llm-app
Build real-time RAG pipelines and search APIs that stay in sync with live business data sources.
Deploy live-updating RAG and search pipelines over Google Drive, Sharepoint, Kafka, and Postgres.
llm-app provides ready-to-run templates for building Retrieval-Augmented Generation (RAG) pipelines over constantly changing data sources. It targets teams that need customer support AI and internal search engines to reflect document edits instantly without rebuilding vector stores. It exposes REST HTTP APIs that hook directly into frontend applications.
What it is
The application listens to continuous updates, additions, and deletions across platforms like Google Drive, SharePoint, AWS S3, and PostgreSQL. It indexes incoming text and media using built-in vector and hybrid search engines without requiring external vector databases. Pre-built templates support multimodal PDF parsing with GPT-4o, adaptive token reduction, and local setups with Ollama.
Who it's for
This tool suits software engineers and AI developers building real-time knowledge retrieval for customer support agents or automated chatbots. It is not suitable for non-technical users seeking a no-code visual builder or fully managed SaaS platform.
Key features
Live Data Connector Synchronization
Tracks real-time file additions, edits, and deletions from SharePoint, Google Drive, S3, Kafka, and databases.
Embedded Search Engine
Includes built-in vector search via USearch and hybrid full-text search via Tantivy without extra server setup.
Multimodal Document Ingestion
Parses tables, charts, and text inside unstructured PDFs using models like GPT-4o.
Adaptive Cost-Saving RAG
Features an adaptive retrieval architecture to lower token usage while maintaining answer accuracy.
Private Offline Execution
Offers offline pipeline templates that run locally using Ollama and Mistral models.
Up and running in 3 steps
Our honest verdict
What's great
- Automatically reflects source document changes without manual re-indexing jobs.
- Eliminates dependencies on separate vector databases, API gateways, and cache layers.
- Provides specialized templates for multimodal PDFs, video search, and SQL conversion.
- Exposes standard REST HTTP endpoints for frontend integration.
Where it falls short
- Requires Python development knowledge and Docker management.
- In-memory indexing demands high server memory for large document collections.
- External model usage incurs API fees from providers like OpenAI or TwelveLabs.
- Does not provide a built-in user management console for non-technical support staff.
Pricing & license
The software is free and open source under the MIT license. Your expenses consist of host server infrastructure costs and any usage fees from third-party LLM providers.
Open remote AI-training roles
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
These reviewed AI tools do the same job instantly — from our scored directory.
Use it, or have us set it up
FAQ
Do I need to install a separate vector database like Pinecone?
No, llm-app uses an embedded in-memory engine powered by USearch and Tantivy, so no extra database infrastructure is required.
How does the tool react when a document is updated in Google Drive?
The underlying Pathway framework streams updates in real time, automatically updating the vector index and cache as soon as changes occur.
Can I run this application without sending data to third-party APIs?
Yes, you can deploy the private RAG template using local models hosted through Ollama.
