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🚨 Embedding Troubleshooting Quick Start

Common Error Messages & Instant Fixes

⚠️ "shapes (768,) and (384,) not aligned"

What it means: Your query embeddings (768D) don't match stored embeddings (384D)

Instant fix:

# Open .env file and change:
EMBEDDING_PROVIDER=huggingface
HUGGINGFACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Restart your application

⚠️ "shapes (384,) and (768,) not aligned"

What it means: Your query embeddings (384D) don't match stored embeddings (768D)

Instant fix:

# Open .env file and change:
EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=nomic-embed-text:v1.5

# Make sure Ollama is running: ollama serve
# Pull the model: ollama pull nomic-embed-text:v1.5
# Restart your application

⚠️ "shapes (1536,) and (384,) not aligned"

What it means: Your query embeddings (1536D) don't match stored embeddings (384D)

Instant fix:

# Open .env file and change:
EMBEDDING_PROVIDER=huggingface
HUGGINGFACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Restart your application

🔧 5-Minute Fix Guide

Step 1: Identify Your Error (30 seconds)

Look at your error message and find the dimension numbers:

  • shapes (X,) and (Y,) → X = query dimensions, Y = stored dimensions

Step 2: Choose Matching Model (1 minute)

Stored Dimensions (Y) Set in .env
384 EMBEDDING_PROVIDER=huggingface
HUGGINGFACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
768 EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=nomic-embed-text:v1.5
1024 EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=mxbai-embed-large
1536 EMBEDDING_PROVIDER=openai
OPENAI_EMBEDDING_MODEL=text-embedding-3-small

Step 3: Update Configuration (2 minutes)

# Edit your .env file
nano .env  # or use your preferred editor

# Find the EMBEDDING_PROVIDER lines and update them
# Save the file

Step 4: Restart Application (1 minute)

# Kill current process (Ctrl+C)
# Restart
uvicorn app:app --reload

Step 5: Test (30 seconds)

# Test with a simple query
curl -X POST "http://localhost:8080/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "chicken recipe"}'

🔍 Alternative: Start Fresh

If you prefer to use a different embedding model permanently:

Option A: Regenerate Database (5 minutes)

# 1. Choose your preferred model in .env
EMBEDDING_PROVIDER=ollama
OLLAMA_EMBEDDING_MODEL=nomic-embed-text:v1.5

# 2. Enable database refresh
DB_REFRESH_ON_START=true

# 3. Restart application (this will rebuild everything)
uvicorn app:app --reload

# 4. IMPORTANT: Disable refresh after startup
DB_REFRESH_ON_START=false

Option B: Switch Vector Store (2 minutes)

# Switch to ChromaDB (will create fresh database)
VECTOR_STORE_PROVIDER=chromadb

# Restart application
uvicorn app:app --reload

⚡ Prevention Tips

Document Your Choice

Add a comment to your .env file:

# Created 2025-08-27 with all-MiniLM-L6-v2 (384 dimensions)
EMBEDDING_PROVIDER=huggingface
HUGGINGFACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

Consistent Development

If working in a team, ensure everyone uses the same configuration:

# Share this in your team chat:
# "Use EMBEDDING_PROVIDER=huggingface with all-MiniLM-L6-v2"

Still stuck? Check the full Embedding Compatibility Guide for detailed explanations.