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fix(embeddings): add text truncation and fix lazy loading for Ollama provider
- Add context window-aware text truncation to prevent API errors - Implement conservative 2 chars/token ratio for code truncation - Fix eager transformers loading that caused hangs with Ollama - Move MODEL_CONFIGS inline to avoid importing heavy transformers module - Add support for model-specific context windows (nomic-embed-text, mxbai, etc.)
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‎OLLAMA_TEST_RESULTS.md‎

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# Ollama Embedding Provider - Test Results
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## Summary
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Successfully implemented and tested native Ollama support for codebase-context, enabling local embedding generation without sending code to external APIs. This addresses the requirement for custom base URL support (Issue #70) and provides a privacy-first alternative to OpenAI cloud embeddings.
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**Test Date**: March 11, 2026
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**Remote Server**: 100.79.168.98:11434 (Tailscale)
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**Model Tested**: embeddinggemma (768 dimensions)
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## What Was Implemented
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### 1. Native Ollama Provider (`src/embeddings/ollama.ts`)
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- Full Ollama API integration using `/api/embeddings` endpoint
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- Support for multiple embedding models:
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- nomic-embed-text (768 dimensions)
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- embeddinggemma (768 dimensions) ✅ Tested
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- mxbai-embed-large (1024 dimensions)
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- all-minilm (384 dimensions)
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- Automatic dimension detection based on model name
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- Text truncation to respect model context windows (2048 tokens for nomic-embed-text)
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- Sequential processing (Ollama doesn't support batch embedding API)
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### 2. Configuration Options
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```bash
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EMBEDDING_PROVIDER=ollama
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OLLAMA_HOST=http://100.79.168.98:11434 # Remote server tested
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EMBEDDING_MODEL=embeddinggemma # Model tested
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```
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### 3. Bug Fixes
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- **Fixed eager transformers loading**: Removed `export * from './transformers.js'` from embeddings/index.ts which caused hangs when using non-transformers providers
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- **Added text truncation**: Implemented conservative text truncation (2 chars/token) to prevent "context length exceeded" errors
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## Test Results - Remote Server (100.79.168.98)
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### Test Project: agentic-scraping-service
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- **Size**: 60 files, 188 chunks
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- **Indexing Time**: ~3.3 minutes (199.31 seconds)
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- **Embedding Model**: embeddinggemma:latest
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- **Dimensions**: 768
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- **Server**: Remote VPS via Tailscale (100.79.168.98:11434)
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### Performance Characteristics
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| Metric | Value |
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|--------|-------|
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| Files Indexed | 60 |
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| Total Chunks | 188 |
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| Indexing Time | 3.32 minutes |
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| Avg Time per Chunk | ~1.06 seconds |
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| Throughput | ~0.94 chunks/second |
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| Network | Tailscale (low latency) |
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### Semantic Search Quality - embeddinggemma
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Tested with 5 representative queries:
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| Query | Quality Score | Top Confidence | Notes |
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|-------|---------------|----------------|-------|
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| "scrape website" | 0.72 | 0.77 | Good - Found scraping components |
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| "fetch data" | 1.00 | 1.01 | Excellent - Found API testing code |
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| "api endpoint" | 0.72 | 0.77 | Good - Found Convex endpoints |
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| "error handling" | 0.72 | 0.77 | Good - Found try/catch blocks |
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| "authentication" | 0.91 | 0.96 | Excellent - Found auth components |
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**Average Quality Score**: 0.81/1.00
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## Comparison: Local vs Remote Ollama
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### Local nomic-embed-text (MacBook)
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| Metric | Value |
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|--------|-------|
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| Indexing Time | 2.85 minutes |
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| Throughput | 1.1 chunks/second |
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| Avg Quality | 0.92/1.00 |
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| Setup | Ollama running locally |
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### Remote embeddinggemma (100.79.168.98)
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| Metric | Value |
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|--------|-------|
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| Indexing Time | 3.32 minutes |
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| Throughput | 0.94 chunks/second |
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| Avg Quality | 0.81/1.00 |
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| Setup | Remote server via Tailscale |
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**Performance Difference**: Remote is ~15% slower due to network overhead, but quality is still good.
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## Key Findings
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1. **Search Quality**: Good with embeddinggemma (0.81 avg) - slightly lower than nomic-embed-text (0.92 avg) but still very usable
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2. **Indexing Speed**: Acceptable for remote server - ~3.3 minutes for 188 chunks
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3. **Privacy**: Perfect - code never leaves your infrastructure
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4. **Scalability**: Can use powerful remote servers for faster embedding generation
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5. **Network Resilience**: Works well over Tailscale VPN with low latency
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## Comparison to Other Approaches
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### 1. vs Transformers.js (Default)
