Indexing External Knowledge Base Documents in Cognigy.AI via REST API with Python

What You Will Build

• A Python module that constructs, validates, and ingests external document payloads into Cognigy.AI vector indices using atomic POST operations.
• This tutorial uses the Cognigy.AI REST API with a custom Python HTTP client wrapper built on httpx.
• The code is written in Python 3.9+ and covers payload construction, schema validation, duplication checks, latency tracking, webhook synchronization, and audit logging.

Prerequisites

• Cognigy.AI tenant URL and API credentials (OAuth 2.0 client ID and secret)
• Required OAuth scopes: kb:write, kb:read, webhooks:write
• Python 3.9 or higher
• External dependencies: httpx==0.27.0, pydantic==2.6.0, python-dotenv==1.0.0, hypothesis (for validation testing if needed), uuid==1.30
• Access to a preconfigured Cognigy.AI knowledge base and vector index with known embedding dimensions (typically 768 or 1536 depending on the selected model)

Authentication Setup

Cognigy.AI authenticates API requests using bearer tokens. The following client handles token acquisition, caching, and automatic refresh when the token expires. It also implements retry logic for rate limit responses.

import os
import time
import httpx
from typing import Optional

class CognigyAuth:
    def __init__(self, tenant_url: str, client_id: str, client_secret: str, scopes: str = "kb:write kb:read webhooks:write"):
        self.tenant_url = tenant_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http = httpx.Client(
            base_url=self.tenant_url,
            timeout=30.0,
            transport=httpx.HTTPTransport(retries=3)
        )

    def _acquire_token(self) -> str:
        response = self.http.post(
            "/api/v1/auth/token",
            data={
                "grant_type": "client_credentials",
                "client_id": self.client_id,
                "client_secret": self.client_secret,
                "scope": self.scopes
            }
        )
        response.raise_for_status()
        payload = response.json()
        self.token = payload["access_token"]
        self.token_expiry = time.time() + payload["expires_in"] - 60
        return self.token

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

    def headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_valid_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

Implementation

Step 1: Initialize Index Client and Configure Constraints

The indexer client wraps authentication and defines vector database constraints. Cognigy.AI enforces maximum document counts per index and requires explicit chunking configurations.

import hashlib
import time
from dataclasses import dataclass
from typing import List, Dict, Any
import httpx
from .auth import CognigyAuth

@dataclass
class IndexConstraints:
    max_documents: int = 50000
    embedding_dimensions: int = 768
    embedding_model: str = "text-embedding-ada-002"
    chunk_size_matrix: Dict[str, int] = None

    def __post_init__(self):
        if self.chunk_size_matrix is None:
            self.chunk_size_matrix = {
                "min_chunk_size": 128,
                "max_chunk_size": 512,
                "overlap_tokens": 32
            }

class CognigyIndexClient:
    def __init__(self, auth: CognigyAuth, kb_id: str, index_id: str, constraints: IndexConstraints):
        self.auth = auth
        self.kb_id = kb_id
        self.index_id = index_id
        self.constraints = constraints
        self.http = httpx.Client(
            base_url=auth.tenant_url,
            timeout=30.0,
            transport=httpx.HTTPTransport(retries=3)
        )

    def _request(self, method: str, path: str, **kwargs) -> httpx.Response:
        headers = self.auth.headers()
        headers["X-Index-Id"] = self.index_id
        response = self.http.request(method, path, headers=headers, **kwargs)
        if response.status_code == 401:
            self.auth.token = None
            return self._request(method, path, **kwargs)
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 5))
            time.sleep(retry_after)
            return self._request(method, path, **kwargs)
        return response

Step 2: Construct and Validate Index Payloads

Index payloads must reference external document URLs, specify chunking parameters, and declare the embedding model. The validation pipeline checks schema compliance, enforces maximum document limits, and verifies embedding dimensions.

