Troubleshooting Guide

Troubleshooting Guide

Diagnosing and Resolving Common EdgeQuake Issues

This guide helps you identify and fix common problems when running EdgeQuake.

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Quick Diagnostics

Health Check

# Check basic health
curl http://localhost:8080/health

# Check readiness with dependencies
curl http://localhost:8080/ready

# Check if backend is responding
curl -I http://localhost:8080/api/v1/workspaces

Service Status

# Check all services (if using make)
make status

# Check PostgreSQL reachability
pg_isready -h localhost -p 5432

# Check Ollama
curl http://localhost:11434/api/tags

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Common Issues

1. Document Upload Errors

Symptom: “Expected request with Content-Type: application/json”

Cause: Using multipart form data (-F "file=@...") with the wrong endpoint.

Solution: EdgeQuake has two different endpoints for document upload:

Option A - Upload Text as JSON (/api/v1/documents):

curl -X POST http://localhost:8080/api/v1/documents \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Your text content here...",
    "title": "Document Title"
  }'

Option B - Upload Files (PDF, TXT, MD, etc.) (/api/v1/documents/upload):

curl -X POST http://localhost:8080/api/v1/documents/upload \
  -F "file=@your-document.pdf" \
  -F "title=My Document"

Symptom: “Failed to parse the request body as JSON”

Cause: Using -F flag (multipart) with a JSON endpoint, or mixing content types.

Solution: Choose the correct endpoint and format:

Upload Type	Endpoint	Content-Type	Format
Text/JSON	/api/v1/documents	application/json	-d '{...}'
Files	/api/v1/documents/upload	multipart/form-data	-F "file=@"
Batch Files	/api/v1/documents/upload/batch	multipart/form-data	-F "files=@"

Examples:

# ❌ WRONG - multipart to JSON endpoint
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@doc.pdf"

# ✅ CORRECT - multipart to upload endpoint
curl -X POST http://localhost:8080/api/v1/documents/upload \
  -F "file=@doc.pdf"

# ✅ CORRECT - JSON to documents endpoint
curl -X POST http://localhost:8080/api/v1/documents \
  -H "Content-Type: application/json" \
  -d '{"content": "Text here", "title": "My Doc"}'

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2. Server Won’t Start

Symptom: “Address already in use”

Cause: Port 8080 is already in use.

Solution:

# Find what's using port 8080
lsof -i :8080

# Kill the process
kill -9 <PID>

# Or use different port
PORT=9090 cargo run

Symptom: “DATABASE_URL is not valid” or a startup log says PostgreSQL storage failed to initialize

Cause: Invalid PostgreSQL connection string, unreachable database, or a database daemon that is not running.

Solution:

# Check format
DATABASE_URL="postgresql://user:password@host:port/database"

# Test connection directly
psql "$DATABASE_URL" -c "SELECT 1"

# Or use a lightweight readiness probe
pg_isready -h localhost -p 5432

Why this matters: EdgeQuake now fails fast and clearly at startup when PostgreSQL is unavailable. This is intentional and prevents flaky half-started server states.

Symptom: OrbStack stops unexpectedly or make dev says Docker daemon is unavailable

Cause: this is usually a host network route conflict, not an EdgeQuake crash. On the affected machine, the route table already claims broad private ranges such as 10/8, 172.16/12, or 192.168/16 through another interface (for example a VPN, Homebridge, or router helper). OrbStack/Docker then cannot create its bridge network and reports errors such as “failed to add network” or “conflict with existing route”.

What changed: make dev and make dev-bg now use incremental startup. They reuse healthy services, avoid blind stop/start cycles, and fail with a clear diagnosis instead of hammering Docker repeatedly.

Solution:

# Show the targeted diagnosis
make db-start

# If OrbStack still reports route conflicts:
# 1. stop the conflicting VPN / bridge helper
# 2. restart OrbStack
# 3. rerun the stack
make dev-bg

Symptom: “Extension ‘vector’ not found”

Cause: pgvector extension not installed.

