Implementing NICE Cognigy.AI RAG Pipelines with Python

What You Will Build

A Python microservice that extracts conversation context from Cognigy.AI, executes hybrid BM25 and vector searches against a Weaviate cluster, ranks retrieved documentation chunks, formats them with citation markers, writes them back via the Cognigy.AI Context API, and serves cached responses for repeated queries to mitigate vector store latency.

Prerequisites

• Cognigy.AI tenant access with a configured OAuth 2.0 client (Client Credentials grant)
• Required OAuth scopes: context:read, context:write, sessions:read
• Weaviate cluster with hybrid search enabled and a pre-populated document collection
• Python 3.10 or later
• Dependencies: requests==2.31.0, weaviate-client==4.4.0, cachetools==5.3.1, hashlib (standard library)

Authentication Setup

Cognigy.AI uses standard OAuth 2.0 client credentials. The token must be cached and refreshed before expiration to avoid unnecessary authentication round trips. The following implementation uses requests with automatic retry logic for transient network failures and 429 rate limit responses.

import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from typing import Optional

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.base_url = f"https://{tenant}.cognigy.ai/api/v2"
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0
        
        self.session = requests.Session()
        retry_strategy = Retry(
            total=3,
            backoff_factor=0.5,
            status_forcelist=[429, 500, 502, 503, 504]
        )
        adapter = HTTPAdapter(max_retries=retry_strategy)
        self.session.mount("https://", adapter)

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

    def get_token(self) -> str:
        if not self.token or time.time() >= self.token_expiry - 60:
            return self._fetch_token()
        return self.token

The _fetch_token method requests a fresh access token and stores the expiration timestamp. The get_token method checks if the token will expire within the next sixty seconds and triggers a refresh if necessary. This prevents mid-request authentication failures during long-running RAG operations.

Implementation

Step 1: Conversation Context Retrieval

Cognigy.AI exposes session context through a dedicated REST endpoint. The service must extract the latest user input and any existing conversation variables to maintain state continuity.

import json
from typing import Dict, Any

class CognigyContextManager:
    def __init__(self, auth: CognigyAuthManager):
        self.auth = auth
        self.base_url = auth.base_url

    def get_session_context(self, session_id: str) -> Dict[str, Any]:
        url = f"{self.base_url}/sessions/{session_id}/context"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}"}
        response = requests.get(url, headers=headers, timeout=10)
        
        if response.status_code == 401:
            self.auth.token = None
            response = requests.get(url, headers={"Authorization": f"Bearer {self.auth.get_token()}"}, timeout=10)
            
        response.raise_for_status()
        return response.json()

    def extract_user_query(self, context_data: Dict[str, Any]) -> str:
        variables = context_data.get("variables", [])
        for var in variables:
            if var.get("key") == "userInput":
                return var.get("value", "")
        return ""

The get_session_context method retrieves the full variable map for a given session. The extract_user_query method scans the variable array for the userInput key, which Cognigy.AI populates during conversation flows. If the key does not exist, the method returns an empty string to prevent downstream failures.

Step 2: Weaviate Hybrid Search Execution

Weaviate supports native hybrid search that combines dense vector similarity and sparse BM25 keyword matching. The alpha parameter controls the weighting between the two methods, where 0.0 prioritizes BM25 and 1.0 prioritizes vector similarity.

import weaviate
from weaviate.classes.query import HybridQuery
from typing import List, Dict, Any

class WeaviateSearchEngine:
    def __init__(self, url: str, api_key: str, collection_name: str = "Documentation"):
        self.client = weaviate.connect_to_weaviate_cloud(url, api_key)
        self.collection = self.client.collections.get(collection_name)
        
    def hybrid_search(self, query: str, alpha: float = 0.5, limit: int = 5) -> List[Dict[str, Any]]:
        if not query.strip():
            return []
            
        try:
            response = self.collection.query.hybrid(
                query=query,
                alpha=alpha,
                limit=limit,
                return_metadata=True
            )
            
            results = []
            for obj in response.objects:
                results.append({
                    "text": obj.properties.get("content", ""),
                    "source": obj.properties.get("source_url", ""),
                    "score": obj.metadata.distance if hasattr(obj.metadata, "distance") else obj.metadata.score,
                    "id": obj.uuid
                })
            return results
        except weaviate.exceptions.WeaviateConnectionError as e:
            raise ConnectionError(f"Weaviate connection failed: {e}")
        except weaviate.exceptions.UnexpectedStatusCodeError as e:
            raise RuntimeError(f"Weaviate query failed: {e}")

