run-llama/rags
Rags
Rags is an orchestration tool for building retrieval-augmented generation pipelines and managing conversational data interfaces. It serves as a system for creating these pipelines from local files and web pages using natural language instructions to query, retrieve, and summarize information from connected datasets.
The project features a multimodal retrieval system that identifies and extracts information across different data types and modalities. It includes a vector search orchestrator to manage chunking strategies and search parameters, alongside a pipeline builder that translates conversational instructions into structured retrieval workflows.
The platform provides capabilities for agent management, including session tracking to isolate conversation states and caches. System configuration is handled through a visual interface and natural language tuning of prompts and model parameters.
Features
- Grounded Chat Interfaces - Provides a chat-based interface for retrieving and summarizing information grounded in connected data sources.
- Agent Session Management - Implements state caches and model configurations to isolate conversations between different agent identities.
- Conversational Data Interfaces - Provides a conversational interface for querying, retrieving, and summarizing information from connected datasets.
- Private Dataset Insight Extraction - Extracts specific answers and patterns from private datasets using vector search and summarization.
- RAG Pipelines - Facilitates the construction of RAG pipelines from local files and web pages using natural language.
- Multimodal Retrieval Systems - Identifies and retrieves information across multiple data types and modalities for LLM agents.
- Natural Language Pipeline Generation - Translates natural language instructions into structured retrieval workflows and data source definitions.
- Text Chunks - Splits large files into overlapping segments to maintain context within model token limits for RAG.
- Semantic Vector Search - Matches user queries to document chunks by comparing high-dimensional embeddings in a vector database.
- Private Data Querying - Enables conversational retrieval and summarization of information from private vector databases.
- Multimodal Indexers - Indexes diverse information types across different formats to enable unified retrieval across a single interface.
- Multimodal Search - Performs searches across multiple data types and modalities to provide comprehensive context for AI responses.
- Vector Search - Extracts answers from large datasets using mathematical similarity in high-dimensional vector spaces.
- Agent Configuration Management - Manages system prompts, chunk sizes, and model parameters to tune AI agent behavior.
- Natural Language Parameter Tuning - Adjusts system settings and generation constraints through a natural language interface.
- Retrieval Configuration Interfaces - Ships a visual interface for adjusting indexing and retrieval strategies without requiring direct code modifications.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
