk['text']}" for chunk in context_chunks
    )
    
    payload = {
        "conversationId": conversation_id,
        "agentId": agent_id,
        "title": "RAG Knowledge Suggestion",
        "content": [
            {
                "type": "text",
                "data": {
                    "text": f"Reference Context:\n{formatted_context}"
                }
            }
        ]
    }
    
    headers = {
        "Authorization": f"Bearer {await auth_manager.get_access_token()}",
        "Content-Type": "application/json"
    }
    
    url = f"{CXONE_BASE_URL}{CXONE_API_PATH}"
    
    # Retry logic for 429 Too Many Requests
    max_retries = 3
    for attempt in range(max_retries):
        async with httpx.AsyncClient(timeout=10.0) as client:
            try:
                response = await client.post(url, json=payload, headers=headers)
                
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                    await asyncio.sleep(retry_after)
                    continue
                    
                response.raise_for_status()
                return response.json()
                
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 401:
                    # Force token refresh on next call
                    auth_manager._access_token = None
                    raise HTTPException(status_code=401, detail="Authentication expired")
                elif e.response.status_code == 403:
                    raise HTTPException(status_code=403, detail="Missing agentassists:write scope")
                else:
                    raise HTTPException(status_code=e.response.status_code, detail=e.response.text)
                    
    raise HTTPException(status_code=503, detail="CXone API rate limit exceeded after retries")

The payload structure matches CXone Assist schema requirements. The conversationId and agentId fields route the suggestion to the correct desktop session. The retry loop reads the Retry-After header when present. If the header is absent, the service falls back to exponential backoff starting at two seconds.

Step 4: Expose FastAPI Endpoint

The final step wires the retrieval and injection logic into a single async route. Input validation handles empty queries and missing identifiers.

@app.post("/assist/push")
async def push_assist(query: AgentQuery, auth: CXoneAuthManager):
    if not query.query_text.strip():
        raise HTTPException(status_code=400, detail="Query text cannot be empty")
        
    context = retrieve_context(query.query_text, top_k=3)
    
    if not context:
        return {"status": "no_match", "message": "No relevant knowledge base chunks found"}
        
    result = await inject_assist_payload(
        auth_manager=auth,
        conversation_id=query.conversation_id,
        agent_id=query.agent_id,
        context_chunks=context
    )
    
    return {"status": "injected", "assist_id": result.get("id"), "chunks_returned": len(context)}

FastAPI automatically validates the Pydantic model. The endpoint returns a lightweight status response. The actual Assist item ID comes from the CXone response payload.

Complete Working Example

The following script combines authentication, embedding, vector search, and API injection into a single production-ready module. Save as main.py and run with uvicorn main:app --reload.

import os
import time
import asyncio
import httpx
import chromadb
import numpy as np
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI(title="CXone RAG Assist Service")

# Configuration
CXONE_BASE_URL = os.getenv("CXONE_BASE_URL", "https://platform.niceincontact.com")
CXONE_API_PATH = "/api/v2/assist/agentassists"
EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2"
COLLECTION_NAME = "kb_chunks"
CHROMA_DB_PATH = "./chroma_db"

# Global instances
auth_manager: CXoneAuthManager | None = None
embedding_model: SentenceTransformer | None = None
chroma_collection: chromadb.Collection | None = None

class CXoneAuthManager:
    def __init__(self, client_id: str, client_secret: str, base_url: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url.rstrip("/")
        self.token_url = f"{self.base_url}/oauth/token"
        self._access_token: str | None = None
        self._token_expiry: float = 0.0
        self._http_client = httpx.AsyncClient(timeout=15.0)

    async def get_access_token(self) -> str:
        if self._access_token and time.time() < self._token_expiry:
            return self._access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "agentassists:write"
        }

        response = await self._http_client.post(self.token_url, data=payload)
        response.raise_for_status()

        token_data = response.json()
        self._access_token = token_data["access_token"]
        self._token_expiry = time.time() + (token_data["expires_in"] - 60)
        return self._access_token

    async def close(self):
        await self._http_client.aclose()

class AgentQuery(BaseModel):
    conversation_id: str
    agent_id: str
    query_text: str

def retrieve_context(query_text: str, top_k: int = 3) -> list[dict]:
    query_embedding = embedding_model.encode(query_text).tolist()
    results = chroma_collection.query(
        query_embeddings=[query_embedding],
        n_results=top_k,
        include=["documents", "metadatas", "distances"]
    )
    
    context_chunks = []
    if results["documents"] and results["documents"][0]:
        for doc, meta, dist in zip(
            results["documents"][0],
            results["metadatas"][0],
            results["distances"][0]
        ):
            context_chunks.append({
                "text": doc,
                "source": meta.get("source", "unknown"),
                "relevance_score": round(1.0 - dist, 4)
            })
    return context_chunks

async def inject_assist_payload(
    conversation_id: str,
    agent_id: str,
    context_chunks: list[dict]
) -> dict:
    formatted_context = "\n\n".join(
        f"[{chunk['source']}] {chunk['text']}" for chunk in context_chunks
    )
    
    payload = {
        "conversationId": conversation_id,
        "agentId": agent_id,
        "title": "RAG Knowledge Suggestion",
        "content": [
            {
                "type": "text",
                "data": {
                    "text": f"Reference Context:\n{formatted_context}"
                }
            }
        ]
    }
    
    headers = {
        "Authorization": f"Bearer {await auth_manager.get_access_token()}",
        "Content-Type": "application/json"
    }
    
    url = f"{CXONE_BASE_URL}{CXONE_API_PATH}"
    
    max_retries = 3
    for attempt in range(max_retries):
        async with httpx.AsyncClient(timeout=10.0) as client:
            try:
                response = await client.post(url, json=payload, headers=headers)
                