The hybrid_search method configures the query with the specified alpha weight and fetches the top limit results. It extracts the content text, source URL, and composite relevance score from each object. The method explicitly handles Weaviate connection and status code exceptions to surface clear error messages for upstream retry logic.

Step 3: Result Ranking and Citation Generation

Raw Weaviate results require deterministic ranking and citation formatting before injection into the LLM prompt. The service sorts results by descending relevance score and appends numeric citation markers that correspond to source metadata.

from typing import List, Dict, Any, Tuple

class CitationFormatter:
    @staticmethod
    def rank_and_format(results: List[Dict[str, Any]]) -> Tuple[str, List[Dict[str, Any]]]:
        if not results:
            return "", []
            
        sorted_results = sorted(results, key=lambda x: x.get("score", 0), reverse=True)
        citations = []
        context_lines = []
        
        for idx, item in enumerate(sorted_results, start=1):
            citation_id = f"[{idx}]"
            source = item.get("source", "Unknown Source")
            text = item.get("text", "").strip()
            
            if len(text) > 500:
                text = text[:500] + "..."
                
            context_lines.append(f"{citation_id} {text}")
            citations.append({
                "id": citation_id,
                "source": source,
                "score": item.get("score", 0)
            })
            
        formatted_context = "\n\n".join(context_lines)
        return formatted_context, citations

The rank_and_format method truncates excessively long chunks to respect LLM token limits while preserving semantic boundaries. It constructs a plain text context block with inline citation markers and returns a structured citation list for downstream tracking.

Step 4: Context Injection and Latency Mitigation

Vector database queries introduce variable latency. The service implements a deterministic cache keyed by a normalized hash of the user query. Cached responses include the formatted context and citation metadata. The service writes results back to Cognigy.AI using the Context API.

import hashlib
from cachetools import TTLCache
from typing import Optional

class RAGPipelineService:
    def __init__(self, auth: CognigyAuthManager, search_engine: WeaviateSearchEngine, ttl_seconds: int = 300):
        self.context_mgr = CognigyContextManager(auth)
        self.search_engine = search_engine
        self.formatter = CitationFormatter()
        self.cache: TTLCache[str, dict] = TTLCache(maxsize=1024, ttl=ttl_seconds)
        self.auth = auth

    def _normalize_query(self, query: str) -> str:
        return hashlib.sha256(query.strip().lower().encode()).hexdigest()

    def process_query(self, session_id: str) -> dict:
        context_data = self.context_mgr.get_session_context(session_id)
        user_query = self.context_mgr.extract_user_query(context_data)
        cache_key = self._normalize_query(user_query)
        
        if cache_key in self.cache:
            cached = self.cache[cache_key]
            self._inject_context(session_id, cached["context"], cached["citations"])
            return cached

        raw_results = self.search_engine.hybrid_search(user_query, alpha=0.5, limit=5)
        formatted_context, citations = self.formatter.rank_and_format(raw_results)
        
        result = {
            "context": formatted_context,
            "citations": citations,
            "source": "live"
        }
        
        self.cache[cache_key] = result
        self._inject_context(session_id, formatted_context, citations)
        return result

    def _inject_context(self, session_id: str, context_text: str, citations: list) -> None:
        url = f"{self.auth.base_url}/sessions/{session_id}/context"
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json"
        }
        payload = {
            "variables": [
                {"key": "rag_context", "value": context_text},
                {"key": "rag_citations", "value": str(citations)}
            ]
        }
        
        response = requests.post(url, json=payload, headers=headers, timeout=10)
        if response.status_code == 413:
            truncated = context_text[:2000]
            payload["variables"][0]["value"] = truncated
            response = requests.post(url, json=payload, headers=headers, timeout=10)
        response.raise_for_status()

The process_query method orchestrates the full pipeline. It checks the cache first, executes the hybrid search on a cache miss, formats the results, stores them in the TTL cache, and injects them into the Cognigy.AI session. The _inject_context method handles payload size limits by truncating the context if the API returns a 413 status.

