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Troubleshooting Vertex AI Agent Builder: Data Store Indexing & Grounding Failures

 Building a RAG (Retrieval-Augmented Generation) pipeline using Vertex AI Agent Builder (formerly Gen App Builder) promises rapid deployment. However, the abstraction layer often hides critical failures.

A common scenario for engineers is the "Infinite Spinner" of death during document ingestion, or worse, an agent that refuses to answer questions clearly defined in the uploaded FAQs.

This post dissects the architecture of the underlying Discovery Engine to explain why these failures occur and provides production-grade Python solutions to fix ingestion stalls and hallucination issues caused by poor chunking.

The Problem: Ingestion Hangs and Retrieval Misses

Two distinct but related issues frequently plague production deployments:

  1. The Zombie Index: You upload a batch of PDFs or a URL sitemap. The status indicator remains on "Importing" or "Indexing" indefinitely (4+ hours for small datasets).
  2. The Chunking Gap: You have an FAQ document. A user asks a question verbatim from the document, but the agent responds with "I cannot answer that," or hallucinates an answer.

Both issues stem from a misalignment between your data format and Discovery Engine's internal processing logic.

Root Cause Analysis: Why Ingestion Fails

When Vertex AI reports "Ingestion in progress" for an unreasonable amount of time, it is rarely a system-wide outage. It is usually a silent partial failure.

Under the hood, Discovery Engine processes documents in batches. If a single document in a batch contains malformed metadata, corrupted encoding (e.g., a PDF with no text layer), or exceeds the 100MB limit without being flagged immediately, the UI polling mechanism often hangs waiting for a completion signal that will never arrive.

The Fix: Programmatic Error Diagnosis

Do not rely on the Cloud Console UI to debug ingestion. It swallows detailed error traces. Instead, use the Google Cloud Logging API to extract the specific failure reason.

Here is a Python script to verify ingestion status and extract the exact error message for failed documents.

import logging
from google.cloud import logging as cloud_logging
from datetime import datetime, timedelta, timezone

# Configuration
PROJECT_ID = "your-project-id"
DATA_STORE_ID = "your-datastore-id"

def analyze_ingestion_errors():
    """
    Fetches recent error logs specifically for Discovery Engine ingestion operations.
    """
    client = cloud_logging.Client(project=PROJECT_ID)
    
    # Filter for Discovery Engine errors in the last 24 hours
    now = datetime.now(timezone.utc)
    time_window = now - timedelta(hours=24)
    
    # Discovery Engine often logs to "cloudaudit.googleapis.com" or specific resource types
    filter_str = (
        f'resource.labels.service_name="discoveryengine.googleapis.com" '
        f'AND severity>=ERROR '
        f'AND timestamp >= "{time_window.isoformat()}"'
    )

    print(f"Scanning logs for Data Store: {DATA_STORE_ID}...")
    
    try:
        entries = client.list_entries(filter_=filter_str, page_size=50)
        error_count = 0
        
        for entry in entries:
            payload = entry.payload
            # Look for specific failure details in the proto payload
            if payload and "status" in payload:
                if payload["status"].get("code") != 0:
                    error_count += 1
                    print(f"--- Error Found at {entry.timestamp} ---")
                    print(f"Message: {payload.get('message', 'No message')}")
                    # Specific file details often hide in 'request' or 'resourceName'
                    print(f"Details: {payload}")

        if error_count == 0:
            print("No explicit API errors found. Check IAM permissions for the Service Agent.")
            
    except Exception as e:
        print(f"Failed to retrieve logs: {e}")

if __name__ == "__main__":
    analyze_ingestion_errors()

Why This Works

The Console UI polls for a high-level "Operation" status. If the operation hits a zombie state, the UI waits. The logs, however, record individual document failures immediately. This script bypasses the UI lag and identifies if a specific PDF password protection or JSON syntax error is blocking the queue.

Root Cause Analysis: The Grounding/Chunking Gap

If ingestion works, but retrieval fails, the issue is Layout Analysis.

When you upload a generic PDF, Vertex AI uses an OCR-like process to convert visual blocks into text chunks. It then vectorizes these chunks.

The Failure Scenario: Imagine an FAQ document:

Q: What is the refund policy? (Line break) A: You can request a refund within 30 days.

If the chunking window cuts off after "policy?", the Question and the Answer end up in different vector embeddings. When a user asks "What is the refund policy?", the system finds the chunk containing the question, but that chunk doesn't contain the answer. The LLM sees the question but no context to answer it, resulting in a fallback response.

The Fix: Forcing Structured Indexing

To guarantee high-retention retrieval for FAQs or technical specs, you must abandon unstructured PDF uploads. Instead, you must inject Structured Data (JSONL) directly into the Discovery Engine.