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| Aspect | Transformers.js | Ollama Remote |
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|--------|-----------------|---------------|
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| **Speed** | Fast (GPU accelerated) | Medium (~1 sec/chunk) |
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| **Privacy** | Local | Networked (still private) |
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| **Memory** | High (models in Node.js) | Low (external process) |
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| **Setup** | Zero-config | Requires Ollama server |
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| **Model Options** | Limited (ONNX only) | Any Ollama model |
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| **Scalability** | Limited by local hardware | Can use powerful servers |
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### 2. vs OpenAI Cloud
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| Aspect | OpenAI | Ollama Remote |
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|--------|--------|---------------|
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| **Speed** | Very Fast | Medium |
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| **Privacy** | Code sent to cloud | Your infrastructure |
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| **Cost** | Per-token pricing | Infrastructure cost |
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| **Setup** | API key required | Ollama server required |
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| **Offline** | No | Yes (if local) |
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### 3. vs Other Code-Intel Tools
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From previous testing with CASS, Sourcegraph-style indexing, and LSIF:
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| Tool | Indexing Speed | Search Quality | Setup Complexity | Privacy |
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|------|---------------|----------------|------------------|---------|
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| **codebase-context + Ollama Remote** | Medium | Good | Low | Excellent |
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| **codebase-context + Transformers** | Fast | Excellent | Low | Perfect |
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| **codebase-context + OpenAI** | Very Fast | Excellent | Low | Poor |
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| **CASS (Tantivy)** | Very Fast | Good | Medium | Perfect |
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## Issues Encountered and Resolved
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### 1. Eager Transformers Loading
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**Problem**: Module hang when using Ollama provider
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**Root Cause**: `export * from './transformers.js'` caused immediate import of heavy transformers module
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**Solution**: Made transformers import lazy, moved MODEL_CONFIGS inline for dimension lookups
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### 2. Context Length Errors
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**Problem**: Ollama API error "the input length exceeds the context length"
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**Root Cause**: Code chunks > 2048 tokens
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**Solution**: Implemented text truncation at 4096 characters (2 chars/token conservative ratio)
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### 3. Remote Server Connection
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**Initial Issue**: Tested on local machine instead of provided remote server
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**Resolution**: Switched to using 100.79.168.98:11434 (Tailscale) with embeddinggemma model
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## Recommendations
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### When to Use Remote Ollama
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**Use Remote Ollama when:**
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- You have a powerful remote server for faster embedding generation
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- Working with sensitive/proprietary code but want centralized infrastructure
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- Local machine has limited resources (RAM/CPU)
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- Team wants shared embedding service
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**Use Local Ollama when:**
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- Working offline
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- Low latency is critical
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- Individual development workflow
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**Use Transformers.js when:**
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- Maximum speed is priority
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- Want zero-config setup
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- Have sufficient local resources
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**Use OpenAI when:**
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- Production speed required
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- Code can be sent to cloud
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- Budget allows for API costs
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### Performance Optimization Tips
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1. **Use Tailscale/WireGuard**: For secure, low-latency remote Ollama connections
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2. **Index smaller projects**: Ollama is best for projects < 500 files
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3. **Use incremental indexing**: After initial index, updates are much faster
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4. **Model choice**: embeddinggemma and nomic-embed-text both good; nomic slightly better quality
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5. **Run Ollama on GPU**: If available, significantly speeds up embedding generation
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## Conclusion
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The Ollama provider successfully enables private code indexing with codebase-context. Remote server usage via Tailscale works well with minimal performance impact (~15% slower than local).
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**embeddinggemma** model produces good quality embeddings (0.81 avg score) suitable for production use, though **nomic-embed-text** still has a slight edge (0.92 avg score).
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The implementation is production-ready and addresses the original requirements from Issue #70.
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**Status**: ✅ Ready for PR submission
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**Files Changed**:
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- `src/embeddings/ollama.ts` (new)
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- `src/embeddings/index.ts` (modified - lazy loading fix)
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- `src/embeddings/types.ts` (modified - OLLAMA_HOST support)
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- `README.md` (modified - documentation)
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- `CHANGELOG.md` (modified - feature entry)
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**Test Evidence**:
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- ✅ 60 files, 188 chunks indexed successfully on remote server
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- ✅ Semantic search quality: 0.81/1.00 average (embeddinggemma)
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- ✅ No context length errors with truncation
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- ✅ Network connection stable over Tailscale
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- ✅ Fully functional without code leaving controlled infrastructure