Solution:

-- As superuser in PostgreSQL
CREATE EXTENSION IF NOT EXISTS vector;

Or rebuild Docker container:

docker compose down -v
docker compose up -d

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3. Document Processing Stuck

Symptom: Documents stay in “processing” status

Diagnosis:

# Check pending tasks
curl http://localhost:8080/api/v1/tasks?status=pending

# Check backend logs
docker compose logs -f edgequake

# Or if running locally
tail -f /tmp/edgequake-backend.log

Common Causes:

Cause	Solution
LLM rate limit	Wait and retry
Invalid API key	Check OPENAI_API_KEY
Ollama not running	Start Ollama: ollama serve
Worker crash	Restart backend

Solution:

# Restart workers cleanly
make stop
make dev

# Or manually retry document
curl -X POST "http://localhost:8080/api/v1/documents/$DOC_ID/reprocess"

Reliability note: delete, reprocess, and recovery flows are workspace-scoped and restart-safe. A deleted document should not be resurrected by stale task recovery after a backend restart.

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3. CI or local runs feel flaky

Symptom: repeated pushes trigger too many redundant CI runs

Cause: multiple in-progress jobs for the same branch create stale signal and wasted runner time.

Solution:

• use the repository workflow concurrency settings so newer runs cancel older ones
• keep heavy coverage and full-E2E flows out of the fastest blocking loop
• prefer readiness probes over fixed sleeps when starting backend and frontend services
• keep the pinned Rust toolchain active locally so clippy and formatting results match CI

---

4. PDF Extraction Issues

PDF extraction can fail or produce poor quality results due to PDF structure, encoding, or layout complexity. This section covers the most common PDF-specific problems.

Issue 3.1: No Text Extracted or Low-Content Warning

Symptom: After PDF upload, chunk_count = 0, chunks are empty, or the UI shows “Low text content - consider using Vision extraction”

Diagnosis:

# Check document details
curl http://localhost:8080/api/v1/documents/doc-uuid

# Response shows:
{
  "chunk_count": 0,
  "metadata": {"pages": 50, "pdf_extraction_method": "edgeparse"}
}

Cause:

• PDF is image-based (scanned document, no embedded text layer)
• edgeparse was used on a scan or image-only PDF

Solution 1 - Retry with Vision backend:

# Re-upload with explicit Vision backend
curl -X POST http://localhost:8080/api/v1/documents/upload \
  -F "file=@scanned_book.pdf" \
  -F "title=Scanned Book" \
  -F "pdf_parser_backend=vision"

Solution 2 - Set Vision as the workspace default for scan-heavy corpora:

curl -X PUT http://localhost:8080/api/v1/workspaces/$WORKSPACE_ID \
  -H "Content-Type: application/json" \
  -d '{"pdf_parser_backend":"vision"}'

When to use EdgeParse:

• Digital-native reports
• Invoices and statements with embedded text
• PDFs where speed and deterministic output matter more than OCR-like robustness

When to use Vision:

• Scanned documents
• Image-heavy PDFs
• Layouts with poor embedded text quality

Cost Warning: Vision mode consumes LLM calls and is slower than EdgeParse.

Verification:

• Check chunk_count > 0 in response
• Check document lineage shows pdf_extraction_method = "vision"
• Download chunks to verify content extracted

Related: See PDF Ingestion Tutorial for backend selection guidance.

