Chipper Project Description

What is the project about?

Chipper is a tool designed to enhance generative AI models with advanced information retrieval capabilities. It provides a framework for building Retrieval-Augmented Generation (RAG) pipelines, enabling AI models to access and utilize external knowledge sources. It can also act as a proxy between an Ollama client and an Ollama instance, extending Ollama's capabilities.

What problem does it solve?

Chipper addresses the limitations of standard generative AI models that rely solely on their pre-trained knowledge. It allows these models to access up-to-date information and specific documents, reducing hallucinations and improving the accuracy and relevance of their responses. It also helps users explore and interact with documents and data using natural language, making it useful for research, creative writing, and other knowledge-intensive tasks. The project was initially created to help the author's girlfriend with her book, using local RAG and LLMs to explore characters and creative ideas while keeping her work private.

What are the features of the project?

Local & Cloud Model Support: Works with locally hosted models (via Ollama) and cloud-based models (via Hugging Face API).
ElasticSearch Integration: Uses ElasticSearch for efficient storage and retrieval of vectorized data (embeddings).
Document Chunking: Splits documents into manageable segments for processing.
Web Scraping: Extracts content from web pages for indexing.
Audio Transcription: Converts audio files to text.
CLI & Web UI: Offers both a command-line interface and a user-friendly web interface.
Dockerized Deployment: Easy deployment and setup using Docker containers.
Customizable RAG Pipelines: Allows users to configure models, query parameters, and system prompts.
Ollama API Proxy: Extends Ollama with retrieval capabilities and enables interoperability with various Ollama clients.
API Security: Provides API key-based and Bearer token authentication for the Ollama API proxy.
Offline Web UI: The web interface can function without an internet connection.
Edge TTS: Client-side text-to-speech using WebAssembly.
Distributed Processing: Chain multiple Chipper instances.

What are the technologies used in the project?

Haystack: An open-source framework for building search systems.
Ollama: A tool for running large language models locally.
Hugging Face: A platform for machine learning models and datasets.
ElasticSearch: A distributed search and analytics engine.
Docker: A platform for containerizing applications.
TailwindCSS: A utility-first CSS framework.
JavaScript: Programming language for the Web UI.
VitePress: Documentation generator.

What are the benefits of the project?

Improved AI Model Accuracy: RAG pipelines provide more accurate and relevant responses by grounding the model in external knowledge.
Privacy and Control: Allows users to leverage powerful AI models while keeping their data local and private.
Extensibility and Customization: Modular architecture allows for easy customization and extension of the RAG pipelines.
Ease of Use: Provides both a CLI and a web UI for easy interaction.
Educational Resource: Serves as a learning tool for AI concepts and RAG pipelines.
Open Source and Community Driven: Encourages contributions and collaboration.

What are the use cases of the project?

Research: Assisting researchers in exploring large datasets and documents.
Creative Writing: Helping writers generate ideas and explore characters/settings.
Question Answering: Building chatbots and question-answering systems with access to specific knowledge bases.
Content Summarization: Summarizing documents and web pages.
Code Generation: Assisting with code generation by providing relevant code snippets and documentation.
Education: Teaching AI concepts and demonstrating RAG pipelines.
Centralized Knowledge Base: Using Chipper as a central knowledge store for multiple Ollama clients.
Extending Ollama Clients: Adding RAG capabilities to existing Ollama clients.