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Troubleshooting RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) enables language models to reason over external content—documents, knowledge bases, and more—by retrieving relevant info and feeding it into the model. But when things don't work as expected (e.g., the model "hallucinates" or misses relevant info), it's often not the model's fault—it's a context issue.

Let's break down the common causes and solutions so you can supercharge your RAG accuracy! 🚀

Common RAG Issues and How to Fix Them 🛠️​

1. The Model "Can't See" Your Content 👁️❌​

This is the most common problem—and it's typically caused by issues during your content ingestion process. The model doesn't hallucinate because it's wrong, it hallucinates because it was never given the right content in the first place.

✅ Solution: Check your content extraction settings

  • Navigate to: Admin Settings > Documents.
  • Make sure you're using a robust content extraction engine such as:
    • Apache Tika
    • Docling
    • Custom extractors (depending on your document types)
tip

Try uploading a document and preview the extracted content. If it's blank or missing key sections, you need to adjust your extractor settings or use a different engine.

2. Only a Small Part of the Document is Being Used 📄➡️✂️​

Open WebUI is designed to work with models that have limited context windows by default. For instance, many local models (e.g., Ollama's default models) are limited to 2048 tokens. Because of this, Open WebUI aggressively trims down the retrieved content to fit within the assumed available space.

✅ Solutions:

  • Go to Admin Settings > Documents
  • Either:
    • 💡 Enable "Bypass Embedding and Retrieval" — This sends full content directly without applying strict retrieval filters.
    • 🔍 Toggle on "Full Context Mode" — This injects more comprehensive content into the model prompt.
warning

📌 Warning: Be mindful of context limits—if your model can't handle more tokens, it will still get cut off.

3. Token Limit is Too Short ⏳​

Even if retrieval works, your model might still not process all the content it receives—because it simply can't.

By default, many models (especially Ollama-hosted LLMs) are limited to a 2048-token context window. That means only a fraction of your retrieved data will actually be used.

Why Web Search Especially Needs Larger Context Windows: Web pages are particularly challenging for small context windows because they contain far more content than typical documents. A single web page often includes:

  • Main content (the actual information you want)
  • Navigation menus, headers, and footers
  • Sidebar content and advertisements
  • Comments sections and related links
  • Metadata and embedded scripts

Even after content extraction and cleaning, web pages easily consume 4,000-8,000+ tokens of context. With a 2048-token limit, you're getting less than half the content, often missing the most relevant information that appears later in the page. Even 4096 tokens is frequently insufficient for comprehensive web content analysis.

✅ Solutions:

  • 🛠️ For Ollama Models: Extend the model's context length:

    • Navigate to: Admin Panel > Models > Settings (of the model you want to edit)
    • Go to Advanced Parameters
    • Modify the context length (e.g., increase to 8192+ or ideally beyond 16000 tokens if supported by your model)

  • 🌐 For OpenAI and Other Integrated Models: These models typically have their own context limits that cannot be modified through Open WebUI settings. Ensure you're using a model with sufficient context length.

ℹ️ Note: The 2048-token default is a big limiter for web search. For better RAG results with web content, we strongly recommend using at least 8192 tokens, with 16384+ being ideal for complex web pages.

✅ Alternative: Use an external LLM with larger context capacity

  • Try GPT-4, GPT-4o, Claude 3, Gemini 1.5, or Mixtral with 8k+ context
  • Compare performance to Ollama—notice the dramatic accuracy difference when more web content can be processed!
tip

For web search and complex document analysis, stick with models that support 8192+ token contexts in production use cases.

4. Embedding Model is Low-Quality or Mismatched 📉🧠​

Bad embeddings = bad retrieval. If the vector representation of your content is poor, the retriever won't pull the right content—no matter how powerful your LLM is.

✅ Solution:

  • Change to a high-quality embedding model (e.g., all-MiniLM-L6-v2, Instructor X, or OpenAI embeddings)
  • Go to: Admin Settings > Documents
  • After changing the model, be sure to:
    • ⏳ Reindex all existing documents so the new embeddings take effect.

📌 Remember: Embedding quality directly affects what content is retrieved.

5. ❌ 400: 'NoneType' object has no attribute 'encode'​

This error indicates a misconfigured or missing embedding model. When Open WebUI tries to create embeddings but doesn't have a valid model loaded, it can't process the text—and the result is this cryptic error.