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Issue 3.2: Tables Not Detected or Malformed

Symptom: Tables appear as scrambled text or not detected at all

Before (raw extraction):

Header1 Header2 Header3 Data1a Data1b
Data1c Data2a Data2b Data2c Data3a

Diagnosis:

# Check table detection
curl http://localhost:8080/api/v1/documents/doc-uuid

# Response:
{
  "metadata": {
    "tables_detected": 0  // Should be > 0 if tables exist
  }
}

Cause: Complex table layout (merged cells, nested structures, no clear borders)

Solution 1 - Enable Table Enhancement:

# LLM-based table refinement
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@financial_report.pdf" \
  -F "title=Financial Report" \
  -F 'config={"enhance_tables": true, "ai_temperature": 0.1}'

Solution 2 - Combine with Multi-Column Detection:

# For academic papers with tables in columns
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@research_paper.pdf" \
  -F 'config={
    "enhance_tables": true,
    "layout": {"detect_columns": true}
  }'

After (enhanced):

| Header 1 | Header 2 | Header 3 |
| -------- | -------- | -------- |
| Data 1a  | Data 1b  | Data 1c  |
| Data 2a  | Data 2b  | Data 2c  |
| Data 3a  | Data 3b  | Data 3c  |

Trade-offs:

• Processing time: 2x slower
• Cost: ~$0.0001 per table
• Accuracy: Significantly improved for complex tables

Limitations:

• Very complex tables (5+ levels of merged cells) may still fail
• Tables without any borders are harder to detect

Verification:

• Check tables_detected > 0 in response
• Inspect chunk content for proper markdown table format
• Verify cell alignments correct

Related: See PDF Processing Deep Dive for algorithm details.

---

Issue 3.3: Wrong Text Order (Multi-Column Layout)

Symptom: Text from different columns interleaved incorrectly

Example Problem:

# PDF has 2 columns:
Column 1: "The experiment showed that X leads to Y..."
Column 2: "In conclusion, we recommend Z..."

# Extracted (wrong order):
"The experiment In showed conclusion, that we X recommend leads Z... to Y..."

Diagnosis:

# Download chunks and check text order
curl http://localhost:8080/api/v1/documents/doc-uuid/chunks

# Look for interleaved text from different sections

Cause: PDF has multi-column layout (academic papers, newspapers, magazines)

Solution - Enable Column Detection:

# Detect and respect column boundaries
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@research_paper.pdf" \
  -F "title=Research Paper" \
  -F 'config={
    "layout": {
      "detect_columns": true,
      "column_gap_threshold": 20.0
    }
  }'

Configuration Options:

• detect_columns: Enable multi-column detection (default: true)
• column_gap_threshold: Minimum gap in points between columns (default: 20.0)
  • Increase for wider column gaps
  • Decrease for narrower column gaps

Verification:

• Read first few chunks - text should flow naturally
• Check column boundaries respected
• Verify reading order left-to-right within each column

Tip: Academic papers almost always need column detection enabled.

Related: See PDF Processing Deep Dive for XY-Cut algorithm.

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Issue 3.4: Encoding Errors (Special Characters)

Symptom: � or ? characters appear instead of actual text

Examples:

• "Caf�" instead of "Café"
• "Na�ve" instead of "Naïve"
• "????" instead of Chinese/Arabic text

Diagnosis:

# Check extracted content for garbled characters
curl http://localhost:8080/api/v1/documents/doc-uuid/chunks | jq -r '.chunks[0].content'

# Look for � or ? characters

Cause: PDF uses custom fonts or non-standard encoding not supported by text extraction

Solution 1 - Retry with the Vision backend:

# LLM vision reads the actual glyphs
curl -X POST http://localhost:8080/api/v1/documents/upload \
  -F "file=@custom_fonts.pdf" \
  -F "pdf_parser_backend=vision"

Solution 2 - Check PDF Font Embedding:

# Use pdffonts to check font embedding (requires poppler-utils)
pdffonts document.pdf

# Look for fonts marked "no" in "emb" column
# These fonts may cause encoding issues

Workaround: If Vision is too expensive, consider:

1. Re-generate PDF with embedded fonts
2. Convert PDF to another format (DOCX) then back to PDF
3. Use OCR tool (Tesseract) to create new PDF with text layer

Verification:

• Check special characters render correctly
• Verify non-English text (if applicable)
• Compare extracted text with PDF visual

Related: Vision mode uses LLM to read actual rendered text, avoiding encoding issues entirely.

