Software Under Development by a Crazy Man
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Multi-modal RAG (Retrieval-Augmented Generation) is an architecture that integrates multiple data types (such as text, images, and audio) to enhance AI response generation. It combines retrieval-based methods, which fetch relevant information from a knowledge base, with generative large language models (LLMs) that create coherent and contextually appropriate outputs. This approach allows for more comprehensive and engaging user interactions, such as chatbots that respond with both text and images or educational tools that incorporate visual aids into learning materials. By leveraging various modalities, multi-modal RAG systems improve context understanding and user experience. |
Ragdoll::CLI
Standalone command-line interface for the Ragdoll RAG (Retrieval-Augmented Generation) system. Provides document import, search, and management capabilities through a simple CLI.
Installation
gem install ragdoll-cliThis will install the ragdoll command-line tool.
Quick Start
-
Initialize configuration:
ragdoll init
-
Set your API key:
export OPENAI_API_KEY=your_api_key_here -
Import documents:
ragdoll import "docs/*.pdf" --recursive -
Search for content:
ragdoll search "What is machine learning?"
Commands
Configuration
# Initialize configuration
ragdoll init
# Show current configuration
ragdoll config show
# Set configuration values
ragdoll config set llm_provider openai
ragdoll config set chunk_size 1000
# Get configuration values
ragdoll config get embedding_model
# Show config file path
ragdoll config path
# Show database configuration and status
ragdoll config databaseDocument Import
# Import files matching a pattern
ragdoll import "documents/*.pdf"
# Import recursively from directory
ragdoll import "docs/**/*" --recursive
# Filter by document type
ragdoll import "files/*" --type pdf
# Available types: pdf, docx, txt, md, htmlSearch
# Basic search
ragdoll search "machine learning concepts"
# Limit number of results
ragdoll search "AI algorithms" --limit 5
# Different output formats
ragdoll search "deep learning" --format json
ragdoll search "AI" --format plain
ragdoll search "ML" --format table # defaultDocument Management
# Add a single document
ragdoll add <path>
# List all documents
ragdoll list
# Limit number of documents shown
ragdoll list --limit 10
# Different output formats
ragdoll list --format json
ragdoll list --format plain
# Check document status
ragdoll status <id>
# Update document metadata
ragdoll update <id> --title "New Title"
# Delete a document
ragdoll delete <id>
ragdoll delete <id> --force # Bypass confirmation
# Show system statistics
ragdoll stats
ragdoll stats --format json
ragdoll stats --format plainRetrieval Utilities
# Get context for RAG applications
ragdoll context "<query>" --limit 5
# Enhance a prompt with context
ragdoll enhance "<prompt>" --context_limit 5Utilities
# Show version information
ragdoll version
# Show help
ragdoll help
ragdoll help import # Help for specific command
# Check system health
ragdoll healthConfiguration
The CLI uses a YAML configuration file located at ~/.ragdoll/config.yml. You can customize various settings:
llm_provider: openai
embedding_provider: openai
embedding_model: text-embedding-3-small
chunk_size: 1000
chunk_overlap: 200
search_similarity_threshold: 0.7
max_search_results: 10
storage_backend: file
storage_config:
directory: "~/.ragdoll"
api_keys:
openai: your_key_here
anthropic: your_key_hereEnvironment Variables
API keys can be set via environment variables (recommended):
export OPENAI_API_KEY=your_key_here
export ANTHROPIC_API_KEY=your_key_here
export GOOGLE_API_KEY=your_key_here
export AZURE_OPENAI_API_KEY=your_key_here
export HUGGINGFACE_API_KEY=your_key_here
export OLLAMA_ENDPOINT=http://localhost:11434Custom Configuration Location
export RAGDOLL_CONFIG=/path/to/custom/config.ymlStorage
Documents and embeddings are stored in a PostgreSQL database managed by the ragdoll-core gem for production performance. Configuration and log files are stored locally in ~/.ragdoll/:
-
~/.ragdoll/config.yml- Configuration settings -
~/.ragdoll/ragdoll.log- Log file (if configured)
Supported Document Types
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PDF files (
.pdf) - Extracts text and metadata -
Microsoft Word (
.docx) - Extracts text, tables, and metadata -
Text files (
.txt) - Plain text import -
Markdown (
.md,.markdown) - Markdown document import -
HTML (
.html,.htm) - Strips HTML tags and imports text
Examples
Import a directory of documentation
# Import all markdown files from a docs directory
ragdoll import "docs/**/*.md" --recursive
# Import mixed document types
ragdoll import "knowledge-base/*" --recursiveSearch and get enhanced prompts
# Basic search
ragdoll search "How to configure SSL certificates?"
# Get detailed results
ragdoll search "database optimization" --format plain --limit 3Manage your knowledge base
# See what's in your knowledge base
ragdoll stats
ragdoll list --limit 20
# Check status of a specific document
ragdoll status 123
# Update document title
ragdoll update 123 --title "Updated Document Title"
# Delete a document
ragdoll delete 123Integration with Other Tools
The CLI is designed to work well with other command-line tools:
# Search and pipe to jq for JSON processing
ragdoll search "API documentation" --format json | jq '.results[0].content'
# Import files found by find command
find ./docs -name "*.pdf" -exec ragdoll import {} \;
# Use with xargs for batch processing
ls *.md | xargs -I {} ragdoll import {}Troubleshooting
Common Issues
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No API key configured:
Error: Missing API key Solution: Set OPENAI_API_KEY environment variable or add to config -
No documents found:
ragdoll stats # Check if documents are imported ragdoll list # See what documents exist
Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/MadBomber/ragdoll-cli.
License
The gem is available as open source under the terms of the MIT License.