Complete Working Example

The following script combines all components into a runnable module. Replace the placeholder credentials with your actual tenant configuration.

import time
import requests
import hashlib
import weaviate
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from cachetools import TTLCache
from typing import Dict, Any, List, Tuple, Optional

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.base_url = f"https://{tenant}.cognigy.ai/api/v2"
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0
        
        self.session = requests.Session()
        retry_strategy = Retry(total=3, backoff_factor=0.5, status_forcelist=[429, 500, 502, 503, 504])
        adapter = HTTPAdapter(max_retries=retry_strategy)
        self.session.mount("https://", adapter)

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

    def get_token(self) -> str:
        if not self.token or time.time() >= self.token_expiry - 60:
            return self._fetch_token()
        return self.token

class CognigyContextManager:
    def __init__(self, auth: CognigyAuthManager):
        self.auth = auth
        self.base_url = auth.base_url

    def get_session_context(self, session_id: str) -> Dict[str, Any]:
        url = f"{self.base_url}/sessions/{session_id}/context"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}"}
        response = requests.get(url, headers=headers, timeout=10)
        if response.status_code == 401:
            self.auth.token = None
            response = requests.get(url, headers={"Authorization": f"Bearer {self.auth.get_token()}"}, timeout=10)
        response.raise_for_status()
        return response.json()

    def extract_user_query(self, context_data: Dict[str, Any]) -> str:
        variables = context_data.get("variables", [])
        for var in variables:
            if var.get("key") == "userInput":
                return var.get("value", "")
        return ""

class WeaviateSearchEngine:
    def __init__(self, url: str, api_key: str, collection_name: str = "Documentation"):
        self.client = weaviate.connect_to_weaviate_cloud(url, api_key)
        self.collection = self.client.collections.get(collection_name)
        
    def hybrid_search(self, query: str, alpha: float = 0.5, limit: int = 5) -> List[Dict[str, Any]]:
        if not query.strip():
            return []
        try:
            response = self.collection.query.hybrid(query=query, alpha=alpha, limit=limit, return_metadata=True)
            results = []
            for obj in response.objects:
                results.append({
                    "text": obj.properties.get("content", ""),
                    "source": obj.properties.get("source_url", ""),
                    "score": obj.metadata.distance if hasattr(obj.metadata, "distance") else obj.metadata.score,
                    "id": obj.uuid
                })
            return results
        except weaviate.exceptions.WeaviateConnectionError as e:
            raise ConnectionError(f"Weaviate connection failed: {e}")
        except weaviate.exceptions.UnexpectedStatusCodeError as e:
            raise RuntimeError(f"Weaviate query failed: {e}")

class CitationFormatter:
    @staticmethod
    def rank_and_format(results: List[Dict[str, Any]]) -> Tuple[str, List[Dict[str, Any]]]:
        if not results:
            return "", []
        sorted_results = sorted(results, key=lambda x: x.get("score", 0), reverse=True)
        citations = []
        context_lines = []
        for idx, item in enumerate(sorted_results, start=1):
            citation_id = f"[{idx}]"
            source = item.get("source", "Unknown Source")
            text = item.get("text", "").strip()
            if len(text) > 500:
                text = text[:500] + "..."
            context_lines.append(f"{citation_id} {text}")
            citations.append({"id": citation_id, "source": source, "score": item.get("score", 0)})
        return "\n\n".join(context_lines), citations

class RAGPipelineService:
    def __init__(self, auth: CognigyAuthManager, search_engine: WeaviateSearchEngine, ttl_seconds: int = 300):
        self.context_mgr = CognigyContextManager(auth)
        self.search_engine = search_engine
        self.formatter = CitationFormatter()
        self.cache: TTLCache[str, dict] = TTLCache(maxsize=1024, ttl=ttl_seconds)
        self.auth = auth