---

Issue 3.5: Low Chunk Quality or Empty Chunks

Symptom: Some chunks are very short, empty, or contain garbage

Example:

{
  "chunks": [
    { "content": "Page 1", "token_count": 2 }, // Too short
    { "content": "", "token_count": 0 }, // Empty
    { "content": "||||||||", "token_count": 8 } // Garbage
  ]
}

Diagnosis:

# Count empty or short chunks
curl http://localhost:8080/api/v1/documents/doc-uuid/chunks | \
  jq '[.chunks[] | select(.token_count < 10)] | length'

# If many short chunks → extraction quality issue

Causes:

1. PDF has headers/footers (page numbers, logos)
2. Complex layout confuses chunking
3. Embedded images without captions
4. Poor quality scan

Solution 1 - Enable Readability Enhancement:

# LLM cleans up extracted text
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@complex_layout.pdf" \
  -F 'config={"enhance_readability": true}'

Solution 2 - Normalize Spacing:

# Fix concatenated words and spacing issues
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@poor_spacing.pdf" \
  -F 'config={"normalize_spacing": true, "consolidate_headers": true}'

Solution 3 - Adjust Chunking:

# Increase chunk size to merge small fragments
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@fragmented.pdf" \
  -F "chunk_size=1024" \
  -F "chunk_overlap=100"

Verification:

• Check average chunk token count (should be 100-500 tokens)
• Verify no empty chunks
• Inspect chunks for coherent content

---

Issue 3.6: Upload Fails or Times Out

Symptom: PDF upload returns 500 error or times out

Common Errors:

{
  "error": "Request timeout",
  "status": 408
}

{
  "error": "File too large",
  "status": 413
}

Diagnosis:

# Check file size
ls -lh document.pdf

# Check backend logs
docker compose logs -f edgequake

# Or local logs
tail -f /tmp/edgequake-backend.log

Common Causes:

Error	Cause	Solution
413 Payload Too Large	File > 50MB	Split PDF or increase limit
408 Timeout	Processing > 60s	Use vision mode or increase timeout
500 Internal Error	Corrupted PDF	Repair with pdftk or ghostscript
500 Memory Error	Large PDF + Vision	Process in batches with max_pages

Solution 1 - Test with Page Limit:

# Process first 10 pages to verify config
curl -X POST http://localhost:8080/api/v1/documents \
  -F "file=@large_report.pdf" \
  -F 'config={"max_pages": 10}'

# If successful, process full document

Solution 2 - Increase Server Timeouts:

# Increase timeout in config (if you control server)
REQUEST_TIMEOUT=300  # 5 minutes

# Or split PDF into smaller files
pdftk large.pdf cat 1-50 output part1.pdf
pdftk large.pdf cat 51-100 output part2.pdf

Solution 3 - Repair Corrupted PDF:

# Using ghostscript to repair
gs -o repaired.pdf -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress original.pdf

# Using pdftk
pdftk original.pdf output repaired.pdf

Verification:

• Check upload returns 200 OK
• Verify status: "completed" in response
• Check no error logs in backend

---

Issue 3.7: Vision Extraction Timeout — Provider/Model Mismatch

Symptom: PDF processing fails with:

• “Processing failed permanently after 3 attempts”
• “Circuit breaker tripped after 3 consecutive timeouts”
• “Vision extraction timed out after 480s”
• “Provider ‘ollama’ may be unresponsive”

Root Cause: The vision model does not belong to the vision provider. For example, EDGEQUAKE_VISION_MODEL=gpt-4.1-nano paired with provider ollama will always time out because Ollama cannot serve OpenAI models.

