ad0c0a438b
Retrieval-Augmented Generation service with: - FastAPI service using lilith-ml-service-base - Vector search via Redis HNSW - Context augmentation and claim extraction - TypeScript client package @lilith/rag-client - Service-addresses integration for port resolution (8111) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2.4 KiB
2.4 KiB
RAG Retrieval Service
Retrieval-Augmented Generation service with vector search and context augmentation.
Overview
This service provides document retrieval, context augmentation, and claim extraction for LLM prompt enhancement. It uses Redis with HNSW indexing for vector search.
Architecture
rag-retrieval/
├── service/src/ # Python FastAPI service
│ ├── api/main.py # FastAPI endpoints
│ ├── config.py # Configuration (uses service-addresses)
│ ├── retrieval/ # Vector search engine
│ ├── augmentation/ # Context formatting
│ └── indexing/ # Document indexing
├── packages/client/ # @lilith/rag-client TypeScript package
└── pyproject.toml # Python package definition
Port
Port 8111 is allocated in lilith-platform/infrastructure/ports.yaml under ml.rag-retrieval.
The service uses lilith-service-addresses for runtime port resolution.
Installation
Python Service
pip install rag-retrieval
TypeScript Client
npm install @lilith/rag-client
Usage
Python Service
# Start the service (requires Redis)
python -m service
TypeScript Client
import { RAGClient } from '@lilith/rag-client';
// Auto-discovers port from service-addresses
const client = await RAGClient.create();
// Retrieve relevant documents
const docs = await client.retrieve({
query: 'creator earnings percentage',
subject_type: 'worker',
limit: 5,
});
// Augment content with verified facts
const augmented = await client.augment({
content: 'Creators keep 85% of earnings',
subject_type: 'worker',
});
console.log(augmented.augmented_prompt);
console.log(augmented.claims);
API Endpoints
POST /retrieve
Search for relevant documents.
{
"query": "search query",
"subject_type": "worker",
"limit": 5
}
POST /augment
Extract claims and augment with verified context.
{
"content": "content with claims",
"subject_type": "worker"
}
POST /index
Index documents into vector store.
{
"path": "/path/to/docs",
"recursive": true
}
GET /health
Health check endpoint.
Dependencies
lilith-ml-service-base- FastAPI scaffoldinglilith-service-addresses- Port resolutionredis- Vector store backendhttpx- HTTP client for embeddings
License
MIT