    def _normalize_query(self, query: str) -> str:
        return hashlib.sha256(query.strip().lower().encode()).hexdigest()

    def process_query(self, session_id: str) -> dict:
        context_data = self.context_mgr.get_session_context(session_id)
        user_query = self.context_mgr.extract_user_query(context_data)
        cache_key = self._normalize_query(user_query)
        if cache_key in self.cache:
            cached = self.cache[cache_key]
            self._inject_context(session_id, cached["context"], cached["citations"])
            return cached
        raw_results = self.search_engine.hybrid_search(user_query, alpha=0.5, limit=5)
        formatted_context, citations = self.formatter.rank_and_format(raw_results)
        result = {"context": formatted_context, "citations": citations, "source": "live"}
        self.cache[cache_key] = result
        self._inject_context(session_id, formatted_context, citations)
        return result

    def _inject_context(self, session_id: str, context_text: str, citations: list) -> None:
        url = f"{self.auth.base_url}/sessions/{session_id}/context"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}", "Content-Type": "application/json"}
        payload = {"variables": [{"key": "rag_context", "value": context_text}, {"key": "rag_citations", "value": str(citations)}]}
        response = requests.post(url, json=payload, headers=headers, timeout=10)
        if response.status_code == 413:
            payload["variables"][0]["value"] = context_text[:2000]
            response = requests.post(url, json=payload, headers=headers, timeout=10)
        response.raise_for_status()

if __name__ == "__main__":
    auth = CognigyAuthManager(
        tenant="your-tenant",
        client_id="your-client-id",
        client_secret="your-client-secret"
    )
    search = WeaviateSearchEngine(
        url="https://your-cluster.weaviate.network",
        api_key="your-weaviate-api-key"
    )
    pipeline = RAGPipelineService(auth, search)
    result = pipeline.process_query("your-session-id")
    print(result)

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token has expired or the client credentials are invalid.
• Fix: Verify the tenant URL matches your deployment region. Ensure the OAuth client has the context:read and context:write scopes assigned. The authentication manager automatically refreshes tokens, but initial credential mismatches will persist.
• Code: The get_token method checks expiration and calls _fetch_token. If the initial fetch fails, inspect the client_id and client_secret values.

Error: 429 Too Many Requests

• Cause: Cognigy.AI or Weaviate has enforced a rate limit due to high query volume.
• Fix: The HTTPAdapter retry strategy automatically backs off for 429 responses. Implement exponential backoff at the application level if retries exhaust. Reduce cache TTL to increase cache hit rates and decrease upstream calls.
• Code: Retry(total=3, backoff_factor=0.5, status_forcelist=[429, 500, 502, 503, 504]) handles transient rate limits.

Error: 413 Payload Too Large

• Cause: The formatted context exceeds Cognigy.AI context variable size limits.
• Fix: The _inject_context method detects 413 responses and truncates the context to two thousand characters. Adjust the truncation threshold based on your tenant configuration.
• Code: The conditional check if response.status_code == 413 triggers automatic truncation and a second POST attempt.

Error: WeaviateConnectionError

• Cause: Network isolation, invalid API key, or cluster downtime.
• Fix: Verify that the host environment has outbound connectivity to the Weaviate cluster on port 443. Confirm the API key has read permissions for the target collection.
• Code: The hybrid_search method catches WeaviateConnectionError and raises a descriptive ConnectionError for upstream logging.

Error: Empty Context Variables

• Cause: The session does not contain a userInput variable or the variable key name differs from your flow configuration.
• Fix: Inspect the raw context payload returned by get_session_context. Update extract_user_query to match your actual variable key. Cognigy.AI flows must explicitly set the input variable before invoking the RAG skill.
• Code: The method iterates through context_data["variables"] and returns an empty string if the key is missing, preventing downstream indexing errors.

Official References

• Cognigy.AI Context API Documentation
• Cognigy.AI OAuth 2.0 Client Credentials
• Weaviate Hybrid Search Guide
• Weaviate Python Client v4 Documentation