rag-system
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Overview
A hybrid semantic-vector retrieval engine that integrates structured knowledge graphs and embedding models for advanced RAG (Retrieval Augmented Generation) capabilities for AI agents.
Installation
docker compose -f docker-compose.demo.yml up -dEnvironment Variables
- FUSEKI_URL
- SIE_EMBEDDING_MODE
- SIE_EMBEDDING_ENDPOINT
- OPENAI_API_KEY
- SIE_VECTORDB_ENDPOINT
- SIESERVER_PORT
- SIE_VECTORDB_ROUTER_MODE
- SIE_CONFIG_MODE
- SERVER_MODE
- SIE_WORKSPACE_DIR
- MCP_WS_URL
- SIE_MCP_MERGE_MANIFEST
- SIE_GRAPHDB_ENDPOINT
- SIE_VECTORDB_BACKEND
- SIE_EMBEDDING_MODEL
- EMBEDDING_MODEL
Security Notes
The system downloads and initializes external ontology files (`site.jsonld`, `.ttl` files) from `www.simplemodeling.org` during `init-fuseki.sh` execution, which introduces a supply chain risk. The Python `chroma_server.py` loads embedding models based on `EMBEDDING_MODEL` environment variable, also presenting a supply chain risk if models are sourced from untrusted locations. The custom `SimpleHttpClient` in Scala, while functional, might not be as thoroughly vetted as widely used HTTP client libraries, potentially leading to unforeseen HTTP-related vulnerabilities. Input to SPARQL queries is URL-encoded, which mitigates direct injection, but the overall reliance on external content for initialization merits caution.
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