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ragbits

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Basic Information

Ragbits is a Python-based toolkit and starter bundle designed to accelerate development of generative AI applications. It provides modular packages and developer tooling for common GenAI needs including prompt management, LLM integration, vector databases, document ingestion, retrieval-augmented generation, agent abstractions, chat interfaces and a command-line for testing and operations. The repository bundles core libraries such as ragbits-core, ragbits-agents, ragbits-document-search, ragbits-evaluate, ragbits-chat and ragbits-cli, and is intended for engineers and teams building production-ready RAG systems, multi-agent workflows and conversational apps. It emphasizes modular installation so teams can install only needed components, and includes quickstart examples demonstrating prompts, document search, RAG pipelines, agentic RAG and a chat API. The project includes templates and a project generator to bootstrap new Ragbits applications and documentation and contributing guidance to extend the stack.

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App URL
https://github.com/deepsense-ai/ragbits
Github Repository
https://github.com/deepsense-ai/ragbits/blob/main/README.md

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App Details

Features
Ragbits offers features to build reliable and scalable GenAI systems. It supports swapping between many LLMs via LiteLLM and running local models, and it provides type-safe LLM calls using Python generics. The stack supports multiple vector stores including Qdrant and PgVector and an in-memory option, plus embedder integrations. Document ingestion handles 20+ formats and can extract tables, images and structured content with VLM support, and connectors exist for S3, GCS and Azure. Ingestion can be scaled with Ray-based parallel processing. For agents and workflows it supports multi-agent coordination via an A2A protocol, Model Context Protocol for real-time data and tools, and automatic conversation history management. Operational features include OpenTelemetry tracing, CLI tools, prompt testing with promptfoo, evaluation tooling, and a deployable chat UI with persistence and live updates.
Use Cases
Ragbits streamlines building, testing and deploying retrieval-augmented and agentic GenAI applications. It reduces engineering work by providing ready-made integrations for LLMs, embedders and vector stores, and reusable abstractions for prompts, agents and tools. Developers can ingest diverse document formats, build vector search indexes, run RAG pipelines, and compose agents that call search tools or external APIs. Built-in developer tools and a CLI enable prompt testing, vector store management and quick iteration, while evaluation components and observability integrations help monitor and improve model performance. The chat interface and API examples show how to expose applications to users with streaming updates and persistence. Templates and a project generator accelerate prototyping so teams can focus on application logic rather than plumbing.

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