finic-ai/rag-stack
Rag Stack
Rag-stack is an enterprise knowledge retrieval system designed to deploy private generative artificial intelligence environments. It functions as a retrieval-augmented generation stack, orchestrating the connection between internal document repositories and open-source language models to enable natural language querying of private organizational data.
The platform distinguishes itself by providing a complete infrastructure for private large language model hosting and vector database management. By utilizing infrastructure-as-code provisioning and containerized microservices, it allows organizations to maintain full control over their data privacy while performing semantic search and inference on their own hardware or cloud clusters.
The system includes comprehensive capabilities for synchronizing unstructured information from external messaging and helpdesk platforms into a centralized, searchable repository. It supports hierarchical access control to ensure that document retrieval remains scoped to specific organizational segments, and it provides a web-based interface for users to interact with these knowledge bases directly.
Features
- Retrieval Augmented Generation - Orchestrates the connection between internal document repositories and language models to enable natural language querying of private data.
- Local Language Model Execution - Deploys open-source large language models on consumer hardware or cloud-based GPU clusters to power private generative artificial intelligence applications.
- Private AI Deployments - Deploys and runs open-source large language models on private infrastructure to maintain complete control over sensitive data.
- Large Language Model Deployments - Provisions open-source large language models on local hardware or cloud-based GPU clusters to perform private inference without relying on external services.
- Private Domain Knowledge Bases - Connects internal documents and organizational data to a conversational interface to enable natural language querying of private company information.
- Vector-Database-Backed Retrievals - Stores document embeddings in a specialized database to enable semantic search and context-aware information discovery across internal knowledge bases.
- Local Language Model Hosting - Executes open-source language models on private hardware or cloud GPU clusters to perform secure text generation without external data exposure.
- Knowledge Base Retrieval - Provides a system for synchronizing and searching organizational data to enable context-aware responses for internal chat interfaces.
- Data Ingestion Pipelines - Synchronizes unstructured information from third-party platforms into a centralized repository to provide a unified source of truth for generative models.
- External System Synchronization - Synchronizes unstructured data from external messaging and helpdesk platforms into a centralized repository for unified information discovery.
- Third-Party Sync Integrations - Connects to third-party applications using secure authentication to ingest and synchronize unstructured documents into a centralized database.
- Vector Databases - Manages self-hosted vector databases to index and search internal knowledge bases based on semantic meaning.
- Vector Embedding Indexes - Deploys high-performance self-hosted vector databases to store and index internal knowledge for accurate retrieval based on semantic meaning.
- Cloud Infrastructure Automation - Deploys infrastructure as code scripts to set up and configure the complete stack required for document retrieval and generation across major cloud providers.
- Infrastructure Deployment Provisioning - Provisions isolated container environments using automated scripts and infrastructure templates to ensure consistent application performance and reliable service delivery.
- Declarative Infrastructure Provisioning - Automates the deployment of cloud resources and containerized environments using declarative templates to ensure consistent and repeatable application hosting.
- Microservices Orchestration - Deploys isolated application components within managed containers to maintain consistent performance and reliable service delivery across diverse cloud environments.
- LLM Hosting - Offers a deployment framework for running large language models on internal infrastructure to ensure data privacy.
- Hierarchical Access Controls - Restricts the scope of document retrieval to specific organizational segments to ensure data privacy and relevance during the generation process.
- Documentation Chat Interfaces - Creates web-based user interfaces that allow people to upload documents and interact with internal knowledge bases using natural language queries.
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.
