rag-query-cli

name	rag-query-cli
description	Interactive CLI for searching and querying documents indexed in a RAG system using Azure AI Search and Azure OpenAI. Supports hybrid search, source tracking, response generation, and UTF-8 compatibility on Windows.

RAG Query CLI — Interactive Document Search

Query your RAG system interactively from the command line.

Overview

Interactive CLI for searching and querying documents indexed in your RAG system using Azure AI Search + Azure OpenAI.

Features

• Hybrid search (keyword + semantic ranking)
• Document retrieval with source tracking
• Response generation with context
• Performance metrics
• Handling of special UTF-8 characters (Windows compatible)

Requirements

• Azure OpenAI account with model deployed
• Azure AI Search instance with indexed documents
• .env file with credentials:
  • AZURE_OPENAI_KEY
  • AZURE_OPENAI_ENDPOINT
  • AZURE_SEARCH_ENDPOINT
  • AZURE_SEARCH_KEY
  • AZURE_SEARCH_INDEX
  • AZURE_OPENAI_MODEL

Installation

# Dependencies in ../.../requirements.txt
pip install -r .github/requirements.txt

Usage

Interactive Query (Recommended)

# From the project root
python .github/skills/rag-query-cli/query.py "Your question here"

# Example
python .github/skills/rag-query-cli/query.py "What is the user onboarding process?"

Direct Execution

from query import RAGExecutor

executor = RAGExecutor()
result = executor.execute("your question", verbose=True)

print(result['response'])
print("Sources:", result['sources'])
print("Metrics:", result['metrics'])

Output

[QUERY] What is the user onboarding process?

[SEARCHING] Searching documents...
[OK] Found 5 relevant documents

[GENERATING] Generating response...
[OK] Response generated

[RESPONSE]
Based on documentation, the user onboarding process involves...

[SOURCES]
   - knowledge/pdfs/Onboarding_Manual.pdf
   - knowledge/procedures/User_Setup.docx

[METRICS]
   Search: 234ms
   Inference: 1523ms
   Total: 1757ms
   Tokens: 412

Advanced Options

Custom Top-K

# Retrieve more context (default is 5)
python .github/skills/rag-query-cli/query.py "question" --top 10

Quiet Mode

# Only answer output
python .github/skills/rag-query-cli/query.py "question" --quiet