https://github.com/jonymusky/natural-language-search-llm
NLS Search is a flexible Natural Language Search API offering vector-based semantic search with support for multiple LLM providers, ideal for integration with e-commerce platforms and SaaS.
https://github.com/jonymusky/natural-language-search-llm
ai artificial-intelligence gpt-4 natural-language-processing openai python qdrant search-engine
Last synced: 6 months ago
JSON representation
NLS Search is a flexible Natural Language Search API offering vector-based semantic search with support for multiple LLM providers, ideal for integration with e-commerce platforms and SaaS.
- Host: GitHub
- URL: https://github.com/jonymusky/natural-language-search-llm
- Owner: jonymusky
- Created: 2024-12-04T00:37:42.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-30T18:03:31.000Z (10 months ago)
- Last Synced: 2025-02-15T03:16:35.248Z (8 months ago)
- Topics: ai, artificial-intelligence, gpt-4, natural-language-processing, openai, python, qdrant, search-engine
- Language: Python
- Homepage:
- Size: 28 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# NLS Search
A powerful Natural Language Search API that provides semantic search capabilities using vector embeddings. The system supports multiple LLM providers and can be easily integrated with various data sources.
## Features
- Natural Language Search using semantic embeddings
- Multiple LLM provider support:
- Ollama (local models)
- OpenAI
- Google Gemini
- Vector-based similarity search using Qdrant
- Configurable model settings per provider
- RESTful API for indexing and searching
- Bulk indexing support from MongoDB
- Automatic vector size handling
- Configurable similarity thresholds
## Requirements
- Python 3.9+
- Docker
- Make
## Quick Start
1. Setup the environment:
```bash
make setup
```
2. Configure your environment variables in `.env`:
```bash
# API Settings
APP_HOST=0.0.0.0
APP_PORT=8000
DEBUG=true
# Vector DB Settings
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_COLLECTION=documents
# MongoDB Settings (optional)
MONGODB_URI=mongodb://localhost:27017
MONGODB_DB=your_database
MONGODB_COLLECTION=your_collection
# Provider Settings
DEFAULT_PROVIDER=ollama
OLLAMA_URL=http://localhost:11434
OLLAMA_MODEL=llama2
OLLAMA_EMBEDDING_MODEL=all-minilm
# Optional Providers
OPENAI_API_KEY=your_key_here
OPENAI_MODEL=gpt-4
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
GEMINI_API_KEY=your_key_here
GEMINI_MODEL=gemini-pro
```
3. Start the services:
```bash
# Start Qdrant (required)
make start-qdrant
# Start MongoDB (optional - for bulk indexing)
make start-mongodb
# Start the API
make start-api
```
## Natural Language Search
The system uses semantic embeddings to understand the meaning of your queries and find relevant documents. Unlike traditional keyword search:
- Understands semantic meaning, not just exact matches
- Handles synonyms and related concepts
- Works with natural language questions
- Returns results ranked by semantic similarity
Example search query:
```bash
curl -X POST "http://localhost:8000/search" \
-H "Content-Type: application/json" \
-d '{
"text": "What are the safety procedures for handling hazardous materials?",
"max_results": 5
}'
```
## Configuration
The system is configured through `config.yaml`:
```yaml
vector_db:
type: qdrant
vector_size: 384 # Matches Ollama's all-minilm model
providers:
ollama:
enabled: true
url: ${OLLAMA_URL}
model: ${OLLAMA_MODEL}
embedding_model: ${OLLAMA_EMBEDDING_MODEL}
vector_size: 384
search:
default_provider: ${DEFAULT_PROVIDER}
max_results: 10
similarity_threshold: 0.3 # Adjust for stricter/looser matching
```
## API Documentation
### Endpoints
- `POST /search`: Search documents using natural language
- `POST /index`: Index a single document
- `POST /bulk-index`: Bulk index documents from MongoDB
For detailed examples and usage scenarios, check out our [Examples Guide](EXAMPLES.md).
## Troubleshooting
### Common Issues
1. **Vector Size Mismatch**
- Error: "Vector size mismatch"
- Solution: Ensure provider's vector_size matches Qdrant's configuration
2. **No Results Found**
- Check similarity_threshold in config.yaml
- Verify documents are properly indexed
- Check provider's embedding model is working
3. **Qdrant Connection Issues**
- Error: "Connection refused"
- Solution: Check status with `make status`
- Start Qdrant with `make start-qdrant`
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.