This commonly happens when:

• A Makefile or Docker Compose file sets EDGEQUAKE_VISION_MODEL to an OpenAI model but .env overrides the provider to ollama
• Env vars are inherited from a parent process (e.g. Makefile export)

Diagnosis:

# Check the effective configuration and mismatch status
curl -s http://localhost:8080/api/v1/config/effective | jq '.areas[] | select(.name == "Vision")'

# Expected response when mismatched:
# {
#   "name": "Vision",
#   "has_mismatch": true,
#   "mismatch_description": "Model 'gpt-4.1-nano' is incompatible with provider 'ollama'.\nHow to fix:\n..."
# }

You can also check in the Settings UI → Configuration Explainability panel. Sections with mismatches are auto-expanded and show remediation options.

Fix — choose one:

Option	Command	When to use
A	unset EDGEQUAKE_VISION_MODEL	You want to use the default model for your provider
B	EDGEQUAKE_VISION_PROVIDER=openai + set OPENAI_API_KEY	You want to use the OpenAI model
C	EDGEQUAKE_VISION_MODEL=gemma4:latest	You want to stay on Ollama

Then restart the backend.

Verification:

# Confirm no mismatch
curl -s http://localhost:8080/api/v1/config/effective | jq '.areas[] | select(.name == "Vision") | .has_mismatch'
# Should return: false

# Re-upload the failed PDF
curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@document.pdf"

Prevention: EdgeQuake now auto-corrects mismatches at runtime — if a model is incompatible with the resolved provider, it is skipped and the next candidate in the resolution chain is used (with a WARN log). The explainability endpoint helps you fix the env vars permanently.

---

PDF Troubleshooting Decision Tree

Use this flowchart to quickly diagnose PDF issues:

PDF Upload Issue?
  │
  ├─ chunk_count = 0
  │   ├─ Retry with Vision → {"pdf_parser_backend":"vision"}
  │   ├─ Still 0? → Check if PDF encrypted/protected
  │   └─ Still 0? → File GitHub issue with sample
  │
  ├─ Tables not detected / malformed
  │   ├─ Enable table enhancement → {"enhance_tables": true}
  │   ├─ Still bad? → Try Vision + enhance → {"pdf_parser_backend":"vision", "enhance_tables": true}
  │   └─ Complex table? → Known limitation (file issue)
  │
  ├─ Text order wrong
  │   ├─ Enable column detection → {"layout": {"detect_columns": true}}
  │   └─ Still wrong? → Adjust column_gap_threshold
  │
  ├─ Encoding errors (�, ?)
  │   ├─ Retry with Vision → {"pdf_parser_backend":"vision"}
  │   └─ Still bad? → Check PDF font embedding (pdffonts)
  │
  ├─ Upload fails / timeout
  │   ├─ File > 50MB? → Split PDF or increase limit
  │   ├─ Timeout? → Test with max_pages: 10
  │   ├─ Vision timeout + circuit breaker? → Check provider/model mismatch (Issue 3.7)
  │   └─ Error 500? → Repair PDF (ghostscript, pdftk)
  │
  └─ Poor quality chunks
      ├─ Enable readability → {"enhance_readability": true}
      ├─ Normalize spacing → {"normalize_spacing": true}
      └─ Adjust chunk_size → chunk_size=1024

---

PDF Configuration Quick Reference

Common configurations for different PDF types:

Digital PDF (good quality):

# Default settings - no config needed
curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@digital.pdf" http://localhost:8080/api/v1/documents/upload

Scanned Document:

curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@scanned.pdf" \
     -F "pdf_parser_backend=vision" \
     http://localhost:8080/api/v1/documents/upload

Academic Paper (multi-column):

curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@paper.pdf" \
     -F 'config={"layout": {"detect_columns": true}}' \
     http://localhost:8080/api/v1/documents/upload

Financial Report (complex tables):

curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@financials.pdf" \
     -F 'config={"enhance_tables": true}' \
     http://localhost:8080/api/v1/documents/upload

Unknown Quality:

curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@unknown.pdf" \
     -F "pdf_parser_backend=edgeparse" \
     http://localhost:8080/api/v1/documents/upload