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

class ChunkingConfig(BaseModel):
    min_chunk_size: int = Field(ge=64, le=1024)
    max_chunk_size: int = Field(ge=128, le=2048)
    overlap_tokens: int = Field(ge=0, le=256)

    @validator("max_chunk_size")
    def max_must_exceed_min(cls, v, values):
        if v <= values.get("min_chunk_size", 0):
            raise ValueError("max_chunk_size must exceed min_chunk_size")
        return v

class DocumentPayload(BaseModel):
    document_id: str
    source_url: str
    format: str = Field(pattern="^(pdf|docx|txt|md|html)$")
    chunking: ChunkingConfig
    embedding_model: str
    metadata_extraction: bool = True

    @validator("embedding_model")
    def validate_model(cls, v):
        allowed_models = ["text-embedding-ada-002", "cognigy-embed-v1", "openai-ada-002"]
        if v not in allowed_models:
            raise ValueError(f"Unsupported embedding model: {v}")
        return v

class IndexBatch(BaseModel):
    documents: List[DocumentPayload]
    index_id: str
    knowledge_base_id: str

    @validator("documents")
    def check_max_limit(cls, v, values):
        constraints = IndexConstraints()
        if len(v) > constraints.max_documents:
            raise ValueError(f"Batch exceeds maximum document limit of {constraints.max_documents}")
        return v

Step 3: Atomic Document Ingestion with Format Verification

Document ingestion uses an atomic POST operation. The API verifies file format compatibility, triggers automatic metadata extraction, and returns an ingestion job identifier. Pagination is handled by splitting batches that approach the maximum limit.

import json
from typing import Dict, Any

class CognigyIndexClient:
    # ... previous methods ...

    def ingest_documents(self, batch: IndexBatch) -> Dict[str, Any]:
        endpoint = f"/api/v1/knowledge-bases/{self.kb_id}/documents/ingest"
        payload = batch.dict()
        
        response = self._request("POST", endpoint, json=payload)
        response.raise_for_status()
        
        result = response.json()
        if result.get("status") != "queued":
            raise RuntimeError(f"Ingestion failed with status: {result.get('status')}")
        
        return {
            "job_id": result["job_id"],
            "document_count": len(batch.documents),
            "ingestion_timestamp": time.time()
        }

    def verify_format_compatibility(self, document_url: str, expected_format: str) -> bool:
        endpoint = f"/api/v1/knowledge-bases/{self.kb_id}/documents/verify"
        response = self._request("POST", endpoint, json={
            "url": document_url,
            "expected_format": expected_format
        })
        response.raise_for_status()
        return response.json().get("compatible", False)

Step 4: Index Validation and Duplication/Dimension Checking

Before committing documents to the vector store, the client validates content uniqueness using SHA-256 hashing and verifies that the target index supports the requested embedding dimensions.

class CognigyIndexClient:
    # ... previous methods ...

    def check_content_duplication(self, document_url: str) -> bool:
        response = self.http.get(document_url, timeout=15.0)
        response.raise_for_status()
        content_hash = hashlib.sha256(response.content).hexdigest()
        
        endpoint = f"/api/v1/indices/{self.index_id}/documents/search"
        existing = self._request("POST", endpoint, json={
            "content_hash": content_hash,
            "limit": 1
        })
        existing.raise_for_status()
        results = existing.json().get("results", [])
        return len(results) > 0

    def verify_embedding_dimensions(self) -> bool:
        endpoint = f"/api/v1/indices/{self.index_id}/metadata"
        response = self._request("GET", endpoint)
        response.raise_for_status()
        metadata = response.json()
        actual_dimensions = metadata.get("embedding_dimensions", 0)
        return actual_dimensions == self.constraints.embedding_dimensions

    def validate_index_pipeline(self, batch: IndexBatch) -> Dict[str, Any]:
        validation_results = {
            "dimension_match": self.verify_embedding_dimensions(),
            "duplicates_found": [],
            "format_validations": []
        }

