Standalone AIQ Service

Running Context Aware RAG AIQ plugin as a service

Exporting environment variables

Export environment variables for our vector and/or graph databases. Also nvidia api key for LLM models.

Vector-RAG

export MILVUS_HOST=<MILVUS_HOST_IP> #milvus host, e.g. localhost
export MILVUS_PORT=<MILVUS_DB_PORT> #milvus port, e.g. 19530
export NVIDIA_API_KEY=<NVIDIA_API_KEY> #NVIDIA API key

Graph-RAG

export GRAPH_DB_URI=<GRAPH_DB_URI> #neo4j uri, e.g. bolt://localhost:7687
export GRAPH_DB_USERNAME=<GRAPH_DB_USERNAME> #neo4j username, e.g. neo4j
export GRAPH_DB_PASSWORD=<GRAPH_DB_PASSWORD> #neo4j password, e.g. password
export NVIDIA_API_KEY=<NVIDIA_API_KEY> #NVIDIA API key

Running Data Ingestion

aiq serve --config_file=./src/vss_ctx_rag/aiq_config/workflow/config-ingestion-workflow.yml --port <PORT>

Running Graph Retrieval

aiq serve --config_file=./src/vss_ctx_rag/aiq_config/workflow/config-retrieval-workflow.yml --port <PORT>

Example Python API calls to the service

Here there are two services running, one for ingestion on port 8000 and one for retrieval on port 8001.

import requests

# Ingestion request
ingestion_url = "http://localhost:8000/generate"
ingestion_headers = {"Content-Type": "application/json"}
ingestion_data = {
    "text": "The bridge is bright blue."
}

ingestion_response = requests.post(ingestion_url, headers=ingestion_headers, json=ingestion_data)
print("Ingestion Response:", ingestion_response.json())

# Retrieval request
retrieval_url = "http://localhost:8001/generate"
retrieval_headers = {"Content-Type": "application/json"}
retrieval_data = {
    "text": "Is there a bridge? If so describe it"
}

retrieval_response = requests.post(retrieval_url, headers=retrieval_headers, json=retrieval_data)
print("Retrieval Response:", retrieval_response.json())