Llm-App

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llm-app is a Data & Analytics tool that provides ready-to-run cloud templates for RAG, AI pipelines, and enterprise search. It is Docker-friendly and synchronizes with live data sources like Sharepoint, Google Drive, S3, Kafka, and PostgreSQL.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is llm-app?

llm-app offers ready-to-run cloud templates for building high-accuracy RAG (Retrieval Augmented Generation) and AI enterprise search applications at scale. These templates are designed to be Docker-friendly and maintain real-time synchronization with various data sources including Sharepoint, Google Drive, S3, Kafka, and PostgreSQL. The platform provides built-in data indexing capabilities, supporting vector search, hybrid search, and full-text search, all performed in-memory with caching. It eliminates the need for separate modules like vector databases, caches, and API frameworks, offering a unified application logic for backend, embedding, retrieval, and LLM tech stacks. Users can test templates locally and deploy them on cloud platforms like GCP, AWS, or Azure.

Best used for

Ideal for developers and data scientists who need to quickly deploy AI applications, integrate live data sources, and implement high-accuracy RAG and enterprise search. Especially valuable for those seeking Docker-friendly solutions with built-in indexing and real-time data synchronization.

Common actions

build AI pipelines

deploy RAG applications

integrate live data

perform enterprise search

index documents

open-sourceautomated workflowworkflowslow-code/no-codecollaborationgithub copilotface swappingdeepfake"AI Agents"

Capabilities

Key features

Ready-to-run cloud templates
RAG AI pipelines
Enterprise search
Docker-friendly
Real-time data sync
Built-in vector search
Multimodal RAG

Target Audience

developerdata scientiststartup founder

Integrations

sharepointgoogle-drives3kafkapostgresql

Pricing & Plans

Open Source

Free

FAQs

What types of AI applications can be built with llm-app templates?

llm-app provides templates for various AI applications, including basic question-answering RAG apps, real-time document indexing services, multimodal RAG pipelines for complex documents like financial reports, and adaptive RAG apps optimized for token cost reduction.

How does llm-app handle data synchronization and indexing?

The platform uses the Pathway Live Data framework to connect and sync with data sources in real-time, handling additions, deletions, and updates. It includes built-in data indexing for vector search, hybrid search, and full-text search, all processed in-memory with caching.

What infrastructure dependencies does llm-app require?

llm-app is designed to have no external infrastructure dependencies. It integrates components like vector databases, caches, and API frameworks directly, offering a unified solution. This means you don't need to set up separate services like Pinecone, Redis, or FastAPI.

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