        for doc in batch.documents:
            is_duplicate = self.check_content_duplication(doc.source_url)
            if is_duplicate:
                validation_results["duplicates_found"].append(doc.document_id)
            
            format_ok = self.verify_format_compatibility(doc.source_url, doc.format)
            validation_results["format_validations"].append({
                "document_id": doc.document_id,
                "compatible": format_ok
            })

        return validation_results

Step 5: Webhook Sync, Latency Tracking, and Audit Logging

The indexer registers webhook callbacks for external repository synchronization, measures ingestion latency, calculates retrieval accuracy rates, and generates structured audit logs for content governance.

import uuid
from datetime import datetime

class CognigyIndexClient:
    # ... previous methods ...

    def register_webhook_sync(self, webhook_url: str) -> str:
        endpoint = "/api/v1/webhooks"
        webhook_id = str(uuid.uuid4())
        payload = {
            "id": webhook_id,
            "url": webhook_url,
            "events": ["index.document.ingested", "index.validation.completed"],
            "knowledge_base_id": self.kb_id,
            "secret": os.getenv("WEBHOOK_SECRET", "default-secret")
        }
        response = self._request("POST", endpoint, json=payload)
        response.raise_for_status()
        return webhook_id

    def track_ingestion_latency(self, start_time: float, job_id: str) -> float:
        elapsed = time.time() - start_time
        endpoint = f"/api/v1/knowledge-bases/{self.kb_id}/jobs/{job_id}/metrics"
        self._request("POST", endpoint, json={
            "metric_type": "ingestion_latency_ms",
            "value": elapsed * 1000,
            "timestamp": datetime.utcnow().isoformat()
        })
        return elapsed

    def calculate_retrieval_accuracy(self, test_query: str, expected_doc_id: str) -> float:
        endpoint = f"/api/v1/vector-search/indices/{self.index_id}/query"
        response = self._request("POST", endpoint, json={
            "query": test_query,
            "limit": 5
        })
        response.raise_for_status()
        results = response.json().get("results", [])
        matched = any(r.get("document_id") == expected_doc_id for r in results)
        accuracy = 1.0 if matched else 0.0
        return accuracy

    def generate_audit_log(self, batch_id: str, validation: Dict, ingestion_result: Dict) -> str:
        log_entry = {
            "audit_id": str(uuid.uuid4()),
            "batch_id": batch_id,
            "timestamp": datetime.utcnow().isoformat(),
            "knowledge_base_id": self.kb_id,
            "index_id": self.index_id,
            "validation_summary": {
                "dimensions_valid": validation["dimension_match"],
                "duplicate_count": len(validation["duplicates_found"]),
                "format_failures": sum(1 for f in validation["format_validations"] if not f["compatible"])
            },
            "ingestion_status": ingestion_result.get("status", "completed"),
            "documents_processed": ingestion_result.get("document_count", 0),
            "action": "index_document_batch",
            "actor": "automated_indexer"
        }
        return json.dumps(log_entry, indent=2)

Complete Working Example

The following script combines authentication, validation, ingestion, and monitoring into a single executable module. Replace the environment variables with your Cognigy.AI credentials.

import os
import json
import time
from typing import List
from cognigy_indexer.auth import CognigyAuth
from cognigy_indexer.client import CognigyIndexClient, IndexConstraints, IndexBatch, DocumentPayload, ChunkingConfig

def run_indexer():
    auth = CognigyAuth(
        tenant_url=os.getenv("COGNIGY_TENANT_URL"),
        client_id=os.getenv("COGNIGY_CLIENT_ID"),
        client_secret=os.getenv("COGNIGY_CLIENT_SECRET")
    )

    constraints = IndexConstraints(
        max_documents=10000,
        embedding_dimensions=768,
        embedding_model="text-embedding-ada-002"
    )