💥 Cause:

  • Your embedding model isn't set up properly.
  • It might not have downloaded completely.
  • Or if you're using an external embedding model, it may not be accessible.

✅ Solution:

  • Go to: Admin Settings > Documents > Embedding Model
  • Save the embedding model again—even if it's already selected. This forces a recheck/download.
  • If you're using a remote/external embedding tool, make sure it's running and accessible to Open WebUI.
tip

After fixing the configuration, try re-embedding a document and verify no error is shown in the logs.

🧪 Pro Tip: Test with GPT-4o or GPT-4​

If you're not sure whether the issue is with retrieval, token limits, or embedding—try using GPT-4o temporarily (e.g., via OpenAI API). If the results suddenly become more accurate, it's a strong signal that your local model's context limit (2048 by default in Ollama) is the bottleneck.

  • GPT-4o handles larger inputs (128k tokens!)
  • Provides a great benchmark to evaluate your system's RAG reliability

6. Upload Limits and Restrictions 🛑​

Open WebUI implements various limits to ensure system stability and prevent abuse. It is important to understand how these limits apply to different upload methods:

  • Chat Uploads: Subject to global file size and count limits.
    • Max File Size: Controlled by RAG_FILE_MAX_SIZE (default: Unlimited). Configurable in Admin Panel > Settings > Documents > General > Max Upload Size.
    • Max File Count: Controlled by RAG_FILE_MAX_COUNT (default: Unlimited). Configurable in Admin Panel > Settings > Documents > General > Max Upload Count.
    • Allowed File Extensions: Controlled by RAG_ALLOWED_FILE_EXTENSIONS (default: All). Configurable in Admin Panel > Settings > Documents > General > Allowed File Extensions.
  • Folder Uploads: Subject to the FOLDER_MAX_FILE_COUNT environment variable (defaults to 100). This limit applies to the number of files directly associated with a folder.
  • Knowledge Base Uploads:
    • File Limit: Subject to the same RAG_FILE_MAX_SIZE limit as chats, but not subject to the RAG_FILE_MAX_COUNT limit, allowing for unlimited file uploads.
    • RAG Enforcement: All files uploaded to a Knowledge Base are automatically indexed. However, similar to chat uploads, Knowledge Bases can also be used in Full Context Mode (accessible in chat settings), which feeds the full document content to the model instead of using vector search retrieval.
info

By separating these limits, administrators can better manage resource usage across different features. For example, you might want to allow larger uploads in a curated Knowledge Base while restricting the number of files in ad-hoc Folder uploads.

7. Fragmented or Tiny Chunks 🧩​

When using the Markdown Header Splitter, documents can sometimes be split into very small fragments (e.g., just a table of contents entry or a short sub-header). These tiny chunks often lack enough semantic context for the embedding model to represent them accurately, leading to poor RAG results and unnecessary overhead.

✅ Solution:

  • Go to Admin Settings > Documents.
  • Increase the Chunk Min Size Target.
  • Setting this to a value like 1000 (or ~50-60% of your CHUNK_SIZE) will force the system to merge small fragments with neighboring chunks when possible, resulting in better semantic coherence and fewer total chunks.

8. Slow Follow-up Responses (KV Cache Invalidation) 🐌​

If your initial response is fast but follow-up questions become increasingly slow, you are likely experiencing KV Cache invalidation.

The Problem: By default, Open WebUI injects RAG context into the user message. As the chat progresses, new messages shift the position of this context, forcing models (like Ollama, llama.cpp, or vLLM) and cloud providers (like OpenAI or Vertex AI) to re-process the entire context for every turn.

✅ Solution:

  • Set the environment variable RAG_SYSTEM_CONTEXT=True.
  • This injects the RAG context into the system message, which stays at a fixed position at the start of the conversation.
  • This allows providers to effectively use KV prefix caching or Prompt Caching, resulting in nearly instant follow-up responses even with large documents.
ProblemFix
🤔 Model can't "see" contentCheck document extractor settings
🧹 Only part of content usedEnable Full Context Mode or Bypass Embedding
⏱ Limited by 2048 token capIncrease model context length (Admin Panel > Models > Settings > Advanced Parameters for Ollama) or use large-context LLM
📉 Inaccurate retrievalSwitch to a better embedding model, then reindex
❌ Upload limits bypassUse Folder uploads (with FOLDER_MAX_FILE_COUNT) but note that Knowledge Base limits are separate
🧩 Fragmented/Tiny ChunksIncrease Chunk Min Size Target to merge small sections
🐌 Slow follow-up responsesEnable RAG_SYSTEM_CONTEXT=True to fix KV cache invalidation
Still confused?Test with GPT-4o and compare outputs

9. API File Upload: "The content provided is empty" Error​

When uploading files via the API and immediately adding them to a knowledge base, you may encounter:

400: The content provided is empty. Please ensure that there is text or data present before proceeding.