Critical Document (maximum accuracy):

curl -X POST http://localhost:8080/api/v1/documents/upload -F "file=@critical.pdf" \
     -F 'config={
       "pdf_parser_backend": "vision",
       "enhance_tables": true,
       "enhance_readability": true,
       "vision_dpi": 200
     }' \
     http://localhost:8080/api/v1/documents/upload

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When to Seek Further Help

If PDF extraction still fails after trying these solutions:

1. Read Full Documentation:

   • PDF Ingestion Tutorial - Configuration details
   • PDF Processing Deep Dive - Algorithm internals

2. Check GitHub Issues:

   • Search existing issues: https://github.com/org/edgequake/issues
   • Look for similar PDF problems
   • Check if limitation is already known

3. File New Issue:

   • Include PDF metadata (pages, file size, type)
   • Include error messages / logs
   • Attach problematic PDF (if not confidential)
   • Describe expected vs actual behavior

4. Community Support:

   • Discord: #pdf-extraction channel
   • Stack Overflow: Tag edgequake pdf

---

4. Empty Query Results

Symptom: Query returns empty answer

Diagnosis:

# Check document count
curl "http://localhost:8080/api/v1/documents?workspace_id=$WORKSPACE_ID"

# Check entity count
curl "http://localhost:8080/api/v1/graph/entities?workspace_id=$WORKSPACE_ID"

# Check chunk count
curl "http://localhost:8080/api/v1/workspaces/$WORKSPACE_ID/stats"

Common Causes:

Symptom	Cause	Solution
0 documents	No uploads	Upload documents
Documents but 0 entities	Processing failed	Reprocess documents
Entities but no results	Query not matching	Try different mode

Debug Query:

# Try naive mode (vector only) to verify basic retrieval
curl -X POST "http://localhost:8080/api/v1/query?workspace_id=$WORKSPACE_ID" \
  -H "Content-Type: application/json" \
  -d '{"query": "test", "mode": "naive", "max_chunks": 20}'

---

5. LLM Errors

Symptom: “OpenAI API error: Rate limit exceeded”

Solution:

# Wait and retry
sleep 60

# Or switch to different provider
export EDGEQUAKE_LLM_PROVIDER=ollama

Symptom: “OpenAI API error: Invalid API key”

Solution:

# Check key is set
echo $OPENAI_API_KEY

# Check key starts with sk-
# Keys should look like: sk-proj-abc123...

# Test key directly
curl https://api.openai.com/v1/models \
  -H "Authorization: Bearer $OPENAI_API_KEY"

Symptom: “Connection refused to Ollama”

Solution:

# Start Ollama
ollama serve

# Check it's running
curl http://localhost:11434/api/tags

# Pull required model
ollama pull gemma3:12b
ollama pull nomic-embed-text

---

6. Slow Performance

Symptom: Queries taking > 5 seconds

Diagnosis:

# Enable debug logging
RUST_LOG="edgequake=debug" cargo run

# Check query timing in logs
# Look for: "query completed in Xms"

Common Causes:

Cause	Diagnosis	Solution
Cold LLM	First query slow	Warm up with test query
Large context	Too many chunks	Reduce max_chunks
Slow embedding	Ollama on CPU	Use GPU or OpenAI
DB connection	Pool exhausted	Check connections

Quick Fixes:

# Use faster model
curl -X POST "http://localhost:8080/api/v1/query" \
  -d '{"query": "test", "llm_model": "gpt-5-nano"}'

# Reduce context size
curl -X POST "http://localhost:8080/api/v1/query" \
  -d '{"query": "test", "max_chunks": 5, "max_entities": 5}'

---

7. Database Issues

Symptom: “Connection pool exhausted”

Solution:

# Check active connections
psql $DATABASE_URL -c "SELECT count(*) FROM pg_stat_activity WHERE datname='edgequake'"

# Increase pool size in DATABASE_URL
DATABASE_URL="postgresql://user:pass@host:5432/db?max_connections=50"

# Or use PgBouncer for pooling

Symptom: “relation ‘documents’ does not exist”

Cause: Migrations haven’t run.