    client = CognigyIndexClient(
        auth=auth,
        kb_id=os.getenv("COGNIGY_KB_ID"),
        index_id=os.getenv("COGNIGY_INDEX_ID"),
        constraints=constraints
    )

    documents = [
        DocumentPayload(
            document_id="doc-ext-001",
            source_url="https://docs.example.com/guide-v2.pdf",
            format="pdf",
            chunking=ChunkingConfig(min_chunk_size=128, max_chunk_size=512, overlap_tokens=32),
            embedding_model="text-embedding-ada-002",
            metadata_extraction=True
        ),
        DocumentPayload(
            document_id="doc-ext-002",
            source_url="https://docs.example.com/api-reference.md",
            format="md",
            chunking=ChunkingConfig(min_chunk_size=128, max_chunk_size=512, overlap_tokens=32),
            embedding_model="text-embedding-ada-002",
            metadata_extraction=True
        )
    ]

    batch = IndexBatch(
        documents=documents,
        index_id=client.index_id,
        knowledge_base_id=client.kb_id
    )

    print("Validating index pipeline...")
    validation = client.validate_index_pipeline(batch)
    print(json.dumps(validation, indent=2))

    if not validation["dimension_match"]:
        raise RuntimeError("Embedding dimension mismatch. Index configuration requires adjustment.")
    
    duplicate_ids = validation["duplicates_found"]
    if duplicate_ids:
        print(f"Skipping duplicates: {duplicate_ids}")
        batch.documents = [d for d in batch.documents if d.document_id not in duplicate_ids]

    print("Registering webhook synchronization...")
    webhook_id = client.register_webhook_sync(os.getenv("EXTERNAL_REPO_WEBHOOK_URL"))
    print(f"Webhook registered: {webhook_id}")

    print("Ingesting documents...")
    start_time = time.time()
    ingestion_result = client.ingest_documents(batch)
    latency = client.track_ingestion_latency(start_time, ingestion_result["job_id"])
    print(f"Ingestion completed. Latency: {latency:.2f}s")

    print("Calculating retrieval accuracy...")
    accuracy = client.calculate_retrieval_accuracy("API authentication flow", "doc-ext-002")
    print(f"Retrieval accuracy: {accuracy}")

    print("Generating audit log...")
    audit_log = client.generate_audit_log(
        batch_id="batch-2024-001",
        validation=validation,
        ingestion_result={"status": "completed", "document_count": len(batch.documents)}
    )
    print(audit_log)

if __name__ == "__main__":
    run_indexer()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired bearer token or invalid client credentials.
• Fix: The CognigyAuth class automatically clears the cached token on 401 responses and re-acquires it. Ensure your client credentials have the kb:write scope assigned in the Cognigy.AI security settings.

Error: 422 Unprocessable Entity

• Cause: Payload schema violation, invalid chunking matrix values, or unsupported embedding model.
• Fix: Validate the ChunkingConfig constraints. The max_chunk_size must exceed min_chunk_size. Verify that embedding_model matches one of the allowed values in the validator. Check the response body for specific field errors.

Error: 429 Too Many Requests

• Cause: Exceeding Cognigy.AI rate limits during batch ingestion or validation queries.
• Fix: The httpx transport is configured with retry logic. The _request method reads the Retry-After header and sleeps before retrying. For large batches, split ingestion calls into chunks of 50 documents and introduce a 200 millisecond delay between requests.

Error: 500 Internal Server Error

• Cause: Vector database constraint violation, typically dimension mismatch or index corruption.
• Fix: Run verify_embedding_dimensions() before ingestion. If the index reports 1536 dimensions but your payload requests 768, update the IndexConstraints object or recreate the index with the correct model directive.

Official References

• Cognigy.AI Knowledge Base API Documentation
• Cognigy.AI Vector Search and Index Management
• Cognigy.AI Webhook Configuration Guide
• httpx Python HTTP Client Documentation
• Pydantic Data Validation Reference