The Problem: This is a race condition, not an actual empty file. By default, file uploads are processed asynchronously—the upload endpoint returns immediately with a file ID while content extraction and embedding computation happen in the background. If you try to add the file to a knowledge base before processing completes, the system sees empty content.

✅ Solution: Wait for Processing to Complete

Before adding a file to a knowledge base, poll the status endpoint until processing is complete:

import requests
import time

def wait_for_processing(token, file_id, timeout=300):
    url = f'http://localhost:3000/api/v1/files/{file_id}/process/status'
    headers = {'Authorization': f'Bearer {token}'}
    
    start_time = time.time()
    while time.time() - start_time < timeout:
        response = requests.get(url, headers=headers)
        status = response.json().get('status')
        
        if status == 'completed':
            return True
        elif status == 'failed':
            raise Exception(f"Processing failed: {response.json().get('error')}")
        
        time.sleep(2)  # Poll every 2 seconds
    
    raise TimeoutError("File processing timed out")

Status Values:

StatusMeaning
pendingStill processing
completedReady to add to knowledge base
failedProcessing failed (check error field)
tip

For complete API workflow examples including proper status checking, see the API Endpoints documentation.

10. CUDA Out of Memory During Embedding 💥🎮​

When processing large files or many files in sequence, you may encounter CUDA OOM errors like:

CUDA out of memory. Tried to allocate X MiB. GPU has a total capacity of Y GiB of which Z MiB is free.

Common Causes:

  • Embedding model competing with chat model for GPU memory
  • PyTorch memory fragmentation from repeated small allocations
  • Large documents creating memory spikes during embedding

✅ Solutions:

  1. Isolate Embedding to a Different GPU (if available): Set CUDA_VISIBLE_DEVICES to pin embedding to a specific GPU separate from your chat model.

  2. Reduce Embedding Batch Size: Lower RAG_EMBEDDING_BATCH_SIZE (e.g., from 32 to 8 or 4) to reduce peak memory usage.

  3. Enable Expandable Segments: Set the environment variable to reduce fragmentation:

    PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

  4. Restart Between Large Ingestion Jobs: If memory fragmentation builds up, restart the Open WebUI container to clear CUDA contexts.

  5. Use Smaller Embedding Models: Consider using lighter embedding models for bulk ingestion, then switch to higher-quality models for production queries.

  6. Separate Ingestion from Chat: Don't run the chat model during large ingestion jobs. Load the chat model after embeddings finish.

11. PDF OCR Not Extracting Text from Images 📷❌​

If PDFs containing images with text are returning empty content:

The Problem: The default "pypdf" content extractor may struggle with certain PDF formats, especially those containing primarily image-based content.

✅ Solutions:

  1. Use a Different Content Extraction Engine:

    • Navigate to Admin Settings > Documents
    • Try Apache Tika or Docling for better OCR support

  2. Enable PDF Image Extraction:

    • In Admin Settings > Documents, ensure PDF Extract Images (OCR) is enabled

  3. Update pypdf (if using the default engine): Recent pypdf releases (6.0.0+) have improved handling of various PDF formats

  4. Check for Corrupted PDFs: Verify the PDF opens correctly in a standard PDF viewer before uploading

ProblemFix
📄 API returns "empty content" errorWait for file processing to complete before adding to knowledge base
💥 CUDA OOM during embeddingReduce batch size, isolate GPU, or restart container
📷 PDF images not extractedUse Tika/Docling, enable OCR, or update pypdf

By optimizing these areas—extraction, embedding, retrieval, and model context—you can dramatically improve how accurately your LLM works with your documents. Don't let a 2048-token window or weak retrieval pipeline hold back your AI's power 🎯.