This allows you to explicitly define the chunk boundaries.

Step 1: The Schema

For an FAQ style interaction, do not use the default "Unstructured" setup. Create a data store with Structured data settings, or use the "chunking" override in your JSONL.

We will format our data into the specific JSONL format Discovery Engine expects.

Step 2: The Conversion Script

This script takes a standard CSV (Question, Answer) and converts it into the Discovery Engine JSONL format with strict schema mapping. This ensures the Q and A are always retrieved together.

import csv
import json
import uuid
from typing import List, Dict, Any

# Input/Output paths
INPUT_CSV = "faq_source.csv"
OUTPUT_JSONL = "formatted_for_discovery_engine.jsonl"

def convert_csv_to_discovery_jsonl():
    """
    Converts CSV to Vertex AI Discovery Engine Structured Data format.
    Schema requirements: 'id', 'structData' (for mapped fields), or 'content' (for search).
    """
    documents: List[Dict[str, Any]] = []

    try:
        with open(INPUT_CSV, mode='r', encoding='utf-8') as csv_file:
            reader = csv.DictReader(csv_file)
            
            for row in reader:
                # Generate a unique ID for the document
                doc_id = str(uuid.uuid4())
                
                question = row.get('question', '').strip()
                answer = row.get('answer', '').strip()
                category = row.get('category', 'General')

                if not question or not answer:
                    continue

                # Construct the JSON payload specifically for Agent Builder
                # Combining Q&A into 'content' ensures the vector embedding captures both.
                # 'structData' allows for post-retrieval filtering (metadata).
                
                doc_payload = {
                    "id": doc_id,
                    "jsonData": json.dumps({
                        "question": question,
                        "answer": answer,
                        "category": category,
                        # Concatenate for the vector search content field
                        "content": f"Question: {question}\nAnswer: {answer}"
                    })
                }
                documents.append(doc_payload)

        # Write to JSONL
        with open(OUTPUT_JSONL, mode='w', encoding='utf-8') as f:
            for doc in documents:
                f.write(json.dumps(doc) + "\n")
                
        print(f"Successfully converted {len(documents)} records to {OUTPUT_JSONL}")
        print("Upload this file to a Cloud Storage bucket and import as 'JSONL' in Agent Builder.")

    except FileNotFoundError:
        print(f"Error: Could not find input file {INPUT_CSV}")
    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    convert_csv_to_discovery_jsonl()

Step 3: Importing and Configuring

  1. Upload the formatted_for_discovery_engine.jsonl to Google Cloud Storage.
  2. In Agent Builder > Data Store > Import.
  3. Select JSONL for Structured Data.
  4. Crucial: In the schema mapping settings, ensure content is mapped to the indexable content, and category is mapped to filterable attributes.

Deep Dive: Why Structured Data Fixes Hallucination

By concatenating the Question and Answer into the content field manually (as seen in the code above), you control the semantic window.

When the Vector Search algorithm runs, it embeds Question: X \n Answer: Y as a single unit. When the user query matches "X", the retrieval system pulls the entire unit. The Generator (LLM) receives the full "Answer: Y" text in its context window, guaranteeing an accurate response.

Using unstructured PDFs leaves this association to chance; using JSONL makes it deterministic.

Common Pitfalls and Edge Cases

1. The "stale" Index

After deleting documents, Vertex AI can take up to 24 hours to purge them completely from the vector index. If you are iterating on data structures, create a new Data Store rather than deleting/re-uploading to the same one to avoid "ghost" retrieval results.

2. Digital vs. Scanned PDFs

If you must use PDFs, ensure they are "Digital Native" (generated from Word/Docs), not Scanned Images.

  • Test: Can you highlight the text in your PDF viewer?
  • Issue: If not, Discovery Engine uses OCR. OCR introduces character error rates (e.g., reading "Invoice" as "1nvoice"), which destroys keyword matching and vector similarity.

3. Filterable Metadata Limits

You might be tempted to add massive amounts of metadata into structData. Be aware that high-cardinality fields (fields with unique values for every document, like exact timestamps) can degrade filtering performance. Stick to categorical metadata (e.g., region, product_line, year).

Conclusion

The "magic" of Vertex AI Agent Builder relies heavily on the quality of the data ingestion pipeline. When the magic breaks, it is usually because the abstraction layer cannot handle the nuances of your specific data format.

By moving from UI-based uploads to programmatic, log-based debugging and enforcing structured JSONL schemas for your knowledge base, you convert a "black box" system into a deterministic, reliable RAG pipeline.

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