LEGACY

SKMS AATIS Module

Agentic Assistance for Testing Information Systems Smart Knowledge Management System 0.0.1

Fully Local & Privacy Friendly, (only outbound is websearches via Bing Web Search API)

See issues page for current known issues.

A single Python-based CLI tool to:

1. generate: Search the web and RAG store, summarize results into a timestamped markdown report.
2. ask: Run a RAG-powered Question & Answer session over your previously saved reports.
3. refresh: Ingest all markdown (.md) reports into a FAISS vector store for future queries.

This tool utilizes AutoGen 0.6.1, the Bing Web Search API (search engine), Ollama (language model), and FAISS (vector database).

---

flowchart TD
  main[main.py CLI] -->|generate| gen[generate_agent.py]
  main -->|ask| ask[qa_agent.py]
  main -->|refresh| refresh[ingest_reports.py]

  gen --> search[BingSearchTool]
  gen --> fetch[FetchWebpageTool]
  gen --> rag1[RagStore]
  gen --> model1[OllamaChatCompletionClient]

  ask --> rag2[RagStore]
  ask --> model2[OllamaChatCompletionClient]

  refresh --> rag3[RagStore]

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Prerequisites

• Python 3.8 or newer
• Git
• Bing Search API Key: Set the BING_SEARCH_API_KEY environment variable to your Bing Search API key.
• Bing Search Endpoint (optional): Set the BING_SEARCH_ENDPOINT environment variable (default: https://api.bing.microsoft.com/v7.0/search).
• Running Ollama server (default URL: http://localhost:11434)

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Quickstart Guide

Step 1: Clone the Repository

git clone https://your.repo.url/project.git
cd project

Step 2: Setup Virtual Environment

Linux/macOS:

python3 -m venv .venv
source .venv/bin/activate

Windows:

python -m venv .venv
.venv\Scripts�ctivate

Step 3: Install Dependencies

pip install --upgrade pip
pip install autogen-agentchat==0.6.1 "autogen-ext[ollama]" requests beautifulsoup4 sentence-transformers faiss-cpu

Step 4: Create Initialization Files

touch tools/__init__.py agents/__init__.py rag/__init__.py

Step 5: Configure Ollama Client

Edit the file config/ollama_config.json:

{
  "model": "llama3",
  "base_url": "http://localhost:11434"
}

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Usage Instructions

Generate a New Report

This command searches the web, summarizes findings using Ollama, and saves the output as a timestamped markdown file:

python main.py generate "quantum computing breakthroughs"

Refresh RAG Database

Ingest all existing markdown reports into the FAISS database:

python main.py refresh

Ask a Question

Use this to query your RAG database and model knowledge:

python main.py ask "What did we learn about quantum entanglement?"

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Configuration Options

• Bing Search API: Provide your API key via the BING_SEARCH_API_KEY environment variable and optionally override the endpoint with BING_SEARCH_ENDPOINT.
• Ollama model settings: Adjust in config/ollama_config.json.
• Embedding model: Set in rag/rag_store.py (default is all-MiniLM-L6-v2).

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Troubleshooting Common Issues

• Connection errors to the Bing Search API or Ollama: Ensure your API key is valid, environment variables are set, and endpoints are correct.
• No reports found during refresh: Confirm that you have run the generate command at least once.
• FAISS errors: Check file permissions or delete and regenerate the rag/index.faiss file.

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You are now ready to use the Unified RAG & Research CLI tool.