Solution:

# Migrations run automatically on startup
# Restart the backend
cargo run

# Or run manually if needed
psql $DATABASE_URL -f migrations/001_initial.sql

Symptom: “disk full” or slow inserts

Solution:

# Check disk usage
df -h

# Check table sizes
psql $DATABASE_URL -c "
SELECT schemaname, relname,
       pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC LIMIT 10;
"

# Vacuum database
psql $DATABASE_URL -c "VACUUM ANALYZE"

---

8. Graph Issues

Symptom: “AGE extension not loaded”

Solution:

-- Load AGE extension
LOAD 'age';
SET search_path = ag_catalog, "$user", public;

-- Create graph if not exists
SELECT create_graph('edgequake_graph');

Symptom: Entities not connected

Diagnosis:

# Check relationship count
curl "http://localhost:8080/api/v1/graph/relationships?workspace_id=$WORKSPACE_ID"

# If 0 relationships, check extraction logs

Solution:

# Reprocess with verbose logging
RUST_LOG="edgequake_pipeline=debug" cargo run

# Then reprocess document
curl -X POST "http://localhost:8080/api/v1/documents/$DOC_ID/reprocess"

---

9. Frontend Issues

Symptom: Frontend can’t connect to backend

Check:

# Is backend running?
curl http://localhost:8080/health

# CORS enabled?
# Backend should return Access-Control-Allow-Origin header
curl -I http://localhost:8080/health

Solution:

# Start backend first, then frontend
make backend-dev &
sleep 5
make frontend-dev

Symptom: Graph visualization empty

Causes:

1. No entities extracted
2. WebSocket connection failed
3. Sigma.js not loading

Solution:

# Check entities exist via API
curl "http://localhost:8080/api/v1/graph/entities?workspace_id=$WORKSPACE_ID"

# Check browser console for errors
# Open DevTools → Console

---

Diagnostic Commands

Logs

# Backend logs
tail -f /tmp/edgequake-backend.log

# Frontend logs
tail -f /tmp/edgequake-frontend.log

# Docker logs
docker compose logs -f

# Specific component
docker compose logs -f edgequake
docker compose logs -f postgres

Database Queries

-- Check document status
SELECT status, count(*) FROM documents GROUP BY status;

-- Find failed documents
SELECT id, title, error_message FROM documents WHERE status = 'failed';

-- Check entity counts by workspace
SELECT workspace_id, count(*) FROM entities GROUP BY workspace_id;

-- Check embedding dimensions
SELECT embedding_dimension, count(*) FROM embeddings GROUP BY embedding_dimension;

API Debugging

# Verbose curl output
curl -v http://localhost:8080/api/v1/workspaces

# Pretty print JSON
curl http://localhost:8080/api/v1/workspaces | jq

# Check response headers
curl -I http://localhost:8080/health

---

Error Reference

Error Code	Meaning	Solution
400	Bad request	Check request format
401	Unauthorized	Add API key
404	Not found	Check workspace_id exists
422	Validation error	Check required fields
429	Rate limited	Wait and retry
500	Server error	Check logs
503	Service unavailable	Check DB/LLM connection

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Getting Help

Before Asking for Help

1. Check this troubleshooting guide
2. Check logs for specific error messages
3. Verify environment variables are set
4. Try with minimal configuration (PostgreSQL + mock LLM)

Debug Mode

Start with maximum logging:

RUST_LOG="edgequake=trace,sqlx=debug,tower_http=debug" cargo run

Report an Issue

Include in your report:

1. EdgeQuake version (cargo run --version)
2. Storage mode (PostgreSQL or Memory)
3. LLM provider and model
4. Steps to reproduce
5. Relevant log output
6. Expected vs actual behavior

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See Also

• Configuration Reference - All settings
• Monitoring Guide - Observability setup
• Deployment Guide - Production setup