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victor-rag

RAG (Retrieval-Augmented Generation) Vertical for Victor AI

A complete RAG implementation showcasing document ingestion, vector search, and knowledge management with Victor AI.

Features

  • 📄 Multi-format Document Ingestion

    • PDF, Markdown, Text, Code files
    • Semantic chunking with configurable overlap
    • Automatic document type detection
  • 🔍 Hybrid Search

    • Vector search (semantic similarity)
    • Full-text search (keyword matching)
    • Combined reranking for best results
  • 💾 Embedded Vector Storage

    • LanceDB (no server required)
    • Persistent local storage
    • Fast similarity search
  • 🎯 Query Enhancement

    • Context retrieval from relevant documents
    • Source attribution and citations
    • Confidence scoring

Installation

# Install with Victor core
pip install victor-ai

# Install RAG vertical
pip install victor-rag

Quick Start

from victor.framework import Agent

# Create agent with RAG vertical
agent = await Agent.create(
    provider="ollama",
    model="qwen2.5-coder:7b",
    vertical="rag"
)

# Ingest a document
await agent.chat("Ingest README.md into the knowledge base")

# Query the knowledge base
result = await agent.chat("What does this project do?")

Available Tools

Once installed, the RAG vertical provides these tools:

  • rag_ingest - Ingest documents into the knowledge base
  • rag_search - Search for relevant document chunks
  • rag_query - Query with context retrieval
  • rag_list - List all indexed documents
  • rag_delete - Delete documents from knowledge base
  • rag_stats - Get knowledge base statistics

System Prompt

The RAG vertical includes specialized prompt contributions:

  • Query Strategy: When to use semantic vs keyword search
  • Citation Format: How to reference sources
  • Document Handling: Chunking strategies by document type
  • Quality Rules: Grounding rules to prevent hallucination

Configuration

The RAG vertical can be configured via environment variables:

# Vector store location
export VICTOR_RAG_DB_PATH=./rag_db

# Chunking configuration
export VICTOR_RAG_CHUNK_SIZE=512
export VICTOR_RAG_CHUNK_OVERLAP=50

# Search configuration
export VICTOR_RAG_TOP_K=5
export VICTOR_RAG_MIN_CONFIDENCE=0.3

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Format code
black victor_rag/

# Type check
mypy victor_rag/

License

Apache License 2.0 - see LICENSE for details.

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