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
                    await asyncio.sleep(retry_after)
                    continue
                    
                response.raise_for_status()
                return response.json()
                
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 401:
                    auth_manager._access_token = None
                    raise HTTPException(status_code=401, detail="Authentication expired")
                elif e.response.status_code == 403:
                    raise HTTPException(status_code=403, detail="Missing agentassists:write scope")
                else:
                    raise HTTPException(status_code=e.response.status_code, detail=e.response.text)
                    
    raise HTTPException(status_code=503, detail="CXone API rate limit exceeded after retries")

@app.on_event("startup")
async def startup():
    global auth_manager, embedding_model, chroma_collection
    auth_manager = CXoneAuthManager(
        client_id=os.getenv("CXONE_CLIENT_ID"),
        client_secret=os.getenv("CXONE_CLIENT_SECRET"),
        base_url=CXONE_BASE_URL
    )
    embedding_model = SentenceTransformer(EMBEDDING_MODEL_NAME)
    try:
        chroma_collection = chromadb.PersistentClient(path=CHROMA_DB_PATH).get_collection(COLLECTION_NAME)
    except Exception:
        raise RuntimeError("ChromaDB collection not found. Populate it before starting the service.")

@app.on_event("shutdown")
async def shutdown():
    await auth_manager.close()

@app.post("/assist/push")
async def push_assist(query: AgentQuery):
    if not query.query_text.strip():
        raise HTTPException(status_code=400, detail="Query text cannot be empty")
        
    context = retrieve_context(query.query_text, top_k=3)
    
    if not context:
        return {"status": "no_match", "message": "No relevant knowledge base chunks found"}
        
    result = await inject_assist_payload(
        conversation_id=query.conversation_id,
        agent_id=query.agent_id,
        context_chunks=context
    )
    
    return {"status": "injected", "assist_id": result.get("id"), "chunks_returned": len(context)}

Set environment variables CXONE_CLIENT_ID, CXONE_CLIENT_SECRET, and CXONE_BASE_URL before execution. The service listens on port 8000 by default.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token has expired or the client credentials are invalid.
• Fix: Verify CXONE_CLIENT_ID and CXONE_CLIENT_SECRET in environment variables. The CXoneAuthManager automatically clears cached tokens on 401 responses, but repeated failures indicate credential rotation or incorrect grant type configuration in the CXone admin console.
• Code: The get_access_token method enforces a sixty-second TTL buffer. If tokens expire prematurely, reduce the buffer or implement a background refresh task.

Error: 403 Forbidden

• Cause: The OAuth client lacks the agentassists:write scope.
• Fix: Navigate to the CXone platform administration, locate the API client configuration, and append agentassists:write to the authorized scopes. Restart the microservice to fetch a new token with the expanded scope.
• Code: The inject_assist_payload function explicitly catches 403 and raises a FastAPI exception with scope guidance.

Error: 429 Too Many Requests

• Cause: CXone enforces per-client and per-tenant rate limits on Assist endpoints. Burst traffic from multiple agents triggers throttling.
• Fix: The implementation reads the Retry-After header and applies exponential backoff. For high-volume environments, implement a request queue with token bucket rate limiting before calling the CXone endpoint.
• Code: The retry loop caps at three attempts. Increase max_retries if your CXone environment has stricter throttling windows.

Error: ChromaDB Collection Not Found

• Cause: The persistent client path does not contain the kb_chunks collection, or the collection was created with a different dimensionality.
• Fix: Run a seeding script that generates embeddings for your knowledge base documents and calls collection.add(). Ensure the embedding model used during seeding matches the runtime model exactly.
• Code: The startup event raises a RuntimeError if the collection is missing. Wrap the lookup in a try-except block and log the exact ChromaDB exception for debugging.

Error: Payload Schema Validation Failure

• Cause: CXone rejects Assist payloads with missing conversationId, agentId, or malformed content arrays.
• Fix: Validate that conversationId and agentId are non-empty strings. Ensure the content array contains objects with type and data keys. CXone does not accept raw strings in the content payload.
• Code: The Pydantic AgentQuery model enforces string types. Add explicit length checks if your CXone environment imposes character limits on Assist text payloads.

Official References

• NICE CXone Agent Assist API Documentation
• NICE CXone OAuth 2.0 Authentication Guide
• ChromaDB Persistent Client Documentation
• Sentence-Transformers Model Hub