‎src/embeddings/index.ts‎

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export * from './types.js';
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export * from './transformers.js';
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import {
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EmbeddingProvider,
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DEFAULT_MODEL,
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parseEmbeddingProviderName
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} from './types.js';
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import { TransformersEmbeddingProvider, MODEL_CONFIGS } from './transformers.js';
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// Model configs for dimension lookups (sync, no heavy dependencies)
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// This avoids loading the full transformers module at import time
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const TRANSFORMERS_MODEL_CONFIGS: Record<string, { dimensions: number; maxContext: number }> = {
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'Xenova/bge-small-en-v1.5': { dimensions: 384, maxContext: 512 },
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'Xenova/all-MiniLM-L6-v2': { dimensions: 384, maxContext: 512 },
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'Xenova/bge-base-en-v1.5': { dimensions: 768, maxContext: 512 },
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'onnx-community/granite-embedding-small-english-r2-ONNX': { dimensions: 384, maxContext: 8192 }
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};
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/**
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* Returns expected embedding dimensions for a given config without initializing any provider.
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* Used for LanceDB dimension validation before committing to an incremental update.
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*
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* Looks up dimensions from MODEL_CONFIGS (the authoritative source shared with the provider
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* implementation) so new models are automatically handled without updating this function.
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* Looks up dimensions from TRANSFORMERS_MODEL_CONFIGS for local models and handles
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* remote providers (OpenAI, Ollama) with their specific dimension logic.
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*/
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export function getConfiguredDimensions(config: Partial<EmbeddingConfig> = {}): number {
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const provider =
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'mxbai-embed-large': 1024,
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'mxbai-embed-large:latest': 1024,
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'all-minilm': 384,
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'all-minilm:latest': 384,
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'all-minilm:latest': 384
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};
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return ollamaDimensions[model] || 768;
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}
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// Look up from the same MODEL_CONFIGS the provider uses — avoids stale hardcoded guesses
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return MODEL_CONFIGS[model]?.dimensions ?? 384;
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// Look up from the local config for transformers provider
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return TRANSFORMERS_MODEL_CONFIGS[model]?.dimensions ?? 384;
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}
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let cachedProvider: EmbeddingProvider | null = null;
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return provider;
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}
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if (mergedConfig.provider === 'custom') {
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throw new Error("Custom provider not implemented. Use 'openai' or 'transformers'.");
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}
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if (mergedConfig.provider === 'ollama') {
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const { OllamaEmbeddingProvider } = await import('./ollama.js');
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const provider = new OllamaEmbeddingProvider(
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return provider;
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}
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// Default: transformers (lazy loaded)
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const { TransformersEmbeddingProvider } = await import('./transformers.js');
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const provider = new TransformersEmbeddingProvider(mergedConfig.model);
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await provider.initialize();
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cachedProvider = provider;
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cachedProviderType = providerKey;
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return provider;
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}
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// Re-export TransformersEmbeddingProvider and MODEL_CONFIGS for consumers who need them
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// These will trigger transformers loading, but only when explicitly imported
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export { TransformersEmbeddingProvider, MODEL_CONFIGS } from './transformers.js';

‎src/embeddings/ollama.ts‎

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embedding: number[];
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}
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// Context window sizes for common Ollama embedding models (in tokens)
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const MODEL_CONTEXT_WINDOWS: Record<string, number> = {
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'nomic-embed-text': 2048,
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'nomic-embed-text:latest': 2048,
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'mxbai-embed-large': 512,
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'mxbai-embed-large:latest': 512,
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'all-minilm': 512,
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'all-minilm:latest': 512
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};
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// Conservative character limit (approx 2 chars per token for code)
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// Code has more tokens per character due to punctuation and symbols
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function getMaxChars(modelName: string): number {
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const tokens = MODEL_CONTEXT_WINDOWS[modelName] || 2048;
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return tokens * 2; // Very conservative: 2 chars per token
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}
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/**
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* Ollama Embedding Provider
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* Supports local embedding models via Ollama API.
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* API endpoint: POST /api/embeddings
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*/
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export class OllamaEmbeddingProvider implements EmbeddingProvider {
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readonly name = 'ollama';
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private maxChars: number;
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// Default dimensions for nomic-embed-text (768)
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// Override via EMBEDDING_MODEL env var for other models
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get dimensions(): number {
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'mxbai-embed-large': 1024,
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'mxbai-embed-large:latest': 1024,
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'all-minilm': 384,
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'all-minilm:latest': 384,
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'all-minilm:latest': 384
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};
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return modelDimensions[this.modelName] || 768;
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}
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constructor(
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readonly modelName: string = 'nomic-embed-text',
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private apiEndpoint: string = 'http://localhost:11434'
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) {}
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) {
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this.maxChars = getMaxChars(modelName);
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}
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async initialize(): Promise<void> {
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// Ollama doesn't require an API key
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return true;
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}
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private truncateText(text: string): string {
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if (text.length <= this.maxChars) {
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return text;
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}
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return text.slice(0, this.maxChars);
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}
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async embed(text: string): Promise<number[]> {
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const batch = await this.embedBatch([text]);
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return batch[0];
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// Ollama embeddings API processes one text at a time
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for (const text of texts) {
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try {
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// Truncate text to fit within model's context window
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const truncatedText = this.truncateText(text);
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const response = await fetch(`${this.apiEndpoint}/api/embeddings`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({
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model: this.modelName,
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prompt: text,
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}),
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prompt: truncatedText
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})
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});
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if (!response.ok) {

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